1. Introduction - Firenze University Press

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4.2. Energy market scenarios The future economic performance, as well as the global emissions of CO2, associated with the different cases studied is dependent on the development of the energy market. Consequently, to identify robust investment options, their performance should be evaluated for varying future energy market conditions. Here, energy market scenarios are used to reflect a variety of possible future energy market conditions. To achieve reliable results from an evaluation using energy market scenarios, the energy market parameters within a given scenario must be consistent, i.e. the energy prices must be related to each other (i.e. accounting for energy conversion technology characteristics and applying suitable substitution principles). Consequently, a systematic approach for constructing such consistent scenarios is facilitated by the use of a suitable calculation tool. In this work the Energy Price and Carbon Balance Scenarios tool (the ENPAC tool) developed by Axelsson and Harvey [26] was used. The scenarios reflect different future energy market conditions for the “average” years 2020 and 2030 and are based on two fossil fuel price levels (low and high) and two CO2 emission charge levels (low and high).The big difference between the two time periods is that for the period with 2020 as its average year, it is assumed that infrastructure for CCS is not established, and therefore the options for CCS are excluded. Tables 4 and 5 present scenario data used. A further description of the ENPAC tool and the scenarios used in this work can be found in Jönsson et al. [18]. Table 4. Key data for the four energy market scenarios used for 2020. Scenario input data 1 2 3 4 Fossil fuel price level a Low Low High High CO2 charge level Low High Low High CO2 charge [€/tonne CO2] 26 67 26 67 Green electricity policy instrument [€/MWh] 26 26 26 26 Resulting prices and values of policy instruments [€/MWh] Electricity 63 85 66 95 DME 47 58 70 81 Bark/by-products/wood chips b 22 37 23 38 Heavy fuel oil (incl. CO2) 37 49 53 65 District heating 15 28 18 28 Biofuel policy instrument 49 61 28 41 Resulting marginal/alternative technologies and their CO2 emissions [kg/MWh] Electricity 722 345 722 722 marginal production of electricity CP NGCC CP CP Biomass 225 237 225 225 marginal user of biomass CP/DME CP/DME CP/DME CP/DME District heating production 224 380 224 224 alternative heat supply technology CCHP/GB CCHP/GB Transportation 273 273 alternative transportation technology Diesel Diesel a Oil prices: Low: 62 USD/barrel, High: 100 USD/barrel. b In the past years the prices of wood by-products and chips have been very similar. 277 CCHP/GB 273 Diesel CCHP/GB 273 Diesel
Table 5. Key data for the four energy market scenarios used for 2030. Scenario input data 1 2 3 4 Fossil fuel price level a Low Low High High CO2 charge level Low High Low High CO2 charge [€/tonne CO2] 35 109 35 109 Green electricity policy instrument [€/MWh] 26 26 26 26 Resulting prices and values of policy instruments [€/MWh] Electricity 68 90 74 98 DME 57 77 88 109 Bark/by-products/wood chips b 27 52 30 56 Heavy fuel oil (incl. CO2) 45 67 67 89 District heating 19 49 27 56 Biofuel policy instrument 46 67 20 41 Resulting marginal/alternative technologies and their CO2 emissions [kg/MWh] Electricity 679 129 679 129 marginal production of electricity CP CP CCS CP CP CCS Biomass 227 244 227 244 marginal user of biomass CP/DME CP/DME CP/DME CP/DME District heating production 242 468 242 468 alternative heat supply technology CCHP/GB CCHP/GB Transportation 273 273 alternative transportation technology Diesel Diesel a Oil prices: Low: 74 USD/barrel, High: 126 USD/barrel. b In the past years the prices of wood by-products and chips have been very similar. 4.3. Sensitivity analysis Table 6 presents the parameters included in the sensitivity analysis. Table 6. Parameters included in the sensitivity analysis. Parameter Denotation Cases District heating production 278 CCHP/GB 273 Diesel CCHP/GB 273 Diesel a 1. The amount of district heating possible to deliver is increased by 100%, to between 20-100 MW depending on season. 2. No possibility to deliver district heat. Capital recovery factor b The capital recovery factor is changed from 0.2 to 0.1. Investment costs for non-commercial technologies Green electricity policy instrument support level Biofuel policy instrument support level Recovery boiler upgrade cost c The investment costs for the non-commercial technologies, i.e. BLG, lignin extraction and CO2 capture and storage, are increased by 25%. d 1. The support is increased by 50%. 2. The support is decreased by 50%. 3. No support is considered. e 1. The support is decreased by 50%. 2. No support is considered. f The investment cost for upgrading the recovery boiler is lowered from 35.4 to 27.4 M€.
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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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Page 275 and 276: Table A2. Extraction equations and
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Page 339 and 340: [5] Erdil E, Ilkan M, Egelioglu F.
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Page 349 and 350: 3.2.2. Input data and assumptions A
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Polygeneration is a term used to de
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The increase in energy utilization
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are available for an entire year, 8
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6. Extensions to core methodology 6
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thermal storage and possible suppor
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laterites. Ore Geology Reviews, v.
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gasification [15], hybrid biomass g
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the second reactor. In this unit, s
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The variables effect was evaluated
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atio, as well as the shift-syngas f
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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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