Numerical and experimental analysis of municipal solid wastes gasification process

Accepted Manuscript Numerical and Experimental Analysis of Municipal Solid Wastes Gasification Process Nuno Couto, Valter Silva, Eliseu Monteiro, Sandra Teixeira, Ricardo Chacartegui, K. Bouziane, P.S.D. Brito, Abel Rouboa PII: S1359-4311(14)01165-X DOI: 10.1016/j.applthermaleng.2014.12.036 Reference: ATE 6230 To appear in: Applied Thermal Engineering Received Date: 2 May 2014 Revised Date: 1 December 2014 Accepted Date: 18 December 2014 Please cite this article as: N. Couto, V. Silva, E. Monteiro, S. Teixeira, R. Chacartegui, K. Bouziane, P.S.D. Brito, A. Rouboa, Numerical and Experimental Analysis of Municipal Solid Wastes Gasification Process, Applied Thermal Engineering (2015), doi: 10.1016/j.applthermaleng.2014.12.036. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT Numerical and Experimental Analysis of Municipal Solid Wastes Gasification Process Nuno Couto a,b , Valter Silva a,b , Eliseu Monteiro b,c , Sandra Teixeira a , Ricardo Chacartegui d , K. Bouziane e , P.S.D. Brito c , Abel Rouboa a,b,f a University of Trás-os-Montes and Alto Douro, Vila Real, Portugal b INEGI, Faculty of Engineering, University of Porto, Porto, Portugal c C3i - Interdisciplinary Center for Research and Innovation, Polytechnic Institute of Portalegre, Lugar da Abadessa, Apartado 148, 7301-901, Portalegre, Portugal d Energy Engineering Department, University of Seville, Seville, Spain e Pôle EREP, Université Internationale de Rabat, 11000 – Salé el Jadida, Technopolis, Morocco f MEAM Department, University of Pennsylvania, Philadelphia, PA, USA Abstract As the quantity of municipal solid waste (MSW) increases with economic growth, problems arise in regard to sustainable management solutions. Thermal treatment presents a valid option for reducing the amounts of post-recycling waste to be landfilled. Incineration technology, besides reducing the total volume of waste and making use of the chemical energy in MSW for power generation, has negative environmental impact from high emission of pollutants. Recent policy to tackle climate change and resources conservation stimulated the development of renewable energy and landfill diversion technology, thereby giving gasification technology development renewed importance. In this work a two-dimensional CFD model for MSW gasification was developed and an Eulerian-Eulerian approach was used to describe the transport of mass, momentum and energy for the solid and gas phases. This model is validated using experimental data from the literature. The numerical results obtained are in good agreement with the reported experimental results. Keywords: Gasification, municipal solid wastes, CFD, Eulerian-Eulerian approach MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT 1. Introduction The amounts of municipal solid waste (MSW) produced increase with economic growth in both industrialised and developed countries, raising the issue of sustainable management solutions [1]. MSW management activities contribute to the generation of greenhouse gas and consequently to the climate change problem. Landfill waste decomposition contributes greatly to the formation of these gases [2-5]. Another environmental problem associated with MSW management systems is the potential generation of dioxins and furans associated to complete combustion of wastes [2]. Thermal treatments are a valid option for reducing the amounts of post-recycling waste to be landfilled, which is considered to be one of the most sanitary disposal methods [3]. It should be noted that biogas production is not an alternative to thermal treatments like incineration or gasification because biogas is produced from the organic fraction of MSW and thermal treatment is applied to the non-organic, non-recyclable fraction [4]. In theory, gasification is a more suitable technology even where the market for thermal product is difficult, however the constraint with gasification of MSW is the technology which is not yet proven at commercial scale. [4] Raw MSW contains a large amount of non-combustible material, and therefore requires pre-processing before sending it to a gasifier. The pre-processing must be able to meet the requirements of the gasifier and be flexible enough to handle MSW variability. This flexibility must be in terms of the type of material handled and its frequency of delivery. The pre-processing area is assumed to be similar to a Refuse Derived Fuel (RDF) MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT facility. Some recyclables and non-combustibles are removed from the MSW to make a higher heating value product that is sized appropriately for gasification [5]. Therefore, incineration continues to be the most common method of thermal treatment for waste- to-energy facilities. However, with the enhancement of environmental restrictions and the development of gasification technology, gasification presents an increasingly efficient and viable alternative to incineration. Gasification is a waste-to-energy conversion scheme that offers a most attractive solution to both waste disposal and energy problems. However, gasification still has some economic and technical challenges, concerning the nature of the solid waste residues and its heterogeneity [4-6]. The greatest strength of gasification is the environmental performance, since emission tests indicate that gasification meets the existing limits and it can also have an important role in the reduction of landfill disposal [3]. Incineration reduces the initial volume of the waste by as much as 85% and offers solutions for problems such as waste odour and leachate. The incineration process creates a large amount of solid residues which are divided into bottom ash and fly ash. Bottom ash represents 85–90% of the total ash produced and is collected at the base of the combustion chamber. This type of ash consists primarily of coarse non-combustible material, unburned organic matter and grate siftings [7]. These are disposed of in sanitary landfills. Fly ash are finely divided particles of ash which are normally entrained in the combustion gases. Fly ash is recovered from the gas stream by a combination of precipitators and cyclones. Incineration technology was developed to reduce the total volume of waste and make use of the chemical energy of MSW for energy generation. However, the incineration process also creates high emissions of pollutant species such as NOx, SOx, HCl, as well as harmful organic compounds and MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT heavy metals. Another problem with MSW incineration is corrosion of the incineration system by alkali metals in solid residues and fly ash [8]. Recent policies to tackle climate change and resource conservation such as the Kyoto Protocol, the deliberations at Copenhagen in 2009 and the Landfill Directive of the European Union have stimulated the development of renewable energy and landfill diversion technology, thereby giving the development gasification technology renewed importance [9]. Gasification is a thermochemical process that involves the oxidation of matter using a fraction of oxidizing agent in low quantities, inferior to the stoichiometric need. Gasification is considered an efficient and environmentally friendly way of extracting energy from different sources of organic materials [10]. Various studies [4-5, 11-13] pointed out that gasification is an emerging but promising technology, especially when compared with commercially-available technologies, such as direct combustion. For instance, Murphy and McKeogh [4], Jones et al. [5] and Lymberopoulos [11], suggested that gasification has better performance, e.g. higher electrical and overall efficiency, lower emissions and lower investment costs than direct combustion. Boustouler and Reynolds [12] corroborates with Lymberopoulos [11] in this regard but also claimed that reduced slagging problems is another advantage of gasification. Roos [13] discussed in more details environmental benefits of biomass gasification, including (i) reduced carbon emissions as a result of improvement in energy efficiency and char addition to soils, (ii) reduced use of fertilizers and runoff of nutrients from soils amended with char-containing ash, and (iii) reduced NOx emissions due to better control of the combustion process. MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT The environmental performance is one of the greatest strengths of gasification technology, which is often considered a comprehensive response to the increasingly restrictive regulations applied around the world [4,9]. Independently-verified emissions tests indicate that gasification is able to meet existing emissions limits and can have a great effect on the reduction of landfill disposal option. Economic aspects are probably the crucial factor for a relevant market penetration, since gasification-based plants tends to have ranges of operating and capital costs about 10% higher than those of conventional combustion-based plants [9]. This is mainly a consequence of the ash melting system and the added complexity of the technology. The technical challenges to overcome for a wider market penetration of commercial advanced gasification technologies can be investigated with the development of numerical simulation methods validated with experimental results of MSW gasification. Gasification involves a set of fairly complex phenomena such as heat and mass transfer, fluid dynamics, and different chemical reactions. Numerous approaches to modelling gasification in CFD [14-19] and non-CFD [20-24] have been made. Currently there are three numerical techniques used for the studying gasification in fluidized beds in literature and these are Eulerian- Lagrangian with single particle or a particle parcel and a group of particles, Eulerian-Eulerian Two Fluid Model and Discrete Element Method within Eulerian-Lagrangian concept [14]. Literature concerning the numerical modelling of fluidized bed gasifier could be divided into three parts based on the geometric regions of fluidized bed furnace. It is dense bed, splash zone and freeboard/riser of fluidized bed units. Regarding dense bed most of studies are done with Eulerian- Eulerian Two Fluid Model approach [19]. Most of the literature in fluidized bed gasification is overlooking three-dimensional behaviours [14]. MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT Cornejo and Farías [15] developed three-dimensional numerical model that describes the process of coal gasification in fluidized-bed reactors using an Eulerian-Eulerian approach. The main contribution of this work was implementing some sub-models within the FLUENT code in order to handle reactive fluidized-beds in complex geometries. Xie et al [16] developed a three-dimensional numerical model to simulate forestry residues gasification in a fluidized bed reactor using an Eulerian–Lagrangian approach. The model predicts product gas composition and carbon conversion efficiency in good agreement with experimental data. Baliban et al. [17] proposed an approach for modeling of a biomass gasifier which is validated for lignocellulosic type of biomass with experimental data. Onel et al. [18] presents a generic gasifier model towards the production of liquid fuels using municipal solid wastes. Using a nonlinear parameter estimation approach, the unknown gasification parameters are obtained to match the experimental gasification results. The results suggest that a generic MSW gasifier mathematical model can be obtained in which the average error is 8.75%. Silva et al. [19] developed a two-dimensional Fluent based model to simulate the gasification of agro-industrial residues. The numerical simulation results were compared and validated versus a set of runs using three kind of biomass residues that were gasified in a bubbling fluidized pilot scale unit. Their results are in good agreement with the experimental data obtained at three different operating conditions. Thermodynamic equilibrium models are very useful tools to study the influence of most parameters for any biomass system because of their gasifier design independence. In MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT general, the thermodynamic equilibrium models consider two approaches both giving the same results: the stoichiometric approach, which requires a clearly defined reaction mechanism that incorporates all chemical reactions and species involved, and the nonstoichiometric approach which is based on the system minimization of the Gibbs free energy [20]. Several equilibrium models have been used to predict syngas composition from different biomass substrates [20-24]. The aim of this paper is to analyse MSW gasification using a numerical method. A twodimensional mathematical model was developed using an Eulerian-Eulerian approach to model the MSW gasification in a fluidized-bed reactor within Fluent [25]. The model takes detailed chemistry into account as homogeneous reactions for the gaseous phase and heterogeneous reactions to the solid phase, and also the modelling of heat and mass transfer and momentum. Pyrolysis is included considering a model with generation of secondary tar. The choice of a fluidized bed reactor is due to the fact that is widely used in industry for converting coal and there is a good understanding of pyrolysis and gasification in this kind of reactor. Fluidized beds are also capable of being scaled up to medium and large scale, overcoming limitations found in smaller scale, fixed-bed designs [26]. In order to validate the numerical results obtained in this work we refer to an experimental study on MSW gasification and melting technology conducted by Xiao et al. [27]. 2. Materials and Methods 2.1 Description of Portuguese waste management The Portuguese MSW management system involves collection, storage, treatment and disposal as shown in the Fig. 1. MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT There are two possible routes for wastes collection - the selected and unselected wastes collection. The selected collection includes ecopoints and door-to-door collection with ecocentres and biodegradable municipal waste collection. Ecopoints are devoted to separate collection based on the use of different containers for glass, paper/cardboard, and plastic/metal, placed together at ecopoints preferably located on public thoroughfares and strategic points. Ecocentres are sorting centres, where the selected wastes from the ecopoints are delivered for recovery. In addition to the materials referred to above as part of mechanical recycling, there are other specific fluxes of wastes (used oils, batteries, electrical and electronic wastes, construction and demolition residues, end-of-life vehicles and used cooking oil). The unselected collection is devoted to the collection of raw MSW. Raw MSW contains a large amount of non-combustible material, and therefore requires pre-processing before sending it to a gasifier. This pre-processing is made in the so-called mechanical and biologic treatment (MBT) station, where the biodegradable waste and the recyclable wastes are separated reducing the amount of waste to landfill. They must be able to meet the requirements of the gasifier and be flexible enough to handle MSW variability. This flexibility must be in terms of the type of material handled and its frequency of delivery. This pre-processing area is assumed to be similar to a Refuse Derived Fuel (RDF) facility. Some recyclables and non-combustibles are removed from the MSW to make a higher heating value product. Transfer stations provide the facilities required for unselected wastes when landfills or the mechanical biological treatment (MBT) station are far away. Therefore, unselected MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT 3.5 Granular Eulerian Model Granular Eulerian model is described by the following conservation equation for the granular temperature [37]: lsasasssssss sss kvIPv t φγταρ αρ +−Θ∇∇+∇+−=           Θ∇+ ∂ Θ∂ ΘΘ ))(.()(:)(.( )( rr 2 3 (13) This equation is obtained from the kinetic theory of gases. The term )(:)( sss vIP r ∇+− τ describes the generation of energy by the solid stress tensor, ls φ is the energy exchange between the fluid phase and the solid phase, aΘ γ the collisional dissipation of energy and )( sa kΘ∇ Θ the diffusion energy ( a k Θ is the diffusion coefficient). The stress in the granular solid phase is achieved by relating the random particle motion and the thermal motion of molecules within a gas accounting for the inelasticity of solid particles. In a gas the intensity of velocity fluctuation determines the stresses, viscosity and pressure of granular phase. 3.6 Chemical Reactions Model The chemical reaction rate coefficients are based on the Arrhenius law. Actually, they are empirical and determined by fitting the experimental data. During the devolatilization and cracking water shift reaction will occur, the gas species react with the supplied oxidizer and among them. The most common homogenous gas-phase reactions are [38]: molkJCOOCO /. 28350 22 +→+ (14) molkJHCOOHCO /1.41 222 + + → + (15) molkJOHCHHCO /1.2063 242 + + ↔ + (16) MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT The Arrhenius rates for each one of these reactions can be expressed as follows, respectively [39, 40]: The Eddy dissipation reaction rate can be expressed using the following equation [25]: The minimum value of these two contributions can be defined as the net reaction rate. The heterogeneous reactions of char (the solid devolatilization residue) with the species O 2 and H 2 O are very complex processes. They demand a mass diffusion balance of the oxidizing species at the surface of the biomass particle with the surface reactions of those species with the char. The composition and the temperature of the gases, as well as the temperature, size and porosity of the particle are important to determining the overall rate of the char. molkJOHOH /2425.0 222 + → + (17) molkJOHCOOCH /7.3522 2224 + + → + (18) CC T exp. r. CO Combustion_CO O 5015 2 16000 10 01      − ×= (19) CC T T exp. r. . CombustionH HO 51 51 15 2 2 2 3430 10 1595 −      − ×= (20) CC T T exp. r CHO CombustionCH 42 1 14 415700 10 5523 −      − ×= (21)                   −      − = T exp. CC CC T exp r H CO H OCO shift_gas_water 3968 02650 1510 2780 22 2 (22)         ∑ ∑         = − M y B, M Y min min k A Mr i,w R,i N i p p R,w r,R R R i,w r,i ndissipatioEddy α α ε ρ α (23) MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT The most used overall simplified heterogeneous reactions are [38]: molkJCOOC /5.1105.0 2 + → + (24) molkJCOCOC /4.1722 2 − → + (25) molkJHCOOHC /3.131 22 − + → + (26) The heterogeneous reactions are influenced by many factors, namely, reactant diffusion, breaking up of char, interaction of reactions and turbulence flow. In order to include both diffusion and kinetic effects the Kinetic/Diffusion Surface Reaction Model [41] was applied. This model weights the effect of the Arrhenius rate and the diffusion rate of the oxidant at the surface particle. The diffusion rate coefficient can be defined as [25]: ( ) [ ] d TT C D p p. 2 750 10 ÷+ = ∞ (27) The Arrhenius rate can be defined as follows: e A r Tp R E Arrehnius         − = (28) The final reaction rate weights both contributions and is defined as follows [25]: rD rD M ZT R A d d Arrehnius Arrehnius ox,w ox p t mp + −= ∞ 0 0 ρ (29) This model was included in the CFD framework by using the User Defined Function tool. 3.6.1. Pyrolysis Both homogeneous and heterogeneous reactions are preceded from pyrolysis reactions. Modeling pyrolysis is crucial for MSW gasification purposes. MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT MSW is thermal decomposed into volatiles, char and tar. There are several approaches to describe this phenomenon and 3 main approaches are usually followed: a single step pyrolysis model, competing parallel pyrolysis and a pyrolysis model with generation of secondary tar. In this model we adopt a pyrolysis model with generation of secondary tar. The MSW is mainly composed by cellulosic and plastic components, where the cellulosic material can be divided in cellulose, hemicellulose and lignin [17, 18] and the plastics are mainly comprised by polyethylene, polystyrene, and polypropylene, among others. To distinguish the several components that comprise the MSW, the pyrolysis reactions of cellulosic and plastic groups are considered individually and following an Arrhenius kinetic expression. The primary pyrolysis equations can be defined as follows: CharTarvolatilesCellulose r ααα 321 1 ++→ (30 ) CharTarvolatilesoseHemicellul r ααα 654 2 ++→ (31 ) CharTarvolatilesLignin r ααα 987 3 ++→ (32) CharTarvolatilesPlastics r ααα 121110 4 ++→ (33) The kinetics for the cellulosic material can be given as follows: ( ) a T E exp A dt a d r i n s i i i i −        − == 1 (34) MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT Where i stands for celulose, hemicellulose and lignin ( r 31− ), A i is the pre-exponential factor, E i is the activation energy and n is the order reaction. The values for each one of these parameters can be found in [42]. Average values were considered. Regarding the kinetic reactions for plastics, data was obtained from [43] by using the following reactions: ρ υ           − ∑ = = RT E exp Ar i n ii 1 4 (35) Where A i , E i and ρ υ are the pre-exponential factor, the activation energy and the volatiles density, respectively, and can be found in [43]. i stands for each one of the plastics that comprise the analyzed MSW along this paper. In this model, it is considered a secondary pyrolysis where is generated volatiles and secondary tar, as follows: arSecondaryTvolatilesimaryTarPr r +→ 5 Because, this secondary pyrolysis is also very difficult to treat, a simplified global reaction is used [44]: ρ 1 4 4 5 10 121 10 559 Tar g T . exp. r        ×− ×= (36) 3.7 Numerical procedure The simulations were performed in an up-flow atmospheric fluidized bed gasifier. This fluidized bed reactor is a tubular reactor of 0.5 m in diameter and 4.15 m of height, internally coated with ceramic refractory materials; MSW enters the reactor at the height of 0.5 m, from its base, and preheated air at 600 K enters the reactor coming from MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT the base through a set of diffusers, warranting a flow of about 70 m 3 /h. The schematics of the fluidized reactor are depicted in Fig. 2 and the bed is made of 70 kg of dolomite. The problem under consideration is solved by using a finite volume method based CFD solver FLUENT. The unstructured quadrilateral cells of non-uniform grid spacing are generated using GAMBIT. The grid is chosen to be sufficiently fine to capture the steep gradients in the vicinity of the cylinder. The second order upwind scheme is used to discretize the convective terms in the momentum and energy equations. The SIMPLE scheme is used for solving the pressure-velocity coupling. The system of algebraic equations is solved using the Gauss-Siedel point-by-point iterative method in conjunction with the algebraic multi-grid method solver. Relative convergence criterion for residuals of 10 -8 was prescribed in this work. 4. Results and discussion Despite having sparse data about municipal solid waste gasification in semi-industrial or industrial facilities, experimental data about MSW gasification in China was gathered from the literature [27]. Based on the characteristics of MSW from China, raw materials were prepared according to the average proportion of organic components (dry basis) for gasification, as displayed in Table 3. In order to perform simulations with the Xiao MSW composition [27] using Fluent code, a global chemical formula is needed. Using the same procedure that was used and described for Lipor MSW and assuming that kitchen garbage, wood and yard waste, paper and textile, are assumed to be cellulosic materials, and the plastics materials with the same relative composition of the Lipor MSW, the global chemical formula is obtained. MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT 4.1 Model validation In order to validate the mathematical model proposed previously, numerical simulations were carried out using the Xiao MSW. The results obtained numerically were compared with the experimental results of Xiao et al. [27]. The results of run 1 are shown in Fig. 3 for the gasification conditions defined in Table 4. Relative errors between numerical and experimental results are depicted in Table 5. The relative error is computed as follows: %=   × 100% (37) The model estimates reasonably all the species in a large spectrum of operating conditions. The largest errors are found for species at minor molar fractions. Despite these numerical results being within the range of values of the experimental results, there are still a few deviations which are justified by some simplifications performed, such as: i) The lack of data on the characteristics of waste has led to the use of constants of other biomasses which may differ from the actual one, ii) The kinetic constants were taken from the literature and can differ greatly from source to source. 4.2 Results of Lipor MSW gasification After validating the numerical model, numerical simulations were carried out and the results obtained for syngas gasification of Lipor`s MSW are shown in Fig. 4 for the gasification conditions defined in Table 6. These results are within the range of literature results [45, 46]. MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT Figure 5 shows the contours of the H 2 , CH 4 , CO 2 , and CO mole fractions in the gas mixture. The highest values for CO 2 are located at the syngas outlet, which is consistent with the results of Qingluan and Rodney [45] and Oevermann et al. [46]. CO shows the opposite trend. For CH 4 its higher value is found immediately above the biomass inlet, where the reduction phase takes place and these gases are formed, which is consistent with the results of Qingluan and Rodney [45]. The values of N 2 are not shown because are closely constant throughout the gasifier due to the unreacted characteristics of this gas, besides its possible combination with oxygen at high temperatures. This behaviour is also consistent with the results of Qingluan and Rodney [45]. The MSW collected by Lipor is representative of a population of 984,047 inhabitants, for a total of 500,000 tonnes of urban solid waste per year. Based on the numerical results of the gasification, it is possible to compute the production of 7.8×10 8 m 3 of syngas during a year based on 500,000 tons of MSW. This is a very interesting factor for Lipor incineration of waste. Lipor consumes about 20,000 m 3 of natural gas per year to spray the waste before entry into the combustion chamber. Therefore, it could be advantageous for Lipor to consider replacing this natural gas with syngas resulting from municipal solid waste gasification. MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT 5. Conclusion In this work, we have modelled gasification as an alternative incineration process for energy recovery from MSW. A two-dimensional mathematical model was developed using the fluent code. An Eulerian-Eulerian approach was used to describe the transport of mass, momentum and energy for the solid and gas phases. The model was tested using gasification experimental data from the literature. The model reproduces the key features of municipal solid waste gasification. The numerical results achieved show reasonable agreement with the experimental results, with some deviations. Numerical simulations were made for the study of gasification of municipal solid waste from the North of Portugal, carried out particularly by Lipor, the entity responsible for the MSW management system in the Porto metropolitan area. These results agree partially well with the experimental data. The very heterogeneous nature of the MSW and the consequent variance of the properties such as elemental composition, density, water content, and structure leads to uncertainties in the modelling of MSW gasification, and should be kept in mind when comparing the numerical results whit the experimental results. For Lipor it would be interesting to consider using a gasification unit instead of the incineration. The associated production of syngas was estimated to be around 7.8 × 10 8 m 3 /year. However, a study of economic viability must be carried out, as well as optimization of the gasification process where the numerical simulation can give a valuable assistance. MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT References [1] I.S. Antonopoulos, A. Karagiannidis, L. Elefsiniotis, G. Perkoulidis, A. Gkouletsos, Development of an innovative 3-stage steady-bed gasifier for municipal solid waste and biomass. Fuel Process Technol 92 (2011) 2389–2396. [2] Smith, A., Brown, K., Ogilvie, S., Rushton, K., Bales, J., Waste management option and climate change, European Communities. Final report to the European Commission. DG Environment, Luxembourg, 2001. [3] P. Sousa, A. Soares, E. Monteiro, A. Rouboa, Prospective application of municipal solid wastes for energy production in Portugal. Energ Policy 71 (2014) 159-168. [4] J.D. Murphy, E. McKeogh, Technical, economic and environmental analysis of energy production from municipal solid waste, Renew Energ 29 (2004) 1043–1057. [5] SB Jones, Y Zhu, C Valkenburg , Municipal Solid Waste (MSW) to Liquid Fuels Synthesis, Volume 2: A Techno-economic Evaluation of the Production of Mixed Alcohols , Richland, WA, Pacific Northwest National Laboratory, 2009. [6] Mohammad Asadullah, Barriers of commercial power generation using biomass gasification gas: A review. Renew Sust Energ Rev 29 (2014) 201–215. [7] A. Karagiannidis, St. Kontogianni , D. Logothetis, Classification and categorization of treatment methods for ash generated by municipal solid waste incineration: A case for the 2 greater metropolitan regions of Greece. Waste Manage 33 (2013) 363–372. MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT P s is the particle phase pressure due to particle collisions p Gas pressure q q r Heat flux q th Specific enthalpy Q pq Heat transfer intensity between phases R Universal gas constant R c Reaction rate S Mass source term due to heterogeneous reactions S k User-defined source terms S q Source term due to chemical reactions S ε User-defined source terms T Temperature U Mean velocity v Instantaneous velocity Y Mass Fraction Y M Contribution of the fluctuating dilatation in compressible turbulence to the overall dissipation rate Other Symbols α Volume fraction MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT β Gas-solid interphase drag coefficient ρ Density ls φ Energy exchange between the fluid phase and the solid phase. k $% D iffusion coefficient k $% ∇Θ (  Diffusion energy )-p ( I+ τ0 ( 1:∇v4 5 (  Generation of energy by the solid stress tensor. γ $% Collisional dissipation of energy τ Tensor stress µ Viscosity γc Stoichiometric coefficient Subscripts g gas phase s solid phase i component MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT Figure 1- Schematic overview of the Portuguese waste management system [3]. Treatment Disposal MBT Incineration Composting Recycling Landfilling Collection Eco centers Selected Unselected R efuse R efuse R efuse Storage Transfer station MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT Figure 2 – Gasifier geometry 1.0 m Syngas MSW Air flow 0.5 m Refractory Reactor Steel wall 4.15 m 0.2 m 0.2 m MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT CO2 H2 N2 CH4 CO C2H4 Molar Fraction, % 0 20 40 60 80 Experimental Numerical Figure 3 - Comparison of the CFD and experimental results for run 1 as defined in Table 4. MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT Gasification Runs 1 2 3 4 5 6 7 8 9 Molar Fraction, % 0 10 20 30 40 50 60 70 CO2 H2 N2 CH4 CO C2H4 Figure 4 - CFD molar fractions of the Lipor MSW for 9 gasification conditions defined in Table 6. MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT Figure 5 – Mole fraction contours for: (a) H 2 , (b) CH 4 , (c) CO 2 , (d) CO. a ) b ) c ) d ) MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT Table1 – Physical characterization of the MSW [31]. . Category % weight Putrefied residues 44.34% Paper 4.74% Cardboard 2.61% Composites 4.68% Textiles 5.73% Sanitary textiles 1.20% Plastics 10.98% Combustive non specified 0.09% Glass 4.29% Metals 2.15% Non-combustive non specified 0.41% Hazardous residues 0.06% Fine elements 8.72% MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT Table 2 - Chemical composition of the MSW. *It was considered the proportion of cellulose, hemicellulose and lignin found in [18]. Category % weight Chemical formula Cellulosic material 85.22% * Polyethylene 11.14% (C 2 H 4 ) n Polyethylene terephthalate 2.05% (C 10 H 8 O) n Polypropylene 0.82% (C 3 H 6 ) n Polystyrene 0.77% (C 8 H 8 ) n MANUS CRIP T ACCEP TED ACCEPTED MANUSCRIPT Table 3 – Xiao et al. [27] MSW characteristics Organic compounds (%) Low heating value (kJ/kg) Kitchen garbage Plastic Wood and yard waste Paper Textile 61 20 10 8 1 17,960