1. Introduction - Firenze University Press

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TSi yi ti1, iI, i NI + 1, (6) The number of TSs is obtained from (7). One is added to the sum of the selected TS boundaries, as the TS boundaries at the beginning and end of the observed time-horizon were excluded within the model: NTS yi 1, (7) iI, iNI 1 The inaccuracy during each time-interval is determined by multiplying the positive difference between the real and approximated supplies with the time-horizon of the time-interval. INi EDi( ti ti1) , iI, i NI + 1, (8) The overall inaccuracy is a result of summating the inaccuracies over the time-intervals: INA IN , (9) iI, iNI 1 i and this overall inaccuracy is constrained and should be less than or equal to the fraction of the initial amount of solar irradiation presented as an area (A0) below the measured profile of Fig 1: INA A , (10) 0 A (( t t) RS 0 i1 i i iI, iNI 1 2.4. Optimisation procedure ) , (11) Optimisation is performed over two stages. During the first stage of optimisation, Equations (1-11) are used with the objective of minimising the number of TSs as follows: min zI NTS , (12) This step requires specifying the acceptable error-level (tolerance) . The procedure applies multi- objective optimisation by the –constraint method, where one objective is considered in the objective function and the other is inserted in the model as an –constraint. The result from optimisation is the minimal number of TSs, min NTSI required to meet any constraint about the inaccuracy limit (10). However, after the first stage, the inaccuracy is not optimal. In order to obtain a further reduction in inaccuracy, in the second stage of optimisation the same model using equations (1–11) is used together with an additional equation (13), which fixes the number of TSs, and the objective as expressed in (14) , NTS min NTSI , (13) min zII INA , (14) Multi-objective optimisation could also be performed over one stage, with the so called weighted sum method as sometimes this is faster. In this case, the objective function would be a weighted sum of NTS and INA with a high enough weight w (e.g. 10,000) for NTS, in order for the minimised NTS to have priority over the minimum of INA. z wNTS INA , (15) 301
2.5. Selecting the number of TSs Selecting the number of TSs depends on the accuracy required. Fig 4 presents the obtained Pareto results from the multi-objective optimisation, where minimal numbers of TSs are shown vs. different tolerances selected. INA [%] 20 18 16 14 12 10 8 6 4 2 0 0 10 20 30 40 NTS 50 Fig. 4: Selecting an acceptable inaccuracy As can be seen from Fig 4, by increasing the tolerance the number of TSs decreases but, however, the inaccuracy becomes too high. On the other hand, if the tolerance is too small, the number of TSs might become too high and, hence, the further steps of integration would be too complex; however, no significant improvement may be achieved. The exact trade-off between the number of NTS and INA depends on the users INA tolerance. As can be seen from Fig 4, generally, a tolerance of between 5 and 10 % should be acceptable as with increase NTS the inaccuracy is not increased significantly. 3. Estimating the supply-loads The supply of the loads is determined separately in each TS. Estimation of the supply-loads depends on the capture system. Different kinds of systems are possible, or even a system coupled with a heat pump [21]. A simplified scheme for capturing was assumed during this work (Fig 5). The heattransfer from the collectors in this model can be (i) direct or (ii) indirect. Direct heat-transfer is feasible when solar thermal-energy is available and there is a demand within the evaluated TS. If the amount of heat is higher than the demand or the heat-transfer is unfeasible in one TS, then the heat is transferred to storage. This heat will be available for covering any heat-demand in the following TSs. The indirect heat-transfer is the described transfer through storage. 302
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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
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Table A2. Extraction equations and
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Page 331 and 332: A) Time Slices for solar thermal en
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Page 349 and 350: 3.2.2. Input data and assumptions A
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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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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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