1. Introduction - Firenze University Press

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methane slip contributes to 13% of the overall impact. For these technologies if the methane loss is reduced then their GWP would improve. 3.2. Sensitivity Analysis Each technology has a final biomethane concentration and methane slip that is inherent to each process. It is therefore of interest to determine whether these characteristics affect their CO2 balance. A sensitivity analysis done for all the 8 technologies showed that there is no correlation between the GWP of the technologies and the percentage of methane loss nor the final biomethane concentration. The data obtained for the two novel technologies, BABIU and AwR consist of laboratory scale data that was scaled up to industrial scale. Therefore one can rightfully assume that once these technologies are developed to the industrial level that the data may not be the same. Though in Table 1 it is possible to see that values such as biogas input, electricity use, biomethane purity and methane loss for BABIU and AwR fall within the range established by the other six technologies that are currently on the market. From Fig. 3 one can see that the electricity use and methane loss in play a small role in the overall CO2 impact of the technologies. Therefore one can assume that while there may be changes once the technologies are commercialized, the effect on the GWP would not be significant. This assumption is supported by a sensitivity analysis conducted where the amount of electricity used by both AwR and BABIU was increased to 0.07 kWh (which is the higher end of the electricity use by commercialized technologies). Applying this new value only reduced the CO2 savings by less than <strong>1.</strong>5 %. 3.3. Uncertainty Analysis 3.3.<strong>1.</strong> Reagent use in AwR As was seen in Fig. 3, one of the largest sources of CO2 for the AwR is the production of the alkaline reagent KOH. Currently, the regeneration rate is around 70%, therefore it was decided to study if improving the regeneration rate would improve the technology enough so that it could be comparable to BABIU and others on the market. As well NaOH is another base that is of interest for this process therefore it was also used in this comparison. The AwR using each base at different regeneration rates were compared to BABIU, AS and HPWS. Global Warming Potential (g of CO 2 equivalent) -1400 -1500 -1600 -1700 -1800 -1900 -2000 -2100 Regeneration rate of base (%) 0 20 40 60 80 100 Fig. 4. Comparison of the global warming potential of using KOH and NaOH at varying regeneration rates in AwR 67 HPWS AS BABIU AwR using KOH AwR using NaOH
As can be seen in Fig. 4 even if for AwR the regeneration rate of both KOH and NaOH is improved to 99%, BABIU is still the technology with the greatest CO2 savings. This is due to the fact that the AwR process has a slightly higher methane slip than BABIU. Though, since both of these technologies are in the development stage the methane slip may improve for both before commercialization. Using NaOH instead of KOH will result in a greater CO2 savings for AwR. While using KOH, AwR passed HPWS at a 65% regeneration rate but NaOH passed HPWS at a 40% regeneration rate. If the regeneration rates of either bases is improved a greater CO2 savings is achieved, though if the regeneration rate is not improved and NaOH is substituted for KOH then an additional savings of 71 g can be achieved. 3.3.2. Transport distance and location of technology A variable in the implementation of the novel technologies that could affect the final CO2 emissions generated is the location of where the technology is installed. This pertains to both the distance between the upgrading plant and a municipal solid waste incinerator (MSWI), and the country where the upgrading plant is located. As the novel technologies depend on waste coming from MSWI it is important to determine how the distance between the MSWI and the location of the upgrading technology affects the GWP. As well, large amounts of the waste are needed to run the system, for BABIU it requires 9 kg of bottom ash (BA) and 1 kg of air pollution control residues (APC) for AwR, per functional unit of 1 kWh of biomethane. It was decided to explore the impact related to transport by truck on a small scale with a distance up to 300km. The electricity production mix of the country where the technology is installed could have an effect on the GWP. For the LCA study the inventory data used was for Spain. We decided to use also the electricity production mix for Italy as the pilot plant of BABIU and AwR are presently located there. BABIU and AwR were compared to HPWS and AS which are the marketed technologies that showed the greatest CO2 savings. Though to ensure proper comparability, the energy mixes of both Spain and Italy were used for all four technologies. As well a travel of 50km by truck was applied to any additional reagents used for AwR, BABIU and the amine used in AS. Global Warming Potential (g of CO 2 equivalent) -1500 -1550 -1600 -1650 -1700 -1750 -1800 -1850 -1900 -1950 -2000 Kilometers travelled 0 25 50 75 100 125 150 175 200 225 250 275 300 BABIU transport of BA HPWS AwR transport of APC AS transport: 50 km amine Fig. 5. Comparison of global warming potential of distance of transport of bottom ash for BABIU and APC of AwR. 68
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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
Page 44 and 45: Engineering Technical Conferences &
Page 46 and 47: generally there are four kinds of m
Page 48 and 49: However, even under the off-design
Page 50 and 51: Fig. 3. 600MW supercritical coal-fi
Page 52 and 53: CO2 rich loading(molCO2/molMEA) 0.4
Page 54 and 55: Net efficiency (%) 40.28 30.29 26.4
Page 56 and 57: Fig. 11. Schematic diagram of heat
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Page 62 and 63: Abstract: PROCEEDINGS OF ECOS 2012
Page 64 and 65: Fig. 1. Solution diagram of „four
Page 66 and 67: K ~ K 1 eK 1a p K 1a iK mK
Page 68 and 69: Oxygen recovery rate, % 105 95 85 7
Page 70 and 71: Calculated optimal compressor press
Page 72 and 73: PROCEEDINGS OF ECOS 2012 - THE 25 T
Page 74 and 75: The net amount of the flue gas leav
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Page 78 and 79: Energy efficiency, % 34,00 33,00 32
Page 80 and 81: points efficiency increase related
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Page 96 and 97: As can be seen in Fig. 5 the impact
Page 98 and 99: References [1] Eurobserv’er, The
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Page 102 and 103: in which CO2 is separated from H2,
Page 104 and 105: dimensions of water droplets and wa
Page 106 and 107: Temperature (°C) 8 7 6 5 4 3 2 1 0
Page 108 and 109: 4. Conclusions In the present paper
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Page 116 and 117: 3.3 - Utilising waste heat All exis
Page 118 and 119: 4.5 - Suomusjärvi olivine deposits
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Page 122 and 123: Fig. 5 Schematic diagram of the PFB
Page 124 and 125: 8. Combined SO2 capture and CO2 min
Page 126 and 127: Fig. 10 Carbonate- (left) and sulph
Page 128 and 129: [13] Ekdahl, E., Idman, H. Statemen
Page 132 and 133: HEAT Fig. 1. Scheme and main reacti
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Page 137 and 138: Fig 3- Aspen Plus® model 110
Page 139 and 140: Table 4. Elemental and XRD analysis
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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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Abstract: PROCEEDINGS OF ECOS 2012
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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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[13] Teir S, Kuusik R, Fogelholm CJ
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In the area of coal technologies al
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Tabel 1. Characteristics quantities
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N m eT 4a ~ T cp T4a 1 ( K
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Auxiliary power rate of ASU, % 40 4
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[8] Buhre B.J.P., Elliott L.K., She
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1.1 - Cement Manufacturing The ceme
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2. Numerical model The purpose of t
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-0.309x15.77x2 2.085x3 240x4 12.53x
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Table 4 - Results of the optimizati
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1. Define the studied system (in th
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cycle) or a lignin extraction plant
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limiting factor for the maximum siz
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Table 5. Key data for the four ener
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Global CO 2 emissions [ktonnes/yr]
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for biofuels and the investment cos
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The possibility to capture CO2 from
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[17] Berglin N, Andersson L. Proces
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Therefore NL Agency (an agency of t
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2.2.2. Pinch analysis Pinch analysi
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3. Case study 3.1. Plant and proces
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Te 1000 mp era 900 tur 800 e [°C 7
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Application of the approach led to
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The advantage of dynamic models is
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e equal at both time intervals and
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2.5. Selecting the number of TSs Se
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A) Time Slices for solar thermal en
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The first step of procedure is to p
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cTSs demand TS solar TSs 0 2 4 6 8
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In the presented paper a framework
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[5] Erdil E, Ilkan M, Egelioglu F.
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district energy network simulation
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were decreased by 10°C.All scenari
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Site 3 Heat sources Heat load Site
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3.2.2. Input data and assumptions A
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Fig.7 shows the operation forecast
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[13] TERMIS Operation User Guide, 7
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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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