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Gaseous emissions from agricultural biomass combustion: a prediction model

Sébastien Fournel1,2, Bernard Marcos1, Stéphane Godbout2, Michèle Heitz1

1Department of Chemical Engineering and Biotechnological Engineering, Faculty of Engineering, Université de Sherbrooke, 2500 boulevard de l’Université, Sherbrooke, QC, Canada.

2Research and Development Institute for the Agri-Environment (IRDA), 2700 Einstein Street, Quebec City, QC, Canada.


This is not a peer-reviewed article.

2013 ASABE Annual International Meeting, Paper No. 131594309, pages 1-11 (doi: 10.13031/aim.20131594309). St. Joseph, Mich.: ASABE.


Written for presentation at the
2013 ASABE Annual International Meeting
Sponsored by ASABE
Kansas City, Missouri
July 21 – 24, 2013

The authors are solely responsible for the content of this meeting presentation. The presentation does not necessarily reflect the official position of the American Society of Agricultural and Biological Engineers (ASABE), and its printing and distribution does not constitute an endorsement of views which may be expressed. Meeting presentations are not subject to the formal peer review process by ASABE editorial committees; therefore, they are not to be presented as refereed publications. Citation of this work should state that it is from an ASABE meeting paper. EXAMPLE: Author’s Last Name, Initials. 2013. Title of Presentation. ASABE Paper No. ---. St. Joseph, Mich.: ASABE. For information about securing permission to reprint or reproduce a meeting presentation, please contact ASABE at rutter@asabe.org or 269-932-7004 (2950 Niles Road, St. Joseph, MI 49085-9659 USA).

Abstract. As the price of the fossil energy resources and the need to reduce the environmental impacts from energy use increase, biomass fuels have regained interest from Quebec’s agricultural sector. Producing and burning energy crops at the farm have become strategies to diversify incomes and decrease dependency to fossil fuels. However, the current absence of emission factors for solid fuel combustion does not allow a sustainable development for energy purposes. Besides, the variety of existing furnaces and biomasses complicates the establishment of such reference values. In order to quantify emissions (CO, CO2, NOx, SO2, CH4, NH3 and HCl) from on-farm combustion of different agricultural biomasses (short-rotation willow, switchgrass, reed canary grass, etc.), a prediction model was established based on the calculation of chemical equilibrium of reactive multicomponent systems. Under constant temperature and pressure, this technique has been judged as relevant for the prediction of product compositions considering inlet conditions in several operations and chemical processes, particularly gasification. The model was first established for wood gasification to be able to compare and validate its results with those of existing models from the literature using the same original data. The model was then adapted to biomass combustion and calibrated with recent results from combustion tests held in the province of Quebec. The preliminary results of the prediction model using data from past combustion experiments with wood, willow and switchgrass revealed good agreement between both measured and predicted values. Other simulation tests are required to increase accuracy of the model.

Keywords.Biomass, combustion, gasification, gaseous emissions, prediction model, chemical equilibrium, Gibbs free energy.

Introduction

As the price of the fossil energy resources and the need to reduce the environmental impacts from energy use increase (Demirbas, 2009; Kaltschmitt and Weber, 2006), biomass fuels have regained interest, especially from Quebec’s agricultural sector in order to develop production systems with lower energy expenses (Brodeur et al., 2008; Lease and Théberge, 2005). Producing and burning energy crops at the farm have become strategies to diversify incomes and decrease dependency on fossil fuels.

However, the current absence of emission factors for solid fuel combustion does not allow a sustainable development for energy purposes (Talluto, 2009). Furthermore, the variety of existing furnaces and biomasses complicates the establishment of such reference values since previous studies (Obernberger et al., 2006; Villeneuve et al., 2012) have revealed that gaseous emissions from biomass combustion differ significantly according to the combustion technology used and the characteristics of the fuel burned (chemical composition, physical properties, etc.). From an experimental point of view, considering all the biomass possibilities would become a laborious and expensive work.

To overcome this situation, thermodynamic equilibrium models can become useful engineering tools to assess how fuel characteristics influence the exit gas composition (Baratieri et al., 2008; Kalina, 2011; Melgar et al., 2007). Actually, when the chemical composition of biomass and the equilibrium temperature are specified, thermodynamic models can simply predict the resulting emissions (Ranzi et al, 2011). To achieve this, some of those models are based on minimization of the Gibbs free energy (Jarungthammachote and Dutta, 2008). This technique is mainly used for determining the chemical equilibrium composition of reactive multi-component closed systems under thermodynamic equilibrium (Néron et al., 2012). Therefore, a global minimum of the Gibbs free energy coincides with the stable equilibrium solution under constant temperature and pressure. The equilibrium problem is then solved as an optimization problem of a non-linear constrained system that must satisfy the restrictions of non-negative number of moles and mole balances (Rossi et al., 2011). Lagrange multiplier method is generally used to compute this constrained optimization problem (Baratieri et al., 2008; Jarungthammachote and Dutta, 2008).

Even though this theoretical approach has some inherent limitations, it was judged as relevant for the prediction of product compositions in several operations and chemical processes as gasification (Altafini et al., 2003; Baratieri et al., 2008; Gautam, 2010; Jarungthammachote and Dutta, 2007, 2008; Kalina, 2011; Melgar et al., 2007; Néron et al., 2012; Rossi et al., 2009, 2011; Zainal et al., 2011) and combustion (de Souza-Santos, 2010). The model based on chemical equilibrium by minimization of the Gibbs free energy was first established for wood gasification to be able to compare and validate its results with those of existing models from the literature using the same original data. The model was then adapted to biomass combustion and calibrated with recent results from wood and agricultural biomass combustion tests held in the province of Quebec in the past few years.

Model development

Gasification and combustion reactions

To develop the model, the chemical formula of biomasses is defined either as CHyOzNa or CHyOzNaSbClc. The former corresponds to a woody biomass with negligible sulfur and chlorine contents and is implied in the global gasification reaction for simulation comparison with other gasification models as described later. The latter term characterizes a general biomass containing carbon, hydrogen, oxygen, nitrogen, sulfur and chlorine. It is utilized in the combustion reaction. Both gasification and combustion reactions (eqs. 1 and 2, respectively) can then be expressed as

CHy Oz Na + w H2 O+ e (O2 + 3.76N2 ) =

n1H2+ n2CH4 + n3CO + n4CO2 + n5H2O + n6N2                      (1)

and

CHy Oz Na Sb Clc + w H2 O + e (O2 + 3.76N2 ) =

n2CH4 + n3CO + n4CO2 + n5H2O + n6N2 + n7O2 +

n8NO + n9NO2 + n10N2O + n11NH3 + n12SO2 + n13HCl,                  (2)

where y, z, a, b and c are respectively the numbers of atoms of hydrogen (H), oxygen (O), nitrogen (N), sulfur (S) and chlorine (Cl) per atom of carbon (C) in the solid fuel, w and e are respectively the amount of moisture and dioxygen in air per kmol of feedstock and ni are the numbers of mole of the species i. Those species correspond to:

  1. Dihydrogen (H2);
  2. Methane (CH4);
  3. Carbon monoxide (CO);
  4. Carbon dioxide (CO2);
  5. Water vapor (H2O);
  6. Dinitrogen (N2);
  7. Dioxygen (O2);
  8. Nitrogen monoxide (NO);
  9. Nitrogen dioxide (NO2),
  10. Nitrous oxide (N2O);
  11. Ammonia (NH3);
  12. Sulfur dioxide (SO2);
  13. Hydrogen chloride (HCl).

They were identified as the main products from either solid fuel gasification or combustion. All inputs on the left-hand side of equations 1 and 2 are supposed entering the combustion system at 25°C. On the right-hand side, the stoichiometric coefficients ni are unknown and the model consists in evaluating the concentrations of the species to calculate them.

Assumptions

The resolution of the stoichiometric coefficients ni using a thermodynamic model was based on the following assumptions:

Minimization of Gibbs free energy

The composition of the gas produced at thermodynamic equilibrium can be estimated using different approaches: kinetic or dynamic models (Gøbel et al., 2007; Ranzi et al, 2011), equilibrium constants (Gautam, 2010; Jarungthammachote and Dutta, 2007; Melgar et al., 2007; Zainal et al., 2001) or Gibbs energy minimization (Altafini et al., 2003; Baratieri et al., 2008; Jarungthammachote and Dutta, 2008; Kalina, 2011; Néron et al., 2012; Rossi et al., 2009, 2011). The key advantage of the latter is that it has a more general application with predictive capability and does not require the selection of appropriate chemical reactions or an extended set of data to train the model (Baratieri et al., 2008; Néron et al., 2012).

The Gibbs energy minimization method is based on the assumption that the Gibbs energy (G) reaches a minimum value at thermodynamic equilibrium (Jarungthammachote and Dutta, 2008; Néron et al., 2012). Considering a closed chemical system with an arbitrary number of species present in one or several phases at uniform temperature (T) and pressure (P) (not necessarily constant) evolving from a non-equilibrium initial state to a closer-to-equilibrium final state and restricting the process to constant T and P, all irreversible process (occurring at constant T and P) evolve in the direction that causes a decrease of G. The equilibrium state of a closed system is the one for which G reaches a minimum with respect to all possible changes at the given T and P. The Gibbs energy minimization method, then, consists in writing an expression for G as a function of the number of moles of the species present and then finding the set of values for the mole number that minimizes this function, subject to the constraints of mass conservation and stoichiometry. Moreover, the expression for G can be extended for open systems, in which the number of moles of the species (ni) can vary because of mass exchange with the surroundings. In this case, it is necessary to introduce a chemical potential (µi) that is a function of the ni moles of the different compounds (Baratieri et al., 2008):

.                  (3)

The next step is to find the values of ni which minimizes the objective function G subject to constraints on the allowable ni. The problem is then solved as an optimization problem of a non-linear constrained system that must satisfy the restrictions of mole balances and non-negative number of moles corresponding to the Karush-Kuhn-Tucker (KKT) conditions (Néron et al., 2012). Based on this principle, the Gibbs free energy minimization is generally solved by the Lagrange multiplier method (Koukkari and Pajarre, 2006).

Thermodynamic properties

whereis the heat capacity at constant pressure for the standard state (kJ kmol-1 K-1) and T0is the inlet temperature of reactants (K). Besides, it is important to note that the arbitrary base selected for calculating the enthalpy value is zero at 25°C in the case of reference elements and thus (298.15) equals zero for all reference elements.

Since heat capacity, enthalpy and entropy are functions of temperature, it would be easier for the model computation that those properties are described in terms of polynomial equations as follows:

,                  (15)

                 (16)

and

.                  (17)

These equations were established by the NASA technical memorandum 4513 (McBride et al., 1993). All coefficients for equations 15 to 17 can also be found in this report.

Energy balance

The energy balance is introduced as an energy constraint when the studied system can exchange heat with the environment. In this case, the equilibrium temperature differs from the initial temperature (Néron et al., 2012). If the heat duty is known, the equilibrium temperature can be obtained from the first law of thermodynamics for the combustion process:

,                  (18)

where Qloss is the heat loss from the combustion process (kJ) andandare respectively the enthalpies of each reactant and each product at the specified temperatures (kJ kmol-1) . Both enthalpies can be calculated by equation 13. The enthalpy of formation of a solid fuel can be calculated by the equation developed by de Souza-Santos (2010) as follows

,                  (19)

whereis the enthalpy of formation of product p under complete combustion of the solid fuel (kJ kmol-1) andis the lower heating value of the biomass (kJ kmol-1) .

Solver

The thermodynamic equilibrium model using Gibbs free energy minimization approach as described before has been developed in Matlab environment. The code first requires entering the necessary input values such as the characterization of biomass and the initial temperature. The nonlinear programming model, comprising the objective function to be minimized (eq. 5) and the constraints (eq. 6), is solved by using the fmincon function contained in Matlab. The fmincon function applies the interior-point algorithm to solve nonlinear programming problems. In different steps, the algorithm obtains Lagrange multipliers by approximately solving the KKT conditions. A detailed description of the use of the fmincon function in Matlab can be found elsewhere (MathWorks, 2013).

For calculating the equilibrium temperature, the initial temperature is assumed and used to perform the minimization of Gibbs free energy. Knowing the predicted gas composition, the energy balance is calculated. Depending on the sign of ?H in equation 18, the reaction temperature is adjusted. The minimization is hence solved iteratively until ?H becomes zero. The complete calculation procedure is illustrated in figure 1.

Figure 1. The calculation procedure.

Simulation results and discussion

Validation of the model applied to gasification process

The first version of the prediction model was developed for biomass gasification considering equation 1 and following the described procedure in the previous section. It was tested by comparing the calculation results with experimental data reported in literature (Altafini et al., 2003; Jayah et al., 2003). The modeling results with both data are shown in tables 1 to 4. These tables also include the predicted values of other authors’ models (Altafini et al., 2003; Jarungthammachote and Dutta, 2007; Melgar et al., 2007) that used the same original data.

Table 1. The comparison of predicted results with the experimental data from Altafini et al. (2003) on sawdust gasification.

Gas compositionAltafini et al. (2003)Jarungthammachote and Dutta (2007)The present model
(% mol dry basis)Experimental dataSynGas modelModified modelOriginal modelModified model
H214.0020.0618.2421.6918.73
CO20.1419.7023.3423.4621.39
CH42.310.001.66[a]0.031.72[a]
CO212.0610.159.829.5711.21
N250.7950.1046.9345.2546.96
[a]The model was calibrated by fixing the amount of methane in the model to a value derived from the experimental results.

Table 2. The comparison of predicted results with the experimental data from Jayah et al. (2003) on rubber wood gasification (MC = 14.0wt% dry basis)

Gas compositionJayah et al. (2003)Jarungthammachote and Dutta (2007)The present model
(% mol dry basis)Experimental dataOriginal modelOriginal modelModified model
H212.518.0319.3617.15
CO18.918.5118.3016.74
CH41.20.110.011.21[a]
CO28.511.4311.8113.00
N259.151.9250.5251.90
[a]The model was calibrated by fixing the amount of methane in the model to a value derived from the experimental results.

Table 3. The comparison of predicted results with the experimental data from Jayah et al. (2003) on rubber wood gasification (MC = 14.7wt% dry basis)

Gas compositionJayah et al. (2003)Melgar et al. (2007)The present model
(% mol dry basis)Experimental dataModelModelOriginal modelModified model
H215.516.417.123.3221.28
CO19.118.319.322.421.02
CH41.11.1[a]0.30.011.16[a]
CO211.411.111.19.9611.02
N252.953.252.344.3445.52
[a]The model was calibrated by fixing the amount of methane in the model to a value derived from the experimental results.

Table 4. The comparison of predicted results with the experimental data from Jayah et al. (2003) on rubber wood gasification (MC = 16.0wt% dry basis)

Gas compositionJayah et al. (2003)Jarungthammachote and Dutta (2007)The present model
(% mol dry basis)Experimental dataOriginal modelOriginal modelModified model
H217.018.0422.2919.97
CO18.417.8620.8219.25
CH41.30.110.011.30[a]
CO210.611.8410.7611.97
N252.752.1546.1347.51
[a]The model was calibrated by fixing the amount of methane in the model to a value derived from the experimental results.

Table 1 deals with the computational simulation of a wood waste downdraft gasifier. The sawdust ultimate analysis results in 52.00wt% C, 6.07wt% H, 41.55wt% O, and 0.28wt% N. The sawdust presents a moisture content (MC) nearly 10.00% (wt% on wet basis). The comparison test was done by setting the temperature and the air/sawdust ratio at 1073 K and 1.957, respectively, as reported by Altafini et al. (2003).

Table 1 shows that the predicted values obtained by the original version of our model generally agree with the experimental data of Altafini et al. (2003) and their SynGas model, except for the cases of H2 and CH4. Actually, the models predicted higher amounts of H2 and lower amounts of CH4 than experimental data. Altafini et al. (2003) noted that this result is generalized to other equilibrium models from the literature. A possible explanation of the differences may have came from the assumptions defined in simplifying the model, such as all gases are assumed to be ideal at a pressure of one atmosphere, ashes are inert, absence of other gases, etc. Besides, from a chemical point of view, CH4 formation at the equilibrium temperature of 800°C is not favored because is positive and approximately equal to 25 kJ mol-1. Therefore, the system needs to absorb energy so that the formation of CH4 could happen. On the other side, the CO and CO2 formation reactions present high values ofat 800°C (–206 kJ mol-1 and –397 kJ mol-1, respectively) comparatively to CH4. Consequently, the formation of CO and CO2 releases an important quantity of energy which allows the system to reach a minimum level of free energy.

To increase the results’ accuracy, Jarungthammachote and Dutta (2007) calibrated the amount of CH4 in their model by fixing a value derived from the experimental data of Altafini et al. (2003), Jayah et al. (2003) and Zainal et al. (2001). The modi?ed model was then used to simulate and compare with the SynGas model and the experimental results of Alta?ni et al. (2003). Table 1 shows that after modifying the model, the amount of H2 significantly reduced as compared to the predicted value from the SynGas model. The amount of CH4 also dramatically increased and was found closer to the experimental value. The same approach was used for the modified version of the present model to finally state the same conclusions.

Tables 2 to 4 present the simulation results of Jarungthammachote and Dutta (2007), Melgar et al. (2007) and the present model (original and modified versions) in comparison with the experimental data of Jayah et al. (2003) on rubber wood gasification at different MC: 14.0%, 14.7% and 16.0% (wt% on dry basis). The wood ultimate analysis results in 50.6wt% C, 6.5wt% H, 42.0wt% O, and 0.2wt% N. The comparison test was done by setting the temperature used for the developed model fixed at 1100 K. The air/sawdust ratios were 2.29, 1.86 and 1.96, respectively for each MC, as reported by Jayah et al. (2003). As in table 1, the values predicted by the thermochemical equilibrium models show good accuracy with experimentally obtained data even though H2 and CH4 contents are slightly different, except if CH4 is fixed in a modified version of the model.

Testing the model with combustion results

The prediction model developed for biomass gasification was adapted to biomass combustion now considering equation 2 instead of equation 1. Data from Godbout et al. (2012) technical report on recent agricultural biomass combustion was used for the simulation tests even though some important input values are missing. This situation is widespread in the literature. Already that there is not many references on the gaseous emissions from agricultural biomass combustion, most of scientific articles on the subject do not report all these essential results for modeling: biomass ultimate and proximate analyses, air flow rate, combustion rate and temperature. Unknown values were therefore estimated when necessary.

Combustion tests were held in April 2011 in an on-farm combustion laboratory in Deschambault, Quebec, Canada. The experiments were carried out in a 60,000 BTU h-1 (17.58 kW) output biomass pellet stove (Enviro Omega model, Sherwood Industries Ltd., Saanichton, BC, Canada). A Fourier transform infrared spectrometer (FTIR; model FTLA2000, ABB, Quebec city, QC, Canada) was used to analyze concentrations (ppmv) of eight gases (CH4, CO, CO2, HCl, NH3, NO2, N2O and SO2) during the combustion of wood, short-rotation willow and switchgrass.

The three biomasses were all pelletized and their characteristics are described in table 5. This table also includes the combustion conditions for each combustion test done with these biomasses. Table 6 presents the measured emissions at the laboratory and the predicted values obtained with the model. For most gases, the model seems generally accurate even if the experimental error on the measured values is high. According to the authors, it reaches almost 100% mainly because of the low flue gas flow rates which were in the detection limits of the iris damper.

Table 5. Characteristics of experimental biomass pellets and combustion conditions during combustion test with three biomasses.

Element or propertyUnitsWoodShort-rotation willowSwitchgrass
BIOMASS
Cwt% dry basis49.848.846.9
Nwt% dry basis0.1140.6320.672
Clwt% dry basis0.0010.0040.014
S*wt% dry basis0.050.060.10
H*wt% dry basis6.26.25.5
O**wt% dry basis43.87544.20446.814
Moisture contentwt% wet basis6.1011.3613.05
Gross calorific valueMJ kg-117.918.018.7
COMBUSTION
Air flow ratem3 min-10.230.210.20
Combustion ratekg h-11.201.921.56
*Estimated from mean concentrations proposed van Loo and Koppejan (2008).
**By difference.

Nevertheless, the model obtained really good results in predicting carbon compounds, especially for CO2 where the difference between the measured and the predicted value is about 7%. CO, as an intermediate in the conversion of fuel carbon to CO2, is oxidized to CO2 if oxygen is sufficiently available. Given that the air flow is low, only a small difference in the quantity of air entering the combustion chamber can drastically change the modeling results. For instance, a simple increase of 0.03 m3 min-1 to reach an air flow of 0.26 m3 min-1 for the wood combustion test implies a drop of predicted CO to less than 1 g kg-1. In these circumstances, the CO results are acceptable even though they do not seem as good as for CO2. Since the emission of CH4 is a result of too low combustion temperatures, too short residence times or lack of available oxygen (van Loo and Koppejan, 2008), the higher emission value measured during wood combustion is likely due to these factors. Theoretically, the real behaviour of the combustion process and possible unfavourable conditions such as poor mixing are hard to predict. With sufficient air and temperature, the predicted CH4 emission approaches zero like it was the case for fast-growing willow and switchgrass experiments. Therefore, it is possible to think that the combustion conditions during wood combustion were sufficient to reach a lower CH4 value like the one obtained by the model.

Table 6. Measured and predicted emissions (g kg-1) from the combustion of three biomasses.

Gas WoodShort-rotation willowSwitchgrass
speciesMeasuredPredictedMeasuredPredictedMeasuredPredicted
CH40.30120.00770.00430.00790.00000.0003
CO21.817.417.418.62.414.67
CO2194918001870176016101710
HCl0.0180.0100.0000.0410.0160.144
NH30.00160.00820.00630.00820.00070.0003
NO20.00660.01000.12840.00970.36160.0004
N2O0.00000.00860.03430.00840.00000.0003
SO20.711.000.531.201.702.00

Nitrogen oxides (NOx)emissions increase with increasing nitrogen content in the fuel (van Loo and Koppejan, 2008). This tendency can be seen in table 6 as measured NO2 for agricultural biomass combustion are much higher than for wood combustion. However, the simulation results for agricultural biomass combustion do not match the values obtained in laboratory. From a chemical point of view, NO2 formation at the simulated equilibrium temperature (˜ 875°C) is not favored becauseis positive. It can then be supposed that under agricultural biomass combustion conditions most nitrogen was rather converted to NO or N2 by the model. This assumption suits to the fact that NO emissions represent about 90% of NOx emissions during biomass combustion (van Loo and Koppejan, 2008). Also, in fuel-rich conditions, NO react with intermediates like NH3 and hydrogen cyanide (HCN) to form N2. When the primary excess air ratio, temperature and residence time are optimized, a maximum conversion of NH3 and HCN to N2 is achieved. Moreover, a nitrogen mass balance reveals that only 15% to 30% of nitrogen is emitted as NOx, the rest being emitted as N2 (AILE, 2012). The low accuracy of the model to predict NO2 could result from these phenomena, but the absence of data on NO and N2 do not allow confirming anything. Future experimental tests should consider acquiring the necessary equipment to measure these gas species. However, the prediction model obtained good results for NH3 and NO2, both gases being only present in small amounts.

Agricultural biomasses contain significant amounts of chlorine comparatively to wood. This difference echoes on HCl emissions as the model predicted higher HCl levels for switchgrass and short-rotation willow combustions. Nevertheless, the relation between fuel chlorine and HCl is not clear by looking to experimental results. In fact, fuel chlorine is not completely converted to HCl. The main fraction is rather retained in salts by reaction with potassium (K) and sodium (Na) (van Loo and Koppejan, 2008). This can explain the difference between measured and predicted values. More work is expected to adjust the model so that it takes into account this fact. A better result for wood combustion is certainly due to its very low chlorine content.

SO2 emissions are directly linked to the content of sulfur in the biomass. Since sulfur content was estimated, it is hard to conclude something on the basis of the present results. However, it is possible to note that predicted values are always higher than measured values. As for HCl, fuel sulfur is only partly released in its main gaseous form (SO2). In fact, a significant portion of sulfur remains in ashes while a minor fraction is emitted as potassium sulphate (K2SO4) or hydrogen sulphide (H2S). Measurements at two district heating plants in Denmark using straw as fuel showed that 57% to 65% of the sulfur was released in the flue gas (Obernberger et al., 2006). Future work should also consider these numbers.

Conclusion or Summary

An equilibrium model based on minimization of the Gibbs free energy was developed to predict the composition of gaseous emissions from biomass gasification and combustion. The general calculation procedure includes an energy balance to calculate the reaction temperature and the minimization of the Gibbs free energy based on this temperature. Given that the equilibrium approach has been mostly used in biomass gasification, the model was validated by comparing the simulation results of the six major components (CO, CO2, CH4, H2O, H2 and N2) with those of other researchers who utilized the same original data for their model. The predicted values and the experimental data showed good accuracy, except for H2 and CH4. The slight differences obtained can be reduced by modifying the model by fixing the CH4 concentration in the gas mixture, based on experimental results. The model was then adapted to combustion and tested with data from past experiments on wood and agricultural biomass combustion. The results of simulation from the model were closer to the experimental data for CO, CO2, NH3 and N2O. The model also showed a certain deviation from the experimental data for CH4, NO2, HCl and SO2. At the moment, the model allows to know the theoretical combustion conditions that allow reaching a complete combustion by keeping the products of incomplete combustion (CO, CH4 and NH3) as low as possible. Future experimental tests considering the measure of hydrogen and sulfur contents, combustion temperature, NO and N2 emissions are needed to calibrate the model in order to improve results’ accuracy.

Acknowledgements

The authors thank the “Ministère de l’Agriculture, des Pêcheries et de l’Alimentation du Québec” (MAPAQ) and the “Fonds de recherche du Québec – Nature et technologies” (FQRNT) for their financial contributions. The authors gratefully acknowledge the Research and Development Institute for the Agri-Environment (IRDA) and the Université de Sherbrooke which provided in-kind contributions for this study. The authors also recognize the professional support provided by IRDA research staff (Joahnn Palacios and Patrick Brassard). Authors also thank Dr. Matthieu Girard for the linguistic revision.

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Nomenclature

anumber of atoms of nitrogen per number of atom of carbon in the feedstock
Ajtotal number of atoms of jth element in reaction mixture
aijnumber of atoms of jth element in a mole of ith species
bnumber of atoms of sulfur per number of atom of carbon in the feedstock
cnumber of atoms of chlorine per number of atom of carbon in the feedstock
Cpspeci?c heat at constant pressure, kJ K-1
eamount of oxygen per kmol of feedstock
Gtotal Gibbs free energy of a system, kJ
Henthalpy, kJ
LLagrangian function
LHVlower heating value, kJ
nnumbers of moles
Ppressure, atm
Qlossheat loss, kJ
Runiversal gas constant, 8.314 kJ kmol-1 K-1
Sentropy, kJ K-1
Ttemperature, K
wamount of moisture per kmol of feedstock
xnumber of atoms of carbon in the
hydrocarbon
ynumber of atoms of hydrogen per number of atom of carbon in the feedstock
yimole fraction of gas species
znumber of atoms of oxygen per number of atom of carbon in the feedstock
Greek letters
?show a difference
?stoichiometric number
µchemical potential
?Lagrange multiplier
Superscripts
quantity per unit mole, kmol-1

standard reference state
Subscripts
compcompound
elemelement
fof formation
iith gas species
jjth chemical element
ktotal number of elements
llth gas species
Ntotal number of gas species
rreactant
pproduct
tottotal