Annals of Warsaw University of Life Sciences - SGGW.

Annalsof WarsawUniversityof LifeSciences– SGGWAgriculture(Agricultural and Forest Engineering No 52)Warsaw 2008

6 G. ViselgaEXPERIMENTAL OBJECTIVEThe experimental objective is to group,analyse and generalise the key trends andneeds of field crop and potato productionmodernisation, and to determine therelationship between them.To investigate the processes ofsoil deep loosening, furrow looseningby rotary implements separating andcrushing the clods, and mulching whengrowing potatoes with permanenttramlines and on enlarged furrows, aswell as to determine possibilities for thereduction of soil packing energy costsper unit of production and for potatolifting improvement.To evaluate the possibilities of thesimplest circular energetic modulus(Fig. 1) of the gantry agriculture, theconditions for the operation of the mainworking parts in the circular trajectoryand to use the best results to make theperspective simplification trends of thetechnological schemes of the powermodulus of the reciprocal movementtype.EXPERIMENTAL METHODSThe following composite parts ofcombined aggregates testing stands wereformed: rotary cultivator – mulchingequipment, tramlining equipment withdeep loosening chisel shares, clod andstone separator, special spur-type roller.These implements can be aggregatedwith 14 kN class MTZ-82 tractorsautonomously or fitted in combinedaggregates.Tramlining equipment is designedto form tramlines and to loosen the soilbetween them while planting potatoes,as well as for localisation of soil rich inhumus or green manure while preparingthe soil for potatoes. It consists of anuniversal frame, two tramline hillers,two support depth control wheels, andthree chisel shares. Tramline hillers areplaced in front of tractor wheels.For the determination of soil hardnesswe used an electronic self-writingpenetrometer CP20 (England) with astandard 12.5 mm diameter cone-shapedtip. Soil resistance to this tip pressingAR p1R p2l 1 =0; α 1 =0.α 2l 2AFIGURE 1. The scheme of the power modulus of the circular gantry system

Inveatigations on soil conservation and precision... 7is recorded in the memory of thisapparatus every 15 mm from the surfaceto the set depth. For measuring of soilhardness distribution in the width of thewhole interrow and for the measuringof furrow profile, besides hardnessmetering equipment, we used 1.5 m longhorizontal plank with legs stuck in thesoil, in which 1,4 m length on both sidesevery 10 mm (with 5 mm sliding) holesof 15 mm diameter were drilled.We investigated the circular (Fig. 1)and shuttle gantry aggregates. Thecircular carriage driven by the electricmotor rotates the cantilever beam aroundthe support centre. The implementmounting cart moves across the beam.Different working implements can bemounted on this cart and they wouldacquire the spiral movement or thatof concentric circles. The shuttle unitswere investigated by laser measurementimplements.EXPERIMENTAL RESULTSMajor engineering soil conservationmeans in field crop and potato production,besides tillage of soil with adequatemoisture regime at optimum terms,education of agricultural producers,control of environmental aspects, canbe grouped into three main parts: meansrelated to machinery improvement,advancement of technologies andreduction of chemical pollution.Firstly, an important and considerablepart is devoted to the reduction ofchassis pressure on the soil. One can findtraditionally used means among themsuch as: doubling of wheels, speciallow-pressure tyres, caterpillar and semicaterpillarchassis. Regardless highenergy costs, on stony soils it is necessaryto remove small stones over 3 cm insize. Our long-term experiments suggestthat from energy and soil conservationpoint of view it is most efficient toremove stones in one time from thewhole arable layer, while preparing thesoil for potatoes by combined complexaggregates. Arable layer is sifted, stonesare separated into fractions: small stonesup to 6–8 cm are crushed and spread inthe soil, bigger stones are removed fromthe field in a hopper. Up to 40% of fuelis economised, potato yield is increasedabout 10% and anti-erosive effect iscreated.An important role is played byadvancement of machinery design –evenly operating ploughs, mouldboardless implements and ploughs ploughingwith mounted rotary soil loosening orclod crushing implements. Optimumoperation regime is of special importancefor actively operating working parts.When preparing the soil byconventional cultivators with passiveworking parts the soil and interrows arepassed several times during the springsoil preparation. Soil hardness increaseswith every pass (Fig. 2).Mulching of green manure crops oilradish and white mustard in the surface10 cm soil layer reduces soil hardness(Fig. 3), weed incidence on the fields(Fig. 4), increases productivity, nutrientcontent and the amount of earth-wormsin the soil as much as 10 times. It is anundoubtedly valuable soil improvementmeans. No increase in the amount ofearth-worms was found after sprayingpotatoes with pesticides.

8 G. ViselgaFIGURE 2. The relationship between soil hardness in the middle of an interrow and the number ofpasses of tractor’s MTZ-82 earthing-up and planting aggregatesFIGURE 3. Effect of mulching on soil hardness in potato furrows before potato liftingFIGURE 4. Effect of soil loosening (a), mulching and cultivation methods (b) on weed incidence

Inveatigations on soil conservation and precision... 9When loosening by a combinedaggregate and planting separately, theamount of clods over 30 mm in diametercollected during potato lifting was 28.5%lower and on average 18.4% lower in allthe experimental treatments than in thecontrol.While setting modernisation trends offield crop production a special attentionshould be drawn to the reduction ofenergy costs. It is equal to the reductionof production costs and enhancement ofprofitability. It goes without saying thaton cultivated, not compacted soils energycosts are always lower. Therefore, all thethree mentioned trends are interrelated.Replacement of organic fertilisersby mulching of green manure crops andgrowing of ecological production are alsoways to save energy costs, as these meansis increased, as it is possible to narrowinterrows and protection zones of somecrops and not to pack the soil. It is atechnology of the future.The operation width of the gantrysystem implements when perpendicularmounted on the beam depends on theirdistance up to the revolution centre Rpand the beam l. The smallest divergenceof the operation width from the designone that is equal to 0.18 m (Fig. 5) willbe when the implements are under thebeam, i.e. l = 0. But in some cases itis difficult to do l = 0 in practice, thenthe implements should be turned bythe radius angle α πarctg R pl= −2depending on the turn. At that time theoperation width of the plough will can becalculated according to equation:b =2 23bp+ anbp+ an+ Rp+ l + 2 3 2 22 244result in lower energy consumption, lowersoil compaction and better suppressionof weeds. Technologies of precisionand gantry agriculture are completelynew. Experimental results of circle andshuttle gantry systems [Viselga 1998]have shown that wheel skidding is aslow as 1%, and soil loosening energycosts can be reduced as much as 20%.Installed power according to the resultsof our tests makes up only 3–5 kW, andfor ploughing and cultivation only about240 kWh/ha is used. In gantry agriculturethe amount of production per area unit2 22 2 an− bp− Rp + l +4⎛2 2( Rp+ l⎜) sin arctg ⎜⎜⎝⎞2bp⎟+2 2an− bp⎟⎠where: a n – the length of the ploughshareblade, b p – the design width of the plough.When the turn radius of gantryimplements is (3–6) m, one side of theprofile of the potato furrow, closer tothe rotation centre, has the smaller areathan the other side of the profile (Fig. 6).When the implements move away fromthe centre, on the contrary, the area of theperipheral side of the furrow profile issmaller than the area of the other side ofthe furrow profile: when the turn radiusis (9–12) m, the furrow asymmetry is13%, and when it is (15–18) m, the

10 G. ViselgaFIGURE 5. The relationship of the operating width of the plough body on its distance to the beam andthe turn radius, when the implements are perpendicular to the beam20ñm1510201510201510500262,2 cm 2 284,4 cm 215304560(3-6)m; (0,16-(9-12)m; (0,47-(15-18)m; (0,79-FIGURE 6. The row profiles and their cross-section areas in dependence of turn radiusfurrow asymmetry is 1%. The furrowasymmetry is insignificant, when thehiller is at (6–12) m distance from therotation centre.Experimental tests showed that theoperation speed has the greatest influenceto the work quality of the hilling bodies,if compared with all the other testedimplements. The operation speed shouldbe not smaller than 0.55 m per secondbecause only at this speed the symmetricalrow profiles may be formed (Fig. 6).From the shuttle modules the simplestare the positional beam with two chassis500299,6 cm 2 266,1 cm 2 01530456050260,8 cm 2 258,9 cm 215304560ñmand longitudinally mounted mowingimplements. The field area is unlimited,the motor power is 3–4 kW.The wheels of positional shuttlemodules precisely copy unevenness offield and the beam has more deviationsof straight movement (Fig. 7). Walkingchassis with length support decreasesthis deviations and quantity of positionalcorrections. The gantry modules andespecially positional shuttle modulesin comparison of the tractors decreasemotor power and deviations from straightmovement and width of rows.

Inveatigations on soil conservation and precision... 11FIGURE 7. The influence of shuttle module chassis type to quantity of positional correctionCONCLUSIONS• Tractors with a front suspensionrod and a front power shaft, evenlyploughing ploughs with mountedloosening implements, mouldboardless aggregates, mulching implements,gantry systems, complex andcombined implements are the meansof field crop production modernisationwhich should be used as widely aspossible.• By one pass of a combined soiltillage aggregate and experimentalpotato growing technology involvingnarrow-wheeled tractors it is feasibleto reduce soil hardness in the zone oftracks 1.5 times and to increase thedepth of friable soil to 22 cm.• Rotary cultivators loosen the soil moreintensively than mouldboard ploughsor passive shares of cultivators. Thesoil loosened by these implementsas well as mulched soil is packedless before potato planting, the clodcontent is lower in them, the weedincidence is 2.5 times lower and thetubers are 18–28 cleaner.• Soil friability, reduction of harmfuleffect of soil compaction by wheelsand mulching of green manure cropsincreased potato yield 40.3% andreduced dirt content.• Very important is the indicator ofincorporation fullness of green manurecrops, as mineralisation of greenmanure in the soil surface is low.• The angle of the deviation of theplough body and other implementsfrom the design position if directlyproportional to the body distance fromthe bar the gantry circular module andwidth is smaller than the design onenot more than 10%. The ploughingdepth at the centre is smaller by 32mm than in the peripheral part.• To avoid the furrow asymmetry at thecentre, the hillers should be as closeas possible to the circular module barand their operation speed should beincreased, at least, up to 0.55 m/s.

12 G. ViselgaREFERENCESBAREIŠIS R., ŠNIAUKA P. 2000: Tiltiniųžemdirbystės sistemų tyrimai. LŽŪUniversiteto mokslo darbai 5 (1).POWAŁKA M. 2007: Changes in soilphysical properties in arable layer underpressure of tractor outfit wheels. Annalsof Warsaw University of Life Sciences– SGGW, Agriculture No 51: 13–17.SKREBELIS S. 2007: Peculiarities ofplowless tillage technology when growingcrops. Annals of Warsaw University ofLife Sciences – SGGW, Agriculture No51: 29–34.VISELGA G. 1998: Tiltines zemdirbystesprincipu taikymo mazuose plotuose tyrimai.Daktaro disertacija. Raudondvaris.VISELGA G. 2006: Research of AccuracyParameters of the Gantry Course. SolidState Phenomena. Vol. 113 “MechatronicSystems and Materials”, p. 50–54.VISELGA G., KAMIŃSKI J.R. 2006: Analysisof soil compaction at potato cultivation.PAN. Zeszyty Problemowe PostepówNauk Rolniczych, 508: 203–208.Streszczenie: Badania konserwującej uprawygleby z wykorzystaniem ciągnika oraz urządzeniabramowego. W porównaniu z narzędziamibiernymi, ugniecioną glebę znacznie intensywniejspulchniają brony aktywne. Brony wirnikowemogą być stosowane zarówno w tradycyjnejuprawie przedsiewnej, jak również podczas siewunasion w mulcz. W uprawie ziemniaków zalecanesą bronowanie i wielokrotne obsypywanie. Zalecasię usunięcie kamieni, szczególnie z pól przeznaczonychpod uprawę ziemniaków. Wielokrotneprzejazdy powodują nadmierne ugniecenie glebyw międzyrzędziach kołami ciągników i maszynrolniczych. Dążąc do ograniczenia liczby przejazdówmaszyn po polu wyposaża się ciągnikiw przedni i tylny TUZ. Umożliwia to stosowaniezłożonych, wieloczynnościowych agregatówuprawowych zawieszanych z przodu i z tyłu ciągnika.To pozwala zmniejszyć liczbę przejazdówdo niezbędnego minimum. Natomiast całkowiteograniczenie ugniatania gleby można uzyskaćpoprzez zastosowanie systemu tzw. rolnictwabramowego. W tym wypadku zabiegi agrotechnicznewykonywane są narzędziami montowanymina wózku przetaczającym się po specjalnychszynach. Stwierdzono korzystny wpływ takiegosystemu uprawy na strukturę i fizyczne właściwościgleby.MS. received June 2008Author’s address:Gintas ViselgaVilnius Gediminas Technical University,Department of Machine Building,LT-03224 Vilnius, J. Basanaviciaus St. 28,Lithuaniae-mail: visgin@one.lt

Annals of Warsaw University of Life Sciences – SGGWAgriculture No 52 (Agricultural and Forest Engineering) 2008: 13–21(Ann. Warsaw Univ. of Life Sci. – SGGW, Agricult. 52, 2008)Precision and energy parameters of the positioned gantry module1 GINTAS VISELGA, 2 JAN R. KAMIŃSKI1 Vilnius Gediminas Technical University, Department of Machine Building, Vilnius, Lithuania2 Department of Agricultural and Forest Machinery, Warsaw University of Life Sciences – SGGW,Warsaw, PolandAbstract: Precision and energy parametersof the positioned gantry module. The courseof the positioned gantry module is maintainedby a laser instrument. A laser beam generatoremitting a vertical beam is placed at the end ofthe experimental field. A laser beam catcher withphoto diodes is mounted on a positioned gantrymodule replaceable support. Course deviationswere assessed in two cases: when laser, straightcoursemaintaining mechanism was mounted atthe same end of the spar as positioning trundle;and when it was mounted at the middle of the spar.Furthermore, we estimated how course deviationsvary when changing inter-axial distance oflaser catcher photodiodes. Electromechanicaltransmitter was used in the tests. Gantry moduleis positioned by a special positioning trundle. It isrun by a 12 V electric motor through a worm self--braking reduction gear.Key words: gantry unit, shuttle movement,accuracy parameters, positioning, straight-linecourse movement.INTRODUCTIONGantry unit allows to dispense withdifferential global positioning system(DGPS), since a straight-line coursecan be maintained by special permanenttramlines using cheaper means suchas gyroscopic system or according tolaser beam. Some researchers confirmthat gantry units can be more precisethan DGPS [Holt, Tillet 1989, Quick1987], and due to the simpler automaticcontrol system, gantry units can be moreeconomical [Viselga 2006, Viselga,Kamiński 2006, Viselga, Bareisis 2003].There are two types of shuttle movementgantry units:••the ones whose chassis together withthe working parts inertly fitted ongantry spar move along the objectand at headlands they are re-arrangedat working width to the adjacent stripof land;with working parts moving acrossthe field during operation with thebeam periodically positioned alongthe object according to the workingwidth of the working parts.The second type has one majoradvantage – the discrepancy betweenenergy needs for the positioning ofspar and for working parts motiondeclines the installed power. In orderto integrate the chief potentials in theshuttle movement gantry module onehas to study the methods for precisionincreasing using automatically controlledmore straight forward means that do notrequire direct involvement of man. In thefuture this would allow to refuse internalcombustion engines in the gantry unitsas well as unreeling electric cables andto change over to the use of other energysources [Viselga 1989].

14 G. Viselga, J.R. KamińskiEXPERIMENTAL OBJECTIVETo identify possibilities to reducedeviations of straight-line coursemovement and positioning in relation tothe effect of micro and macro unevennessof the field, by choosing chassis type,control methods and parameters, andto estimate technological and energeticefficiency of the model.EXPERIMENTAL METHODSThe course of the positioned gantrymodule (PGM) is maintained by alaser instrument UKL – 1 (УКЛ – 1).A laser beam generator emitting avertical beam is placed at the end of theexperimental field. A laser beam catcherwith photo diodes is mounted on a PGMreplaceable support (Fig. 1). When themodule deviates from the course, laserbeam is passed into one of the marginalphotodiodes and a signal is formed, whichis passed into the control panel. Thecontrol panel switches off transmitter ofone or the other chassis and when PGMspar turns perpendicularly to the straightlinecourse, the transmitter of the chassisis turned on again.PGM is positioned by a specialpositioning trundle. It is run by a 12 Velectric motor through a worm self--braking reduction gear.When positioning trundle is running,a horizontal round tube inertly joinedwith it, moves along the bracket withterminal switches. When running, PGMbracket with terminal switches movesalong the tube of stopped positioningtrundle. Having moved on motionlimiters, the terminal switch of theFIGURE 1. Technological scheme of PGM: 1 – spar; 2 – chassis; 3 – working parts stretcher; 4 – ropeof working parts stretcher; 5 – working parts; 6 – working parts fixing carriage; 7 –axis of turning ofworking parts; 8 – back disconnectors with the switchover contact; 9 – flexible cable; 10 – cable drum;11 – horizontal tube with motion limiters; 12 – laser catcher with photodiodes; 13 – positioning trundle;14 – motor of working parts stretcher; A – distance between centres of photodiodes; B – distance ofcatcher’s photodiodes to the spar (B = 1 m); C – catcher’s distance from a wheel positioned the chassis;l s – spar length (l s = 18 m)

Precision and energy parameters of the positioned gantry module 15electric motor switches off transmitterof the chassis and switches on motorsof working parts carriage stretcherand of positioning trundle transmitter.When positioning trundle has run the setpositioning distance, motion limiters onthe tube press the terminal off switch andthe trundle stops. The distance that PGMruns between stops, is set by shiftingmotion limiters on the tube of positioningtrundle. When the working parts carriagehas moved to the end of its motion, theterminal switch is pressed, the motorof working parts carriage stretcher’stransmitter is switched off and motors ofchassis transmitters are switched on. Theabove-described cycles revolve.Precision of PGM stops in setpositions was measured in series of threereplications. Command to stop by asignal of an electric control scheme wasgiven by a mechanism of positioningtrundle.Precision of PTM stops in a setposition was assessed in the followingway:•••on stopping, special marks weremade on the soil surface according tospecial supports in tramlines;afterwards a line was stretched withinthe length of the experimental plot,parallel to tramlines at marks in orderto maintain measuring straightness inthe course direction;the distance between the marks wasmeasured by a tape-measure stretchedat the line ties.PGM straight – line course deviationswere measured along the whole length ofthe experimental plot by stretching theline and tape-measure, leaving the samedistance from the middle of the tramlineat the ends. The imprint of the middle ofchassis support wheels protector in thesoil of tramlines was considered as themiddle of tramline. The distance fromthe line to the middle of the tramline wasmeasured by a ruler every 0.5 m. PGMcourse deviations were assessed in threereplications in two cases: when laser,straight-course maintaining mechanismwas mounted at the same end of thespar as positioning trundle (C = 0); andwhen it was mounted at the middle of thespar (C = l s /2 = 6 m). Furthermore, weestimated how course deviations varywhen changing inter-axial distance oflaser catcher photodiodes.Electromechanical transmitter wasused in the tests.Average soil moisture content in thetramlines was: 6.1–12.6% at the 50 mmdepth, 8.7–13.2% at the 50–100 mmdepth.EXPERIMENTAL RESULTSThe right and the left chassis of PGMmove on the surfaces with differentevenness.According to the experimental dataof tramlines unevenness measuring andcomputer chassis simulation programmedeveloped in the Matlab environment, weobtained positioning deviation results ofPGM individual chassis run between positionsdistances or number of positionsto permissible set position (Fig. 2). Theyenable to compare the effects of wheelchassis tramlines levelling on the correctionsof the set straight-line course.In the case of wheel chassis thewheels are in contact with the brokenline of tramlines.

16 G. Viselga, J.R. KamińskiNumber of positions to corrections80706050403020100both tramlines of chassis not levelledone tramline levelled1 2 3 4Succession of correctionsFIGURE 2. The effects of tramline on the frequency of course correctionWhen moving along natural nonlevelledtramlines both tracks needlevelling, because levelling of onetramline even increases the number ofpositions to corrections and increases theinter-difference of the distance done bythe chassis in the direction of the courseduring correction. This results from thefact that when both chassis move alongnon-levelled tramlines their unevennesscompensate one another.When positioning trundle andphotodiodes of laser catcher with A = 30mm inter-axial distance were mountedon the same chassis (C = 0), mean coursedeviation of this chassis amounted to67.6 mm, and mean square deviation±15.0 mm (Fig. 3). Straight-line coursedeviations of the other chassis weredetermined by the unevenness of itstramline and other already-mentionedfactors, therefore its mean straight-linecourse deviation was higher (102.2mm), and mean square deviation wasconsiderably higher ±33.9 mm.When photodiodes are mounted in themiddle of the spar (C = 6 m) and the leftchassis is positioned by the trundle, meandeviation from the straight-line course ofthe right chassis significantly declines,compared with the first case (75.6 mm)(Fig. 4). Its average straight-line coursedeviation are smaller (25.5 mm). Meansquare deviations of straight-line coursedeviations were 4.1 mm bigger for theright chassis.Precision of positioning depends onthe speed of PGM. When increasing thespeed from 0.08 m/s to 0.24 m/s, thedistance between the left, positioned bya trundle, chassis stops in the positionsincreased by on average 31 mm, and thatof the right chassis by 20 mm. When thespeed is increased from 0.08 m/s to 0.39m/s, this distance increases by 61 mm and55 mm, respectively. Average positioningsquare deviation of the chassis positionedby a trundle is lower and at a speed of0.08 m/s it reached on average ±32 mm.An increase in the chassis speed resultsin an increases in mean square deviation.At a speed of 0.38 m/s it was ±50 mm.Average square deviations of thedistance between positions of the right

Precision and energy parameters of the positioned gantry module 1718,51817,519,51920,520160 .21 21,5 22140120100806040200Course deviationof chassis, mm33,544,55Left chassisRight chassis5,566,57177,516,58168,515,51514,51413,513 12,5121111,51010,59,59Distance coveredby the chassis, mFIGURE 3. Course deviation, when positioning trundle and photodiodes of laser catcher were mountedon the left chassis17,51716,51818,519 120 .1008060Course deviationof chassis, mm33,544,55Left chassisRight chassis16405,515,52061506,514,57147,513,58138,512,59 Distance covered129,5 by the chassis, m11,511 10,510FIGURE 4. Course deviation, when photodiodes are mounted in the middle of the spar and the leftchassis is positioned by the trundle

18 G. Viselga, J.R. Kamińskichassis positioned according to the bendof the spar were by on average 1.6––2.1 times higher due to differences intramline unevenness.The distance between photodiodes,when A = 20–30 mm did not have anysignificant effect on the positioningprecision of the right chassis positionedby a trundle. Mean deviations rangedbetween 62–64 mm, and mean squaredeviations amounted to 19–26 mm, meandeviations of the other chassis rangedwithin wider limits of 29–63 mm, andmean square deviation when increasingthe distance between photodiodes from50 to 67 mm, increased by 1.3 times,from 81 to 104 mm.On the basis of the above-mentioneddata we can find that positioning speedis a decisive factor for positioningprecision. With increasing speed, inertiaforces increase when stopping or startingduring positioning, which increasespositioning deviations.Variation of asynchronous motorspower of electromechanical transmitteris rather typical (Fig. 5a). Duringpositioning, the power depended onthe speed of chassis. During automaticactuation of chassis motors the powerslightly exceeded the mean value. At0.083 m/s speed of chassis the powerinappreciably fluctuated and amountedto on average 0.08 kW. During the coursecorrections when motors temporarilyswitched off, the power declined to zero.Duration of corrections was about 0.8––1.0 s. The power of individual motorsof chassis slightly differed, most likelydue to the different distances coveredduring skid.Motor power of electromechanicaltransmitter of rope stretcher of implementcarriage at actuation moments wasat its peak, up to 4.8 kW, and duringstabilisation of implement tractionresistance, declined to 0.74 kW. Thelength of peak was 0.8–1.2 s. Due tothese power peaks, the working width ofthe implements was limited.An increase in positioning speedof PGM with an electromechanicaltransmitter to over 0.1 m/s wascomplicated due to the impacts duringtransitional processes.Comparison of power utilisationgraphs presented in Figure 5 suggeststhat chassis speeds in the case ofelectromechanical transmitter were4.7 times lower, and the power ofchassis differed by 3.7 times, i.e. withincreasing speed the power increasesless. Implement speeds in the case ofhydromechanical transmitter were by1.76 times higher, and the power 3times higher compared with implementcarriage pulling using electromechanicaltransmitter. This increase in poweroccurred due to the flow in throttletransmitter. When throttling is reduced,i.e. when revolution frequency is reducedby a mechanical transmitter, the powerdoes not increase significantly.Low power requirement of chassisenables to position chassis even bya muscle power of man. The power ofimplement carriage stretcher is higherand increases with increasing the workingwidth of implements. The total requiredpower can be fully generated bysolar energy photoconverters, havingmounted over the spar and chassis. Thearea of PGM cover fitted up from thephotoconverters l s × 2b would be 80–100m 2 for 20 m long spar and would protectPGM mechanism from precipitation. In

Precision and energy parameters of the positioned gantry module 194,0N, kWN max =4,77kW3,53,0positioningN=N k +N dWork of implement carriageN=N padpositioningN=N k +N d2,5a)2,01,5implement of carriage (P pv) N pad )left of left chassis chassis (N k) (P k)right of right chassis chassis (Nd)(P d)totaltotalpower(P)(N)correctioncorrection1,00,50,00 10 20 30 40 t, s 507,0N, kW6,0positioningwork of implement carriagepositioningwork of implement carriage5,04,0b)3,02,01,0correctioncorrection0,0130 135 140 145 150 155 t, s 160a – in electromechanical transmitter (mean parameters: v chas. = 0,083 m/s N važ = 0,89 kW, v pad = 0,57 m/s,N pad = 0,74 kW)b – in hydromechanical transmitter (v chas. = 0,390 m/s, N chas. = 3,29 kW, v impl. = 1,00 m/s, N impl. = 2,21 kW,intensity of corrections –3)FIGURE 5. Power variation fragment of asynchronous motorsthe long run, it is more viable to use theenergy of chemical fuel converters forPGM or to combine it with the use of solarenergy.For the calculation of energy input,it is necessary to estimate the capacitiesand power of implements W impl. andchassis W chas :W impl. = 0.36 l s B p /t p , == 0.36 B p v impl. , ha/h (1)W chas. = 0,36q/t == 0,36 l p v chas. , ha/h (2)where: l p – positioning distance, m.Since we cannot add these capacitiesor calculate their average, to find totalPGM capacity we have to calculateworking time input per ha of implementsand positioning and to add it. Total PGMlabour efficiency is calculated as aninverse value of total working time input.

20 G. Viselga, J.R. KamińskiE.g. in this way we calculate, that a 40m-long spar will provide a possibility forPGM to operate at 0.42 ha/h net labourefficiency.Summing up energy input of PGMpositioning and implements in workingpositions we can determine total energyinput.Having adopted the earlier-mentionedperspective PGM parameters and networking efficiency: for implements 0.45ha/h and chassis 7 ha/h, we determinedthat energy input of implements workequals 8.0 kWh/ha, and that of chassispositioning 0.6 kWh/ha. Therefore, totalenergy input can make up only 8.6 kWh/ha.CONCLUSIONS• When automatically controllingpositioned gantry module there has tobe a straight-line course maintenancesystem, e.g. according to the laserbeam.• Accuracy of positioning increasesat reduction of speed of movementchassis and with that of the connectedforces of inertia of the gantry unit.• Straight-line course deviations of theright chassis are reduced by distancingphotodiodes from the straight-linecourse positioned left chassis C > 0and reduction of their inter-axial distanceA. Mean square deviation whenincreasing the distance between photodiodesfrom 50 to 67 mm, increasedby 1.3 times, from 81 to 104 mm.• Energy input for a single-time soilloosening at 10 cm of the perspectivePGM amounts to 8.0 kWh/ha, forchassis positioning 0.6 kWh/ha, thetotal energy input can amount only toabout 8.6 kWh/ha.REFERENCESHOLT J.B., TILLETT N.D. 1989: The developmentof a nine metre span gantry forthe mechanized production and harvestingof cauliflowers and other field vegetables.Journal of agricultural engineeringresearch. Vol. 43, p. 125–135.QUICK R.G. 1987: Engineering an agriculturalfuture. Agricultural engineering.Australia. Vol. 16, p. 8–11.VISELGA G. 2006: Research of AccuracyParameters of the Gantry Course. SolidState Phenomena, Vol. 113 „MechatronicSystems and Materials“, p. 50–54.VISELGA G., BAREISIS R., SNIAUKAP. 2003: Investigation of positioningaccuracy of shuttle gantry tillage modules.Bioagrotechnical systems engineering.Research papers. Warsaw University oftechnology. Poland. No 2–3 (11–12),p. 127–134. (In Russian).VISELGA G. 1998: Investigation of theutilization of the principles of gantryagriculture in small fields. Doctoralthesis. Raudondvaris. (In Lithuanian).VISELGA G., KAMIŃSKI J.R. 2006: Analysisof soil compaction at potato cultivation.Zeszyty Problemowe PostępówNauk Rolniczych, 508: 203–208.Streszczenie: Precyzja i parametry energetycznepozycjonowanego modułu bramowego. Sterowaniepozycjonowaniem urządzenia bramowego(wózka narzędziowego) odbywa się za pomocąurządzenia laserowego. Generator laserowejwiązki promieniowania emitujący pionową wiązkępromieni, umieszczony jest na końcu uprawianegopola. Odbiornik laserowej wiązki promieniz fotodiodami zamontowany jest na module nastawczymurządzenia bramowego. Dokładnośćpozycjonowania została zbadana dla dwóch przypadków,gdy urządzenie laserowe zostało zamontowanena końcu wraz z toczącymi się rolkami,oraz gdy było zamontowane centralnie. Oszaco-

Precision and energy parameters of the positioned gantry module 21wano wielkość odchyleń przebiegu procesu w zależnościod zmian wzajemnego położenia emiteralaserowego i fotodiod. W teście użyto nadajnikaelektromagnetycznego. Moduł sterujący zamocowanybył na specjalnych rolkach prowadzących.Do napędu wykorzystano silnik elektryczny zasilanyprądem o napięciu 12 V z zabezpieczeniemtermicznym.Jan R. KamińskiWydział Inżynierii Produkcji SGGWKatedra Maszyn Rolniczych i Leśnych02-787 Warszawa, ul. Nowoursynowska 164Polande-mail: jan_kaminski@sggw.plMS. received June 2008Authors’ address:Gintas ViselgaVilnius Gediminas Technical University,Department of Machine Building,LT-03224 Vilnius, J. Basanaviciaus St. 28,Lithuaniae-mail: visgin@one.lt

Annals of Warsaw University of Life Sciences – SGGWAgriculture No 52 (Agricultural and Forest Engineering) 2008: 23–30(Ann. Warsaw Univ. of Life Sci. – SGGW, Agricult. 52, 2008)Methods for evaluation of breaking up of maize chaff separatedon the sieve separatorALEKSANDR LISOWSKI, KRZYSZTOF ŚWIĄTEK, KRZYSZTOF KOSTYRA,JAROSŁAW CHLEBOWSKIDepartment of Agricultural and Forest Engineering, Warsaw University of Life Sciences – SGGW,Warsaw, PolandAbstract: Methods for evaluation of breaking upof maize chaff separated on the sieve separator.The work aimed at determination of breakingup degree of maize chaff and grain with the useof the sieve separator, fabricated according toANSI/ASAE S424.1 Standard, and at comparisonof the methods for evaluation of chaff particlelength distribution. As the quality indices ofevaluation of maize breaking up degree therewere taken: geometric mean of chaff length,standard deviation of length and index of maizegrain breaking up. These indices were determinedon the basis of mass distribution. The threevarieties of maize plants were investigated: SAN,LG2244 and REDUTA of moisture content 62.8,59.4 and 63.2%, respectively, harvested with twoself-propelled forage harvesters. It was found thatthe chaff length ranged from 7.82 to 12.92 mm,depending on forage harvester used and maizevariety. The ANSI/ASAE S424.1 method fordetermination of geometric mean of chaff lengthwas verified with the use of Rosin-Rammlermodel, by calculating hypothetical dimensionof sieve mesh, which passed through a half ofmaterial being sifted. The obtained results provedusability of both the methods for determination ofthe chaff length distribution.Key words: breaking up, sieve separator, chaffdistribution, method.INTRODUCTIONMaize silage is a basic feed used in nongrazingfeeding of cattle. To achieve thebest feeding results, the maize must beproperly broken up prior to ensilaging(Michalski 1997). As it is evident frommany research findings, in order to ensurebetter absorption of particular nutrientsone should try to get short chaff andhighest degree of maize grain breaking up.The forage harvesters equipped with thedrum chopping unit combine the optimalbreaking up of maize plants and grainduring harvesting. The sieve separator(analyzer) can be used in evaluation ofchaff particle length and uniformity aswell as maize grain breaking up degree.The work aimed at determination ofbreaking up degree of maize chaff and grainwith the use of sieve separator fabricatedaccording to own technical documentationand ANSI/ASAE S424.1 Standard, andalso at comparison of the methods forevaluation chaff length distribution.As the quality indices of evaluationof maize breaking up degree there weretaken: geometric mean of chaff length,standard deviation of length and index ofmaize grain breaking up. These indiceswere determined on the basis of massdistribution.MATERIAL AND METHODSAnalysis of breaking up was executedfor three maize varieties: SAN, LG2244and REDUTA of moisture content

24 A. Lisowski et al.62.8, 59.4 and 63.2%, respectively.The varieties SAN and LG2244 wereharvested with forage harvester ClassJaguar 690 SL equipped with rowindependentattachment Champion3000; the mass productivity amountedto 71 t/h. The plants of REDUTA varietywere harvested with forage harvesterClass Jaguar 682 S equipped with 4-rowattachment; mass productivity amountedto 62 t/h. Both harvesters were equippedwith the drum chopping unit.In order to determine the percentagemass ratio of particular parts of amaize plant, 10 plants of each varietywere randomly chosen, divided intohomogeneous components and weighedon an electronic scale. Mean values aregiven in Table 1.The investigated chaff samples weretaken immediately after harvest from 5different places of the trailer (accordingto a single envelope method). Meansamples of each variety of volumeamounted to 10 liters were measured.The samples were put into the separator’scharging hopper (Fig. 1) to separatethem into fractions according to length.The separator was equipped with theshoe with rectangular sieves of squareshape mesh (Sar 2007) and dimensions406 × 565 mm. The shoe was driven byTABLE 1. Averaged parameters of plant materialParameterMaize varietyMaize variety SAN LG2244 REDUTAMass of whole plant[g] 730.06 559.65 549.20Mass of leaves at stem [g] 112.63 82.09 72.27Mass of stem [g] 271.46 237.12 184.49Mass of panicle [g] 2.72 1.59 3.96Mass of cob with leaves [g] 341.25 229.21 276.46Mass of leaves at cob [g] 32.53 14.06 15.84Mass of cob with grain [g] 281.94 212.42 246.53Mass of grain [g] 214.01 161.58 200.22Mass of torus [g] 64.93 40.32 46.04Length of plant [mm] 2401.00 2383.10 2741.00Number of grains on cob [pcs] 481.90 438.90 501.70Length of panicle [mm] 321.00 375.10 414.10Height of cob fixing [mm] 530.00 699.60 871.00Length of cob [mm] 209.00 198.90 165.50Diameter of cob [mm] 48.80 42.76 52.15Diameter of torus [mm] 18.00 14.69 14.26cutting 26.20 22.65 22.63250 [mm] 24.45 21.19 21.60500 [mm] 22.20 20.26 20.38Diameter of stem at height 750 [mm] 19.45 18.54 18.851000 [mm] 16.75 15.53 16.691250 [mm] 15.20 13.09 14.081500 [mm] 12.05 10.02 12.00

Methods for evaluation of breaking up of maize chaff separated... 25FIGURE 1. Sieve separator: 1 – base, 2 – gear box, 3 – electric motor, 4 – inverter, 5 – rubber shockabsorber, 6 – frame of ground wheels, 7 – guide of circular section, 8 – housing with linear bearing, 9– sieve, 10 – sieve shoe, 11 – eccentric mechanismeccentric mechanism, which allowed forhorizontal movement of sieves (imitatinghand sifting), consisted of complexmotion: rotary and to-and-fromotion.The separation time of each sampleamounted to 120 s, starting from themoment of stabilization of separator’selectric motor speed. Material particleswere separated on sieves dependingon their length; the sieve parametersand lengths of particles remaining onparticular sieves are given in Table 2. Theshoe movement frequency was equal to2.4 Hz (144 cycles per minute). Rotationalspeed of electric motor was controlled withinverter and monitored on electronic gauge.Upon completion of separation, the massof mixture on each sieve and the bottom(Fig. 2) was weighed on an electronic scalewith accuracy 0.05 g. Besides, the wholegrains remaining on sieves were pickedup and weighed.The measurements for each varietywere repeated 30 times and the resultswere averaged. If material on the firstsieve weighed less than 1% of entire

26 A. Lisowski et al.TABLE 2. Parameters of sievesNo ofsieveDimension of square opening[mm]Mesh diagonal, X i[mm]Mean length of particle[mm]1 19 26.9 482 12.7 18.0 223 6.3 8.98 12.74 3.96 5.61 7.15 1.17 1.65 3.04Bottom – – 0.82X iFIGURE 2. Effect of maize plant mixture separation (on sieves there are given mesh dimensionsin mm)sample, it was not considered in analysisof the chaff length; if mass of materialexceeded 1%, the length of particularparticles was measured with a slidecaliper.The effect of maize grain breaking upwas evaluated on the basis of breakingup index (Niewęgłowski 2006):mpuz− Σmcikz=⋅100 (1)mpuzwhere:k z – breaking up index of maize grain, %,m p – mass of chaff sample, g,u z – mass ratio of grains in the wholemaize plant,m ci – mass of unbroken maize grains oni-sieve, g.Mass ratio of grains in the wholemaize plant was determined basing onhand hulling of grains of 10 plants. Incombination with unbroken grain mass,

Methods for evaluation of breaking up of maize chaff separated... 27it enabled to determine effectivenessof maize grain breaking up by forageharvester.Geometric mean of chaff particlelength X gm and standard deviation S gmwere calculated according to ANSI/ASAE S424.1 Standard with equations:−1 Σ( m XXilog i)gm = logΣmi(2)1⎡2mi Xi − X ⎤−1Σ (log log gm)2Sgm= log ⎢⎥⎢ Σm⎣i ⎥⎦(3)where:m i – mass of chaff on i-sieve, g,X i – mean length of particle on i-sieve,mm.Length of particles on the first sieve(X l ) measured with a slide caliperaveraged to 48 mm for all varieties. Themean length of particle on the bottom(X 6 ) amounted to 0.82 mm (half ofsieve diagonal of smallest dimension).Geometric means of lengths for theremaining sieves were calculated withequations:1Xi = [ Xi ⋅X( i−1)] 2 (4)where:X i – mesh diagonal of i-sieve (i = 2÷5), m,X (i-1) – mesh diagonal of sieve abovei-sieve, mm.In order to verify the geometricmean of chaff length calculated withANSI/ASAE S424.1 method there wasdetermined the particle mean value withthe use of modified dependence of Rosin-Rammler method. Thus, the hypotheticalvalue of sieve mesh X 50 (mesh diagonal)was evaluated, which passed through ahalf of material being sifted:bX− ⎛ XQ w = − ⎝ ⎜⎞⎟1 250 ⎠(5)where:Q w – cumulated frequency of undersievemass,X – mesh diagonal, mm,b – regression coefficient.By finding the double logarithmfor equation (5) there was obtained thefollowing linear equation of regressioncoefficients b and C:( )⎛log log 1−Qw⎞⎜⎟ =⎝ log 05 . ⎠= b⋅logX −b⋅ log X50== b⋅ log X + C(6)Knowing b coefficient and freeterm C in the equation (6) one couldcalculate X 50 . Regression coefficientswere determined with statistical methodswith the use of Statgraphics Plus v.4.1program.RESULTS OF MEASUREMENTSAND CALCULATIONSThe obtained breaking up indexes k zwere close to 1. In samples of SAN andLG2244 varieties of plants harvestedwith forage harvester Class Jaguar 690SL with row-independent attachment,the single grains were found (one piece

28 A. Lisowski et al.in three and two repetitions, respectively,in 30 samples), while in 14 samples (outof 30) of REDUTA variety, harvestedwith forage harvester Class Jaguar 682 Swith 4-row attachment, there were foundin total 23 unbroken grains (averagebelow 1 piece per sample).The mean chaff mass on sieves andtheir percentage ratio are presented inTable 3.X− ⎛ 1.779SAN: Q w = − ⎝ ⎜ ⎞⎠ ⎟1 2 11 . 12because: X 50 = 11.12 mm b = 1.79X− ⎛ 2.297LG2244: Q w = − ⎝ ⎜ ⎞⎠ ⎟1 2 782 .because: X 50 = 7.82 mm b = 2.297TABLE 3. Chaff mass on sieves for particular varietiesNo ofsieveMassSAN LG 2244 REDUTASieve Q w Mass Sieve Q w Mass Sieveresidueresidueresidue[g] [%] [–] [g] [%] [–] [g] [%] [–]1 52.28 2.47 0.975 0 0.00 1.000 65.48 3.96 0.9602 251.4 11.85 0.857 139.94 6.71 0.933 229.01 13.87 0.8223 1169.96 55.17 0.305 1157.48 55.51 0.378 941.68 57.02 0.2514 322.88 15.22 0.153 406.98 19.52 0.183 267.01 16.17 0.0905 264.95 12.49 0.028 306.87 14.72 0.035 131.85 7.98 0.010Bottom 59.27 2.80 – 73.75 3.54 – 16.56 1.00 –Total 2120.74 100 – 2085.02 100 – 1651.59 100 –Q wIn the case of LG2244 variety thechaff mass on the first sieve amountedto less than 1% of the whole sample;therefore, it was assumed as 0.Substituting values of Tables 2 and3 to Equations (2) and (3) there wasobtained:Using Rosin-Rammler model (5) thefollowing equations were obtained:SAN: X gm = 9.93 mmS gm = 2.08 mmLG2244: X gm = 8.65 mmS gm = 2.04 mmREDUTA: X gm = 11.41 mmS gm = 1.87 mmX− ⎛ 2.122REDUTA: Q w = − ⎝ ⎜ ⎞⎠ ⎟1 2 12 . 92because: X 50 = 12.92 mm b = 2.122Figure 3 presents graphical representationof cumulated frequency of undersievemass Q w calculated with dependence(5). The smallest mesh dimension(7.82 mm) needed to sift 50% of samplemass for particular maize varieties wasfound for LG2244 variety, proving itsbest breaking up.

Methods for evaluation of breaking up of maize chaff separated... 291Frequencies of under-sieve mass, Q w0,90,80,70,60,50,40,30,20,10RedutaLG 2244San0 2 4 6 8 10 12 14 16 18 20 22 24 26Mesh diagonal, X i [mm]FIGURE 3. Cumulated frequencies of under-sieve mass for various maize varieties based on Rosin--Rammler modelSUMMARYThe obtained results enable tofind that both forage harvesters werecharacterized by good breaking upefficiency. Almost 100% of grains werebroken, while optimal length of cutamounted to about 10 mm with relativelysmall scatter. Mean geometric lengths ofparticles differed from those calculatedwith Rosin-Rammler equation. Forvarieties SAN and REDUTA, X gm valueswere lower than X 50 by 12 and 13.2%,respectively, while for LG2244 varietyX gm was bigger than X 50 by 9.6%.However, these differences were notvery big, since they were contained in therange of scatter determined by standarddeviation. It proved the usability of boththe methods in determination of chafflength distribution.REFERENCESMICHALSKI T. 1997: Wartość pastewnaplonów kukurydzy w zależności od sposobówi terminów zbioru. Zeszyty ProblemowePostępów Nauk Rolniczych,450: 133–162.ANSI/ASAE S424.1 MAR98: Method of determiningand expressing participle sizeof chopped forage materials by screening.NIEWĘGŁOWSKI K. 2006: Wpływ czynnikówtechnicznych i eksploatacyjnychna wskaźniki jakościowe rozdrabnianiaroślin kukurydzy zbieranych sieczkarniąpolową. Praca doktorska, maszynopis.Warszawa SGGW.SAR Ł. 2007: Projekt konstrukcyjny separatorasitowego. Praca inżynierska,maszynopis. Warszawa SGGW.Streszczenie: Celem pracy było określenie stopniarozdrobnienia sieczki oraz ziarna kukurydzyza pomocą separatora sitowego wykonanego

30 A. Lisowski et al.według normy ANSI/ASAE S424.1 oraz porównaniemetod oceny rozkładu długości cząsteksieczki. Za wskaźniki jakościowe oceny stopniarozdrobnienia kukurydzy przyjęto średnią geometrycznądługości sieczki, odchylenie standardowedługości oraz wskaźnik rozdrobnienia ziaren kukurydzy.Wskaźniki te określono na podstawierozkładu masowego. Zbadano rozdrobnienie roślinkukurydzy odmian SAN, LG2244 i REDU-TA, o wilgotności odpowiednio 62,8, 59,4 i 63,2%zbieranych dwiema sieczkarniami samojezdnymi.Stwierdzono, że długość sieczki zawiera się wzakresie 7,82-12,92 mm i zależy od zastosowanejsieczkarni oraz odmiany kukurydzy. Metodęwyznaczenia średniej geometrycznej długościsieczki według ANSI/ASAE S424.1 zweryfikowanoposługując się modelem Rosina-Rammlera,obliczając hipotetyczny wymiar oczka sita, przezktóry przechodzi połowa masy przesiewanegomateriału. Uzyskane wyniki świadczą o przydatnościobydwu metod do opisywania rozkładu długościsieczki.MS. received May 2008Authors’ address:Szkoła Główna Gospodarstwa WiejskiegoKatedra Maszyn Rolniczych i Leśnych02-787 Warszawa, ul. Nowoursynowska 166,tel. +22 5934527,e-mail: aleksander_lisowski@sggw.pl

Annals of Warsaw University of Life Sciences – SGGWAgriculture No 52 (Agricultural and Forest Engineering) 2008: 31–37(Ann. Warsaw Univ. of Life Sci. – SGGW, Agricult. 52, 2008)Economic efficiency of growing and technological processesfor cerealsJAROSLAV JÁNSKÝ, IVA ŽIVĚLOVÁDepartment of Business and Economics, Mendel University of Agriculture and Forestry, Brno, CzechRepublicAbstract: Economic effi ciency of growing andtechnological processes for cereals. The paperdeals with the draft of silviculture/technologicalprocesses for selected crops in organic system offarming. The impact of recommended silviculturetechnologies on economics of selected field cropsgrowing was carried out in order to increasethe competitiveness as well as the comparisonto economic results in conventional system offarming, which can contribute to increasing shareof cereals grown on arable land thereby meetingincreasing demand for bio-products of organicorigin in the Czech Republic.Key words: organic farming, cereals, silviculture/technological processes, economic efficiency.INTRODUCTIONGrowth of crops is complex and includesa range of forms. Field crops are themost important among them. Thesesecure the main part of human nutrition.Nevertheless, field crops it is possibleto consider as „ecosystems in which isman not only important force, but forthis kind of ecosystem also the necessarycondition for its existence. Man usesfield crops so, that significant share ofits energy, which the crops gain from thephotosynthesis process, gets out duringthe harvesting from the place of rise andregulates the stream of energy into theplaces of need (Jánský 2005).The agriculture in the European conditionsshould be multifunctional, sustainableand competitive. Approximationor achieving the sustainable agriculturedemands: decrease of inputs, increasingthe efficiency of all used sources andgreater use of natural processes as biologicalnitrogen fixation, circulation ofnutrients, prevention in plant protectionand so on. These general principles mustbe realized in individual growing technologies.The system of organic farming,in adequate extent and in selectedconditions together with observance ofenvironment friendly or equilibriumrules in agro-systems is one of the ways,which could play very important rolefor sustainability of agriculture but withlower yields.GOAL AND METHODOLOGYThe result of this paper is formulation ofrecommended silviculture/technologicalprocesses of selected cereals in organicsystem of farming. Evaluation of theimpact of recommended silviculturetechnologies on selected cereals growingand comparison with economic resultsin conventional systems of farmingwas carried out for increasing thecompetitiveness. It is assumed that theachieved results contribute to increasethe share of crops grown on arable land

Economic effi ciency of growing and technological processes for cereals 33growing stages it is possible to harrow innecessary cases and with lower effect.Inter-row distance is important forweed regulation. Line weed controlneeds minimal inter-row distance of 16cm and more. Inter-row distance of 8––12 cm could be use together with weedregulation done by harrowing. Vegetationgrown in narrower rows inhibits betterthe weeds. The use of narrow rows is lesslabor intensive. Line weeding is possibleto choose while greater occurrence ofweeds.Recommended growing andtechnological processes for cerealsgrowing and its evaluation for winterwheatWheat is the most important foodcereal and as food is used in corn andbeat growing regions. Wheat flower isused in food industry for bread bakingand for other wide range of bakery andpasta. The grain is processed into groutand semolina, together with millingwaste is grain valuable feed. The grainis in smaller amounts used as industrymaterial for starch production, alcoholand beer production. Wheat straw is usedas bedding.It is the most demanding for soilfertility and water accessibility out ofall crops. The use of nutrients per onehectare is clear from Table 1. It uses verygood deep and heavier soils with largewater capacity. Very light or shallow andpeaty soils are not suitable for it.TABLE 1. Nutrients consumption (kg/ha)N P 2 O 5 K 2 OOrganic farming 60 29 58Conventional farming 119 57 114Winter form of wheat reacts the mostsensibly from all crops to the crop grownin prior season. Very good prior-cropsfor winter wheat in this means are cropswith wide leafs or crops fertilized withmanure. For example it can be perennialfodder crops (besides the drier areas,where it can worsen the water regime forconsequent crop). Other very good cropsare leguminous crops, pulse-cerealsmixed green crops, early and semi-earlypotatoes and corn for silage. These kindsof crops are important for the qualityof winter wheat, especially concerningthe amount of aleurone. The highestamounts of aleurone are after clover andpulse crops.Winter wheat is grown by number offarmers in the organic system of farming.The efficiency of farming is determinedabove all by costs and revenues. Theoverview of average costs per one hectareof harvested area while following abovementioned technological processes insystem of organic farming is clear fromTable 2.TABLE 2. Structure of average costs and revenuesfor winter wheat in system of organic farmingCosts per 1haIndicatorof harvestedarea (CZK)Seeds 1 765Fertilizers 1 113Other direct material costs 210Direct material costs in total 3 088Costs of growing technology 4 619Indirect costs 2 735Costs in total 10 442Marginal contribution 619Costs of major product (CZK/t) 3 829Per hectare yield (t/ha) 2.40Average farmers’ price (CZK/t) 3 469

Economic effi ciency of growing and technological processes for cereals 35TABLE 4. Characteristics of costs and revenues for winter wheat in the sample file of organic farmersIndicator Average Minimal value Maximal value MedianSeeds 1 787.60 750.00 2 440.00 1 600.00Fertilizers 1 307.10 875.00 5985.00 1 859.00Other direct material costs 35.00 0.00 520.00 24.00Direct material costs in total 3 129.71 1 625.00 8 945.00 3 483.00Costs of growing technology 5 643.39 948.00 21 518.00 4 343.00Indirect costs 1 618.16 x x 5 048.00Costs in total 10 391.26 7 407.00 24 203.00 12 874.00Marginal contribution –443.46 x x 5 043.70Costs of major product (CZK/t) 6 178.08 2 332.64 8 048.37 4 315.59Per hectare yield (t/ha) 2.03 1.14 4.15 3.10Average farmers’ price (CZK/t) 4 110.33 3 199.99 5 000.04 4 151.52is by 276 CZK less than it is set inrecommended technological process.Marginal contribution presented bymedian is in the sample file higher thanthe average shown in Table 2, i.e. by1062 CZK per one hectare. It is causedby higher per hectare yield and lowervariable costs.Even when the comparison of thesample file of organic farmers withthe set recommended value is for thesample file favorable, in comparisonto conventional farmers is organicallygrown winter wheat by far less efficient,which is possible to deduce from data inTable 5.Total per hectare costs shown byorganic farmers have by 3972 CZKlower average value than companies inconventional system of farming. In themeans of median it is less by 1489 CZK.The main reason for lower costs is theabsence in use of chemical protectivemeans by organic farmers as well asman-made fertilizers, which is clear fromindexes in Table 6.TABLE 5. Structure of average costs andrevenues of winter wheat in conventional systemof farmingCosts per 1 haIndicatorof harvestedarea (CZKSeeds 1 078Fertilizers 2 509Other direct material costs 2 659Direct material costs in total 6 246Costs of growing technology 5 824Indirect costs 2 293Costs in total 14 363Marginal contribution 3 155Costs of major product (CZK/t) 2 667Per hectare yield (t/ha) 4.74Average farmers’ price (CZK/t) 3 212Different per hectare yield is exposedinto costs per one ton of grain. Perhectare yield is in this sample file oforganic farmers in the range of 1.14 till4.15 tons per hectare, while in the systemof conventional farming it is in average4.74 tons. Different is average farmers’price as well, in the sample file of organic

36 J. Jánský, I. ŽivělováTABLE 6. Comparison of average costs andrevenues of winter wheat for companies farmingin organic and conventional conditionsIndicatorIndexSeeds 1.64Fertilizers 0.44Other direct material costs 0.08Direct material costs in total 0.49Costs of growing technology 0.79Indirect costs 1.19Costs in total 0.73Marginal contributionxCosts of major product (CZK/t) 1.44Per hectare yield (t/ha) 0.51Average farmers’ price (CZK/t) 1.08farmers it is 3200 till 5000 CZK per onetone of grain – the average is only 4110CZK. From the median in the level of4152 CZK it is possible to conclude thatorganic farmers are partially selling theorganic winter wheat as organic product.Nevertheless, the winter wheat seemsto be unprofitable crop in the system oforganic farming.CONCLUSIONImportant aspect that influences the decision-makingof farmers about transformationinto organic farming is economicresults. For better awareness inthis area it is necessary to compare theeconomic results of individual sectors inconventional as well as organic systemsof farming and thus to get objective datafor consequent decision-making abouthow to contribute to the development oforganic farming especially on the arableland, which represents in the conditionsof the Czech Republic 7.5% out of totalarea of organically farmed land.The result of this paper is formulationof recommended silvicultural/technologicalprocesses of selected cereals in organicsystem of farming. Similar analysis as itwas carried out for winter wheat was alsosolved in the research project for other cerealsas spring wheat, spelt, winter barley,oats, rye, triticale and some other plantsgrown on arable land.Evaluation of the impact of recommendedsilvicultural technologies onselected field crops growing and comparisonwith economic results in conventionalsystems of farming was carriedout for increasing the competitiveness,which can contribute to increasethe share of crops grown on arable landthereby meeting increasing demand forbio-products of organic origin in theCzech Republic.REFERENCESJÁNSKÝ J.: Analysis of the current situationin sales of selected organic products in theCzech Republic. Zemědělská ekonomikač. 7, 51, Praha 2005, p. 309–313.Jánský J., Živělová I: Analýza vztahu cena nákladů na vybrané rostlinné bioprodukty.Sborník příspěvků z mezinárodníhovědeckého semináře „Manažment a ekonomikaekologickej polnohospodárskejvýroby“, SPU Nitra, 2005, p. 39–42.(zborník anotácií).Streszczenie: Ekonomiczna efektywność technologiiuprawy zbóż. W pracy przedstawiono projektprzebiegu technologii uprawy zbóż w systemierolnictwa organicznego w aspekcie ekonomicznym.Otrzymane wyniki porównano z efektamiekonomicznymi dla rolnictwa tradycyjnegow Republice Czeskiej i stwierdzono, że technologierolnictwa organicznego mogą być konkurencyjneprzy rosnącym popycie na biomasę pochodzącąz upraw roślin zbożowych.

Economic effi ciency of growing and technological processes for cereals 37MS. received June 2008Authors’ addresses:Jaroslav JánskýDepartment of Business and EconomicsFaculty of EconomicMendel University of Agriculture and ForestryBrnoZemědělská 1, 613 00 Czech Republice-mail: jansky@mendelu.czIva ŽivělováDepartment of Business and EconomicsFaculty of EconomicMendel University of Agriculture and ForestryBrnoZemědělská 1, 613 00 Czech Republice-mail: zivelova@mendelu.cz

Annals of Warsaw University of Life Sciences – SGGWAgriculture No 52 (Agricultural and Forest Engineering) 2008: 39–44(Ann. Warsaw Univ. of Life Sci. – SGGW, Agricult. 52, 2008)Effect of storage conditions on biological value of wheat and barleygrainCZESŁAW WASZKIEWICZ, MICHAŁ SYPUŁADepartment of Agricultural and Forest Engineering, Warsaw University of Life Sciences – SGGW,Warsaw, PolandAbstract: Effect of storage conditions onbiological value of wheat and barley grain. Therewas compared the rate of mould attacking of wheatand barley grain during storage at temperatures20, 25 and 30°C and relative air humidity 94and 99%. The samples were put in a climaticchamber and constant storage conditions weremaintained. Using linear regression method therewere determined equations describing the rate ofgrain attacking, as a function of storage time forparticular storage conditions. Basing on carriedout analysis of investigation results it was provedthat the wheat grain was earlier attacked by mouldand it lost its germination capacity earlier, whencompared to barley grain.Key words: grain, wheat, barley, germinationcapacity, mould.INTRODUCTIONGrain is a live organism, with continuoustransformations going inside it. Thepurpose of proper storage is to inhibitbiological processes to the highestpossible extent and to eliminateunfavourable environmental factors,which limit duration of the safe storage.The biochemical processes occurring ingrain are directly influenced by moisturecontent, air temperature, contact withair and condition of grain (degree ofdamage) (Janowicz 2005; Ryniecki1998; Waszkiewicz 1986).The best-known criteria for safestorage are: degree of mould development,degree of deterioration of germinationability and amount of carbon dioxideemission and the connected loss in graindry mass (Ryniecki 1998).A general equation for calculation ofpermissible duration of wheat storagedepending on storage conditions anddegree of mechanical damage of grainwas developed by Al-Yahya (1999), whoassumed the amount of cargo dioxideemission as an evaluation criterion.The time of loosing 1% of grain masswas taken in this equation as the indexfor determination of admissible time ofwheat storage there.Wilcke et al. (2000) determinedpermissible duration of maize grainstorage with the use of Thompsonequation; they calculated the loss of graindry mass as a function of time and amountof carbon dioxide emission, and assumedthe time of loosing 0.5% of grain dry massas permissible time of storage.In references one can find a generalmathematical model for prediction thevitality of grain of high content of starch,protein and oil depending on storageconditions; the equilibrium relativemoisture content was used in modeldescription instead the grain moisturecontent (Chen and Jayas 2000).The most severe criterion for grainis development of mould, leading to

40 Cz. Waszkiewicz, M. Sypułaspoilage of grain by deterioration ofits sowing, feeding and technologicalvalue.Therefore, this work aimed atdetermination of the effect of storageconditions for wheat and barley grainon mould development and changes ingermination energy and capacity duringstorage. The temperature, air humidityand time of storage were taken asparameters for determination of storageconditions.MATERIAL AND METHODSThe investigations were carried out onwinter wheat grain of Flair variety andspring barley grain of Justyna varietyharvested in 2006. Grain was put intoclimatic chamber KPK200 of Feutronmake and was stored in galvanizedcontainers at fixed temperature and airmoisture content. Investigations wereexecuted at the following air parametersin the chamber:––3 temperature levels (20°C, 25°C and30°C),2 levels of relative humidity (94%and 99%).Relative air humidity set up in thechamber to 94% and 99% enabled tosteady the equilibrium moisture contentof grain at given temperature duringstorage. The equilibrium moisturecontent ranged from 20.2 to 23.6% forwheat grain and from 19.8 to 24.8%for barley grain, depending on storageparameters.Temperature and air humidity in thechamber were measured with the use ofhytherograph LB-701 of LAB-EL make,with maximal error of temperaturemeasurements 0.4°C and maximalerror of moisture content measurements1.5%.Then, at specified time intervals3 grain samples were taken out ofthe chamber, each of 100 pieces, andthere was determined the number ofmould attacked grains and germinationcapacity.The mould attacked grain wasevaluated visually with the use ofmagnifying glass, searching forcharacteristic symptoms of mould(white, then green, green and yellow,and brown or black). After countingof mould-attacked seeds, there wascalculated their percentage ratio in entireamount of seeds taken from the samples.The germination capacity and energy forwheat and barley grain were investigatedaccording to PN-79/R-95950 with theuse of Jacobsen’s germination apparatus.The germination energy was determinedafter 3 days (barley) and 4 days (wheat)of keeping grain in the apparatus, whilethe measurements on germinationcapacity were executed after 7 and 8days, respectively.RESULTS OF INVESTIGATIONSAND DISCUSSIONThe multifactor analysis of varianceproved that all three consideredindependent variables (temperature,relative air humidity, time of storage)influenced significantly the moulddevelopment and germination capacity.The results presented in Figure 1 pointout that temperature 30°C and humidity99% are the most favourable conditionsfor mould development, since after twodays 10% of wheat grain was attacked,

Effect of storage conditions on biological value of wheat and barley grain 41a) b)rate of mould development[%]7060wheat5040302010barley00 5 10 15storage time [days]rate of moulddevelopment [%]50403020100wheatbarley0 5 10 15 20storage time [days]c)rate of mould development[%]100806040wheat20barley00 10 20 30storage time [days]FIGURE 1. Changes in mould development as a function of storage time at air humidity 99% and temperature:a) 30°C, b) 25°C, c) 20°Cand after 12 days this figure increased toabove 50%. For barley grain this processwas even quicker; at the same parameters30% of grain was attacked by mould.At the remaining temperature (20°Cand 25°C) the mould creation processwas slower than at 30°C, while the firstsymptoms of mould were found after 3days on mechanically damaged grain.After 12 days of storage mould wasfound on 42% of seeds at temperature25°C and 26% at temperature 20°C,and after three weeks all the grain wasattacked. It was also found that themould process was slower in the initialperiod, and later on it was quicker. This isbecause the mouldy grain is a good focusof infection for the adjacent grain. Therate of mould development in barley waslower under the same storage conditions;at temperature 20°C after three weeksonly 40% of grain was affected.Figure 2 presents comparison betweenrate of mould development on consideredgrain at temperature 20°C and 25°Cat constant relative air humidity 94%.After 16 days of storage at temperature25°C the percentage of attacked wheatgrain amounted to 70%, while for thebarley grain to 30%. The rate of moulddevelopment in the same time at lowertemperature (20°C) was slower andamounted to 17 and 12% for wheat andbarley, respectively.The rate of mould development as afunction of storage time was describedby empirical formulae and presented inTable 1.

42 Cz. Waszkiewicz, M. Sypułaa) b)note rate of moulddevelopment [%]80706050403020100wheatbarley0 4 8 12 16 20rate of moulddevelopment [%]80706050403020100wheatbarley0 5 10 15 20 25 30 35 40 45 50 55storage time [days]storage time [days]FIGURE 2. Changes in mould development as a function of storage time at air humidity 94% andtemperature: a) 25°C, b 20°CTABLE 1. Empirical dependencies for rate of mould development during grain storageCereals Regression equationApplication rangeCorrelationcoefficient Rstorageair humiditystorage timetemperaturewheat y = 4.1863t s + 1.9104 0.988 99% 30°C 2 – 12 daysbarley y = 3.0661 t s – 12.3 0.989 99% 30°C 5 – 14 dayswheat y = 4.7229 t s – 9.5714 0.983 99% 25°C 3 – 12 daysbarley y = 2.1111 t s – 5.1111 0.985 99% 25°C 3 – 16 dayswheat y = 5.2615 t s – 25.344 0.947 99% 20°C 3 – 20 daysbarley y = 2.1656 t s – 10.163 0.978 99% 20°C 3 – 24 dayswheat y = 5.1521 t s – 19.727 0.970 94% 25°C 3 – 17 daysbarley y = 2.167 t s – 5.7895 0.981 94% 25°C 3 – 16 dayswheat y = 2.5737 t s – 21.263 0.980 94% 20°C 6 – 36 daysbarley y = 1.3187 t s – 10.802 0.973 94% 20°C 6 – 54 dayswhere: y – ratio of mould attacked grain in the sample [%], t s – storage time [days]The obtained high correlationcoefficients for regression equationsallow for their application for practicalpurposes.The results of germination energyand capacity enabled to evaluate theusability of wheat and barley grainas sowing material during storage athumidity 99% and temperature 30°C.Considering diagrams on Figure 3 andthe obtained equations (Tab. 2) onecan find that germination energy andcapacity decrease with time of storagein the climatic chamber. This is provedby nature of equations and negativevalue of correlation coefficient. After47 days of barley grain storage thegermination energy decreased to 67%,while germination capacity to 79%. Thesame figures for wheat grain amountedto 12% and 16%, respectively. Theobtained results were strongly influencedby mould development, enhanced byprevailing storage conditions, at whichthe grain were loosing its sowing value.In the investigated period the difference

Effect of storage conditions on biological value of wheat and barley grain 43a) b)Ek, Zk [%]100806040200ZkEk0 5 10 15 20 25 30 35 40 45 50storage time [days]Ek, Zk [%]1009080706050403020100EkZk0 5 10 15 20 25 30 35 40 45 50storage time [days]FIGURE 3. Changes in germination capacity and germination energy during storage: a) – wheat,b) – barleyTABLE 2. Regression dependencies of germination energy and capacity on storage time at temperature30°C and relative humidity 99%Cereal Parameter EquationCorrelationcoefficient RStorage timePszenica Germination energy E k = –1.794 t s + 89.59 –0.933 0–47 daysGermination capacity Z k = –1.84 t s + 94.03 –0.936 0–47 daysJęczmień Germination energy E k = –0.40 t s + 86.15 –0.912 0–48 daysGermination capacity Z k = –0.387 t s + 95.68 –0.910 0–48 daysbetween germination energy andgermination capacity did not exceed 5%for wheat and 10% for barley.Dependencies of germination capacityand germination energy during storageon storage time are described by regressionequations presented in Table 2.CONCLUSIONS• The considered storage conditions(temperature, air humidity and timeof storage) significantly influencedegree of mould infection of wheatand barley grain. Under the samestorage conditions, the rate of moulddevelopment on wheat grain is biggerthan on barley grain.• The germination capacity and energydeteriorate with storage time; in entireinvestigated period the germinationenergy was lower on the average by10% for barley and by 4% for wheatthan their germination capacity.• The obtained empirical equationsallow for fairly accurate descriptionof the mould development rate andchanges in grain germination capacitydepending on storage time.REFERENCESAL-YAHYA S.A. 1999: Deterioration rates ofwheat as measured by CO2 production.Can.Agricult.Eng. Vol. 41 (3), 161–166.CHEN C., JAYAS D.S. 2000: Relatingequilibrium relative humidity and

44 Cz. Waszkiewicz, M. Sypułatemperature to seed longevity. Agricult.Eng. J. Vol. 9 (3-4), 129–138.JANOWICZ L. 2006: Wpływ zmian mikrobiologicznychna jakość ziarna zbóżw czasie przechowywania. Przegląd zbożowo-młynarski,4 s. 33.RYNIECKI A., 1998: Warunki bezpiecznegoprzechowywania ziarna Przegląd zbożowo-młynarski,10 s. 31–32.WASZKIEWICZ Cz., 1986: Analiza i dobórtechnologii konserwowania ziarna zbóżdla potrzeb gospodarstwa rolnego. Rozprawynaukowe i monografie. WydawnictwoSGGW-AR, Warszawa.WILCKE W.F., GUPTA P., MERONUCKR.A., MOREY R.V. 2000: Effect ofchanging temperature on deterioration ofshelled corn. Trans. ASAE, Vol. 43 (5),1195–1201.Streszczenie: Wpływ warunków przechowywaniana wartość biologiczną ziarna pszenicy i jęczmienia.Dokonano porównania tempa porażenia pleśniąziarniaków pszenicy i jęczmienia podczas ichprzechowywania w temperaturach 20, 25 i 30°Coraz przy wilgotności względnej powietrza 94i 99%. Stałe warunki przechowywania byłyutrzymywane przez umieszczenie próbek ziarnaw komorze klimatycznej. Metodą regresji liniowejwyznaczono równania opisujące tempo porażeniaziarniaków w funkcji czasu przechowywania dlaposzczególnych warunków przechowywania. Napodstawie przeprowadzonej analizy wynikówbadań wykazano, że warunki przechowywania(temperatura i wilgotność powietrza oraz czasprzechowywania) istotnie wpływają na stopieńporażenia pleśnią ziarna pszenicy i jęczmienia.Przy tych samych warunkach przechowywaniatempo rozwoju pleśni na ziarniakach pszenicy jestwiększe w porównaniu do jęczmienia.MS. received June 2008Authors’ address:Katedra Maszyn Rolniczych i LeśnychSzkoła Główna Gospodarstwa Wiejskiego02-787 Warszawa, ul. Nowoursynowska 164

Annals of Warsaw University of Life Sciences – SGGWAgriculture No 52 (Agricultural and Forest Engineering) 2008: 45–50(Ann. Warsaw Univ. of Life Sci. – SGGW, Agricult. 52, 2008)Investigations on drying of new pumpkin varietiesMARIUSZ SOJAK, SZYMON GŁOWACKIDepartment of Fundamental Engineering, Warsaw University of Life Sciences – SGGW, Warsaw,PolandAbstract: Investigations on drying of newpumpkin varieties. The paper presents preliminaryinvestigations and verification of models fordrying of new pumpkin varieties in the first dryingperiod, with consideration to shrinkage in volume,and in the second drying period. In calculationsthere were used results of measurements onkinetics of drying (in forced convection) ofpumpkin particles (cross-cut perpendicularlyto fibres) in the shape of a plate of thickness5 and 10 mm, at drying medium temperature80°C, drying medium speed 1.2 m·s –1 . The resultsof measurements on changes in pumpkin particlevolume were also used in calculations. The dryingprocess was executed in a tunnel dryer.Key words: pumpkin, convectional drying, forcedconvection, model, shrinkage.NotationK – drying coefficient in the secondperiod, min –1 ,N – coefficient, dimensionless,a – coefficient, dimensionless,b – maximum value of shrinkagecoefficient, dimensionless,k 0 – constant drying rate, min –1 ,M s – dry mass of solid particle, %,u – moisture content of solid particle,kg·kg –1 ,u e – equilibrium moisture content ofsolid particle, kg·kg –1 ,u 0 – initial moisture content of driedparticle, kg·kg –1 ,u cr – critical water content, kg·kg –1 ,δ – relative error, %,∆ – error, dimensionless,τ – time of drying, min,τ cr – time of drying while water content,u = u cr , min.INTRODUCTIONIn recent years one can find a constantlyincreasing interest in the products madeof pumpkin fruits, expressed by feed,pharmaceutical and food industries. Tomeet new requirements of consumers, inDepartment of Plant Genetics, Breedingand Biotechnology of WULS therewere created new pumpkin varietiesof improved quality in terms of: taste,nutritional and technical value (softexternal cover, improved colour offlesh and cover, reduced volume ofseed pocket. Investigations were carriedout on drying of two edible pumpkinvarieties (Justynka and Amazonka) andone feeding variety (Ambar). In hithertoreferences, apart from the Authors’investigations, there is lack of data onpumpkin drying, particularly on dryingof new varieties. More detailed learningand scientific explanation of the processis important with respect to both thecognitive and utilization aspects. Thedrying process coefficients determinedin this work can be used in optimizationof convectional drying process and alsoin improvement of dryers’ design.

46 M. Sojak, Sz. GłowackiMODELLING OF VEGETABLECHIPS DRYINGProcess of convectional drying ofvery moist solid bodies occurs in twosignificantly different drying periods. Inthe first period the process is influencedby conditions of water particle transportfrom the surface of body subjected todrying through the boundary layer ofgas; in conventional second period ofdrying, the process is influenced bythe conditions of internal diffusion ofthese particles to body surface. In fact,transition from the first to second periodproceeds constantly. This period is calleda transitory period and it is not welllearned theoretically, while only partsof its mathematical models are known(Jaros and Pabis 2006). The kineticmodel considering the effect of dryingshrinkage was checked in this work inthe range (u 0 , u cr ) (Pabis 1999):u() τ⎡N⎛ b= ub Nu k ⎞⎢1⎤⎜ τ⎟− ⎥0 1− 1− ⎢1−⎝ 0 ⎠bb0 1−⎥⎣⎦(1)This model was proved with maximallocal error not bigger than 11% in therange of water content from 7.5 to about2 kg·kg –1 and for coefficient b = 0.056,determined in drying process of anotherpumpkin variety (Sojak 2000).The exponent N value in equation(1) can be determined eg.: by trial-anderror method or it can be calculated(Pabis and Jaros 2002) basing on resultsof measurements on changes in shapeparameters (Sojak and Jaros 1999).The second model to be verified forthe range (u 0 , u cr ) is:u() τ = ue + ( ucr −ue) exp(− Kτ)(2)Equation (2) was proved withmaximal local error not bigger than 15%in the range of water content from about2 to about 0.02–0.16 kg·kg –1 .In respect to maintaining of processcontinuity in point u = u cr , drying speedsin the first and second periods must beequal, thus, (du/dτ ) I = (du/dτ ) II (Sojak2000).Then, coefficient of drying speed canbe calculated (Jaros and Pabis, 2006)from equation:K =k0ucr− ue( )⎛ Nb−−−1⎜Nu k ⎞1 1 cr ⎟⎝ 0 0 τ (3)⎠VERIFICATION OF MODELSFOR THE FIRST AND SECONDDRYING PERIODSFigure 1 presents results of threerepetitions of measurements on changesin water content in pumpkin slices ofthickness 5 and 10 mm at drying mediumtemperature 80°C and drying mediumspeed 1.2 m·s –1 . Changes in water contentwere described with the mean functionsin the form of cubic polynomials selectedso, that relative error was lower then 1%;their graphical interpretation is presentedin Figure 1.Coefficient N in model (1) wasdetermined by subsequent approximationsso, that model relative error of watercontent was possibly lowest according tomethod given by Jaros and Pabis (2006).The value of k 0 coefficient of initialdrying speed was determined by linearregression method, basing on watercontent initial measurements.

Investigations on drying of new pumpkin varieties 4787u [kg . kg -1 ]65432J, 5 mmJ, 10 mmZ, 5 mmZ, 10 mmB, 5 mmB, 10 mm100 100 200 300 400τ [min]FIGURE 1. Graphical representation of formulae approximating results of water content measurements(u) in time (τ) for pumpkin chips o varieties Justyna (J), Amazonka (Z) and Ambar (B) of slice thickness5 and 10 mm, dried at temperature 80°C and drying medium speed 1.2 m·s –1The course of drying speed wasdetermined basing on measurements onwater content. Figure 2a presents dryingspeed of pumpkin chips of Justynkavariety in slices of thickness 10 mmdepending on drying duration, while inFigure 2b the drying speed is dependenton instant water content calculated frommeasurements.Analysis of drying speed ofinvestigated samples points out(especially on diagrams of b type)that at water content of 2 kg·kg –1 themechanism of mass exchange is changedand also the drying speed decreasesrapidly. Therefore, it was assumed thatthe critical content is equal to or is closeto 2 kg·kg –1 .a) b)k, K [min -1 ]0,070,060,050,040,030,020,010,000 100 200 300 400τ [min]k, K [min -1 ]0,070,060,050,040,030,020,010,000 1 2 3 4 5 6 7 8 9u [ kg . kg -1 ]FIGURE 2. Diagrams of drying speed in I and II drying periods (thin line, k and thick line, K, respectively)for pumpkin chips of Justynka variety of thickness 10 mm, dried at temperature 80°C and dryingmedium speed 1.2 m·s –1

48 M. Sojak, Sz. GłowackiFigure 3 presents diagrams of localrelative and absolute errors determiningaccuracy of calculations on water contentof models (1) and (2) compared to valuescalculated from measurements.is not acceptable, which is pointed outby absolute and relative error valuescalculated in relation to measurementresults. Error values of both modelsincrease along with an increase inδ [%]60504030201000 100 200 300 4000-0,3 0,0 0,3τ [min]Δ [−]FIGURE 3. Diagrams of local relative (δ) and absolute (∆) errors for water content calculations of modelI (thin line) and II (thick line) of drying period for pumpkin chips of Justynka variety for slice thickness5 mm, temperature 80°C, drying medium speed 1.2 m·s –1 and coefficients: N = 1.6, b = 0.056u [kg . kg -1 ]8642The value of relative error was takenas 0.3 kg·kg –1 , assuming that this valuedetermines the range of water content,where models (1) and (2) can be regardedas verified; this value should not beexceeded.The time τ cr after which the driedsamples achieve water content 2 kg·kg –1was determined basing on formulaeapproximating results of measurements.It was found that changes in dryingspeed can be described with a linearequation of large value of correlationcoefficient. It results in the value of Ncoefficient in model (1) equal to 2. Thevalues of this coefficient, for which themodel of I drying period is regarded asverified are presented in Table 1.The models of changes in watercontent (1) and (2) are highly dependenton coefficient N. If its value is taken as 3,the accuracy of drying process modelingcoefficient N values (Fig. 4).The carried out logical analysis ofmodels (I) and (II) with considerationto equation (3) showed the sufficientaccuracy of modeling the drying processfor values of drying shrinkage values bincluded in interval [0,0; 0,1];it is provedby relative and absolute error valuesfor both models, calculated in relationto measurement results. However, thisaccuracy calculated for b values includedin interval (0,1; 1,0] can not be accepted,basing on the relative and absolute errorvalues (Fig. 5).In Table 1 there are presented valuesof particular drying parameters and modelcoefficients of drying periods I and II,for which these models were consideredas verified. Additionally, the percentcontent of dried matter was included forparticular varieties of pumpkin fruits.

Investigations on drying of new pumpkin varieties 49TABLE 1. Values of parameters of pumpkin chips drying for which models (1) and (2) were verifiedVarietyParametersJustynka Amazonka AmbarThickness [mm]5 10 5 10 5 10u 0 [kg⋅kg –1 ] 7.63 7.63 7.32 7.32 5.01 5.01τ cr [min] 113 177 125 207 75 147u cr [kg⋅kg –1 ] 1.8 2.1 2.0 1.8 2.0 2.0N 1.60 2.40 1.40 2.80 2.60 1.10k [min –1 ] 0.063 0.043 0.049 0.038 0.052 0.021b 0.056 0.056 0.056 0.056 0.056 0.056u e [kg⋅kg –1 ] 0.020 0.040 0.110 0.040 0.060 0.160K [min –1 ] 0.0222 0.0107 0.0192 0.0098 0.0161 0.0110M s [%] 11.59 11.59 12.02 12.02 16.65 16.65δ [%]N=1,6N=3,0N=5,060504030201000 100 200 300 400τ [min]u [ kg . kg -1 ]N=1,6N=3,0N=5,086420-0,6 -0,3 0,0 0,3 0,6Δ [-]FIGURE 4. Diagrams of local relative (δ) and absolute (∆) errors for water content calculations ofmodel I (thin line) and II (thick line) of drying period for pumpkin chips of Justynka variety for slicethickness 5 mm, temperature 80°C, drying medium speed 1.2 m·s –1 and coefficients: N = 1.6, 3.0, 5.0;b = 0.056b=0,056b=0,1b=0,3b=0,056b=0,1b=0,3δ [%]60504030201000 100 200 300 400τ [min]u [kg . kg -1 ]86420-0,6 -0,3 0,0 0,3 0,6Δ [-]FIGURE 5. Diagrams of local relative (δ) and absolute (∆) errors for water content calculations of modelI (thin line) and II (thick line) of drying period for pumpkin chips of Justynka variety for slice thickness5 mm, temperature 80°C, drying medium speed 1.2 m·s –1 and coefficients: N = 1.6, b = 0.056, 0.1, 0.3

50 M. Sojak, Sz. GłowackiSUMMARYKinetic models of convectional dryingof solid bodies considering the dryingshrinkage were proved empirically withresults of measurements on drying ofpumpkin chips of varieties Amazonka,Ambar and Justyna in the tunnel dryer. Asthe logical analysis points out and carriedout investigations prove, the models aresensitive to changes in coefficients N andb values. For investigated varieties therewere obtained significantly differentvalues of coefficient N, while the samemaximal shrinkage value b (determinedin previous investigations for Auravariety) was taken. The coefficient bvalues can differ in various varieties,characterized by different content ofdry matter. Therefore, it is advisable tocarry out further investigations in orderto determine the above coefficients foreach variety and to re-verify the models(1) and (2), as well as to determine otherparameters describing the drying process(eg. coefficient of water diffusion).REFERENCESSOJAK M., JAROS M. 1999: The verificationof the model of the first drying period ofpumpkin. International Conference ofPhD Students. University of Miszkolc,Section proceeding agriculture. Hungary.pp. 155–161. ISBN: 963-661-375-3SOJAK M. 2000: Matematyczny model konwekcyjnegosuszenia dyni spożywczej.Rozprawa doktorska. SGGW Warszawa.PABIS S. 1999: Koncepcja teorii konwekcyjnegosuszenia warzyw. KonwekcyjneSuszenie Warzyw – Teoria i Praktyka,pod red. Stanisława Pabisa. PTIR Kraków1999, s. 9–31. ISBN: 83-905219-2-XPABIS S.; JAROS M. 2002: The first periodof convection drying of vegetables andthe effect of shape-dependent shrinkage.Biosystems Engineering. 81(2), pp. 201––211. www.idealibrary.com. ISSN: 1537––5110.JAROS M., PABIS S. 2006: Theoreticalmodels for fluid bed drying of cutvegetables. Biosystems Engineering.93(1), pp. 45–55. www.sciencedirect.com. ISSN: 1537–5110.Streszczenie: Badania suszenia nowych odmiandyni. W pracy przedstawiono wstępne wynikibadań oraz weryfikację modeli pierwszegookresu suszenia z uwzględnieniem skurczuobjętościowego oraz drugiego okresu suszenianowych odmian dyni. Do obliczeń wykorzystanowyniki pomiarów kinetyki suszenia, w konwekcjiwymuszonej, cząstek dyni (krojonych prostopadledo włókien) w kształcie płyty o grubości 5 oraz 10mm, w temperaturze czynnika suszącego 80°C,dla prędkości czynnika suszącego 1,2 m·s –1 , orazpomiarów zmian objętości cząstek dyni. Processuszenia przeprowadzony został w suszarcetunelowej.MS. received June 2008Authors’ address:Mariusz Sojak, Szymon GłowackiWydział Inżynierii Produkcji SGGW,Zakład Podstaw Nauk Technicznych,ul. Nowoursynowska 164, 02-787 Warszawa,Poland.e-mail: mariusz_sojak@sggw.pl

Annals of Warsaw University of Life Sciences – SGGWAgriculture No 52 (Agricultural and Forest Engineering) 2008: 51–57(Ann. Warsaw Univ. of Life Sci. – SGGW, Agricult. 52, 2008)Analysis of optimal values of air stream supplied in the clustersMARIA MAJKOWSKA 1 , ADAM KUPCZYK 21 Department of Applied Mathematics, Warsaw University of Life Sciences – SGGW, Warsaw, Poland2 Department of Production Management and Engineering, Warsaw University of Life Sciences –SGGW, Warsaw, PolandAbstract: Analysis of optimal values of air streamsupplied in the clusters. A mathematical model forvacuum drop in the form of Bernoulli equationwith the assumed constant velocity of ascent of afree bubble was used in determination of optimalair streams to get the minimal vacuum drops.Analysis of these values allowed for determinationof supplied air stream corresponding to a givenmilking speed, depending on tube diameter andthe height of liquid rise.Key words: mechanical milking machine, airstream, pressure changes, optimization.INTRODUCTIONIt was found in hitherto investigations(Majkowska 2007b) that the minimalvacuum drops (min ∆p) in a long milk tubeare realized at properly adjusted valuesof supplied air stream Q p (min∆p). Thisresult confirms the previous Nordergenhypothesis (1980). The cluster operatingin a given cow-shed serves the cows ofdifferent milking capacity Q m . Even ifthe animals are grouped according tomilking capacity, it differs between thecows within a given group.The problem arises, how to selectthe supplied air stream to achieve theleast drops, and what is the connectionbetween the stream Q p (min∆p) allowingfor minimal drops and design parametersof milking machine.AIM OF WORKThe work aims at determination ofsupplied air stream to get the leastvacuum drops, and at analysis of theeffect of long milk tube diameter andthe height of liquid rise on optimal airstream value Q p (min∆p).MATERIAL AND METHODSThe model presented by Kupczyk(1999) for vacuum drops in the cluster,described with Bernoulli equation for thelost height, is of the form:Δpkol = ( 1− α pdpm)ρmgH+l 2dpm u+ ( 1− αMpdpm)λρm+D 2u+ ( 1−αpdpm)ξρm M22where:α pdpm – volumetric coefficient of air inlong milk tube,α mdpm – volumetric coefficient of milk inlong milk tube,l dpm – length of milk tube [m],D – diameter of long milk tube [m],ζ – coefficient of local losses,λ – coefficient of local loss dependent onrelative tube roughness and Re number,

52 M. Majkowska, A. KupczykH – height of rise [m],ρ m , ρ p – density of milk and air [kg . m –3 ],g – acceleration of gravity [m . s –2 ],u M – reduced velocity of mixture [m . s –1 ].This equation was simplified bysubstitution of free rise velocity with aconstant one (Majkowska, 2007a). Thevacuum drop ∆p kol occurs in factors:Q Qu u u m p pM = m + p = + NAdpmAdpmpu pα pdpm =12 , uM+ v ∞pwith p= p kolr + Δ 2[kPa]where:Q m , Q p – stream of milk [kg . min –1 ] andair [m 3. h –1 ],u p , u m – reduced velocities of air andmilk [m . s –1 ],p N =100 [kPa].The Bernoulli equation was reduced toa fourth degree polynomial in relationto ∆p kol . The following task wasformulated:(let ∆p kol = x, J – optimization task)J = min x, where x is connected byQ pidentity:43wx4( Qp) x + wx3( Qp)x +2+ wx2( Qp) x + wx1( Qp)x++ ww( Qp)= 0The solution for this task is minimalvacuum drop and the corresponding airstream (Majkowska 2007b).EFFECT OF LIQUID RISEHEIGHT ON MINIMAL DROPVALUES AND CORRESPONDINGSUPPLIED AIR STREAMThe height of liquid rise is one ofimportant factors influencing the vacuumdrops, also the minimal drops as well asthe supplied air stream value Q p (min∆p).Dependence of the air stream minimizingthe vacuum drop on milking rate Q pmin∆p(min∆p(Qm)). For a given height ofliquid rise H, independently of the milktube diameter D, this dependence isnonlinear and convex, with extremumin the neighbourhood of a pointcharacteristic for height H (Fig. 1). Inthe can milking machine H = 0.4 m, themaximal air stream minimizing the dropsQ* pmin ∆p for each diameter correspondsto vacuum drop about 2 kPa. In milkingmachine with highly arranged milking tube,the maximal values Q* pmin ∆p correspondto vacuum drop about 10 kPa (Fig. 1).This result confirms observations ofKupczyk (1999).Considering hypothetical heights ofliquid rise H in the range from 0.4 mto 1.9 m one can find that the vacuumdrops corresponding to Q* pmin ∆p (H i )values are set almost along a straight lineof constant inclination angle coefficientindependent of diameter D (Fig. 2).Change in diameter does not influencethe linearity of rise of minimal drops,corresponding to maximal air streamsfor a given diameter, with an increase inheight.

Analysis of optimal values of air stream supplied in the clusters 5310.9D=0.02Q p min delta P [m 3 h –1 ]0.80.70.60.50.4D=0.020.30.2D=0.0130.1D=0.01300 5 10 15 20 25 30minimum delta P [kPa]FIGURE 1. Supplied air stream minimizing vacuum drops as a function of minimal drops for variousmilking rates (ranging from 0.5 to 12 kg min –1 ); Q pmin ∆p (min∆p(Q m )), while H = 0.4 m, H = 1.9 m110001000090008000minimum delta P [kPa]70006000500040003000200010000.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2FIGURE 2. Minimal vacuum drops for Q* pmin ∆p (H i ) presented for diameter D ranging from 0.013 mto 0.016 mH [m]

54 M. Majkowska, A. KupczykEFFECT OF DIAMETER ONMINIMAL DROPS VALUESAND CORRESPONDING VALUESOF SUPPLIED AIR STREAMThe range of changes in minimal drops atdefined height H depending on diameterD dpm was analyzed. The bigger diameter,the lower scatter of minimal vacuumdrops. Therefore, at bigger diameter,independent of milking rate, one cana0.2determine the expected minimal vacuumdrop more precisely. Similarly, with anincrease in diameter the range of suppliedair, realizing the minimal drops (scatter∆Q p = Q* pmin ∆p – Q pmin ∆p ), decreases(Fig. 3).The range of supplied air streamminimizing the drops can be determinedfor each diameter and height of liquidrise at investigated milking rates (from0.5 to 12 kg min –1 ). Assuming a constantQ*p min P – Qp min P [m 3 h –1 ]0.180.160.140.120.10.080.06D=0.02D=0.0120.040.02b0.3505 10 15 20 25 30minimum delta P [kPa]Q*p min P – Qp min P [m 3 h –1 ]0.30.250.20.150.1D=0.0120.05D=0.0201 2 3 4 5 6 7 8minimum delta P [kPa]FIGURE 3. Difference between maximal supplied air stream among the drop minimizing streams andthe air stream minimizing vacuum drop for subsequent milking rates and diameters from 0.012 to 0.02m: (Q* pmin ∆p (min∆p(Q m )) – Q pmin ∆p (min∆p(Q m )) [m 3·h –1 ], for H = 1.9 m (Fig. 3a) and H = 0.4 m(Fig. 3b)

Analysis of optimal values of air stream supplied in the clusters 553530Qp=0.09Qp=0.19Qp=0.26D=0.01225deltaP20151050 2 4 6 8 10 12Qm1816Qp=0.43Qp=0.47Qp=0.53D=0.01614deltaP1210860 2 4 6 8 10 12QmFIGURE 4. Vacuum drops obtained by three selected values of supplied air stream as a function ofmilking rateair stream for each milking rate at thelevels of three selected values of therange of streams optimizing drops, onecan find that the vacuum drops differinconsiderably. The calculated vacuumdrops for the selected diameters at H == 1.9 m are presented in Figure 4a, b.A significant effect of the heightof liquid rise on maximal air streamminimizing the vacuum drops can befound. This dependence is ascending,nonlinear and convex for each diameterof long milk tube (Fig. 5).CONCLUSIONS• The vacuum drop corresponding to agiven maximal air stream, suppliedfrom the air streams minimizingdrops, is constant for defined height

56 M. Majkowska, A. Kupczyk0.55D=0.0160.50.45D=0.015Qp minP0.40.350.3D=0.014D=0.0130.250.20.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2HFIGURE 5. Air stream minimizing vacuum drops Q* pmin ∆p (H i ) for various diameters of long milktubeof liquid rise and independent ofdiameter of long milk tube.• It changes linearly as a function of theheight of liquid rise for each diameter,with constant angle coefficient.• One can point out the range ofsupplied air stream to obtain theminimal air streams, if milkingmachine parameters H, D aredetermined. Within this range ofsupplied air stream the vacuum dropsdiffer inconsiderably.• An increase in diameter of long milktube causes a decrease in scatter ofminimal vacuum drops and a decreasein the range of supplied air streamrealizing the minimal drops.• An increase in the height of liquid risecauses an increase in minimal vacuumdrops (almost linear) and an increasein supplied sir stream realizing theminimal drops, while an increase inmaximal stream values is nonlinear.REFERENCESKUPCZYK A. 1999: Doskonalenie warunkówdoju mechanicznego ze szczególnymuwzględnieniem podciśnieniaw aparacie udojowym. Rozprawa habilitacyjna,SGGW Warszawa.MAJKOWSKA M. 2007a: Spadki podciśnieniaw długim przewodzie mlecznymaparatu udojowego obliczane na podstawieuproszczonego równania Bernoulliego.Problemy Inżynierii Rolniczej 2(56).MAJKOWSKA M. 2007b: Minimalizacjaspadków podciśnienia w kolektorzeaparatu udojowego w oparciu o modelmatematyczny, Vol. 6/1 ARAE, OficynaWydawnicza DaborNORDERGEN S.A. 1980: Cyclic VacuumFluctuations In Milking Machines.Dissertation. Hohenheim.Streszczenie: Analiza optymalnych wartościstrumienia powietrza dopuszczanego w aparatachudojowych. Minimalne spadki podciśnieniaw długim przewodzie mlecznym wyznaczone napodstawie równania Bernoulliego na wysokośćstraconą, w którym przyjęto prędkość wznoszenia

Analysis of optimal values of air stream supplied in the clusters 57pęcherza jako stałą, określają wielkość strumieniapowietrza dopuszczanego. Dla różnych prędkościdoju przeanalizowane zostały wielkości strumieniapowietrza w funkcji wysokości podnoszeniacieczy, średnicy długiego przewodu mlecznego.Spadki podciśnienia różnią się nieznacznie, gdystrumień powietrza jest z zakresu wartości minimalizującychspadki.MS. received June 2008Authors’ addresses:Maria MajkowskaKatedra Zastosowań Matematyki SGGWul. Nowoursynowska 16602-787 WarszawaAdam KupczykKatedra Organizacji i Inżynierii Produkcji SGGWw Warszawieul. Nowoursynowska 16602-787 Warszawa

Annals of Warsaw University of Life Sciences – SGGWAgriculture No 52 (Agricultural and Forest Engineering) 2008: 59–65(Ann. Warsaw Univ. of Life Sci. – SGGW, Agricult. 52, 2008)Optimization of selection of reconditioned parts in repair of injectionpumpMAREK KLIMKIEWICZDepartment of Production Management and Engineering, Warsaw University of Life Sciences – SGGW,Warsaw, PolandAbstract: Optimization of selection ofreconditioned parts in repair of injection pump.The paper presents possibility of application ofdynamic programming in optimization of selectionof the new and reconditioned machine parts. Theminimization of spare part costs was carried outat assumed reliability of equipment. The methodwas presented on the example of injection pumprepair.Key words: reconditioning, optimization, injectionpumps.INTRODUCTIONIt is proved by many practical examplesand also by numerous research works,that application of reconditioned parts inequipment subjected to repair is profitable.However, an appropriate quality of repairmust be ensured. The reconditioned partsare used with respect to their lower priceand protection of environment in the pointrecycling by reconditioning (Bocheńskiand Klimkiewicz 2001a). Processesof spare part reconditioning occur instrict connection with the processes ofrepair and disposal of capital assets, aspresented in Figure 1 (Bućko and Guść1988). Confirmation of advisability ofreconditioning development can be alsofound in highly developed countries. Itcan be assumed that reconditioning isprofitable and technically justified as faras the following is concerned:– parts of older vehicles and out ofproduction,– large and expensive elements, e.g.:bodies, heads, fuel systems,– parts of special vehicles (shortproduction series),New spare partsRepairReconditioningScrap materialReconditioningspare partsRunning down ofcapital assetsFIGURE 1. Place of reconditioning in circulation of spare parts (Bućko and Guść 1988)

60 M. Klimkiewicz– units and parts of low reconditioning developed for each problem. In optimalcosts – starters, generators, valves selection of new and reconditioned partsetc.Reconditioning requires lower energyand material inputs than productionfor the equipment being repaired one canadapt an algorithm applied in allocationof new parts reliability, presented byand they can be estimated as several Kapur and Lamberson (1980).percent in relation to a new product. Thereconditioning costs are influenced bytechnology, size of production, continuityof supply, and other factors. It is assumedthat total costs of reconditioning shouldnot exceed about 60% of the costs of aAlgorithm of optimizationIt was assumed that the needed value ofreliability index (probability of properoperation) of the equipment is ỹ, andthat:new part fabrication. The equipment 0 < ỹ < 1being repaired, as a system, mustThe least values of reliability indicesfulfill the reliability requirements,for particular elements were designatedwhich depend on system structure andas xreliability of basic components. The i , and:reliability indices can be obtained in 0 < x i < 1exploitation or laboratory investigations. Further designations:With respect to short series of the parts y i – variable level of reliability indexbeing reconditioned and heterogeneity value for i-element, and:of population, the reliability indices x i ≤ y i < 1can often be evaluated on the basis of G i (x i ,y i ) – costs connected to applicationreconditioning technology used. Many of parts of reliability index value x i or y i ,reconditioning methods enable to give y i * – optimal level of reliability indexthe parts such properties, that their value for i-element at minimal costs.durability can be higher than that of the The optimization aims at determinationoriginal parts.of value z as a minimal cost function:OPTIMIZATION OF PARTSELECTION BY DYNAMICPROGRAMING METHODKnowing the reliability indices valuesone can select parts of various qualityto equipment being repaired with theuse of optimization methods, e.g. thedynamic programming method, using theBellman’s principle (1957). In dynamicprogramming method a general purposealgorithm for solution is not used. Adetailed proceeding method should benz = min ∑ Gi( xi, yi)i=1with limitations:n∏ yi≥ ỹ and 0 < x i ≤ y i ≤ 1, i = 1, ..., n.i=1The algorithm of dynamic programmingcan be presented in the form ofgraphical model (Fig. 2).Each i-element represents simultaneouslythe stage k of task solving, therefore,it was assumed that i = k. S k is a set

Optimization of selection of reconditioned parts in repair of injection pump 61x k y k 1Final stageD kInitial stageS n = 1k=1, ...nkS 0 = S k y k = S k-1 S k-1G k (x k , y k )FIGURE 2. Graphical model for solution of dynamic programming task (source: own elaboration basedon Kapur and Lamberson 1980)of possible reliability indices s k at stagek, and:1 = s n ≥ s n–1 ≥ , ..., s k ≥, ..., s 1 ≥ s 0 = ỹThe s k value determines reliabilityindex to be taken for a given element atk-stage of solution to ensure the requiredlevel of system reliability.A set D k of such possible decisions d kat stage k is determined to achieve:x k ≤ y k < 1 k = 1, ..., nTransformation function T k representsthe following conversion of final state atstage k into initial state:T k (s k ,d k ) = s k y k = s k–1k = 1, ..., n (2)while the profit function R k (functiondetermining the effect depending on initialstate at stage k and decision undertakenat this stage) will be expressed with thefollowing dependence:R k (s k ,d k ) = [G k (s k ,y k ) + f k–1 (s k–1 )],k =1, ..., n (3)where f k (s k ) – transition functiondetermining optimal effect obtainedfrom the initial stage to k-stage.The basic functional dependence inthis case has the form of the followingrecurring function:fk( sk) = min[ Gk( sk, yk) + fk−1( sk−1 )] ,ykk =1 , ..., n(4)while:ỹyk≥Sk⋅ yk−1where:f 0 (s 0 ) = 0, s n = 1 s k–1 = s k · y k(5)Verification calculationsThe injection pump of distributor DPAtype is a precise device consisted of over100 elements. This pump, as majority ofmechanical systems, is characterized byin-line reliability structure, which can beschematically presented by the in-lineconnected blocks:1 2 nThe function of reliability structureof such system can be expressed asfollows:nSx ( 1, ..., xn)= ∏ xi(6)i=1

62 M. Klimkiewiczwhere: S(x l,…, x n ) – function of reliabilitystructure of system,x i – realization of vector coordinate ofreliability state of logic value 0 or 1.Probability of proper operation of thesystem R s (t) under defined exploitationconditions can be calculated by expandingdetermination of the vector coordinatesof reliability state of logic values 0 or 1into values of the range , with thefollowing dependence:nR(t)= s ∏ Ri () t(7)i=1where: R i (t) – probability of properoperation of element during time t underthese conditions.Intensity of pump damage is equalto the sum of intensity of damage ofparticular elements. Serious failures ofthe pump leading to substantial financialinputs result usually from damageof hydraulic head, body or cam ring;therefore, with consideration to purposeof calculations one can assume that theindex of dependence (7) will undertakethe form:Rs ()= t R1()⋅ t R2()⋅ t R3 () t (8)where: R 1 (t), R 2 (t), R 3 (t) – probabilityof proper operation of hydraulic head,cam ring and body of the pump in time t,respectively.Probability of proper operation ofthe pump elements will be evaluated forthe half-year period, which means theguarantee period given by majority ofworkshops repairing the fuel systems.Evaluation of indices for properoperation of elements was based onexploitation investigations carried outin service workshops for fuel equipment(Bocheński and Klimkiewicz 2001b c).To simplify designations and toensure their conformity with dynamicprogramming algorithm, these indiceswill be called below as reliability anddesignated with symbol y i . Reliabilityin selection of parts was evaluated withconsideration to damage occurred duringassembling and adjustments in serviceworkshops as well as during exploitation.The current market prices of spare partswere taken as the costs of elements. Thereliability indices and prices of parts arepresented in Table 1.The maximal values of reliability forthe three mentioned elements of pump(new parts) were evaluated as follows:hydraulic head R(t) = 0.99, cam ring R(t)= 0.999, body R(t) = 0.999. Therefore,TABLE 1. Values of reliability ndices and costs of New and reconditioned partsHydraulic head Cam ring Bodyy 1yG(reliability1 (S k ,y 1 )2yG(reliability 2 (S k ,y 2 )3G(reliability 3 (S k ,y 3 )cost [PLN]cost [PLN]cost [PLN]– R 1 (t))– R 2 (t))– R 3 (t))0.975 100 0.980 20 0.991 300.980 130 0.999 100* 0.999 100*0.988 1500.990 350** – New elements.

Optimization of selection of reconditioned parts in repair of injection pump 63the hydraulic head is a weak elementand it determines the reliability of entireequipment. As it is evident from theproduct low of reliability (equation 7),reliability of the entire system can notbe higher than reliability of the mostdeceptive element, therefore, it can notexceed the value R(t) = 0.99. It wasassumed in the model that reliability ofthe complete injection pump must behigher than R(t) = 0.96.Tables 2–4 present the values of allpossible reliability indices for variouselements, which ensure reliability of thesystem.This value is obtained for reliabilityy 3 * = R 3 (t) = 0.991; therefore, thereconditioned body of cost 30 PLN isselected (y i * – optimal level of reliabilityindex for i-element at minimal costs).Then, from Table 6 there is selected theelement of reliability y 2 * = R 2 (t) = 0.999for function f 2 (s 2 ) = 230. This is the camring of cost equal to 100 PLN. Finally,from Table 5 for the function f 1 (s 1 ) = 130there is selected the element of reliabilityy 1 * = R 1 (t) = 0.98. This is the hydraulichead of cost 130 PLN. The total cost ofthe parts will amount to 260 PLN.TABLE 2. Transformation at stage 3: there is determined system reliability, which can be taken at stage2: s 3 = 1, s 2 = s 3 · y 3y 3s 30.991 0.9991.000 0.991 0.999TABLE 3. Transformation at stage 2: s 1 = s 2 · y 2y 2s 20.980 0.9990.991 0.9712 0.9900.999 0.9790 0.998TABLE 4. Transformation at stage 1: s 0 = s 1 · y 1y 1s 10.975 0.980 0.988 0.9900.9712 – – – 0.96150.9790 – – 0.9673 0.96920.9900 – 0.9702 0.9781 0.98010.9980 0.9731 0.9780 0.9860 0.9880In Tables 5–7 the conversion functionswere determined. It is evident from Table7 that conversion function:f3( s3) = min[ G3( s3, y3) + f2( s2)]y3is equal to 260.Assuming various values of reliabilityfor the complete injection pump andperforming the simulation with the useof Excel program, there were obtainedthe total costs of elements ensuring therequired probability of proper operationof the pump (Tab. 8).

64 M. KlimkiewiczTABLE 5. Matrix of costs at stage 1: G 1 (s 1 ,y 1 ) and vector f 1 (s 1 )y 1s 10.9750 0.980 0.988 0.990 f 1 (s 1 )0.9712 – – 350 3500.9790 – – 150 350 1500.9900 – 130 150 350 1300.9980 100 130 150 350 100TABLE 6. Matrix of costs at stage 2: G 2 (s 2 ,y 2 ) and vector f 2 (s 2 ) = G 2 (s 2 ,y 2 ) + f 1 (s 1 )y 2s 20.9800 0.9990 f 2 (s 2 )0.9910 20+350 100 + 130 2300.9990 20+150 100 +100 170TABLE 7. Matrix of costs at stage 3: G 3 (s 3, y 3 ) and vector f 3 (s 3 ) = G 3 (s 3, y 3 ) + f 2 (s 2 )y 3s 30.991 0.999 f 3 (s 3 )1 30 +230 100 + 170 260TABLE 8. Results of simulation on total costs of assembled elements for different values of requiredsystem reliabilityRequired system reliability 0.988 0.985 0.98 0.975 0.97 0.96 0.95 < 0.95Calculated reliability 0.988 0.986 0.986 0.978 0.970 0.970 0.951 0.9469Total cost of elements 550 350 350 280 260 260 180 150SUMMARYApplication of dynamic programmingmethod allowed for objective selectionof the new and reconditioned parts toensure proper reliability of the injectionpump. The model based on this methodcan be successfully used in selection ofspare parts for other equipment. An advantageof dynamic programming is reductionof labour inputs of the solutions,when compared to other methods. Thedynamic programming releases fromcombinatorial investigating of all solutions,which must be applied in otheroptimization methods, since every stageis considered separately and an optimalsolution is selected at every stage.Simulation of total costs of assembledparts for different values of requiredreliability enables to select the desirablevariant of spare parts’ set depending oncustomer expectations.REFERENCESBELLMAN R. 1957: Dynamic Programming.Princeton University Press. 400.BOCHEŃSKI C., KLIMKIEWICZ M.2001a: Regeneracja części maszyn jakojeden ze sposobów recyklingu MateriałyKonferencyjne – I Międz. Konf. Nauk-

Optimization of selection of reconditioned parts in repair of injection pump 65-Tech. „Problemy Recyklingu. Rogów.p. 55–63.BOCHEŃSKI C., (Kier. projektu bad.)KLIMKIEWICZ M. 2001b: Badaniawpływu właściwości fizykochemicznychpaliwa do silników wysokoprężnych nacharakterystykę wtrysku i trwałości elementówukładu paliwowego konwencjonalnegoi Common Rail. Projekt bad.KBN nr 9T12D00716. Maszynopis cz. II,WIP, Warszawa. p. 128.BOCHEŃSKI C., KLIMKIEWICZ M.2001c: Problemy smarowania i zużyciapomp wtryskowych w aspekcie stosowanychpaliw. Motoryzacja i EnergetykaRolnictwa. II Międz. Konf. Nauk. Tech.„MOTROL 2001”. Tom 4. p. 13–19.BUĆKO J., GUŚĆ A. 1988: Rachunek ekonomicznejefektywności regeneracjiczęści wymiennych. Krajowa Konf. n.t.:Regeneracja Części Maszyn. Łódź 1988.s. 1–14.KAPUR K., LAMBERSON L. 1980:Nadeżnost i projektirowanie sistem. Mir,Moskwa. ss. 604Streszczenie: Optymalizacja doboru regenerowanychczęści do naprawy pompy wtryskowej.Modelowanie doboru regenerowanych części donaprawianej pompy wtryskowej przeprowadzono,stosując programowanie dynamiczne. Za pomocątej metody można w obiektywny sposób dobraćczęści nowe i regenerowane, aby zapewnićodpowiednią niezawodność pompy wtryskowej.Metodę zaprezentowano na przykładzie naprawypompy wtryskowej.MS. received June 2008Author’s address:Katedra Organizacji i Inżynierii ProdukcjiSGGW02-787 Warszawa, ul. Nowoursynowska 166

Annals of Warsaw University of Life Sciences – SGGWAgriculture No 52 (Agricultural and Forest Engineering) 2008: 67–71(Ann. Warsaw Univ. of Life Sci. – SGGW, Agricult. 52, 2008)Properties and structure of spheroidal chilled cast iron weldedby frictionRADOSŁAW WINICZENKODepartment of Fundamental Engineering, Warsaw University of Life Sciences – SGGW, Warsaw,PolandAbstract: Properties and structure of spheroidalchilled cast iron welded by friction. Investigationswere carried out on friction welding of spheroidalcast iron “modified” from the front. Theinvestigations aimed at “decreasing” of free statecarbon content in welding zone. The sampleswere welded by friction and subjected to strengthtest, hardness test and structural test with theuse of scanning electron microscope (SEM). Anincrease in friction time improved the strength ofconnections, however, the temperature achievedin a central part of connection was insufficient tobreak up the cementite into equivalent phases.Key words: spheroidal cast iron, friction welding,strength of connection.INTRODUCTIONFriction welding is a method of welding,which has been recently used to connectgrey cast iron with both the flakegraphite and the spheroidal graphite(Dette and Hirsch 1990; Shinoda, Endoand Kato 1999). The hitherto trialsaimed from one side at simplification ofconstruction of casts and the resultingdecrease in quantity of scrap, from theother side at obtaining a high mechanicalstrength during exploitation of a givenelement. The classical examples of suchapplications are joints of articulatedtelescopic shafts transferring power fromtractor to agricultural machines, suctionand exhaust valves of internal combustionengines, hydraulic cylinders, pistonrods, parts of gears, driving shafts andturbine shafts (Brochure ManufacturingTechnology, INC (MTI), 1999).Many authors have used the spacersmade of low-carbon steel in order toconnect the spheroidal cast iron with theuse of friction heat (Kaczorowski andWiniczenko 1999; Winiczenko 2003).Regardless of type of material, thesetechnologies complicate the weldingprocess, increase its time and maketechnology more expensive. Therefore,author of this work proposes anothermethod, so called “chilling” of cast iron,in order to “decrease” free state carboncontent in welding zone.MATERIAL AND METHODSThere was investigated the spheroidalcast iron of 400 – 15 type of the followingchemical composition: C – 3.78%, Si– 2.60%, Mn – 0.15%, P – 0.05 %, S– 0.01%, Cr – 0.03%, Ni – 0.02%, Si– 2.60%, Cu – 0.05%, Mg – 0.036%.The iron castings were fabricated inFounding Department of MechanicalPlant PZL-Wola by the followingmethod: the inductive crucible furnaceof capacity 3 t, special low-manganesecrude iron 30% + process scrap 15% andthe rest – steel scrap, spheroidizing attemperature 1530°C, spheroidizer type

68 R. Winiczenko611A of granulation 1÷10, modificationat temperature 1450°C with the use ofmodifier ZL80ZN(0.4/2) of PECHINEYmake, put to the bottom of ladle with theuse of a sinking device. Then, the samplesof properly shaped fronts was welded ina friction welder ZT-14 at Universityof Technology and Life Sciences inBydgoszcz.RESULTS AND THEIR ANALYSISTo simplify the friction welding processthere was attempted a direct welding,in which surfaces of samples beingconnected were chilled in order toremove the free state carbon, namely inthe form of graphite. This operation isrelatively simple technologically, sinceit consists in placing the chill leadingto a local solidification of cast iron in ametastable system.As a result, a series of spheroidalcast iron samples were obtained,chilled at distance from 3 to 4 mm. Itwas expected that lack of releasing offree state carbon will remove a basicobstacle, namely the graphite layer beingdeposited on the connection surface. Itwas also considered that depending ontemperature and time of its maintainingon the interface, the cementite couldbreak up into austenite and carbon “instatu nascendi”, which could be releasedin the form of ultra-dispersive emissionsof incandescence carbon during longperiods of temperature maintained aftercompletion of welding.Strength test resultsThe initial trials did not yield the expectedresults, since welding time was too short.Warming up of very hard chilled layer toappropriate temperature turned out to beimpossible with application of parametersgiving good results during welding withthe use of a spacer. With respect to limitedparameters of the welder, an increase induration of friction process was the onlyway for obtaining proper temperature inthe plane of connection. This improved250Tensile strength Rm [ MPa]2001501005000 20 40 60 80 100 120 140Time of friction welding t [s]FIGURE 1. Results of tensile strength of cast iron chilled samples (I stage of welding)

Properties and structure of spheroidal chilled cast iron welded by friction 69the strength of connections, presentedin Figure 2, although the strength ofconnection was not fully satisfactory.higher than that measured at distance of2.5 mm from the surface of cylindricalsample. This suggests that temperature250Tensile strength Rm [ MPa]2001501005000 20 40 60 80 100 120 140Time of friction welding t [s]FIGURE 2. Results of tensile strength of cast iron chilled samples (II stage of welding)Micro hardness testConsidering the course of chilled samplewelding one should referred to the resultsof hardness tests (Fig. 3). It is evidentthat hardness in the zone of interface,measured in asis of sample is considerablyachieved in central axis of connectionwas insufficient to cause breaking upof cementite ledeburite into equivalentphases (Fig. 4b). This process occurredin zones situated at bigger distance fromthe sample axis, but was not completelyMicro hardness HV15004504003503002502001501005000 1 2 3 4 5 6 7 8 9Distance from interface [mm]FIGURE 3. Results of micro hardness of spheroidal cast iron (chilled) welded by friction2,5 mm from edgeof specimenon axis of specimen

70 R. WiniczenkoabFe 3 CFIGURE 4. Secondary cracks in envelope structure zone of chilled cast iron: a – magnification × 2000,b – microstructure of chilled cast iron welded by friction, with visible Fe 3 C of various morphology,magnification × 250sufficient (Fig. 3), since the hard centralpart of sample made impossible suchswelling of the sample, that a permanentconnection between external fragmentsthe sample could be created. It should benoted that the welding time can not betoo long, since it could result in excessiveprocess of breaking up of cementite intoaustenite and carbon “in statu nascendi”already during friction. If it occurred,the created carbon would be depositedon surface of interface and annihilatethe previous action, aimed at its removalduring welding.It should be noted that in that casethe hardness of cast iron in the axis ofsample and close to plane of connectionis distinctly higher than hardness atdistance 2.5 mm. It could be caused bythe fact that in the axis of sample thetemperature was too low for breakingup the cementite Fe 3 C in the chilledlayer into more stabile phases. However,higher peripheral speed in zones situatedat distance 2.5 mm from the surface ofsample enabled to release the frictionheat, sufficient to create in relativelyshort time in the chilled layer theprocesses analogical to those occurringduring production of pearlitic malleablecast iron.Observations and microstructurestudiesObservations on connections welded byfriction and chilled from the front pointout at cleavable nature of fractures,where initiation of cracks occur onthe boundaries of former austenitegrains (Fig. 4a). As it was evident fromstereoscope tests, in this case the breakof sample occurred along the grains ofcementite Fe 3 C, which did not break upentirely and occurred on the weldingline.The advantageous trend observedin Fig. 2 points out the direction offurther investigations, although attemptstowards connecting of decarburizedcast iron seem more perspective, atleast theoretically. In this way, it wouldbe possible to create a specific ferritic

Properties and structure of spheroidal chilled cast iron welded by friction 71spacer, perfectly connected with nativematerial; this spaces should ensure betterconditions, or at least the same as duringfriction welding with the use of Armcoiron or low-carbon constructional steel.CONCLUSIONSBasing on carried out investigationsthere were formulated the followingconclusions:• The spheroidal cast iron can bedirectly welded by friction, withoutapplication of special heat treatmentsprior to welding.• The strength of connection increaseswith an increase in welding time.• The investigations on direct connectingof ferritic cast iron “chilled” fromthe front. The possibility of optimizationof welding parameters (rotationalspeed n or friction force Pt) shouldbring the rational advantages.• “Decarburize” spheroidal cast ironin the welding zone. One can expect,that the produced surface layer willbe ferritic, which means that carboncontent in this zone will be equal toalmost zero, while the hardness ofthis layer will be small.REFERENCES“Friction welding”. 1999. Brochure-ManufacturingTechnology.INC(MTI).DETTE M., HIRSCH J. 1990. Reibschweissenvon Konstruieren aus Kugelgraphitgussmit Stahlteilen. Schweissen undSchneiden, 42, Vol. 11.KACZOROWSKI M., WINICZENKO R.1999. Procesy towarzyszące zgrzewaniutarciowemu żeliwa sferoidalnego ze stalą1H18N9T, II Międzynarodowa Konf.nt.: Nauka dla Przemysłu Odlewniczego,Kraków.SHINODA T., ENDO S., KATO Y. 1999.Friction welding of cast iron and stainlesssteels. Welding International, Vol. 13, No2, p. 89–95.WINICZENKO R. 2003. Łączenie żeliwasferoidalnego za pomocą zgrzewaniatarciowego. Inżynieria Rolnicza 11(53),237–243.Streszczenie: Właściwości i struktura zabielonegożeliwa sferoidalnego zgrzanego tarciowo. W pracyprzeprowadzono badania nad zgrzewaniem tarciowymżeliwa sferoidalnego „modyfikowanego” odczoła. Celem badań było „zmniejszenie” zawartościwęgla w stanie wolnym w strefie zgrzewania.Próbki zostały zgrzane tarciowo, a następnie poddanebadaniom wytrzymałościowym, twardościi strukturalnym z wykorzystaniem skaningowegomikroskopu elektronowego (SEM). Zwiększenieczasu tarcia korzystnie wpłynęło na wytrzymałośćpołączeń, aczkolwiek temperatura osiągniętaw centralnej części złącza była niewystarczającado rozpadu cementytu na fazy równowagowe.MS. received August 2008Author’s address:Radosław WiniczenkoKatedra Podstaw Inżynierii, Wydział InżynieriiProdukcji, SGGW ,ul. Nowoursynowska 166, 02-787 Warszawaemail: rwinicze@poczta.onet.pl

Annals of Warsaw University of Life Sciences – SGGWAgriculture No 52 (Agricultural and Forest Engineering) 2008: 73–79(Ann. Warsaw Univ. of Life Sci. – SGGW, Agricult. 52, 2008)Hyperspectral imaging for chilling injury detection in Red DeliciousapplesPart 1: Establishment of a hyperspectral imaging systemGAMAL ELMASRY 1 , NING WANG 2 , CLEMENT VIGNEAULT 31 Agricultural Engineering Department, Faculty of Agriculture, Suez Canal University, Ismailia, Egypt2 Department of Biosystems and Agricultural Engineering, Stillwater, Oklahoma, USA3 Horticulture Research and Development Centre, Agriculture and Agri-Food Canada, Quebec, CanadaAbstract: Hyperspectral imaging for chillinginjury detection in Red Delicious apples. Part 1:Establishment of a hyperspectral imaging system.A hyperspectral imaging system was establishedto acquire and preprocess apple images, as wellas to extract apple spectral properties. The fruitcolor was measured for both the external peel andthe internal flesh. The difference between the fleshresponses of both classes was clear from theirspectral responses, especially in the visible range,owing to the browning effect of chilling injurydevelopment. However, there was no significantdifference between normal and injured fruits interms of all color parameters (R, G, B, L*, a*, andb*).Key words: Hyperspectral imaging, artificialneural network (ANN), apple, optimal wavelength,firmness, chilling injury.INTRODUCTIONApple susceptibility to defects isaffected by the nature of the applevariety, the growing conditions, thecultural methods, and the harvesting,postharvest, handling and storageconditions. External defects are usuallyeasy to detect visually, especially whenthey present some contrast with normaltissues. Chilling injury is a physiologicaldamage to fruit cell membranes thatmay occur at any time owing to harmfulenvironmental conditions duringthe growing season, transportation,distribution or storage, at the retail store,or even in a home refrigerator. Themembrane damage is often followedby a cascade of secondary effects, suchas an increase in ethylene production,an increase in respiration, a decreasein photosynthesis, and an alteration ofcellular structure causing the fruits tobe more susceptible to diseases. Earlydetection and diagnosis of chilling injuryis rather difficult, as the injured produceoften looks sound as long as it remainsin low temperatures. Symptoms becomeevident when the produce is placed inwarmer temperatures. Symptoms mayappear almost immediately, or theymay take several days to develop (Skog1998).Increased demands for objectivity,consistency and efficiency in defectdetection techniques have necessitatedthe introduction of computer-basedtechniques that can be reasonablesubstitutes for human inspection. Theideal method for detecting injured fruitsshould be rapid, precise, reliable and nondestructive.The hyperspectral imagingtechnique integrates spectroscopy anddigital imaging techniques to providespectral and spatial information

74 G. Elmasry, N. Wang, C. Vigneaultsimultaneously for the surface of intereston a target object. A hyperspectral imageconsists of a series of sub-images, eachone representing the intensity distributionat a certain spectral band. This techniquehas been implemented in severalapplications, such as the inspection ofpoultry carcasses (Chao et al. 2001; Parket al. 2004), defect detection and qualitydetermination in fruits and vegetables(Li et al. 2002; Cheng et al. 2004; Liu etal. 2006), and the estimation of physical,chemical and mechanical properties invarious commodities (Park et al. 2003;Lu 2004; Nagata et al. 2005).The ultimate objective of thisresearch was to establish sufficientlyrobust models for the detection ofchilling injury in apple using the toolsof hyperspectral imaging and ANN. Thespecific objectives for this paper wereto: a) establish a hyperspectral imagingplatform for apple chilling injury; (b) todevelop strategies on image acquisitionand preprocessing; (c) to explore spectralsignatures for normal and chillinginjuredapples; and (d) to evaluate colorof apple samples.for another 24 hours to allow injurydevelopment. The other 32 fruits werestored at room temperature (20 ±1°C)and used as control (normal) samples.The external surface of the injured fruitslooked normal visually in terms of colorand texture.Hyperspectral imaging systemHyperspectral images of the apples(normal and injured) were acquiredusing a lab-scale hyperspectral imagingsystem (Fig. 1) that consisted of acharge-coupled device (CCD) camera(PCO-1600, PCO Imaging, Germany)connected to a spectrograph (ImSpectorV10E, Optikon Co., Canada) coupledwith a standard C-mount zoom lens. Thesensitivity of this optic assembly waswithin the spectral range of 400 to 1000nm. The camera faced downward at adistance of 400 mm from the target. Thesample was illuminated through a cubictent made of white nylon fabric to provideuniform lighting conditions. The lightsource consisted of two 50 W halogenlamps mounted at a 45° angle from thehorizontal line, fixed at 500 mm aboveMATERIALS AND METHODSApple samplesApple fruits of the Red Delicious varietywere purchased from local retail stores. Atotal of 64 fruits free from any abnormalfeatures such as defects, bruises, diseasesand contamination were selected.Chilling injury was stimulated in 32fruits by keeping them in a cold storageat –1°C for 24 hours. The injured fruitswere removed from the cold storageand kept at room temperature (20 ±1°C)FIGURE 1. The hyperspectral imaging system:a) a CCD camera, b) a spectrograph with a standardC-mount zoom lens, c) an illumination unit,d) a light tent and e) a PC supported with the imageacquisition software

Hyperspectral imaging for chilling injury detection... 75the sample and spaced 900 mm apart ontwo opposite sides of the sample. Thesample position corresponded with thecenter of the field of view of the camera.The spectral images were collected ina dark room where only the halogen lightsource was used. The exposure time wasadjusted to 200 ms throughout the test.Each spectral image collected was storedas a three-dimensional image (x, y, λ).The spatial components (x, y) included400 × 400 pixels, and the spectralcomponent (λ) included 826 bands within400 to 1000 nm. The hyperspectralimaging system was controlled by a laptopPentium M computer (processor speed:2.0 GHz; RAM: 2.0 GB) preloaded andconfigured with the Hypervisual ImageAnalyzer® software program (ProVisionTechnologies, Stennis Space Center, Mo.,USA). All the spectral images acquiredwere processed and analyzed using theEnvironment for Visualizing Imagessoftware program (ENVI 4.2, ResearchSystems Inc., Boulder, Co., USA).The hyperspectral images werecalibrated with a white reference and adark reference. The dark reference wasused to remove the dark current effect ofthe CCD detectors, which are thermallysensitive. The calibrated image (R) wasthen defined using Equation (1):R R o −=D ×100 (1)W − Dwhere Ro is the acquired hyperspectralimage, D is the dark image (with 0%reflectance) collected by turning off thelight source by means of completelyclosing the lens of the camera, and W isthe white reference image taken from astandard white reference board (Teflonwhite board with 99% reflectance). Thecalibrated images were used to extractinformation about the spectral propertiesof normal and injured fruits with aview to optimizing the identification ofchilling injury, the selection of effectivewavelengths, the prediction of firmness,and classification.Extraction of fruit spectral propertiesThe spectral characteristics of an appledescribe its response to incident radiation.When an apple is subjected to visible andinfrared radiation, 80% of the radiationis reflected from the external surface ofthe fruit, and 20% penetrates inside theapple; of that, 1% is re-emitted. Thereflected and re-emitted radiation can bemeasured and recorded as an absorption/reflectance spectrum (Bochereau et al.1992). This spectrum is relevant to thechemical composition of the apple, andspectra collected from apples at differentquality levels can therefore be quitedifferent.The first step in detecting chillinginjury is to extract the spectral signaturesrepresenting chilling-injured fruits andthose representing normal fruits. Thedetailed steps in extracting the spectralsignature are illustrated in Figure 2. Theimage at 550 nm with the best contrastbetween the apple and background wasselected from the spectral space andsegmented to act as a mask to excludebackground pixels. The white pixels inthe mask were used as an area of interest(AOI) to extract the spectral data fromthe calibrated hyperspectral image. Themean reflectance spectrum from theAOI of each hyperspectral image wascalculated by averaging the spectralvalue of all pixels in the AOI. In total,

76 G. Elmasry, N. Wang, C. VigneaultFIGURE 2. Extraction of the fruit spectral signature: a) selecting 550 nm image, b) binarization (definingthe AOI), c) applying the mask, d) calculating the fruit spectral signature using only those at thewhite pixels in the mask64 average spectra (400–1000 nm)representing the 64 tested fruits werecalculated and stored for selection ofthe wavelength and development of theANN model.Fruit color measurementTo demonstrate the visual changes thatoccurred during the chilling injurydevelopment process, fruit color wasmeasured for both the external peel andthe internal flesh. The color image wasconstructed for each fruit by combiningthe red (650 nm), green (500 nm) andblue (450 nm) band images from thecalibrated hyperspectral data space toform an RGB (red-green-blue) image. Allthe RGB images were transformed intothe L*a*b* format, where L* stands forcolor lightness (0 indicates black and 100indicates white), a* defines the positionbetween green and red (0 indicates greenand 255 indicates red), and b* indicatesthe position between blue and yellow (0indicates blue and 255 indicates yellow).The RGB values were transformed intoa* and b* color components in order toproduce a better identification of colorchanges according to Vízhányó andFelföldi (2000). The color conversionprocess was conducted by means ofa program written using MATLAB 7(Release 14, The MathWorks Inc., Mass.,USA).RESULTS AND DISCUSSIONSSpectral characteristicsFigure 3a shows the average spectralsignature of both normal and injuredfruits obtained from the apple surfaces.There were no significant differencesbetween the normal and the injuredfruits in the visible range (400–700nm), indicating that it was impossible todetect injured fruits using the traditionalmachine vision systems that utilizegray-scale or color cameras. However,the distinction between normal andinjured fruits was obvious in the nearinfraredregion (700–1000 nm) owingto chemical changes that occur duringchilling injury development. The internalspectral responses of the fruit flesh

Hyperspectral imaging for chilling injury detection... 77that were obtained after removal of thepeel are also illustrated (Fig. 3b). Thedifference between the flesh responsesof both classes was clear, especially inthe visible range, owing to the browningeffect of chilling injury development.both normal and injured fruits, becausethere were no significant differencesbetween the two classes for all colorparameters (R, G, B, L*, a*, and b*).The results presented in Table 1 are inagreement with the spectral signaturesaRelative reflectance, %100806040200NormalInjured400 500 600 700 800 900 1000Wavelength, nmRelative reflectance, %100806040200NormalInjured400 500 600 700 800 900 1000Wavelength, nmFIGURE 3. Spectral characteristics of normal and injured apples for a) the external surface and b) theinternal fleshbColor differencesThe color differences between normaland injured fruits for the external surfaceas well as for the internal flesh are shownin Table 1. The external surface (fruitpeel) exhibited the same appearance forof the fruits in the visible range shownin Figure 6a. However, there was asignificant difference between the fleshof the normal fruits and the flesh of theinjured fruits in all color parameters.If parameter a* is considered to be anTABLE 1. Means and standard deviations (SD) of color characteristics and of normal and chilling--injured fruits for surface and fleshExternal surfaceInternal fleshNormalMean ± SDInjuredMean ± SDNormalMean ± SDInjuredMean ± SDR 1 0.626 ± 0.045 a 0.616 ± 0.037 a 0.814 ± 0.021 a 0.735 ± 0.050 bG 1 0.187 ± 0.024 a 0.198 ± 0.019 a 0.557 ± 0.023 a 0.395 ± 0.065 bB 1 0.187 ± 0.022 a 0.186 ± 0.020 a 0.218 ± 0.026 a 0.118 ± 0.035 bL* 51.740 ± 8.57 a 52.460 ± 8.09 a 164.88 ± 4.50 a 132.75 ± 14.61 bA* 156.86 ± 6.61 a 155.93 ± 5.54 a 148.13 ± 2.98 a 159.76 ± 5.19 bB* 144.64 ± 4.82 a 144.77 ± 4.40 a 182.21 ± 1.43 a 180.13 ± 3.12 bValues with same superscript letters within each row are not significantly different, α = 0.05.RrR G B g GR G B b B= ; = ; =+ + + + R + G + B

78 G. Elmasry, N. Wang, C. Vigneaultindicator of browning (0 indicates greenwhile 255 indicates red), the effect ofthe chilling injury in terms of fleshbrowning can be evaluated. The a* valueof normal flesh was 148.13 ± 2.98, avalue that significantly differed from thatof the injured flesh (159.76 ± 5.19). Thehigher a* value of injured fruits indicatesbrowning of the flesh compared withthe flesh of the normal fruits. Based onthe spectral and color characteristics ofnormal and injured fruits, it could beinferred that distinguishing injured fruitsfrom sound ones by means of visualmethods is rather difficult.CONCLUSIONSA hyperspectral imaging system witha spectral range of 400–1000 nm wasestablished for the detection of chillinginjury in Red Delicious apple. Theapple images were preprocessed, andthe spectral data was extracted. Thedifference between the flesh responsesof both classes was clear, especially inthe visible range, owing to the browningeffect of chilling injury development.There was no significant differencebetween normal and injured fruits interms of all color parameters (R, G, B,L*, a*, and b*).REFERENCESBOCHEREAU L., BOURGINE P.,PALAGOS B. 1992: A method forprediction by combining data analysisand neural networks: Applicationto prediction of apple quality usingnear infra-red spectra. AgriculturalEngineering Research. 51(2), 207–216.CHAO K., CHEN Y.R., HRUSCHKA W.R.,PARK B. 2001: Chicken heart diseasecharacterization by multi-spectralimaging. Transactions of ASAE. 17(1),99–106.CHENG X., CHEN Y.R., TAO Y., WANGC.Y., KIM M.S., LEFCOURT A.M.2004: A novel integrated PCA and FLDmethod on hyperspectral image featureextraction for cucumber chilling damageinspection. Transactions of ASAE. 47(4),1313−1320.LI Q., WANG M., GU W. 2002: Computervision based system for apple surfacedefect detection. Computers andElectronics in Agriculture. 36(2002),215–223.LIU Y., CHEN Y.R., WANG C.Y., CHAND.E., KIM M.S. 2006: Development ofhyperspectral imaging technique for thedetection of chilling injury in cucumbers;spectral and image analysis. AppliedEngineering in Agriculture. 22(1),101–111.LU R. 2004: Multispectral imaging forpredicting firmness and soluble solidscontent of apple fruit. Postharvest Biologyand Technology. 31(1), 147–157.NAGATA M., TALLADA J.G., KOBAYASHIT., TOYODA H. 2005: NIR hyperspectralimaging for measurement of internalquality in strawberries. ASAE Paper No.053131, ASAE Meeting, Tampa, Fla.,USA.PARK B., ABBOTT J.A., LEE K.J., CHOIC.H., CHOI K.H. 2003: Near-infrareddiffuse reflectance for quantitative andqualitative measurement of solublesolids and firmness of Delicious and Galaapples. Transactions of ASAE. 46(6),1721–1731.PARK B., WINDham W.R., LawrenceK.C., Smith D.P. 2004: Hyperspectralimage classification for fecal and ingestaidentification by spectral angle mapper.ASAE Paper No. 043032, ASAE/CSAEMeeting, Ottawa, Ont., Canada.SKOG L.J. 1998: Chilling Injury ofHorticultural Crops. Ministry of

Hyperspectral imaging for chilling injury detection... 79Agriculture, Food and Rural Affairs,Report No. 98-021, Ontario, Canada.VÍZHÁNYÓ T., FELFÖLDI J. 2000:Enhancing colour differences in imagesof diseased mushrooms. Computersand Electronics in Agriculture. 26(2),187–198.Streszczenie: Nadwidmowy system obrazu dlawykrywania uszkodzeń podczas wychładzaniaw jabłkach red delicious. Część 1: Założenianadwidmowego systemu obrazu. Opracowanonadwidmowy system obrazu w celu uzyskaniai wstępnej obróbki obrazów jabłek oraz określeniaich właściwości widmowych. Kolor owocówmierzono w skórce zewnętrznej, jak i w wewnętrznymmiąższu. Różnice pomiędzy reakcjąmiąższu obydwu klas określono na podstawie ichreakcji widmowych, szczególnie w zakresie widzialnym,dzięki efektowi brązowienia w wynikuuszkodzeń podczas schładzania. Nie stwierdzonojednak istotnych różnic pomiędzy zdrowymii uszkodzonymi owocami w zakresie parametrówbarwy (R, G, B, L*, a* i b*).MS. received June 2008Authors’ addresses:Gamal ElMasryAgricultural Engineering DepartmentFaculty of AgricultureSuez Canal University,P.O. Box. 41522, Ismailia, Egypte-mail: gmisry@yahoo.comNing WangDepartment of Bioresource EngineeringMcGill University21,111 Lakeshore RoadSainte-Anne-deBellevueQuebec H9X 3V9, CanadaDepartment of Biosystems and AgriculturalEngineeringStillwater, Oklahoma, 74078 USAe-mail: ning.wang@okstate.eduClément VigneaultHorticulture Research and Development CentreAgriculture and AgriFood Canada430 Gouin BoulevardSaint-Jean-sur-RichelieuQuebec J3B 3E6, Canadae-mail: vigneaultc@agr.gc.ca

Annals of Warsaw University of Life Sciences – SGGWAgriculture No 52 (Agricultural and Forest Engineering) 2008: 81–88(Ann. Warsaw Univ. of Life Sci. – SGGW, Agricult. 52, 2008)Hyperspectral imaging for chilling injury detection in Red DeliciousapplesPart 2: Selection of optimal wavelengths for chilling injury detectionGAMAL ELMASRY 1 , NING WANG 2 , CLEMENT VIGNEAULT 31 Agricultural Engineering Department, Faculty of Agriculture, Suez Canal University, Ismailia, Egypt2 Department of Biosystems and Agricultural Engineering, Stillwater, Oklahoma, USA3 Horticulture Research and Development Centre, Agriculture and Agri-Food Canada, Quebec, CanadaAbstract: Hyperspectral imaging for chillinginjury detection in Red Delicious apples. Part2: Selection of optimal wavelengths for chillinginjury detection. Hyperspectral imaging (400––1000 nm) and artificial neural network techniqueswere investigated for the detection of chillinginjury in Red Delicious apples. Feed-forwardback-propagation ANN models were developedto select the optimal wavelength(s), classify theapples and detect firmness changes due to chillinginjury. The five optimal wavelengths selectedby ANN were 717, 751, 875, 960 and 980 nm.With the spectral and spatial responses at theselected five optimal wavelengths, an averageclassification accuracy of 98.44% was achievedfor distinguishing between normal and injuredfruits. The correlation coefficients betweenmeasured and predicted firmness values were0.93, 0.91 and 0.92 for the training, testing andvalidation sets, respectively.Key words: hyperspectral imaging, artificial neuralnetwork (ANN), apple, optimal wavelength,firmness, chilling injury.INTRODUCTIONChilling injury is a physiologicaldamage to fruit cell membranes thatmay occur at any time owing to harmfulenvironmental conditions duringthe growing season, transportation,distribution or storage, at the retail store,or even in a home refrigerator. However,early detection and diagnosis of chillinginjury is rather difficult, as the injuredproduce often looks sound as long as itremains in low temperatures. Increaseddemands for objectivity, consistency andefficiency in defect detection techniqueshave necessitated the introduction ofcomputer-based techniques that canbe reasonable substitutes for humaninspection. The hyperspectral imagingtechnique integrates spectroscopy anddigital imaging techniques to providespectral and spatial informationsimultaneously for the surface of intereston a target object. This technique hasbeen implemented in defect detectionand quality determination in fruits andvegetables (Kim et al. 2002; Polderet al. 2002), and the estimation ofphysical, chemical and mechanicalproperties in various commodities (Lu2004). Artificial neural network (ANN)models are developed to simulate theorganizational principles of the humanbrain and nervous system. An ANNconsists of a large number of computingelements (called nodes) that are linkedtogether. The overall performance of anANN is determined by the structure andthe strength of the connections (Bronset al. 1993). ANNs have an advantage

82 G. Elmasry, N. Wang, C. Vigneaultin solving problems in which someinputs and corresponding output valuesare known but the relationship betweenthe inputs and outputs is not wellunderstood or is difficult to translateinto a mathematical function. Despitemathematical differences, ANNs haveproven to be stronger than statisticalclassifiers in the identification andclassification of agricultural produce(Jayas 2000), where non-coherenceor non-linearity often exists. Kavdýrand Guyer (2004) developed a backpropagationneural network (BPNN)with the texture features extracted fromspatial distribution of color/gray levelsto detect defects (leaf roller, bitter pit,russet, puncture and bruises) in Empireand Golden Delicious apples.The ultimate objective of this studywas to establish sufficiently robust modelsfor the detection of chilling injury in appleusing the tools of hyperspectral imagingand ANN. The specific objectives were:a) to establish an ANN model for theselection of the optimal wavelength(s)for identifying normal apples againstinjured apples; c) to develop ANNs formonitoring firmness changes in applesbased on spectral images at the selectedoptimal wavelength(s); and d) to developANNs for fruit classification accordingto firmness levels.MATERIALS AND METHODSApple imagesApple images were collected throughthe procedures described in ElMasry,et al. 2007a. Images from all 64 fruits(normal and injured) were used to trainand test an ANN for the selection of theoptimal wavelength(s). To increase therobustness of the ANN models, another20 Red Delicious apples were purchasedin order to validate the ANN models andalgorithms that were developed. In 10of those apples, chilling injuries werestimulated using the previously describedprocedures. The other 10 fruits were keptat room temperature and used as normalfruits.Firmness measurementAfter spectral image acquisition, thefirmness of each fruit was measured withan Instron Universal Testing Machine(Model 4502, Series IX Automated MaterialsTesting System, Instron Corporation,Mass., USA) using an 11 mm diameterplunger according to the standardmethod (ASAE, 1994). After removal ofthe fruit peel, the plunger was pressedinto the fruit flesh to a depth of 9 mmat a speed of 50 mm/min. The maximumforce extracted from the force-deformationcurve was used to indicate the fruitfirmness. The firmness test was conductedat two opposite positions on the equatorof the fruit surface and subsequentlyaveraged. The average maximum forcewas used as the firmness index of thefruit.Data volume reduction and optimalwavelength selectionThe major disadvantage of thehyperspectral imaging technique is thathandling the huge amount of data extractedfrom hyperspectral images requires extratime and resources. It is imperative thatefficient manipulation procedures beused to reduce data dimensionality to itslowest level without losing functionality.In this study, instead of the entire image

Hyperspectral imaging for chilling injury detection... 83volume (400 × 400 × 826) being used,a reduced data cube with dimensions of400 × 400 × n, where n is the numberof selected optimal wavelengths, wasformed. The success of a classificationalgorithm based on the reduced data cubedepends on the quality of the selectionof the optimal wavelengths at whichthe spectral signatures can best describeeach class. Although several wavelengthselection techniques have been derivedby researchers (e.g. Liu et al. 2003; Mehlet al. 2004; Chong and Jun 2005), thechoice of a particular method dependson the nature of the problem, the size ofthe data set, ease of implementation andeconomic feasibility. In this study, anANN was used for data volume reductionand wavelength selection on the basisof the fact that the network can changeand adjust its knowledge by adjustingits weights according to the presentedsamples of data.ANN Model 1 for selectionof wavelength(s)A multilayer BPNN, ANN Model 1, wasdeveloped to differentiate injured applesfrom normal ones. Back-propagationis the canonical feed-forward networkin which an error signal is fed backthrough the network, altering weights asit goes; back-propagation is therefore anumerically intensive technique.Weights between the simpleprocessing units of nodes were adjustedby iterating input patterns throughoutthe network until the error between thenetwork output and the targeted outputwas minimized. ANN Model 1 consistedof three layers: an input layer, an outputlayer and a hidden layer. The input layerhad 826 nodes representing the spectralresponses of a fruit at each of the 826wavelengths. Owing to the large volumeof data, only one hidden layer with fivenodes was used. The number of nodeson the output layer was determinedpredominantly by the number of classesunder investigation. Thus, two nodeswere used: normal fruits (coded as 1) andinjured fruits (coded as 0). A sigmoidfunction was used as a transfer functionbetween the input and hidden layers,and a linear transfer function was usedbetween the hidden and output layers. Thenetwork was trained for at least 20.000epochs or until the error measurementapproached 0.01%. Among the 64 testedfruits, 42 fruits (both normal and injured)were randomly chosen as a training set totrain the ANN model. The other 22 fruits(both normal and injured) were usedas a testing set to test the model. Thisprocedure was run three times on thesame 64 fruits; each time, 42 differentfruits were randomly selected for training,and the rest were used for testing. Theoutcomes of the three replications wereaveraged to calculate the importance ofeach variable.The importance of each variable(wavelength) for the ANN model wasevaluated using an index calculated byEquation (1):n n H ⎡⎛p ⎞ ⎤⎢⎜I pI ⎟pO ⎥∑ /⎢ ∑j jk jj ⎜⎝k ⎟ ⎥= 1⎠M =⎣⎢= 1 ,⎦⎥n p ⎛ n n H ⎡⎛p ⎞ ⎤ ⎞⎜ ⎢⎜I p/ I ⎟ij , piO ⎥ ⎟∑∑ ⎢ ∑i jk , j , k j= = ⎜⎝= ⎟ ⎥1⎜1⎝ ⎣⎢1⎠ ⎦⎥⎟⎠(1)where M is the importance measure forthe input variable, np is the number of

84 G. Elmasry, N. Wang, C. Vigneaultinput variables (826), nH is the numberof hidden layer nodes (5 nodes), 219 I pjis the absolute value of the hidden layerweight corresponding to the pth inputvariable and the jth hidden layer, and Ois the absolute value of the output layerweight corresponding to the jth hiddenlayer.Each input variable had five weightvalues corresponding to the five nodesin the hidden layer of the ANN. Theindex M value was calculated for eachinput node and then normalized in the0 to 1 range. The higher the M value,the more important the node (variable/wavelength) for the classification ofinjured and normal fruits.ANN Model 2 for firmness predictionBased on the number of wavelengthsselected by ANN Model 1, a new feedforwardBPNN, ANN Model 2, wasestablished to predict the firmness ofthe fruits. The input layer consisted ofthe reflectance features at the selectedoptimal wavelengths. The hidden layercontained three nodes. The output layercontained only one node to representthe fruit firmness value. Sigmoid andlinear functions were used as transferfunctions, and over-fitting was avoidedby using Bayesian regularizationtraining algorithm. The model wastrained using 42 randomly selected fruitsand tested with the remaining 22 fruits.The maximum number of iterations intraining was set to 5.000.ANN Model 3 for classification ofapplesANN Model 3 was a revised version ofANN Model 2. The spectral responses atthe five optimal wavelengths were usedas input nodes. The output layer wasmodified to have two nodes: normal andinjured classes. The network was trainedusing 42 randomly selected fruits andtested with the remaining 22 fruits, witheach group containing both normal andinjured fruits. The maximum number ofiterations in training was set to 5.000.The detailed procedures for detectingchilling injury and firmness changes inapple fruits are illustrated in Figure 1.Test for robustness of the ANNmodelsThe capability of the three ANNmodels for optimal wavelength selection,firmness prediction and classificationwas validated with a group of 20fruits purchased at a later stage in theexperiment and called the validationset. Chilling injury was stimulated in 10fruits using the same method describedpreviously. The same procedures wereused for hyperspectral image acquisition,image preprocessing and firmnessmeasurement. Model robustness wasevaluated by comparing the correlationcoefficients between the predicted andthe actual firmness and among theclassification accuracies obtained fromthe training, testing and validation sets.RESULTS AND DISCUSSIONSOptimal wavelength selectionANN Model 1 used the entire spectralrange (826 wavelengths) in the rangeof 400 to 1000 nm to generate greatperformance for detecting chilling injuryeffect, with 100% classification successfor both the training and the testingsamples. Figure 2 presents the results

Hyperspectral imaging for chilling injury detection... 85FIGURE 1. Flowchart of the key steps involved in chilling injury detection algorithmobtained, along with the highest Mvalues. The wavelengths correspondingto the highest M values, 717, 751, 875,960 and 980 nm, were chosen as theoptimal wavelengths.Firmness predictionFigure 3a shows the performance ofANN Model 2 in firmness prediction forthe training (42 fruits) and the testing (22fruits) sets. The correlation coefficientFIGURE 2. The importance measure (M) values used to select the optimal wavelengths

86 G. Elmasry, N. Wang, C. Vigneaultbetween measured and predicted firmnessvalues was 0.93 and 0.91 for the trainingand testing sets, respectively. The rootmean square error (RMSE) was 8.26and 9.4 N for the training and testingsets, respectively. Because of the highcorrelation coefficient of ANN Model 2for firmness prediction, this model canbe applied to the detection of firmnesschange due to chilling injury effect. Table2 shows the confusion matrix for theclassification of the 64 fruits (training +testing) into normal and injured classes.A high classification accuracy of 98.44%was obtained with ANN Model 3.Model robustnessFigure 3b shows the fruit firmnesspredicted by ANN Model 2 for thevalidation set. The correlation coefficientbetween the actual and predictedfirmness for the validation set was 0.92with an RMSE value of 10.09 N. Forclassification, ANN Model 3 achieved100% success for categorization of thevalidation set into the two classes (normaland injured). Both results are therefore inagreement with the training and testingsets, indicating the robustness of themodels for classification and firmnessaprediction of normal and injured RedDelicious apple fruits.CONCLUSIONSA hyperspectral imaging system witha spectral range of 400–1000 nm wasestablished for the detection of chillinginjury in Red Delicious apple. The appleimages were preprocessed, and thespectral data was extracted. There wasno significant difference between normaland injured fruits in terms of all colorparameters (R, G, B, L*, a*, and b*).Artificial neural network (ANN) modelswere developed for optimal wavelengthselection, fruit classification and firmnessprediction. Five optimal wavelengths(717, 751, 875, 960 and 980 nm) wereselected based on the maximum weightassigned to the input nodes in ANN Model1. The ANN models were trained, testedand validated with different fruit sets toevaluate the robustness of the models.With the selected optimal wavelengthsinstead of the whole spectral range (826wavelengths), the correlation coefficientsbetween the actual and predicted firmnessobtained using ANN Model 2 were 0.93,bFIGURE 3. Apple firmness prediction by neural network for: a) the training and testing sets and b) thevalidation set

Hyperspectral imaging for chilling injury detection... 87TABLE 2. Confusion matrix for fruit classification using ANN Model 3From/to Normal Injured Total % correctNormal 32 0 32 100Injured 1 31 32 96.88Total 33 31 64 98.440.91 and 0.92 for the training, testingand validation sets, respectively. ANNModel 3 achieved an accuracy of 98.44%and 100% in distinguishing normal frominjured fruits for the training + testing setand the validation set, respectively.In summary, the experimental resultsdemonstrate that the spectral imagingsystem associated with ANN cansuccessfully distinguish between chillinginjuredfruits and normal fruits, as well asdetect firmness changes. Considering theimportance of the data volume reductionobtained, spectral imaging systems usingthe selected wavelengths open a newavenue for more optimistic applicationsin commercial implementations fordetecting various quality disorders indifferent types of produce.REFERENCESASAE, 1994: Compression test of foodmaterials of convex shape. ASAEStandards. ASAE, St. Joseph, Mich.,USA. ASAE S368.2. p. 472–475.BRONS A., RABATEL G., ROS F., SÉVILAF., TOUZET C. 1993: Plant gradingby vision using neural networks andstatistics. Computers and Electronics inAgriculture. 9(1), 25–39.CHONG L.G., JUN C.H. 2005: Performanceof some variable selection methodswhen multicollinearity is present.Chemometrics and Intelligent LaboratorySystems. 78(1), 103–112.HAHN F., LOPEZ I., HERNANDEZ G.2004: Spectral detection and neuralnetwork discrimination of Rhizopusstolonifer spores on red tomatoes.Biosystems Engineering. 89(1), 93–99.JAYAS D.S., PALIWAL J., VISEN N.S.2000: Multi-layer neural networks forimage analysis of agricultural products.Agricultural Engineering Research.77(2), 119–128.KAVDÝR Ý., GUYER D.E., 2004:Comparison of artificial neural networksand statistical classifiers in apple sortingusing textural features. BiosystemsEngineering. 89(3), 331–344.KIM J., MOWAT A., POOLE P., KASABOVN. 2000: Linear and non-linear patternrecognition models for classification offruit from visible–near infrared spectra.Chemometrics and Intelligent LaboratorySystems. 51, 201–216.LIU Y., WINDHAM W.R., LAWRENCEK.C., PARK B. 2003: Simple algorithmsfor the classification of visible/nearinfraredand hyperspectral imagingspectra of chicken skins, feces, andfecal contaminated skins. AppliedSpectroscopy. 57(12), 1609–1612.LU R. 2004: Multispectral imaging forpredicting firmness and soluble solidscontent of apple fruit. Postharvest Biologyand Technology. 31(1), 147–157.MEHL P.M., CHEN Y.R., KIM M.S., CHAND.E. 2004: Development of hyperspectralimaging technique for the detection ofapple surface defects and contaminations.Journal of Food Engineering. 61(1),67–81.NAGATA M., TALLADA J.G., KOBA-YASHI T., TOYODA H. 2005: NIR hyperspectralimaging for measurement ofinternal quality in strawberries. ASAE Pa-

88 G. Elmasry, N. Wang, C. Vigneaultper No. 053131, ASAE Meeting, Tampa,Fla., USA.POLDER G., VAN DER HEIJDENG.W.A.M. YOUNG, I.T. 2002: Spectralimage analysis for measuring ripeness oftomatoes. Transactions of ASAE. 45(4),1155–1161.Streszczenie: Nadwidmowy system obrazu dlawykrywania uszkodzeń podczas wychładzaniaw jabłkach Red Delicious. Część 2: Wybór optymalnychdługości fal dla wykrywania uszkodzeńpodczas wychładzania. Przeprowadzono badanianadwidmowych obrazów (400–1000 nm) i techniksztucznej sieci neuronowej przy wykrywaniuuszkodzeń jabłek odmiany Red Delicious w wynikuwychładzania. Opracowano modele ANNze sprzężeniem do przodu i wsteczną propagacjąw celu wybrania optymalnej długości fali (fal),klasyfikacji jabłek i określenia zmian jędrnościw wyniku uszkodzeń podczas wychładzania. Zapomocą modeli ANN wybrano 5 optymalnychdługości fal: 717, 751, 875, 960 i 980 nm. Na podstawiewidmowych i przestrzennych reakcji przywybranych pięciu optymalnych długościach fal,podczas odróżniania zdrowych i uszkodzonychowoców uzyskano średnią dokładność klasyfikacjiwynoszącą 98,44%. Wartości współczynnikówkorelacji pomiędzy zmierzoną i prognozowanąjędrnością dla poszczególnych zestawów: uczenia,testowania i atestacji wynosiły odpowiednio:0,93; 0,91; i 0,92.MS. received June 2008Authors’ addresses:Gamal ElMasryAgricultural Engineering DepartmentFaculty of AgricultureSuez Canal University,P.O. Box. 41522, Ismailia, Egypte-mail: gmisry@yahoo.comNing WangDepartment of Bioresource EngineeringMcGill University21,111 Lakeshore RoadSainte-Anne-deBellevueQuebec H9X 3V9, CanadaDepartment of Biosystems and AgriculturalEngineeringStillwater, Oklahoma, 74078 USAe-mail: ning.wang@okstate.eduClément VigneaultHorticulture Research and Development CentreAgriculture and AgriFood Canada430 Gouin BoulevardSaint-Jean-sur-RichelieuQuebec J3B 3E6, Canadae-mail: vigneaultc@agr.gc.ca

Annals of Warsaw University of Life Sciences – SGGWAgriculture No 52 (Agricultural and Forest Engineering) 2008: 89–93(Ann. Warsaw Univ. of Life Sci. – SGGW, Agricult. 52, 2008)Experimental verifying of mathematic model for biomasscombustionMARTIN POLÁK 1) , PAVEL NEUBERGER 1) , JIŘÍ SOUČEK 2)1 Department of Mechanics and Engineering, Technical Faculty, Czech University of Life Science inPrague, Czech Republic2 Research Institute of Agricultural Engineering, Prague, Czech RepublicAbstract: Experimental verifying of mathematicmodel for biomass combustion. Among allalternative energy sources Czech Republic hasthe biggest potential in biomass. If we want useit effectively it is need to fulfil all requirementsof this fuel. Created model describes combustionprocess from the point of view of imperfectburning and it is based on elementary analyse offuel. This model was compared with results fromexperimental combustion. Difference betweenmodel and reality is in the maximum 5%, whilethis is valid for samples with worse burningstability. On the opposite, samples with goodstability show smaller deviation, ranging 1–2 %.Key words: biomass, combustion, emission,calculation model.INTRODUCTIONSignificant marker for evaluation andoptimisation of biomass combustion isan amount and composition of flue gases.It depends partly on burner constructionand partly on fuel composition. Burningprocess calculation models stemmingfrom fuel elementary analysis areimportant for solving many problemsin burner designing and controllingcombustion process. It is possible tocreate calculation model by two ways:1) with help of stechiometric equationsand dates from elementaryanalysis,2) with help of approximate equationsbased on fuel heat value.In case of heterogenous fuelscombustion only the first mentionedpossibility could be taken into account.Outcomes of this model are total airamount, generated flue gases amountand its composition for 1 kg of burnedfuel. Model takes into account perfectand imperfect burning.Calculation modelAn essence of the model is to describecombustion process of any solidfuel based on its elementary analysisconsidering perfect and imperfectburning. It is possible to compare datesfrom model with dates from experimentto see how the model reflects the reality.Some substantial equations are infollowing.Flue gas composition for perfectburning is:22.27V CO2 = ⋅ C+ 0. 003⋅V vv min ⋅ m12.01λ[m 3·kg –1 ] (1)21.89VSO 2= ⋅S[m 3·kg –1 ] (2)32.06

90 M. Polák, P. Neuberger, J. Souček44.81 22.41VHO2= ⋅ H + ⋅ W +403 . 18.02+ ( 104−1)⋅V vs ⋅ m. min λ [m 3·kg –1 ] (3)22.40V N2= ⋅ N + 0 7805⋅V vs ⋅ m28.01[m 3·kg –1 ] (4)VO= O2 2 min ⋅ λ m [m3·kg –1 ] (5)where V v min is minimal volume of air,λ m is coefficient of excess of air.In the case of imperfect burning partof carbon burns to CO and part remainsunburned. The part, which is burnt toCO:Va = CO 12.01⋅ ⋅C110 ⋅6 22.37[–] (6)VO2 skO 2 sk = ⋅100[%]Vsv skVHOH2O 2 sksk = ⋅100 [%]Vsv skMATERIAL AND METHODSMeasuring of burning characteristicsof given samples were done during theexperiment. Concentration of each fuelgases component was monitored by gasanalyzer TESTO 350XL (Fig. 1) withranges: CO (0–400 000) ppm; NO x(0–3000) ppm; NO 2 (0–500) ppm;O 2 (0–25)%;CO 2 (0 – 50)% (direct measurement)Obtained values are defined as medianfrom measured data during continualmeasurement of stabile state burner.. min λ FIGURE 1. Analyzer TESTO 350XLwhere V CO is CO concentration in fluegases [mg·m –3 ].The part, which is unburnt:ΔVbv min= ⋅C−a881 . 2[–] (7)Then, the real concentrations ofparticular emission are:VCO2 skCO 2 sk = ⋅ 100 [%] (8)Vsv skVN2 skN 2 sk = ⋅100[%]Vsv skVSO2 skSO 2 sk = ⋅100[%]Vsv sk

Experimental verifying of mathematic model for biomass combustion 91Experimental combustionAn experimental combustion wasdone with boiler VERNER A25 (Fig.2). VERNER A25 is heat water boilerfor pellets which are supplied withscrew conveyer through back side ofcombustion chamber. Plate bottom ofchamber is equipped with saw-like gratebar which removes ash in defined cycles.Side and upper walls are covered withceramic slabs. Air is forced by fan underthe grate as a primary and through sidewalls as secondary.experiment. Concentration of CO 2 influe gasses was used as a comparisonvalue. This component is in majority ofemission measurement being calculatedafterwards and thus its real amountusually stays a secret. Analyzer TESTO350XL that we used is equipped withsensor for direct measuring of CO 2 andtherefore this value could be used in thecomparison.Comparison of separate values, e.g.of model and reality is apparent fromFigure 3.FIGURE 2. Hot water boiler VERNER A25Tested fuelPellets and chopped straw from mixtureof permanent grass hay and other fuelswere used as trial fuel. List of fuelsamples and its basic characteristics isenclosed in Table 1.DISCUSSION AND CONCLUSIONThe results of calculation model werecompared to data obtained duringObtained and calculated values differin the maximum by 5%, while this isvalid for samples with worse burningstability. On the opposite, samples withgood stability show smaller deviation,ranging 1–2%. Calculation model withsuch a deviation is thus well suitable forapproximate burning calculation. But forexact determination of separate items ifwould be probably necessary to adjustthe calculation – apparently with regardto unburned fuel part.

92 M. Polák, P. Neuberger, J. SoučekTABLE 1. Fuel sample and its elementar analysisFuel samplehay + 10% coal,pellets 15 mmhay + bark (1:3),choppedhay + populus (1:1) ++ 20% coal,pellets 15 mmhay + populus (1:3),choppedhay + sorrel (1:3),choppedWaterVolatilecombustibleNon-volatilecombustibleAsh[%] [%] [%] [%]Total fuel heat[MJ//kg]Heating value[MJ//kg]C H N S O Cl[%] [%] [%] [%] [%] [%]8.7 66.8 18.5 5.9 17.7 16.1 44.3 6.4 1.1 0.1 33.1 0.38.3 68.3 19.6 3.8 17.5 16.1 43.5 5.4 0.4 0.1 38.6 0.19.4 65.9 18.4 6.3 18.3 16.8 47.4 5.8 0.9 0.2 29.9 0.26.5 74.0 15.1 4.3 18.0 16.6 46.1 5.8 0.7 0.1 36.5 0.17.3 69.8 17.5 5.4 16.9 15.4 43.6 6.1 0.6 0.1 36.7 0.1hay + 10%coal, pellets15 mmhay + bark(1:3) –choppedFIGURE 3. Comparison of model and realityhay +populus(1:1) + 20%coal, pellets15 mmhay +populus(1:3) –choppedhay + sorrel(1:3) –chopped

Experimental verifying of mathematic model for biomass combustion 93This article was created within thescope of project of National Agencyof Agriculture Research No QF4079:“Logistic of bioenergy material”REFERENCESPASTOREK Z.; KÁRA, J.; JEVIČ P. 2004:Biomasa – obnovitelný zdroj energie.FCC PUBLIC Prague, Prague, 288.JEVIČ P.; ŠEDIVÁ Z.; SLADKÝ V. 1998:Emise při energetickém využití biomasy,Energie l/98, Prague.POLÁK M. 2005: Biomass for heatproduction. Ph.D. thesis, CUA Prague,113 p.POLÁK M.: Fytomass as a fuel for smallscale boilers. In: Naukovyj visnik NAU73/2004. Kyiiv 2004, p. 246–252.POLÁK M. 2004: The practical experiencewith fytomass combustion in small scaleboilers. In: Collection of abstracts ofInternational conference – Science andresearch – Tools of global developmentstrategy, CUA in Prague, p. 38.Streszczenie: Doświadczalna weryfi kacja modelumatematycznego spalania biomasy. RepublikaCzeska posiada ze wszystkich źródeł alternatywnychnajwiększy potencjał w biomasie. Jeśli tenpotencjał chcemy efektywnie wykorzystywać, tonależy spełnić wszystkie wymogi, które to źródłoposiada. Opracowany model opisuje processpalania na podstawie analizy elementarnej paliwaprzy czym jest zakładane niedoskonałe czylirealne spalanie. Wyniki z modelu są następnieporównywane z wynikami spalania eksperymentalnego.Różnica między modelem i rzeczywistościąwynosi najwyżej 5%, przy czym ta wartośćodpowiada paliwu z niższą stabilnością spalania.W stosunku do paliwa z dobrą stabilnością spalaniaróżnica nie przekracza 1–2%.MS. received December 2007Authors’ addresses:Ing. Martin Polák, Ph.D.Technical FacultyCzech University of Life ScienceKamýcká 129Prague 6 – Suchdol 165 21Czech Republice-mail: karel@tf.czu.czJiří SoučekResearch Institute of Agricultural EngineeringDrnovská 507Praha 6 - 161 01e-mail: jiri.soucek@vuzt.cz

Annals of Warsaw University of Life Sciences – SGGWAgriculture No 52 (Agricultural and Forest Engineering) 2008: 95–101(Ann. Warsaw Univ. of Life Sci. – SGGW, Agricult. 52, 2008)Results of verification of the slaughter waste anaerobic fermentationprocess1 JAROSLAV KÁRA, ZDENĚK PASTOREK 1 , RADOMÍR ADAMOVSKÝ 21 Research Institute of Agricultural Engineering, Prague, Czech Republic2 Faculty of Engineering, Czech University of Life Sciences, Prague, Czech RepublicAbstract: Results of verifi cation of the slaughterwaste anaerobic fermentation process. The articledeals with the analysis of results of verificationof a one-stage anaerobic digestion at mesophileand thermophile conditions in two series differingin retention time (26 and 37 days). Substratesconsisting of various proportions of poultrycrushed bones, pork ligaments, and slurry of beefcattleand pigs (1:1) were used. Stabilized nondrainedremainder of an anaerobic digestion wasused as inoculum. Results of verification showedeffectiveness of slaughter waste processingin biogas stations. However, slaughter wasteprocessing requires installation of equipment fora thermal modification of the feed-in substrate(warming up to 70°C for the duration of 1 hour).Key words: anaerobic fermentation, slaughterwaste, bio-degradable waste, biogas, biogasstations; retention.INTRODUCTIONThe basic of biogas production inagriculture is processing of wasteagricultural products (mainly livestockexcrements but also phytomass), othersector of biogas production is biologicallydegradable municipal waste andbiologically degradable industrial waste,particularly from food industry plants.Currently the attention is concentratedon the slaughterhouse waste processing.Processing of that waste in agriculturalbiogas plants could significantly improvetheir economy.In the past years, animal by-productsas well as slaughterhouse waste andwastewater have not been taken as wasteanymore. We have realized that is afeedstock to be treated in order to getits energy potential. With a rising threatof diseases such as bovine spongiformencephalopathy (BSE) in cattle, tighterlegislation and an effort to use non-wastetechnologies with an effective energygain there has also been raising demandon deeper research activities regarding tothis problems.Anaerobic digestion has become anestablished and proven technology asa means of managing solid as well asliquid organic waste.In this chapter, there are quotedsome results of research experimentsand information regarding to anaerobicdigestion of ABP:• The effect of hydraulic retentiontime (HRT) and loading on anaerobicdigestion of poultry slaughterwastes was studied by Salminen andRintala (2002). The experiment wascarried out in semi-continuously fedlaboratory-scale digesters at 31 o C.The effect on process performancewas highly significant: Anaerobicdigestion appeared feasible with aloading up to 0.8 kg volatile solids(VS)/m 3·d and an HRT of 50–100

96 J. Kára, Z. Pastorek, R. Adamovskýdays. The specific methane yieldwas high, from 0.52 to 0.55 m 3 //kg VS added . On the other hand, at ahigher loading, in the range from 1.0to 2.1 kg VS/ m 3·d, and as shorterHRT, in the range from 25 to 13 days,the process appeared inhibited and/or overloaded, as indicated by theaccumulation of volatile fatty acids(VFA) and long-chain fatty acids(LCFA) and the decline in the methaneyield. However, the inhibition wasreversible. The nitrogen in the feed,ca. 7.8% of the total solids (TS), wasorganic nitrogen with little ammoniapresent, whereas in the digestedmaterial ammonias accounted for 52––67% (up to 3.8 g/l) of total nitrogen.The TS and VS removals amountedto 76% and 64%, respectively.• A new generation mathematicalmodel called modelwas modified in order to describe asystem dynamics of slaughter wastedegradation (Vavilin 2003). Salminenet al. (2000) used this modifiedversion for studying of anaerobicbatch degradation of solid poultryslaughterhouse wastes.• Broughten et al. (1998) studiedanaerobic digestion of sheep tallow.The experiment was carried out inbatch reactors operating at mesophilic(35ºC) and thermophilic (50ºC)temperatures with sheep tallow atlevels up to 59% of the volatilesolids. Tallow was rapidly fermentedto LCFA and VFA at 35ºC, but wasrefractory at 50ºC. Oleic acid wasfermented to palmitic, stearic andacetic acid. Methanogenesis wasdelayed by characteristic adaptationperiods before LCFA and VFAwere completely degraded. Thisdemonstrated that wastes withhigh lipid contents are amenable tostabilization by mesophilic batchdigestion.• Dohányos et al. (2003) are studyingtwo methods of meat and bonemeal (MBM) treatment – pyrolysisand anaerobic digestion and theircombination, eventually. Thepreliminary experiments of anaerobicdigestion were carried out withclassically produced MBM at 140ºCand MBM pyrolysed at 200ºCand 285ºC. The biogas yield wasdetermined by batch experimentswith digested sludge as inoculum.The results showed very goodbiodegradability of MBM and MBMpyrolysed at 200ºC, biogas productionreached 0.37 l and 0.452 N·m 3 /kg ofTS, respectively.• Raizada et al. (2003) observed performancesof fixed and fluidized bedreactors used for the degradation oforganic waste (rumen content) by atwo-stage anaerobic digestion processat different loading rates (2–12kg COD m –3·d–1 ) under mesophiliccondition. The performance of thefixed bed reactor was better in comparisonto the fluidized bed reactorand as no propionic acid accumulationwas observed in contrast to thefluidized bed reactor.• Farinet and Forest (2003) mentionbrief descriptions of two slaughterhousetreatment plants based on anaerobicdigestion in Africa. One is locatedin Senegal, the other in Egypt.Both have equipment for digestatecomposting. The authors suppose arising further development of this

Results of verifi cation of the slaughter waste anaerobic fermentation process 97combined treatment of slaughterhousewaste in Africa, mainly becauseof the high levels of energy price andcompost demand.• Ashare et al. (1983, in Straka et al.2003) studied available values ofBOD of various types of waste frommeat processing industry. They foundout that available BOD of this wastehave a great potential e.g. for cattle– blood has 2.3 kg BOD/t LWK (liveweight killed), intestinal contenthas 2.5 kg BOD/t LWK; for poultry– 15.3 kg BOD/ t LWK. The specificmethane production gained fromthis waste is very high due to a highcontent of fat.MATERIALS AND METHODThe tests were carried out on the singlestageanaerobic digestion principle using5% dry-matter charges under mesophilicand thermophilic conditions in two seriesdiffering in retention times (experiments1 and 2 respectively).The following materials were thesubject of verification:••••poultry bone pulp (39.8% drymatter),pork sinews (15.2% dry matter),cattle and pig slurry mixed in aproportion 1:1,stabilized non-dehydrated residueafter anaerobic digestion, used asinoculum.The poultry pulp and pork sinews werecut up to particles of 12 mm. The slurryand digestate were mixed in a proportion1:1 in both the charges for the smallreactors (0.002 m 3 ) and the large reactors(0.1 m 3 ). The results of verification in thesmall reactors are quoted in this paper.The substrate proportions for each batchare shown in Table 1; Table 2 shows thematerial compositions of the batches.The retention times, or times of presenceof each batch in the reactor, were 26 daysand 37 days during Experiments 1 and 2respectively.The biogas production in the smallreactors was measured using a gasmeter constructed at the AgriculturalTechnology Research Institute inPrague.Samples of the stabilized residue weretested for the presence of bacteria ofTABLE 1. Substrate proportions in batches, for small reactorsReactorProportion[% mass]Poultry bone pulp[% mass]Pork sinews[% mass]1a 100 0 02a 90 10 03a 80 20 04a 70 30 05a 60 40 06a 90 0 107a 80 0 208a 70 0 309a 60 0 40

98 J. Kára, Z. Pastorek, R. AdamovskýTABLE 2. Material composition of charges in small rectors in both experimentsReactorProportions Poultry bone pulp Pork sinewsWater[g][g][g][g]1a 1250.0 0.0 0.0 750.02a 803.6 89.3 0.0 1107.13a 555.6 138.8 0.0 1305.64a 397.7 170.5 0.0 1431.85a 288.5 192.3 0.0 1519.26a 1034.5 0.0 114.9 850.67a 851.1 0.0 212.7 936.28a 693.1 0.0 297.1 1009.89a 555.6 0.0 370.3 1074.1the Salmonella and Enterobacteriaceaegenera in microbiological analyses atthe Microbiology Laboratory of theNational Medical Institute in Prague. Themicrobiologic methods applied complywith the requirements of EC Regulation1774/2002 (Salminen, Rintala, Lokshina,Vavilin 2000).RESULTS AND DISCUSSIONThis paper only presents in detailthe results of observations of biogasproduction. The biogas production wasmeasured daily; nevertheless, cumulativeproduction related to 1 kg of dry matteris quoted for the sake of clarity. Themeasurement results are for the smallreactors, verifying the concentrations ofthe additives, the poultry bone pulp, andthe pork sinews.The tested samples contained poultrybone pulp in concentrations of 10%,20%, 30%, and 40%, and pork sinews inidentical concentrations. Figures 1 to 4show the results of experiments for bonepulp and pork sinews in the mesophilicrange. The experimental developmentswere very similar to each other, butdiffered in certain details. The sampleswere processed in single-stage reactorsunder mesophilic and thermophilicconditions (the developments underthermophilic conditions are not quotedhere as they were very similar, only theretention times were reduced).The composition of samplescontaining the bone pulp seems optimal interms of biogas production and methanecontent. The highest cumulative biogasproduction rate was achieved in thesample containing 40% of poultry bonepulp (381.5 litres per kg of dry matter,and 561.0 litres per kg of dry matter,respectively) after 26 days.The samples containing 10% and 20%of pork sinews showed a satisfactoryproduction of both biogas and methane.The best result was achieved with thesample containing 10% of pork sinews(460.5 liters per kg of dry matter,and 641.4 liters per kg of dry matter,respectively) after 26-day retention.The samples containing 30% and 40%of pork sinews were characterized bylow biogas production rates and highmethane contents (70–80%). These

Results of verifi cation of the slaughter waste anaerobic fermentation process 99450400Biogas production l.kg -1 of d.m.3503002502001501005001 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26Content of poultry crushed bones:days0% crushed bones 10% crushed bones 20% crushed bones30% crushed bones 40% crushed bonesFIGURE 1. Cumulative biogas production, poultry bone pulp, Experiment 1 (Biogas production [litresper kg of dry matter])600500Biogas production l.kg -1 of d.m.40030020010001 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37Content of poultry crushed bones:days0% crushed bones 10% crushed bones 20% crushed bones30% crushed bones 40% crushed bonesFIGURE 2. Cumulative biogas production, poultry bone pulp, Experiment 2

100 J. Kára, Z. Pastorek, R. AdamovskýBiogas production l.kg -1 of d.m.4003503002502001501005001 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26daysContent of pork sinews:0% 10% sinews 20% sinews 30% sinews 40% sinewsFIGURE 3. Cumulative biogas production, pork sinews, Experiment 1600Biogas production l.kg -1 of d.m.50040030020010001 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37daysContent of pork sinews:0% sinews 10% sinews 20% sinews 30% sinews 40% sinewsFIGURE 4. Cumulative biogas production, pork sinews, Experiment 2samples posed considerable difficultiesin terms of frothing. The large reactorsvalidated the results in the small reactors,only the frothing of the samples was notas marked, and the maximum biogasproduction occurred ten days earlier inthe thermophilic process (Vavilin 2003).The stabilized residue after anaerobicdigestion was analyzed chemically andmicrobiologically, and its suitability forsoil application was ascertained.It can be stated that poultry bone pulpand pork sinews are suitable materialsfor anaerobic digestion provided thatthe appropriate mass proportions in thefermented mixture are maintained.

Results of verifi cation of the slaughter waste anaerobic fermentation process 101CONCLUSIONSIt is effective to use slaughterhousewaste in order to increase the efficiencyof biogas stations. Stabilized residueafter anaerobic digestion was analyzedchemically and microbiologically, andits suitability for soil application wasascertained. However, processing ofslaughterhouse waste in a biogas stationrequires the installation of a unit forthermal treatment of the input substrate,i.e., heating the material to 70°C for onehour.This paper has been developed as aresult of execution of Czech Ministryof Agriculture projects nos. QG 50039:‘Processing confiscated and other wasteusing the biogas process’, and QD3160: ‘Research into new technologicalprocedures for improved efficiency ofagricultural and food processing wasteutilization’.REFERENCESBROUGHTEN M.J., THIELE H.J., BIRCHJ.E., COHEN A. 1998: Anaerobic batchdigestion of sheep tallow. Water Research,Vol. 32, No 5: 1423–1428.DOHÁNYOS M., ZÁBRANSKÁ J.,STRAKA F. 2003: Possibilities of safetreatment and utilization of veterinarysanitation waste. [In:] IWA – WorkshopAnaerobic digestion of slaughterhousewastes. Proceedings, NarbonneFARINET J.L., FOREST F. 2003: Agroenergeticvalorization of slaughterhousewastes in Africa. [In:] IWA – WorkshopAnaerobic digestion of slaughterhousewastes. Proceedings, Narbonne.SALMINEN E.A., RINTALA J.A. 2002:Semi-continous anaerobic digestion ofsolid poultry slaughterhouse waste: effectof hydraulic retention time and loading.Water Research, Vol. 36: 3175–3182.SALMINEN E.A., RINTALA J.A., LOK-SHINA L.Y., VAVILIN V.A. 2000:Anaerobic batch degradation of solidpoultry slaughterhouse waste. WaterScience and Technology, Vol. 41, No3: 33–41.STRAKA F., DOHÁNYOS M., ZÁBRAN-SKÁ J., DĚDEK J., MALIJEVSKÝ A.,NOVÁK J., ODLŘICH J. 2003: Bioplyn.GAS s.r.o., Říčany.VAVILIN V.A. 2003: Modelling of AnaerobicDegradation of Slaughterhouse Waste.[In:] IWA – Workshop Anaerobicdigestion of slaughterhouse wastes.Proceedings, Narbonne.Streszczenie: Wyniki sprawdzania procesu fermentacjianaerobowej odpadów ubojowych. Artykułzajmuje się analizą wyników sprawdzaniajednostopniowego rozpuszczania anaerobowegow warunkach ze średnimi wymaganiami odnośniewilgotności i warunkach ciepłolubnych w dwuseriach, odróżniających się okresem retencyjnym(26 i 37 dni). Zostały sprawdzone substraty, składającesię z różnych stosunków drobiowej, rozmiażdżonejmasy kostnej, ścięgien wieprzowych,gnojówki bydła wołowego i prosiąt (1:1). Stabilizowana,nieodwodniona reszta po rozpuszczeniuanaerobowym została wykorzystana jako inoculum.Wyniki sprawdzania wskazały na efektywnośćprzerabiania odpadów ubojowych w stacjachbiogazowych. Przerabianie odpadów ubojowychwymaga jednakże instalowania urządzeń do przeróbkicieplnej substratu wejściowego (podgrzaniedo 70°C przez 1 godzinę).MS. received December 2007Authors’ address:Ing. Jaroslav Kára, CSc.Česká zemědělská univerzita v Praze, TechnickáfakultaKamýcká 129, 165 21 Praha 6 – SuchdolCzech Republice-mail: jaroslav.kara@vuzt.cz

Annals of Warsaw University of Life Sciences – SGGWAgriculture No 52 (Agricultural and Forest Engineering) 2008: 103–106(Ann. Warsaw Univ. of Life Sci. – SGGW, Agricult. 52, 2008)New fermentation source in the technology of fermentednon-alcoholic beveragesELENA TSED, ZOYA BASILENKO, LIDIA KOROLEVA, ELENA LEBEDOK,IRINA IVANOVAThe Mogilev State University of Foodstuffs, Mogilev, BelarusAbstract: New fermentation source in thetechnology of fermented non-alcoholic beverages.Decoding and identification of microbecomposition of trivially called rice fungus as anew fermentation source have been studied. It hasbeen found out that Orуzamyces indici РГЦ isan associative consortium of microorganisms ofvarious groups: Zygosaccharomyces fermentatiNaganishi, Pichia membranaefaciens Hansen,Lactobacillus paracasei subsp. рaracasei,Leuconostoc mesenteroides subsp. dextranicum,Acetobacter aceti, and it is potentially ableto bring about the fermentation processes.Technology of new fermented non-alcoholicbeverage “Klukovka” of higher biological valuehas been developed.Key words: Oryzamyces indici РГЦ , rice fungus,fermented non – alcoholic drink “Klukovka”,cranberry, microbe composition, organolepticcharacteristics, biochemical characteristics.INTRODUCTIONDevelopment of new non-alcoholic beveragesenriched with biologically activeingredients and possessing purposefullygiven properties is one of the current prioritytrends in non-alcoholic beverageproduction.Up-to-date market of the Republicof Belarus is characterized by a widevariety of non-alcoholic beverages. Unfortunately,they are mainly producedwith the use of synthetic or identical tonatural food additives – the flavouring,colouring, sweetening and conservingagents. Such beverages have simplifiedtechnology of production, longer shelfbut their biological and food value isvery dubious.At present there is much information,that a number of food additives thatare most widely used in non-alcoholicindustry (benzoic acid and its salts,citric acid, cyclamic acid and its salts)are regarded as cancerogenic substancesinfluencing a human body. They seriouslythreaten the consumer’s health [1].That is why drinking beveragescontaining harmful substances for a longperiod of time affects negatively a humanbody, and specially children, who are themain consumers of such products.In this connection, the researchworks aimed at development of naturalnon-alcoholic beverages containing noartificial food additives and enrichedwith biologically active substances ofendogenous origin are very perspectiveand actual.In this case the fermented nonalcoholicbeverages of plant originand enriched with microbiologicalbiocomplex metabolites are speciallyimportant. This is caused by the fact thatquite a number of biologically activesubstances (vitamins, amino acids,organic acids, minerals) necessary for

104 E. Tsed et al.normal functioning of a human bodyare produced during the fermentationprocess; finally, it gives the higherbiological value, the treatment and theprophylactic value to the fermentedbeverages.Studies on development of new technologiesof non-traditional fermentationalcohol – free beverages that possessthe treatment and prophylactic propertiesare carried out at the Mogilev StateUniversity of Foodstuffs. Therefore, theOrуzamyces indici РГЦ polyculture ofmicroorganisms, trivially called rice fungus(Indian sea rise, Japanese rise) havebeen investigated for several years.Thus, the purpose of this investigationwas to study a new non-traditionalfermentation source and possibility ofusing it as a fermenting agent in theproduction of non-alcoholic fermentationbeverages.RESULTSIn appearance the rice fungus lookslike transparent jelly-like granulesof various size; it resembles the ricegrains in the shape and is consideredto be a natural symbiotic polyculture ofmicroorganisms that was formed in thecourse of evolution. The rice fungus isa widely cultivated home-made product,however, scientific investigations onthe composition and properties of thisbioculture are not available.It is only known that a home-madeinfusion on the basis of rise funguspossesses both the pleasant organolepticproperties and a whole range oftreatment and prophylactic ones. Itaids in normalization of metabolicdisturbances and reduction of bloodsugar content, enhances the performanceefficiency, normalizes acidity of a gastricjuice, relieves insomnia, muscle-skeletaldiseases, rheum arthritis, stomatitis,pyelonephritis; it also possesses theantiviral, canceridical and immunestimulatingeffects [2].Microbe composition of polycultureOrуzamyces indici РГЦ has been identified;it has been found that the risefungus is an associative consortium ofmicroorganisms belonging to varioustaxonomy groups: yeast (Zygosaccharomycesfermentati Naganishi, Pichiamembranaefaciens Hansen), lactic acidbacteria (Lactobacillus paracasei subsp.рaracasei, Leuconostoc mesenteroidessubsp. dextranicum), acetic acid bacteria(Acetobacter aceti). While examiningphysiological characteristics it has beenfound that microorganisms of symbioticassociation of polyculture Orуzamycesindici РГЦ utilize a significant amountof carbohydrates forming the plant substrates.Physiological, biochemical, andtechnological properties of this bioculturehave been studied. Metabolic propertiesof rice fungus have been determined.This information makes it possible tocontrol substrata fermentation processeseffectively and to obtain the productof properties given purposefully. Thebioculture of rice fungus Orуzamycesindici РГЦ has been found to be a veryprospective fermentation source inthe production of the fermented nonalcoholicbeverages [3].It has also been found that during itsvital activity the rice fungus produces awide range of substances, many of thempossessing a high biological activity

New fermentation source in the technology of fermented non-alcoholic beverages 105and being essential components formaintaining metabolism in human body.Thus, synthesis of free amino acid duringvital activity of rice fungus has beenstudied.We identified that when cultivated,the rice fungus synthesized almostall known amino acids, including 8essential ones, such as threonine, valine,methionine, leucine, lysine. Moreover,2 amino acids essential for children –arginine and histidine were determined.It is important because children areamong main consumers of non-alcoholicbeverages.Taking into account the wholecomplex of experimental data, the newproduction technology for the fermentednon-alcoholic beverage “Klukovka”,made of non-traditional fermentationsource Orуzamyces indici РГЦ withaddition of cranberry juice has beendeveloped and patented (Tab. 1).Biochemical analyses of the newbeverage showed that non-alcoholicfermentation beverage produced frompolyculture of rise fungus Orуzamycesindici РГЦ as a fermented agent andadditionally enriched with cranberry juicepossessed pleasant organoleptic propertiesand considerable biological value.This was attributed to the contentof the whole range of such biologicallyactive compounds as amino acids,vitamin P, vitamin C, folic acid, reducingsubstances, etc. in beverage. They takepart in metabolism processes of humanbody (Tab. 2).In particular, reducing substances takepart in energy cell metabolism, aminonitrogen – in the synthesis of proteinsof cell structure; vitamin C promotes theTABLE 1. Organoleptic characteristics of non-alcoholic drink “Klukovka”IndexAppearanceColorTasteAromaDescriptionOpaque liquid with no foreign bodies peculiar to the product. A little naturalsediment may be presentRoseSour and sweetCranberry aromaTABLE 2. Biochemical characteristics of non-alcoholic drink “Klukovka”IndexValueReducing sugars content [g/100 ml] of non-alcoholic beverage 1.8Amino acid composition [mg/100 ml],including:HistidineArginineProlineMethionineCystineLysine231.6658.8 ±11.836.8 ±7.469.3 ±13.917.4 ±3.579.0 ±15.812.2 ±2.4Vitamin P content [%] 0.98Vitamin C content [mg %] 11.00Folic acid content [m·kg/100 ml] of non-alcoholic beverage 0.15

106 E. Tsed et al.carbohydrate – protein metabolism, aidsin clotting of the blood, intensifies tissueregenerations and synthesis of steroidhormones – collagen and procollagen. Itpromotes easier iron adsorption, enhancesbody’s adaptation ability and resistance toinfection; vitamin P decreases capillarypermeability, maintains blood vesselsand makes biological effect of vitamin Cmore efficient.The folic acid takes part in regulationof cell fission process and proteinmetabolism, stimulates the immunesystem, regulates fat metabolism in live.A deficiency of folic acid mainly affectsthe hemopoietic anemia, resulting inbleeding gums, various hemorrhagesetc. The lack of folic acid is one of mostcommon deficiencies among modernpeople.Moreover, the fact that citric acid andpreservatives are not used in the recipeof non-alcoholic beverage allows toincrease not only economic efficiencydue to food product price reduction, but,first of all, to get social effects aimed atbringing the human body into a healthystate due to consumption of naturalfood products containing no exogenouschemical ingredients.Thus, production and popularizationof naturally fermented non-alcoholicbeverage will permit to diversify the foodproducts with an appeal to the consumerswho take care of their health.REFERENCESCHEPURNOY I.P. 2005: Identificatsia yfalsificatsia prodovolstvennyh tovarov,M., Izdatelsko – torgovaya korporatsia“Dashkov y K o ”, 460.POLEVAYA M.A. 2005: Indiyskiy ris– tselebniy ris. Zhdorovje serdtsa ysosudov, vosstanovlenie posle infarcta,SPb. ID “Ves”, 128.TSED E.A., PRIBYLSKIY V.L.,YAKIREVICH L.M., RIDEVSKAYAL.I., KAMINSKAYA N.A. 2001: Risoviygrib-osnova bezhalcoholnyh napitkov,Pivo y Napitky, 5, 38.Streszczenie: Nowe źródło fermentacji w technologiisfermentowanego napoju bezalkoholowego.Przeprowadzono badania dekodowania i identyfikacjiskładu mikroorganizmów w grzybku ryżowymjako nowego źródła fermentacji. Stwierdzono,że Orуzamyces indici РГЦ stanowi kompleksmikroorganizmów należących do różnych grup:Zygosaccharomyces fermentati Naganishi, Pichiamembranaefaciens Hansen, Lactobacillus paracaseisubsp. рaracasei, Leuconostoc mesenteroidessubsp. dextranicum, Acetobacter aceti. Może onwywołać proces fermentacji. Opracowano technologięnowego sfermentowanego napoju bezalkoholowego“Klukovka” o wyższej wartościbiologicznej.MS. received June 2008Authors’ address:Irina IvanovaDepartment of the Automation of TechnologicalProcesses and Productione-mail: mti@mogile.byMogilev State University of Foodstuff3 Schmidt Avc.Mogilev 212027Belarys

Annals of Warsaw University of Life Sciences – SGGWAgriculture No 52 (Agricultural and Forest Engineering) 2008: 107–114(Ann. Warsaw Univ. of Life Sci. – SGGW, Agricult. 52, 2008)Agricultural business extension aided by the Case-Based ReasoningmethodTADEUSZ WAŚCIŃSKI 1 , ANNA MICHALCZYK 21 Department of Production Management and Engineering, Warsaw University of Life Sciences –SGGW, Warsaw, Poland2 Faculty of Management, University of Warsaw, Warsaw, PolandAbstract: Agricultural business extension aidedby the Case-Based Reasoning method. The paperaims at presentation of a concept of computerizedinformation system, aiding the managementwith knowledge and experience for agriculturalconsulting purposes. The Case-Based Reasoningmethod is presented as a basis for informationsystem development. Additionally, applicationof uncertain and approximate knowledge wasassumed.Key words: agricultural extension, knowledgemanagement, information system, artificialintelligence, Case-Based Reasoning, approximateknowledge.INTRODUCTIONAchieving a success in contemporaryagribusiness calls for knowledge,initiative, flexibility and innovation,thus, there occurs a wider demand foragricultural extension on the increasedeffectiveness and competitivenessof farms. Therefore, agriculturalextension aims at helping the farmers inundertaking correct decisions, especiallydifficult under conditions of uncertainty,when every advice can be burden withpossible errors resulting in economiclosses. The risk of errors increases if anadvisor have no appropriate knowledgeor experience in considered scope; underthis situation the agricultural extensionunit can provide the best advice byintroducing solutions from the fieldof knowledge management, includinginformation systems supporting decisionmaking under conditions of uncertainty.It can result in improvement of decisionmaking process, shortening of timeneeded for solving the critical problems,increasing productivity and reducingcosts [2, 12, 13, 17]. Taking the aboveinto consideration one can formulate thefollowing research thesis: Knowledgeand experience properly utilized inagricultural consulting minimizes therisk of economic failure connected withthe given advice. Verification of thisresearch thesis was attempted by creationof the information system as a toolsupporting management with knowledgeand experience of agricultural advisorin decision making process based onthe Case-Based Reasoning methodwith consideration to uncertain andapproximate knowledge.MANAGEMENT OFKNOWLEDGE AND EXPERIENCEKnowledge management is an effectiveprocess of learning connected withsearching, utilization and dissemination

108 T. Waściński, A. Michalczykof knowledge (open and hidden one),with the use of appropriate technologiesand cultural environment in orderto increase intellectual capital andorganization efficiency [12]. Experienceis a knowledge from the past, created byproblems and their solutions, thus, as aspecific form of knowledge it is subjectedto management and can be supportedby technological factor. ExperienceManagement as a sub discipline ofknowledge management belongs to thegroup of subjects investigated at presentin the world [7, 10, 11], especially inthe field of informatics and intelligentsystems, however, not separatedfrom organization and managementsciences. It deals with processes whichcontrol creation, storing, utilization andreuse of both the human and machineexperiences.In agricultural extension units theknowledge is a product. The adviceseekingfarmer expects to get advicebased not only on current knowledge,but also on the branch experience, whilethe advice-based decisions will sooneror later yield advantageous economicresults. It is expected that the advisor willuse his own open knowledge acquiredduring service and also his experienceand the connected hidden knowledge,while solving the current problems hewill remember his past experiences andthe solutions proposed in the similarsituation. If they were successful, hewould try to act similarly, if not hewould avoid them trying to proposeother solutions using his experience.The presented mechanism of experienceutilization corresponds to inference byanalogy and is based on rememberingthe past cases understood as a pair:problem – applied solution and skilfuldetermination of similarity betweenproblems.The experience exists in everyextension unit as a knowledge acquiredduring carried out activity, appliedand practically utilized. Many cases(advices) are similar to each other, thus,the previous experiences would facilitatetheir solving. Lack of experience of a newemployee can cause the financial lossesfrom wrong advice, while codification ofexperience and automatic mechanismsforcing its use will not only save timeneeded to solve the future problem, butalso will stabilize the unit’s operationafter quitting jobs by the experiencedadvisors. It will additionally facilitateacquiring, processing and sharing thehidden knowledge and also increasingknowledge of employees, their efficiencyand operational effectiveness [17].Management of data, information andknowledge, including management ofexperience in contemporary enterprisewill be practically very difficult or evenimpossible without information support.The most useful are data base systemsoriented on aiding the user in realizationof decision making processes, createdwith the use of artificial intelligencemethods [12, 16]. Artificial intelligencetechniques enable to imitate the humanthinking process, aiming at solving thecurrent problem on the basis of collectedknowledge and experience. As it isevident from literature review [15], theartificial intelligence knowledge-basedsystems, especially expert systems,positively influence productivity andstaff efficiency, optimize the results of

Agricultural business extension aided by the Case-Based Reasoning method 109undertaken decisions, increase rate ofwork by an increase in speed of decisionmaking, and increase the experiencevolume. A decrease in efforts of problemsolving was found also, both in termsof costs and time [17]. The most usefultechnique in experience management,which imitates the mechanism forexperience utilization (thinking byanalogy) is the Case-Based Reasoningmethod (CBR) [7, 10, 11, 16].CASE-BASED REASONINGCase-Based Reasoning is an artificialintelligence method of the field ofsolving problems, learning and alsothe technique supporting knowledgemanagement, particularly experiencemanagement [1, 8, 9]. This method isbased on observing the expert’s thinking,reaching to the past memory for thesolutions of known problems (cases)and using these patterns during solvingcurrent problems. The case is a pair:problem and its solution, while the casebase collects experience in the form ofcases, without specifying the rules takenas basis for decision making. This featuredistinguishes the system with base casefrom expert system, where knowledgeis expressed with the use of rules. Theessence of CBR is: solving the currentproblem by acceptance of solutions usedin the past [1, 8, 9], thus, it assumes thatsimilar problems have similar solutions.Imitating the human thinking, CBR ispresented as a cycle consisted of thefollowing phases (stages):• Searching (retrieving) in the casebase for the case(s) most similar toconsidered one;• Utilization (reusing) of this casesolution in solving the currentproblem;• Evaluation of usability (revising) ofold solution(s) in order to match itto considered problem or adaptation(modification) of solutions;• Memorizing (retaining) of consideredproblem with applied solution as anew case (experience) for furtherutilization during solving newproblems in future.CBR is used in solving of manyproblems as: diagnostics, classification,interpretation, planning, designing,teaching, etc. It is assumed that CBRbasedsystem are used in the fieldscharacterized by: regularity, repeatability,similarity of phenomena, continuity ofreality subjected to modeling. The CBRmethod and its application is investigatedat present by many researchers, seekingvarious solutions for its development [7,10, 11]. The authors think that agriculturalextension is such an area.CONCEPT OF INFORMATIONSYSTEM – RESULTS OFINVESTIGATIONSDevelopment of information systemwith CBR method application shouldinclude: case definition, selection ofcase representation, determination ofthe way for probability measurement,selection of technique for seeking out thesimilar cases and method for adaptationof solutions. To investigate CBRapplication in supporting management ofknowledge and experience of employeesof agricultural extension units, the

110 T. Waściński, A. Michalczykfollowing assumptions were made basingon references:Assumption 1. The authors intendto achieve the model of system in thegeneral form, thus, they passed overthe problem of case definition. The caserepresentation based on feature vector[3] was assumed in the form of structureObject-Attribute-Value. Therefore, thecase base was defined as follows:CB=< U, A, D >(1)where: U is a finite set of cases, A is anon-empty, finite set of attributes describingthe problem, D is a non-empty,finite set of attributes describing theproblem solution; for each attribute a∈A,a: U→U a , where U a is a set of values ofproblem description attribute a; for eachd∈D, d: U→U d , where U d is a set of valuesof decision attribute d.Assumption 2. Seeking out the similarcases according to nearest-neighbourretrieval method [8, 9] by browsing thebase, case after case, in order to find themost similar case.Assumption 3. Measuring similaritybetween cases is based on the concept:similarity of cases results from similaritywithin their features, propertiesdescribing the problem. Measurement ofsimilarity was based on local similarityaccording to formula [8, 9]:∑wsim ac acsim c c a A a a ( ( ), ( '))(, ') = ∈∑ wa∈Aa(2)where A is a set of features, attributesdescribing the problem, sim a :U a xU→[0.1] is a local measure determined forattribute a∈A, U a is a set of values takenfor each a∈A, w a ≥0 is significance ofattribute a∈A.It should be noted that in equation(2), the measures of local similarity sim adepend on type of values assumed withinthe feature. It is assumed additionallythat the measure of case similarity is arefl exive function (each case is similar toitself) and a symmetrical function (if casec is similar to case c’, then c’ is similarto case c), while attribute’s significanceenables to determine the effect of featureon similarity of cases. It is assumedthat local similarities and significanceof attributes are known and must begiven by the system’s user or calculatedwith the use of machine learningtechniques. Analysis of referencesenables to conclude that by this methodone can compare similarity of cases ofheterogeneous, mixed features, bothquantitative and qualitative, with the useof real, unprocessed data.Assumption 4. Interactive adaptationof solutions with participation of system’suser [8, 9].RESULTS OF INITIALINVESTIGATIONSThe authors’ investigations carried outtowards development of an informationsystem based on CBR at undertakenassumptions point out at some problemsin determination of local similarity bythe system’s users (equation 2), whocan much easier determine the fact ofsimilarity between values of a givenattribute than its accurate values or thefunction (measure) taken in calculations.

Agricultural business extension aided by the Case-Based Reasoning method 111This is because a man in his everydaylife rather estimates than counts, usingan approximate knowledge (in this paperit was assumed after (4) that approximateknowledge is based on approximate databurden with error).The obtained results and conclusionspoint out at the need of supplementingthe classical measurements of similaritywith measures including uncertainand approximate knowledge. As it isevident from references [4, 5], solvingof this problem can be attempted byassuming that the cases are elementsof tolerance spaces, which – as specifictype of approximation spaces – allow forconvenient comparison between variousobjects’ similarities and for applicationof approximate knowledge.The tolerance space (TS) [4] is thetuple 1 TS ≤ U, sim, p >, where U is anon-empty set of objects, sim:UxU→[0.1] is a measure of tolerance, p is athreshold value and p∈[0.1]. Similaritymeasure is a tolerance measure if it isa reflexive and symmetrical function,thus, for each u,u ∈ U there are fulfilledconditions sim(u,u) = 1 and sim(u,u’) =sim(u’,u).The set of similar objects notdiscriminated with object u∈U, forwhich there exists:Iu ( ) = { u' ∈U; simuu ( , ') ≥ p}(3)is an environment (neighbourhood) ofthis object.1 Tuple – “rearranged, finite set of elements” or“rearranged set of n-elements”, sources: http://pl.wikipedia.org.wiki/Krotka of 23.03.2008, MarciniakM., Szaniawski J.: Słownik angielsko-polskidla informatyków. WNT, Warszawa 1990, p. 243.A very important feature of tolerancespace is the fact that similarity ofcomplex structures is based on similarityof simple, elementary set of values. Itmeans that tolerance spaces determinedon elementary sets induce the tolerancespace for complex structures usedfor their development. This feature isvery important since in CBR methodin classical approach the similarity ofcases results from similarity of theirproblems, and this results from similarityof comparable features describing theproblem.In carried out investigations it wasassumed that besides the case base thereis also known additionally the similarityof features of investigated problem,expressed with tolerance spaces. Then,this similarity is transferred on problemsimilarity, thus, on case similarity. Thisassumption is also taken in the systemmodel, which can be presented asfollows.Assumptions for the system.There is the base of cases CB definedaccording to equation (1) and there isalso additional knowledge on similaritywithin the problem features, expressedwith tolerance spaces TS a ≤ U a ,sim a ,p a >for each a∈A, then this similarity istransferred on similarity of cases , thus,tolerance spaces TS a determine thetolerance space above the base of casesTS CB ≤ U,sim,p>.Introduction of above assumptionto the system allows for application ofobject environments (equation 3), inorder to determine similarity betweencases, which can be described withequation (4):

112 T. Waściński, A. Michalczyk∑wS ac acsim c c a A a a ( ( ), ( '))(, ') = ∈(4)∑ wa∈Aawhere⎧1Sa( x, y)= ⎨⎩0dla y ∈ Ia( x)dla y ∉ Ia( x)where w a is attribute’s significance,while function S a , which determinebelonging to environments I a of attributea∈A (equation 3) allows for applicationof “being not discriminated” within agiven feature.It should be noted that a classicalmeasure of similarity for CBR accordingto equation (2) is a measure of toleranceand can be used in TS. Definitions oftolerance measures together with theirclassification and characteristic can befound in references [4, 14].Cycle of inference CBR TS . Undertakenassumption that CB cases are elementsof the tolerance space affects notonly the measurement of similarity, butalso the cycle of inference, which wasproved in carried out experiments. Thefour-phase cycle CBR TS was obtained,with its first phase retrieving TS expandedin relation to a classical phase by possibilityof searching for the cases not discriminatedwith the case being considered (itsenvironment). It enables to find quicklyexistence of precedent, in the context ofcases collected in the base. In order tosearch for the new problem, an algorithmof environment retrieving is proposed forbrowsing the base, case after case, to findall cases not discriminated with the casebeing considered (for which equation 3is valid). It was assumed that the remainingCBR TS phases are compatible withphases 2, 3 and 4 of classical CBR.Model of the system. Theabove assumptions and carried outinvestigations and conclusions enableto define the information system, aidingthe management with knowledge andexperience for agricultural consultingsystem =< CB,{ TSa : a ∈ A}, TSCB, CBRTS,I >purposes, according to equation (5):(5)where CB, TS a , TS CB form the knowledgebase and are conform with undertakenassumptions of the system, while cycleof inference CBR TS is mechanism ofinterference and I is interface of theuser.The carried out computer experimentsproved usability of the proposedmodel of the system as a tool aidingthe management with knowledge andexperience of employees of the consultingunit. This procedure aimed at provingthat contemporary techniques and toolsof software engineering can fulfill therequirements of system realizationaccording to a given concept.With respect to undertaken scopeof work the model was presented ina basic version and is open to furtherdevelopment. The authors think thatintroduction of tolerance space to CBR isan initial point to further considerations,e.g. to application of approximate settheory, and contributes to developmentof this method. This problems will besubjected to further investigations.

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114 T. Waściński, A. Michalczykw procesie decyzyjnym – opartego na metodzieCase Based Reasoning z uwzględnieniem możliwościposługiwania się wiedzą niepewną i przybliżoną.W artykule omówiono zagadnienia z obszaruzarządzania wiedzą i doświadczeniem orazprzedstawiono metodę Case-Based Reasoning(CBR) jako podstawę budowy systemu informatycznego.Zaprezentowano koncepcję systemuinformatycznego opartego na metodzie CBR i założeniu,że przypadki (cases) są elementami przestrzenitolerancji co daję możliwość posługiwaniasię wiedzą niepewną i przybliżoną.Authors’ addresses:Tadeusz WaścińskiKatedra Organizacji i Zarządzania Produkcją,Wydział Inżynierii Produkcji SGGWul. Nowoursynowska 16402-787 WarszawaAnna MichalczykKatedra InformatykiWydział Informatyki i EkonomiiWyższa Szkoła Informatyki i Ekonomii TWPul. Barczewskiego 1110-061 OlsztynMS. received June 2008