1. Introduction - Firenze University Press

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Combination of TSs for process demand and solar thermal-energy Integration of solar thermal-energy within each combined TS Estimation of storage size The trade-off between the inaccuracy and number of TSs should be evaluated, in order to determine the number of TSs. 2. Determining the number of time slices 2.<strong>1.</strong> Problem formulation The approximation inaccuracy decreases with an increasing number of TSs. On the other hand, the aim is to minimise the number of TSs, in order to simplify the computations during the following steps of the integration procedure. Therefore, the task can be defined as obtaining the minimum number of TSs with acceptable accuracy. The solar irradiation (G) measurements or the temporal variation of the captured heat flow could be used to identify the TS for solar energy availability. 2.2. Approximation of the irradiation profile This procedure is based on optimising the load-levels and selecting items from a discrete superset of candidate time boundaries. These represent the measured Solar Irradiation – G data in dependence of time, t[h], by constructing a high-precision piecewise-constant profile [20] (Fig 1). G [W/ 700 m2 ] 600 500 400 300 200 100 0 4 6 8 10 12 14 16 18 t 20 [h] 299 measured data discretization Fig 1: Discretisation of the measured profile/ input data for optimising the number of TSs When using a large number of time-intervals, the inaccuracy of this transformation is minimised and can be ignored. However, such high-accuracy would require very intensive computation Therefore, the piecewise–constant load profile to be obtained has to contain a significantly smaller number of TSs. The supply is approximated separately at each time-interval by the minimisation of any inaccuracy represented by those areas occurring between the approximated and real inputsupply profiles (Fig 2). The boundaries of the time-intervals are the candidate boundaries for the final TSs. If there is a difference between two consecutively approximated supply levels, the time-boundary is also a TS boundary. When two time-intervals are joined into one TS, the approximated supply-levels should
e equal at both time intervals and the time-interval period boundary candidate is deselected as a TS boundary (Fig 3). Fig 2: Determining the inaccuracy between the input and approximated supply 2.3. MILP model formulation 300 Fig 3: Acceptance/ rejection of the candidate time period boundary as a TS boundary A two-stage MILP model has been developed for minimising the number of TSs at acceptable inaccuracy. During the first stage, the number of TSs is minimised, depending on the tolerance of inaccuracy specified by the models’ users. During the second stage, the inaccuracy is minimised at a fixed minimum number of TSs, determined during the first optimisation stage. Initially there is NI number of time-intervals and, hence, NI +1 boundaries of time-intervals indexed by the following index and set: i for the time-boundaries of the time-intervals, iI. The difference between the real input-supply and approximated-supply is calculated during each time period separately: SD RS – AS , iI, (1) i i i Because the difference, SDi can have a positive or negative value, it can be represented as the difference between the positive variables PDi and NDi: SD PD – ND , iI, (2) i i i Note that, when the SDi has a positive value, the NDi is zero, as a result of minimising the inaccuracy. When SDi has a negative value, the PDi is zero. For minimal inaccuracy the difference between the real and approximated supply should be the lowest possible. EDi PDi NDi, iI, (3) In (3) the positive value is obtained for the difference between real and approximated supply load. Further equations relate to the accepting / rejecting of the time-interval boundary as a TS boundary. The decision is made by the binary variable yi. When there is a positive (4) or negative difference (5) between the two consecutively-approximated supply loads, there is a TS boundary and the value of yi is <strong>1.</strong> If there is no difference between these supplies, there is no TS boundary and the value of yi is 0. ASi1ASi LV yi , iI, i NI + 1, (4) ASi1ASi LV yi , iI, i NI + 1, (5) In order to present the selected TS boundaries, the binary variable is multiplied by the observed time-period boundary:
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Proceedings e report 90
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ECOS 2012 : the 25 th International
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Advisory Committee (Track Organizer
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The 25 th ECOS Conference 1987-2012
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VOLUME VI CONTENT VI. 1 CARBON CAPT
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-----------------------------------
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» Personal transportation energy c
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» Excess enthalpies of second gene
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VOLUME IV IV . 1 - FLUID DYNAMICS A
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» Exergy analysis and genetic algo
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» Optimal lighting control strateg
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» Stability and limit cycles in an
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PROCEEDINGS OF ECOS 2012 - THE 25 T
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Fig. 2. The exergy destruction of M
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ineffectiveness of the catalyst, in
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[ P EXmth EX SNG] ESR F where EXm
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Fuel inputkW 728221 203771 237719 3
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Not only at desined Rc (the ratio o
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4. DISCUSSION The graphic exergy an
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Figure 9(a) illustrates the energy
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Engineering Technical Conferences &
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generally there are four kinds of m
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However, even under the off-design
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Fig. 3. 600MW supercritical coal-fi
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CO2 rich loading(molCO2/molMEA) 0.4
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Net efficiency (%) 40.28 30.29 26.4
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Fig. 11. Schematic diagram of heat
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28.67%. In addition, through heat i
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Applied Thermal Engineering 2010;30
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Abstract: PROCEEDINGS OF ECOS 2012
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Fig. 1. Solution diagram of „four
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K ~ K 1 eK 1a p K 1a iK mK
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Oxygen recovery rate, % 105 95 85 7
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Calculated optimal compressor press
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The net amount of the flue gas leav
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Figure 3. Idea for lignite drying b
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Energy efficiency, % 34,00 33,00 32
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points efficiency increase related
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Figure A1b. Thermoflex flow sheet o
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Figure A3a. Thermoflex flow sheet o
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Figure A4. Thermoflex flow sheet of
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2.1. Life Cycle Assessment 2.1.1. G
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these factors. A sensitivity analys
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methane slip contributes to 13% of
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As can be seen in Fig. 5 the impact
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References [1] Eurobserv’er, The
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in which CO2 is separated from H2,
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dimensions of water droplets and wa
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Temperature (°C) 8 7 6 5 4 3 2 1 0
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4. Conclusions In the present paper
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penalty, but without separate captu
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0.5 - 0.7 kg/kg, resulting typicall
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3.3 - Utilising waste heat All exis
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4.5 - Suomusjärvi olivine deposits
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material will change from a few % t
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Fig. 5 Schematic diagram of the PFB
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8. Combined SO2 capture and CO2 min
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Fig. 10 Carbonate- (left) and sulph
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[13] Ekdahl, E., Idman, H. Statemen
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HEAT Fig. 1. Scheme and main reacti
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(raw) materials pre-heating and AS
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Fig 3- Aspen Plus® model 110
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Table 4. Elemental and XRD analysis
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the ÅA CSM route. The content of M
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moisture - 0,2237 ash - 0,0876 LHV
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2.2 CFB plant For the current momen
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The CO2 emission factor which indic
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The CO2 emission factor calculated
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Appendix A Fig A.1. Simulation mode
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(b) Fig. A.2. Simulation model of C
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Table A.1. Calculated parameters at
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References [1] Pfaff I., Oexmann J.
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with a CC installation could be avo
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2.3. Basic CHP plant indices. The b
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3. Off-design calculations The main
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Fig. 6. CHP plant efficiency Fig. 7
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amount of steam delivered to the he
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[5] Lukowicz H., Mroncz M.: Analysi
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water removal is feasible by coolin
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Figure 2. Sketch of the suggested P
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a b Figure 5. a) Exergy balance of
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The basis of comparison of each met
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Figure 7a. Impact of S/CATR on PCU
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However, more energy duty is requir
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3.1. JACOBIAN-ASPEN Interface ASPEN
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Fig. 3. Triple Pressure Bottoming S
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As seen from Table 1, the mass flow
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4. Conclusion Fig. 5. Partial Emiss
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[19] Yantovski E., Shokotov M., Sho
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investigation and the developing of
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University of L’Aquila and German
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hydrogen combustion technology at d
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eversibility of a pre-treated synth
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Fig. 1. TG curves collected for spe
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(see Fig 5). Carbon dioxide uptake
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period is reduced to the first 15 c
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CO2 capture capacity up to 150 th c
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the way for biogas utilisation is t
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projectand is described in the foll
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4. Pilot plant tests In order to de
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Concerning the absorption reaction,
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with Regeneration (AwR), consists i
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[25] Aspen Plus 2004.1. Cambridge,
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systems for fuel drying waste strea
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heated in a heat exchanger placed i
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Fig. 2. Lower heating value of fuel
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Acknowledgements The results presen
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large amount of CO2 [1,5-7]. APCr a
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N 1 i ( x x i n 1 m ) 2.3. Carbon
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DTA analysis of the untreated APCrs
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Table 4. Relative Error (%) of the
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CaO HCl CaCl H O 193kJ / mol (
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[1] M. Fernández Bertos, S.J.R. Si
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2. Process development 2.1. Backgro
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to dissolve the chloride salts prec
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The remaining 25% (5 ml/min) of the
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Table 6. Experimental conversions o
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p p 1 1. 5 p H 1 1. 5 2O p mi
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Calculating from the Aspen Plus mod
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[11] Mattila, H.-P., Experimental s
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Mg2SiO4(s) + 2CO2(g) →2MgCO3(s) +
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(Mg/Fe) of the mineral rock, Mg-sil
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Figure 2. Effect of Mg/Fe ratio of
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Figure 4 shows that an increase in
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Figure 5. Process flow diagram of M
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2. Only cold utility is needed belo
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extraction process at 400 °C (~ 62
Page 275 and 276: Table A2. Extraction equations and
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Page 279 and 280: In the area of coal technologies al
Page 281 and 282: Tabel 1. Characteristics quantities
Page 283 and 284: N m eT 4a ~ T cp T4a 1 ( K
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Page 291 and 292: 2. Numerical model The purpose of t
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Page 299 and 300: 1. Define the studied system (in th
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Page 305 and 306: Table 5. Key data for the four ener
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Page 317 and 318: 2.2.2. Pinch analysis Pinch analysi
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Page 323 and 324: Application of the approach led to
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Page 329 and 330: 2.5. Selecting the number of TSs Se
Page 331 and 332: A) Time Slices for solar thermal en
Page 333 and 334: The first step of procedure is to p
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Page 337 and 338: In the presented paper a framework
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Page 341 and 342: PROCEEDINGS OF ECOS 2012 - THE 25 T
Page 343 and 344: district energy network simulation
Page 345 and 346: were decreased by 10°C.All scenari
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Page 349 and 350: 3.2.2. Input data and assumptions A
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Page 355 and 356: Polygeneration is a term used to de
Page 357 and 358: The increase in energy utilization
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Page 361 and 362: 6. Extensions to core methodology 6
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Page 367 and 368: Abstract: PROCEEDINGS OF ECOS 2012
Page 369 and 370: gasification [15], hybrid biomass g
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Fig. 4. Effect of operating tempera
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SDG has slight effect on the syngas
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[26.] Castle WF. Air separation and
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1. Introduction - Firenze University Press

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