Multi-Objective Optimization of a Combined Power Plant Fueled by Syngas Produced in a Downdraft Gasifier

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73rd Conference of the Italian Thermal Machines Engineering Association (ATI 2018), 12-14 73rd Conference of the Italian Thermal Machines September 2018, Engineering Pisa, Italy Association (ATI 2018), 12-14 September 2018, Pisa, Italy

Multi-Objective Optimization of aa Combined Power Plant Fueled by Multi-Objective Optimization of Combined Power Plant Fueled by The 15th International Symposium on District Heating and Cooling Syngas Produced in a Downdraft Gasifier Syngas Produced in a Downdraft Gasifier a a Lorenzo Dambrosio , Roberto Micera , Bernardo Fortunato , Marco Torresia Assessing the a,∗ feasibility of using the heat demand-outdoor Lorenzo Dambrosioa,∗, Roberto Miceraa , Bernardo Fortunatoa , Marco Torresia Politecnico di Bari, Via Orabona 126/b-70126 Bari,Italia temperature function for diaBari, long-term district heat demand forecast Politecnico Via Orabona 126/b-70126 Bari,Italia a a

I. Andrića,b,c*, A. Pinaa, P. Ferrãoa, J. Fournierb., B. Lacarrièrec, O. Le Correc Abstract Abstract a IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal In this paper technical-economic feasibility is analyzed about a gas-steam combined-cycle, fueled by syngas produced in a local b Veolia Rechercheis&analyzed Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, In this paper technical-economic feasibility about a gas-steam combined-cycle, fueledFrance by syngas produced in a Fuel, local downdraft gasifier. At first, the downdraft gasifier model is briefly described, where a biomass mix of Refused-Derived c Département Systèmes Énergétiques et Environnement IMT Atlantique, 4 rue Alfred Kastler, 44300ofNantes, France downdraft gasifier. At first, the downdraft gasifier model is briefly described, where a biomass mix Refused-Derived Fuel, RDF, and sewage sludge is transformed into syngas; then, the economic model is introduced with the graph about the economic RDF, sewage is transformed into syngas; then, from the economic model is introduced with the graph this aboutpaper the economic trendsand of the mostsludge important plant components. Starting the Design of Experiment (DOE) analysis, proposes trends of the most important plant components. Starting from the Design of Experiment (DOE) analysis, this paper proposes a multi-objective optimization methodology to obtain the Pareto Frontier of micro gas turbine technologies. In particular, this astudy multi-objective optimization methodology to obtain the Pareto Frontier of micro gas turbine technologies. In particular, this allowed us to determine which micro gas turbine technology is more suitable to attract investment capital. The considered Abstract study allowedparameters us to determine which micro gas turbine technology is more to attract capital. The considered performance are: the nominal power of the cycle; the total plant suitable efficiency; the net investment revenues; the pay back period. On performance parameters are: thevariables nominal are power of the cycle; theturbine total plant efficiency; the net revenues; theratio pay back period. On the other hand, thenetworks input design represented intake compression anddecreasing the biomass District heating are variables commonly addressed in by thethe literature as onetemperature, of the mostthe effective solutions for the the other hand, the input design are represented by the turbine intake temperature, the compression ratio and the biomass chemical composition. greenhouse gas emissions from the building sector. These systems require high investments which are returned through the heat chemical composition. sales. Due to the changed climate conditions and building renovation policies, heat demand in the future could decrease, cprolonging � 2018 The The Authors. Authors. Published byperiod. Elsevier Ltd. Ltd. the investment return © Published by Elsevier c 2018 � 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND (https://creativecommons.org/licenses/by-nc-nd/4.0/) This an open the CCthe BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Theismain scopeaccess of thisarticle paperunder is to assess feasibilitylicense of using the heat demand – outdoor temperature function for heat demand This is an open access article under the CC BY-NC-ND licensecommittee (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of Lisbon the scientific scientific of the 73rd 73rd Conference of thedistrict ItalianThermal Thermal Machines Selection and peer-review under responsibility of the committee the the Italian Machines forecast. The district of Alvalade, located in (Portugal), was of used a Conference case study. of The is consisted of 665 Selection andAssociation peer-review(ATI under responsibility of the scientific committee of the as 73rd Conference of the Italian Thermal Machines Engineering 2018). Engineering Association (ATI 2018). buildings that vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district Engineering Association (ATI 2018). renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were Keywords: Biomass, RDF, Sludge, Syngas, Thecnical feasibility;, Economical feasibility, DOE; Pareto. compared Biomass, with results from a dynamic demand model,Economical previouslyfeasibility, developed andPareto. validated by the authors. Keywords: RDF, Sludge, Syngas, heat Thecnical feasibility;, DOE; The results showed that when only weather change is considered, the margin of error could be acceptable for some applications (the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the 1.The Introduction 1.decrease Introduction in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and renovation scenarios of considered). OnI,the othercoal hand, intercept increased for 7.8-12.7% per ships, decadetrains (depending on the At the beginning World War small andfunction biomass gasifiers were built for vehicles, and small coupled scenarios). The values War suggested could beand usedbiomass to modify the function parameters for the ships, scenarios considered, and At the beginning of World I, small coal gasifiers were built for vehicles, trains and small electric generators. In the following years, the research activities was mainly carried out by amateur enthusiasts, since improvegenerators. the accuracyInofthe heat demand estimations. electric following years, the research activities was mainly carried out by amateur enthusiasts, since

the low cost of gasoline did not favor the use of this fuel, slowing down its technological development. The return of the low of gasoline did favor the use of this afuel, slowing downfor itssyngas technological of the war cost and blocking of not oilbyin Europe produced renewed interest deriveddevelopment. from biomassThe andreturn coal. At © 2017 The the Authors. Published Elsevier Ltd. the war and the blocking of oil in Europe produced a renewed interest for syngas derived from biomass and coal. At the end of theunder war,responsibility there were more 700.000 gas generators from wood, trucks and buses Europe [1]. and Peer-review of thethan Scientific Committee of The 15th International Symposium on in District Heating the end of interest the war,in there were has more than 700.000 gas generators from wood, trucks andyears, buses in Europe [1]. Lately biomass increased as a source of renewable energy. In recent several individuals and Cooling. Lately interest in biomass has increased as a source of renewable energy. In recent years, several individuals and groups have built versions of small downdraft gasifiers and managed them as demonstrators. groups have built versions of small downdraft gasifiers and managed them as demonstrators. Keywords: Heat demand; Forecast; Climate change ∗ Corresponding ∗ Corresponding

author. Tel.: +39-080-596-3406 ; fax: +39-080-596-3411. Tel.: +39-080-596-3406 ; fax: +39-080-596-3411. E-mail address:author. [email protected] E-mail address: [email protected] 1876-6102� 2017The TheAuthors. Authors.Published Publishedby byElsevier ElsevierLtd. Ltd. c©2018 1876-6102 1876-6102 � © 2018 The TheAuthors. Authors. Published by Elsevier Ltd. c under 1876-6102 2018 Published by Elsevier Ltd. Peer-review responsibility ofthe theCC Scientific Committee of The 15th International Symposium on District Heating and Cooling. This is an open access article under BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection responsibility of the scientific committee of theof73rd of the Italian MachinesMachines Engineering Selection and andpeer-review peer-reviewunder under responsibility of the scientific committee the Conference 73rd Conference of the Thermal Italian Thermal Selection and peer-review of the scientific committee of the 73rd Conference of the Italian Thermal Machines Engineering Association (ATI 2018). under Engineering Association (ATI responsibility 2018). Association (ATI 2018). 10.1016/j.egypro.2018.08.055

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Fig. 1. Cycle-Tempo flow chart of the proposed gasification model.

Many organizations focused their interest on small-scale gasifiers to be used in the least developed countries, such as the World Bank, the U.S. Agency for the International Development and equivalent organizations in European Union. The Producer Gas Roundtable (of the Beijer Institute in Stockholm) has published many documents on gasification, gathering international technical skills [2], [3]. A very active development area for small gasifiers is certainly the one of power generation in developing countries, which have biomass resources but cannot easily afford conventional fuels. They do not have an electrical distribution grid, so power systems from 10 to 1000 [kW] can be very attractive. Finally, the new gasifier developments can extend their use to other new industrial and civil areas, for example the use of biomasses such as industrial sludge or organic waste, furthermore facilitating their disposal. This paper proposes a Multi-Objective optimization iterative procedure concerning the performance optimization of a Syngas External Combustion, SyEXC, plant. Specifically the performance variables are represented by the total power of the plant, the first-principle yield, the net revenues and the payback period. On the other hand, the design variables, are: the biomass mixture entering the gasifier, the turbine inlet temperature and the compression ratio. 2. Description of the plant The gasification process is the conversion of a solid carbonaceous material, such as a biomass, in a gaseous energy carrier through a partial high temperature oxidation. The exhaust gas produced from this process, is mainly composed by carbon monoxide (CO), carbon dioxide (CO2 ), hydrogen (H2 ), methane (CH4 ), water vapor (H2 O), nitrogen (N2 ), other hydrocarbons, such as ethylene (C2 H4 ) and ethane (C2 H6 ), and finally by other substances such as ash, coal particles, tar and oils. Air, oxygen, water vapor or a mixture of these, can be used as gasification agents. By means of condensation and after-treatments, the exhaust gas becomes the final syngas. The syngas is suitable to be used as a common gaseous fuel, being easy to convey and transport, or to be used in other industrial processes. The process takes place within a reactor and can be divided into two main stages: a first stage of pyrolysis and the second stage the actual gasification. In the first stage, pyrolysis (a thermochemical decomposition of biomass at temperatures above 623 [K]) promotes distillation. Volatile vapors are mainly composed by gaseous hydrocarbons, hydrogen, carbon monoxide, carbon dioxide, water vapor and tar. What remains from the pyrolysis process is mainly char (a complex mixture consisting of carbon, ash, sulfur compounds and residual volatile hydrocarbons), which, in the second stage together with pyrolysis products, reacts at high temperature with

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Fig. 2. (a) SyEXC layout; (b) Thermodynamic cycle in a T-s plane.

the gasification agent, enriching the syngas with combustible species. This last stage is the most important of the entire gasification process, being the slower one, thus determining its kinetics and, consequently, both the dimensioning and the performance of the reactor. The thermodynamic model was studied by Fortunato et al. [4], [5], by means of the commercial software CycleTempo (TU Delft, the Netherlands), The flow-chart of the proposed gasification is reported in Fig. 1. It is actually a SyEXC (Syngas External Combustion) plant with analogies to the ones proposed by A. Datta et al. [6] and Soltani et al. [7]. The advandage is that a high purity of the syngas is mandatory only in the case of internal combustion, in order to avoid fouling or possible corrosion of the most valuable parts of the gas turbine (e.g. the turbine blades). In the SyEXC plant, the raw syngas can be directly conveyed into the external combustor, where it is burned with the air coming from the gas turbine. As sketched in Fig. 2 the high temperature reached in the external combustor allows the heating (both by radiation and convection) of the air coming from the compressor of the Joule-Brayton cycle. The air (3) is then expanded in the turbine (4) and conveyed into a regenerator for the combustion air of the external combustor, from which comes out at T 4′′′ . A combustor, fed by ligno-cellulosic biomass, heats the air from the conditions 4”’ up to the conditions 4” (again, it is imposed that T 4′′ is equal to T 4 ). Then, this re-heated air passes through a HRSG, producing superheated steam (9), which expands in the steam turbine. Finally, leaving the boiler at conditions 4’, the air is partially discharged into the atmosphere, whereas the remaining part is used for the combustion of the syngas. Table 1 shows the data used for the SyEXC plant simulation and the assumptions, while Table 2 shows the biomass chemical composition of RDF [8] and sludge [9]. The remaining parameters, not shown in the table, were chosen as project variables for the analysis (T 3 = T IT , β). Table 1. Performance. Performance p1 = 1.013 bar T 1 = 291 K ηis,c = ηis,t = 0.87 ηm,c = ηm,t = 0.98 p10 = 0.05 bar ∆T pp = 5 K

p9 = p8 = 50 bar p4 = 1.013 bar η steam turb = 0.87 ∆T app = 70 K T 4 − T 6 = 30 K T 5 = 1573 K

T 4 = T 4′′ = T OT T 7 = T 2 = 50 K LHV f ur = 15 MJ/kg G bio = 0.15 kg/s η f ur = 0.87 T water = 290K

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Table 2. Biomass chemical composition. Biomass chemical composition Weight percentage on dry basic RDF Sludge

C 48.23 39.48

H 6.37 6.19

O 28.48 25.46

N 1.22 3.93

S 0.76 1.45

Cl 1.13 −

Ash 13.81 23.51

Moinsture 20% 11.75

LHV [kJ/kg] on humid basic 12900 14893.70

Fig. 3. (a) Cost of microturbines with annular wrap-around recuperator; (b) Cost of microturbines with rear-mounted recuperator.

3. Economic model The economic competitiveness of a plant is measured in terms of the Cost Of Electricity, the COE: COE =

C plant Pad f

+ CO&M + C +

C f ur η f ur

+ CCH4

(1)

Pel h

where C plant is the annualized plant cost cost with   1 1 1− Pad f = i (1 + i)N

(2)

is the annual depreciation factor of the SyEXC plant, being i the discount rate and N the years of the return on capital. As far the other variables in (1) they are specified as follows: CO&M indicates the operating and maintenance costs, C is annual cost of insurance, land rent and waste transportation, C f ur represents the cost of lignocellulosic biomass, whereas η f ur the efficiency of the biomass boiler. Moreover, Pel is the nominal power and h indicates the operating time (hours). Concerning CCH4 term, it indicates cost of the natural gas and it has been included for completeness. However, in the present case, no natural gas has been added during the gasification process The cost of plant is the most important factor in this equation and it is equal to the sum of the costs of the individual components of the plant. In order to define it, a market research has been carried out, deriving specific cost trends as the characteristic size of the plant changes (Fig. 3). Figures 3 (a) and (b) show the trends of the two considered gas turbine technologies: (a) microturbine with annular wrap-around recuperator; (b) microturbine with rear-mounted recuperator [10]. They differ for the T IT achieved, being [1073 − 1223] [K] the T IT interval for the first technology and [1223 − 1423] [K] for the second. The costs of electric generator Cgen and pump C pump are calculated with Enea equations [11]: Cgen = C0g



Pel Pel,0

0.67

C pump = C0p



Pel Pel,0

0.67

(3)

where: C0g = 200 [ke], Pel,0 = 5000 [kW], C0g = 14 [ke] and Pel,0 = 200 [kW] The plant is featured by two other heat exchangers and a condenser for the HRSG. For these components, the estimate made by Lozza [12] of 600 [e/m2 ] was used.

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Fig. 4. Specific cost trends: (a) steam turbine; (b) gasifier; (c) syngas boiler.

The cost of civil structures and auxiliaries is equal to 60% of the cost of the plant. Moreover, the maintenance cost considers two components: a fixed one and a variable (with electric production) one. From [9] the following values have been taken into account: CO&M f ix = 10.5 [e/kW], CO&M var = 3.5 [e/MWh]. It was considered a variable rate loan with Euribor value about −0.33% (date June 1st 2017) and a mean spread 3% for biomass engine [13]: an amortization rate of 2.67% is used, for a 12-year return on capital. Other important economic parameters considered are the annual Net Revenue and the Payback Period: Rnet = Rgross − Cost,

PBP =

C plant Rnet

(4)

According to existing plants whose component costs can be found in the literature, in the present paper, the following specific cost trends have been considered: specific cost of steam turbine (Fig. 4 (a)), specific cost of gasifier (Fig. 4 (b)) and specific cost of syngas boiler (Fig. 4 (c)). 4. Multi-objective optimization Multi-objective optimization involves more than one objective function to be optimized simultaneously. The design of experiment, DOE, is an active statistical method, based on the realization of a series of tests where the independent variables, (the input variables), vary according to a precise order. If no better information is available, then an equallyspaced (Carthesian) DOE is carried out [14], [15]. Many authors, e. g. [14], provide a complete description of the theory behind the multi-objective optimization problem and the DOE technique. Nevertheless, in order to compare two or more solutions, it is worthy to recall the concept of non-dominated solution. Let X = [X1 , X2 , . . . , XN ] and Y = [Y1 , Y2 , . . . , Y M ] be the design variable input and the corresponding performance variable output, respectively. Suppose all the performance variables Yi , (i = 1, . . . , M) have to be minimized (for different optimizations the changes are straightforward). Considering two different performance variables vectors, X1 and X2 , corresponding to the design variable vectors Y1 and Y2 , respectively, if ∀i ∈ [1, . . . , M] ∋′ Yi1 < Yi2

(5)

then the solution X2 is referred as dominated by solution X1 . Moreover, if ∃i ∈ [1, . . . , M] ∋′ Yi1 > Yi2

(6)

then the solution X2 is referred as non dominated by solution X1 . Finally, if ∃i, j ∈ [1, . . . , M] i  j ∋′ Yi1 < Yi2 , Y 1j > Y 2j

(7)

then the solutions X1 and X2 are referred as non dominated each other. Comparing all the possible solutions, according to non-dominated solution relation, it is possible to obtain the set of all the non-dominated solutions that constitutes the so-called Pareto Frontier. Of course, it does not make practical sense to compare all the possible solutions, therefore the proposed optimization strategy will start considering only the solutions included in the DOE to build the 1 st level Pareto Frontier (Fig. 5 (a)) [14], [15], [16], [17].

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The basic idea to improve the non-dominated solutions, represented by the 1 st level Pareto Frontier, is to detect, in the design variable space, the directions along which the performance variables exhibit the best improvements. In order to pursue this goal, a subset of solution points, apart from 1 st level Pareto Frontier, have to be determined. The best subset of solution points is represented by the DOE sub-optimal solutions. This sub-optimal solutions subset can be found removing the non-dominated solutions from the starting DOE and then performing the non-dominated relation (5)-(7) to the remaining DOE solutions. At this point it is possible to couple solution points coming from the sub-optimal solutions subset and 1 st level Pareto Frontier, respectively. This let us observe how the sub-optimal frontier solutions evolve to the 1 st level Pareto Frontier in both the performance and design variable space. In order to have a better detection of the fore-mentioned optimal directions, few other solutions have been added to the optimal solutions just perturbing the design variable of 1 st level Pareto Frontier (Fig. 5 (c), shows an exemple of 1 st level perturbations). Then, carrying out the non-dominated relation (5)-(7) to this new DOE, it is possible to build the 2nd level Pareto Frontier (Fig. 5 (b)). Finally, comparing similar solutions coming from the sub-optimal and the Pareto Frontiers (see Fig. 5 (d)), one can detect few preferential directions in which the solutions seem to improve. Of course this process can be iterated, but it will be stopped when the performance variable improvements are quite small from one iteration to the next. 5. Results In this section the results of the proposed Multi-Objective optimization will be presented. The variable to be optimized are: the total power of the plant, Ptot , the first-principle yield, ηI , the net revenues, Rn and the payback period, PBP. The optimization process can be carried out varying the design variables, that are represented by: the biomass mixture entering the gasifier, the turbine inlet temperature T IT and the compression ratio β. Applying the procedure described in the previous section, three preferential directions in which the solutions seem to improve have been detected. For instance, Fig. 5 (e) shows the improved Pareto Frontier together with the 1 st and 2nd level Pareto and Sub-optimal Frontiers for comparison, in Rn -Power plane. Table 3 summarizes the resulting non-dominated solutions. In particular table 3 reports in the first column the number of the test, in the second the T IT , in the third the compression ratio and in the fourth column the percentage of RDF in the biomass mixture. The remaining four column represent the performance variables: the annual net revenue, the plant nominal power, the PBP and the efficiency, respectively. This Non-dominated solutions, which result by applying the proposed optimization procedure, satisfy the non-dominated relation (5)-(7). As a consequence, they are non-dominated, even slightly, at least in one of the four performance variables. As concluding remarks, non-dominated solutions present a variable mixture with a low percentage of waste, the compression ratio is equal to or less than 7, while it is advisable to use a high turbine intake temperature, higher than 1173 [K]. Moreover, it can be observed that the non-dominated solutions have a T IT mainly between 1223 [K] and 1373 [K], this result allows to define which technology to choose. Table 3. Pareto Frontier non-dominated solutions. Solution number

T IT [K]

β

%RDF in the mix

Rn [ke]

Ptot [kW]

PBP [years]

ηI

5 37 38 39 40 42 89 104 106 107

1173 1223 1223 1123 1223 1323 1323 1273 1373 1373

4,0 5,5 6,0 6,5 7,0 5,5 6,0 5,8 4,0 4,0

30 20 20 20 20 20 30 20 20 40

664.574 654.498 638.159 634.087 608.506 664.852 642.724 672.324 848.936 718.674

1020 1010 986 964 943 1049 1109 1062 1344 1336

3,87 3,42 3,41 3,40 3,39 4,03 4,34 4,04 4,73 5,57

0,352 0,376 0,374 0,371 0,368 0,381 0,387 0,383 0,397 0,397

Observing Fig. 6 (a) [10] and Fig. 6 (b) [18], it is noted that the first technology works in temperature range [1073 − 1223] whereas the second in [1223 − 1423]. It is quite evident that the non-dominated solutions obtained

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Fig. 5. (a) 1st level Pareto Frontier; (b) 2nd level Pareto Frontier; (c) TIT-RDF% DOE levels; (d) Pareto and Sub-optimal Frontiers in Rn-Power; (e) New Pareto, 1st and 2nd level Pareto and Sub-optimal Frontiers in Rn-Power.

Fig. 6. (a) Rapresentative curve array drawn for single-shaft engine with radial flow turbomachinary in power size of about 100 [kW] rated at sea level and 15 [C] [10]; (b) Technological development of gas turbine according to turbine intake temperature, TIT [18]

have a turbine intake temperature between 1223 and 1373 [K], so it is preferable to have a type of plant of the second type: a metallic engine with a stainless steel recuperator.

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6. Conclusion In this paper, an energy production plant was analyzed with the aim of enhancing the gasification of a biomass mixture composed of various percentages of Refused-Derived Fuel, RDF, and sewage sludge. A thermodynamic model based on the commercial software, Cycle-Tempo, was used. The feasibility analysis of the plant SyEXC has seen the realization of the economic trends of the most important components of the plant, including the gasifier, the two turbines of the combined cycle, the syngas boiler and the biomass furnace. This allowed the calculation of the cost of electricity production, COE. The comparison was made when the turbine intake temperature, TIT, the compression ratio, β, and the biomass mix were changed. A design of experiment, DOE, has been used, in order to detect of the most favorable conditions for the use of the plant. This research was carried out using the Pareto method, a multi-objective optimization method that aims to compare the various simulations by defining non dominated solutions. The resulting Pareto Frontier defines the ranges of existence for the chosen design quantities: for the biomass, a mixtures with a low percentage of waste is preferred; a turbine intake temperature between 1223 [K] and 1373 [K] mainly; a compression ratio lower than 7. By focusing on temperature, it was possible to observe how the analysis carried out channeled the choice on a system with a metallic engine with stainless steel recuparator. References [1] Thomas B. Reed and Agua Das, “Handbook of Biomass Downdraft Gasifier Engine System.”, A Product of the Solar Technical Information Program (1987). [2] Kjellstrom, B., “Producer Gas 1980: Local Electricity Generation from Wood and Agricultural Residues.”, FV-80-0035/01, The Beijer Institute, Stockholm, Sweden, (1981). [3] Kjellstrom, B., Stassen, H., and Beenackers, A.A.C.M., “Producer Gas 1982: A Collection of Papers on Producer Gas With Emphasis on Applications in Developing Countries, Producer Gas Conference.”, Sri Lanka, ISBN:91-86618-00-8, The Beijer Inst., Stockholm, (1983). [4] Fortunato B., Brunetti G., Camporeale S.M., Torresi M. and Fornarelli F. “Thermodynamic model of a downdraft gasifier.” Energy Conversion and Management 140 (2017): 281–294. [5] Fortunato B., Camporeale S.M., Torresi M., Fornarelli F., Brunetti G. and Pantaleo A. M.“A combined power plant fueled by syngas produced in a downdraft gasifier.” GT2016-58159, In Proceedings of ASME Turbo Expo 2016: Turbomachinery Technical Conference and Exposition, Seoul, South Korea, (2016) doi:10.1115/GT2016-58159. [6] Datta A., Ganguly R. and Sarkar L. “Energy and exergy analyses of an externally fired gas turbine (EFGT) cycle integrated with biomass gasifier for distributed power generation.” Energy 35, (1) (2010): 341–350. 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