A mathematical model of biomass downdraft gasification with an integrated pyrolysis model

Fuel 265 (2020) 116867

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Full Length Article

A mathematical model of biomass downdraft gasification with an integrated pyrolysis model

T

Marta Trninića, , Dragoslava Stojiljkovića, Nebojsa Manića, Øyvind Skreibergb, Liang Wangb, Aleksandar Jovovića ⁎

a b

University of Belgrade, Faculty of Mechanical Engineering, Kraljice Marije 16, 11020 Belgrade, Serbia SINTEF Energy Research, Postboks 4761 Torgarden, NO-7465 Trondheim, Norway

ARTICLE INFO

ABSTRACT

Keywords: Gasification Pyrolysis Biomass Steady state Modelling

A zero-dimensional mathematical model for downdraft gasification of biomass in fixed or slowly moving bed gasifiers was developed and implemented in Engineering Equation Solver (EES). The proposed model does not consider the gasification process as a black box, it takes into account the main gasification sub-processes (drying, pyrolysis, gasification) and their products. The model successfully predicts conversion behaviours of different types of biomass during a gasification process in terms of yield and composition of products. The model has been validated with published experimental data from the literature, showing good agreement. In addition, a sensitivity analysis was carried out using the model, varying different operating parameters (gasification temperature, equivalence ratio, air preheating temperature, steam injection amount, oxygen enrichment degree etc.). The simulation results are presented and discussed. The developed model can be considered a useful tool to simulate the influence of a wide variety of biomass feedstocks and operating parameters on gas characteristics.

1. Introduction Biomass is one of the most important environmentally friendly and renewable energy sources. The potential offered by biomass for solving some of the world's energy and environmental problems is widely recognised [1]. Biomass can be combusted directly to generate heat or can be converted into more valuable gas and liquid products. Therefore, biomass energy can cover different kinds of energy needs, including fuelling vehicles, process heat for industrial facilities, and generating electricity and heat in the household sector [2]. Among the different thermochemical paths, biomass gasification is continuously receiving attention due its advantages compared to other conversion paths. A gasification process is a partial thermal oxidation, which results in mainly gaseous products (carbon dioxide, hydrogen, carbon monoxide, water vapour, methane and other gaseous hydrocarbons), and small quantities of charcoal, ash, and condensable compounds-tars [3]. The quality of produced gas from gasification, called producer gas, vary as a function of gasifying medium (air, oxygen, steam, carbon dioxide or a mixture of these) and the operating conditions. Installation of small, low-cost and efficient gasifier-engine systems can be an attractive alternative to direct combustion, considering achievable electric efficiency and costs related to storage and transport

⁎

of biomass fuels [4]. The producer gas, after cleaning and conditioning, can be used as a fuel in gas engines and turbines owing to its acceptable thermochemical combustion properties (flame speed and knock tendency) [5]. Gasification is also considered as a cleaner and more efficient technology than combustion, since it enables higher electric performances at smaller scales and due to its very acceptable combustion properties coupled to a conventional Rankine cycle [6], giving lower NOX and SOX emissions, and possibilities for CO2 capture [7]. However, biomass gasification must overcome some barriers for commercial implementation. The main ones are the removal treatment of particles and tars, and issues related to the production, logistics, and pretreatment of the biomass feedstock. In order to optimize the gasification process, a better knowledge and understanding of the effect of the biomass properties and the gasifier operating parameters on the producer gas quality and the gasifier performance is needed [8]. In order to optimize design of complex gasification processes and to operate them efficiently it is necessary to explore and understand the basic gasification mechanisms. Consequently, it is necessary to simulate biomass gasification processes for scale-up, industrial control strategies, and performance evaluation after modifying the operating conditions [9]. Mathematical modelling of a gasification process is proved to be a relatively fast and economic solution compared to the direct

Corresponding author. E-mail address: [email protected] (M. Trninić).

https://doi.org/10.1016/j.fuel.2019.116867 Received 26 June 2019; Received in revised form 27 November 2019; Accepted 12 December 2019 0016-2361/ © 2019 Elsevier Ltd. All rights reserved.

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Nomenclature

nC , T nH , G nH , T nH , H2 O

Acronyms and abbreviations

Yi, biomass, waf Mass fraction of ith element in biomass, wet ash-free kg/ kg Yi, biomass, daf Mass fraction of ith element in biomass, dry ash-free kg/ kg Y jp, products, daf Mass fraction of jth pyrolysis product, dry ash-free kg/ kg Y jg, products, daf Mass fraction of jth gasification product, dry ash-free kg/kg YA Ash content in biomass/product, dry basis kg/kg YM , wb Moisture content of biomass in dry ash free basis kg/kg YH2 O Moisture content in gas obtained in the process of pyrolysis kg/kg Y H2 O Moisture content in gas obtained in the process of gasification kg/kg YC , G Mass fraction of C in gas, after pyrolysis kg/kg YC , CC Mass fraction of C in charcoal, after pyrolysis kg/kg YC , T Mass fraction of C in tar, after pyrolysis kg/kg YH , G Mass fraction of H in gas, after pyrolysis kg/kg YH , T Mass fraction of H in tar, after pyrolysis kg/kg YH , H2 O Mass fraction of H in water vapour, after pyrolysis kg/kg YO, G Mass fraction of O in gas, after pyrolysis kg/kg YOp, T Mass fraction of O in tar, after pyrolysis kg/kg YO, H2 O Mass fraction of O in water vapour, after pyrolysis kg/kg YN 2 Mass fraction of N2, after pyrolysis kg/kg YCO Mass fractions of CO, after pyrolysis kg/kg YCO2 Mass fractions of CO2, after pyrolysis kg/kg YCH 4 Mass fractions of CH4, after pyrolysis kg/kg YH2 Mass fractions of H2, after pyrolysis kg/kg YH2 O Mass fractions of water, after pyrolysis kg/kg YCC Mass fractions of charcoal, after pyrolysis kg/kg YT Mass fractions of tar, after pyrolysis kg/kg YC' , G Mass fraction of C in gas, after gasification kg/kg YC' , CC Mass fraction of C in charcoal, after gasification kg/kg YC' , T Mass fraction of C in tar, after gasification kg/kg Y H' , G Mass fraction of H in gas, after gasification kg/kg YHg , T Mass fraction of H in tar, after gasification kg/kg YHg , H 2O Mass fraction of H in water vapour, after gasification kg/kg YOg, G Mass fraction of O in gas, after gasification kg/kg YOg, T Mass fraction of O in tar, after gasification kg/kg YOg, H 2O Mass fraction of O in water vapour, after gasification kg/kg Y N' 2 Mass fraction of N2, after gasification kg/kg ' YCO Mass fractions of CO, after gasification kg/kg ' YCO2 Mass fractions of CO2, after gasification kg/kg ' YCH Mass fractions of CH4, after gasification kg/kg 4 Y H' 2 Mass fractions of H2, after gasification kg/kg Y H' 2O Mass fractions of water, after gasification kg/kg ' YCC Mass fractions of charcoal, after gasification kg/kg YT' Mass fractions of tar, after gasification kg/kg YAIR Mass fraction of air kg/kg YSTEAM Mass fraction of steam kg/kg nC , biomass, daf Molar fraction of C in biomass in dry ash free basis kmol/kmol nH , biomass, daf Molar fraction of H in biomass in dry ash free basis kmol/kmol nO, biomass, daf Molar fraction of O in biomass in dry ash free basis kmol/kmol nN , biomass, daf Molar fraction of N in biomass in dry ash free basis kmol/kmol Molar fraction of C in gas, after pyrolysis kmol/kmol nC , G nC , CC Molar fraction of C in charcoal, after pyrolysis kmol/kmol

nO,M,wb n O, G n O, T nO, H2 O nN , G nC , G nC' , CC nC' , T nH,steam nH' , G nH' , T nH' , G nO,steam nO,air nO' , G nO' , T nO' , H 2O nN , air nNg , G T K1 K2 K3

Molar fraction of C in tar, after pyrolysis kmol/kmol Molar fraction of H in gas, after pyrolysis kmol/kmol Molar fraction of H in tar, after pyrolysis kmol/kmol Molar fraction of H in water vapour, after pyrolysis kmol/ kmol Molar fraction of O in moisture content of biomass in dry ash free basis kmol/kmol Molar fraction of fraction of H in gas, after pyrolysis kmol/ kmol Molar fraction of H in tar, after pyrolysis kmol/kmol Molar fraction of H in water vapour, after pyrolysis kmol/ kmol Molar fraction of N in gas, after pyrolysis kmol/kmol Molar fraction of C in gas kmol/kmol Molar fraction of C in charcoal kmol/kmol Molar fraction of C in tar kmol/kmol Molar fraction of H in steam kmol/kmol Molar fraction of H in gas kmol/kmol Molar fraction of H in tar kmol/kmol Molar fraction of H in water vapour kmol/kmol Molar fraction of O in steam kmol/kmol Molar fraction of O in air kmol/kmol Molar fraction of H in gas kmol/kmol Molar fraction of H in tar kmol/kmol Molar fraction of H in water vapour kmol/kmol Molar fraction of N in air kmol/kmol Molar fraction of N in gas kmol/kmol Temperature oC The equilibrium constant for the water–gas shift reaction The equilibrium constant for the methanation reaction The equilibrium constant for the steam reforming reaction

Subscripts and Superscripts i C H O N j CC TAR G CH4 CO CO2 H2 H2O

Chemical elements (C, H, O, N) Carbon Hydrogen Oxygen Nitrogen jth pyrolysis/gasification products (CC, TAR, G, H2, H2O, CO, CO2, CH4) Charcoal, dry ash-free Tar (lumped condensable organic compounds) Total gas obtained in the process of pyrolysis/gasification Methane Carbon monoxide Carbon dioxide Hydrogen Moisture content in gas obtained in the process of pyrolysis/gasification

Abbreviations

LHV LHVCC LHVT LHVG af daf db waf wt RMSE

2

Low heating value MJkg−1 Low heating value of charcoal MJkg−1 Low heating value of tar MJkg−1 Low heating value of gas MJkg−1 Ash free Dry ash free Dry basis Wet ash free |Weight Root mean-square value

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construction of pilot units. Mathematical models, based on theoretical and experimental work and practical operation, aim to analyse the thermochemical processes during the gasification of the biomass and to evaluate the influence of the main input variables on the properties of gas products (i.e., gas composition and heating value) [9]. Different kinds of models have been developed for gasification systems, including computational fluid dynamic (CFD), artificial neural networks (ANN), thermodynamic equilibrium and kinetic models [10]. A detailed review of biomass gasification models was presented by Baruah and Baruah [11], Patra and Sheth [12], Sikarwar et al. [13]), and only a brief description of thermodynamic equilibrium models is therefore given here. The comparison of different mathematical models showed that the thermodynamic equilibrium model (TEM), is the simplest and can be used as an effective preliminary tool to analyse the effect of process parameters and different biomass types on a gasification process. TEM, contrary to kinetic, ANN and CFD models, are characterized by a higher level of flexibility and applicability. Moreover, less computational intensity is required in a TEM [14]. These models calculate the composition at the highest stability of the products of a reaction, a condition defined as thermodynamic equilibrium which is met at the level of the products’ minimum chemical potential [15]. In practice, the lack of ideal conditions along with design restrictions, e.g. retention time, prevent the output products to reach thermodynamic equilibrium [16]. In this regard, these models usually overestimate the yields of H2 and CO, underestimate those of CO2, and predict an outlet stream free from CH4, tars, and charcoal. Nonetheless, thermodynamic equilibrium calculations, which are also independent of gasifier design, may provide useful insights, e.g. the influence of the most important process parameters. Further, the long residence time needed in fixed bed gasifiers suggest that the process propagate at a rather slow rate while the producer gas composition in practise ends up not too far from equilibrium [17]. In the literature, it can be found that various researchers utilized thermodynamic equilibrium models for the simulation of biomass gasification processes. Moreover, a variety of modified models has been proposed to upgrade the thermodynamic equilibrium model and produce a better agreement with experimental data. One of the approaches is the use of a quasi‐equilibrium temperature (QET) assuming that the gasification occurs at a temperature lower than the actual gasifier temperature. This approach was introduced by Gumz [18]. For fluidized bed gasifiers, the average bed temperature can be used as the process temperature, whereas for downdraft gasifiers, the outlet temperature at the throat exit should be used [19]. Li et al. [20]

found that the kinetic carbon conversion for coal gasification in the temperature range 747–877 °C is seen to be comparable to equilibrium predictions for a temperature about 250 °C lower. Kersten [21] showed that for the charcoal gasification reaction with H2, CO2, and H2O for temperatures in the range of 740–910 ˚C, the measured unconverted carbon was similar to equilibrium predictions when evaluated at temperatures in the range of 450–580 ˚C. However, according to Prins et al. [19] such an approach is impractical as the temperature used for calculation appears to be independent from the real process temperature and hardly is predictable without experimental data. Another approach to modify an equilibrium model, consists of multiplying the equilibrium constants by correction factors determined from experimental results. Zainal et al. [22], who cited Vakalis et al. [15] applied the RAND equilibration algorithm with the addition of several calibration factors, i.e. the surface reactivity of the charcoal, the production of CH4 and assumed a linear correlation between moisture and H2 production. This model was used for prediction of the producer gas compositions after downdraft gasification. Jarungthammachote and Dutta [23] developed the modified thermodynamic equilibrium model based on the equilibrium constant for predicting the composition of a producer gas in a downdraft gasifier. They used coefficients for correcting the equilibrium constant of the water–gas shift reaction and the methane reaction in order to improve the model. Those coefficients were obtained from the comparison between the model and the results of other researchers’ experiments (Zainal et al. [22], Altafini et al. [24], Jayah et al. [25]). In Vaezi et al. [38], in order to improve the model accuracy for producer gas composition prediction, the equilibrium constants are multiplied by certain factors obtained from a comparison between calculated results and those of the experiments performed by Ashizawa et al. [26]. Li et al. [27] proposed a phenomenological model adapted from the pure equilibrium model, incorporating experimental results regarding unconverted carbon and methane to account for nonequilibrium factors. Huang and Ramaswamy [28], developed two downdraft gasification models, with and without considering charcoal. The equilibrium models were modified by using coefficients for correcting the equilibrium constant of the water–gas shift reaction and the methane reaction. Those coefficients were obtained from the comparison between the model and the results of Altafini et al. [24], Francisco et al. [29] and Jayah et al. [25]. The product gas compositions predicted by the first model were in good agreement with experimental values. However, the results of the second model considering charcoal, are far from the experimental data, and specifically, the predicted value of CO is much higher than in experimental data, while the value of CO2

Fig. 1. Overall mass balance for the biomass gasification process. 3

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is much lower. Costa et al. [30] developed a model that considers tar and charcoal content in biomass gasifiers. The accuracy of the model results is improved by proper calibration, namely by modifying the equilibrium constants for the water–gas shift, methane and Boudouard reactions, through correction factors that represent the degree of approach of the analysed system towards equilibrium. To this aim, the developed model is coupled with a genetic algorithm, to search for the optimal correction factors able to minimize the error between the computed and the experimentally measured product yields and temperatures [30]. Correcting the rate of reactions has been a common and partially successful technique. However, a large amount of experimental data is required for the development of calibration factors [15]. Most of gasification models consider the thermal decomposition process as a black box. Based on a set of input parameters the output results, e.g. gas composition, is generated without consideration of detailed decomposition and reaction mechanisms. The operation of a biomass gasifier depends on several complex chemical reactions, including several steps like: drying, pyrolysis, thermal cracking of tars to gas and secondary charcoal, gasification of charcoal, and partial oxidation of combustible gas, tars and charcoal. In this regard, in this paper, the gasification model is no longer considered as a black box, it involves main gasification sub-processes (drying, pyrolysis, gasification) and their products. The developed model, based on thermodynamic equilibrium calculations, has been validated with experimental published data of other authors, and provides the opportunity to evaluate downdraft gasification processes, in fixed or slowly moving bed gasifiers, as well as effects of variations in biomass properties and operating conditions.

presented in Supplementary Material. 6) Gas products consists of CO2, CO, H2, CH4, N2, and H2O. 7) Setting the amount of produced CH4 = 2 vol% as an initial guess, needed for the iterative solution process. 8) No heat losses are considered from the gasifier, i.e. adiabatic condition. 9) The air for the gasification process is considered as dry air, containing only 21 vol% O2 and 79 vol% N2 (the traces of water vapour, CO2, Ar, and various other components are not considered). 10) Biomass is assumed to enter the gasification process at 25 °C and 1 atm. The objectives of this model are: 1) To define and predict characteristics of the downdraft biomass gasification process. 2) To predict the yields of gas, charcoal, and tar produced during gasification. 3) To predict the composition of the gas covering conditions typically found in gasification, including pyrolysis (350–950 °C). 4) To evaluate the influence of main input variables, such as moisture content and air/fuel ratio, temperature of the process, gasifying medium, etc. Regarding this, an empirical predictive model is developed to describe the general trends of product distribution as a function of temperature, which is based on balance of elements, energy balance and empirical relationships.

2. Methods

2.1. The overall mass balances

As already mentioned, a real gasification system differs from an ideal reactor at chemical equilibrium. For this reason, the pure thermodynamic equilibrium model, described elsewhere (e.g. Zainal et al. [22] and Melgar et al. [31]), has been modified to increase the results’ accuracy. The gasification model consists of a series of sub-processes, each containing one process (biomass drying, pyrolysis, gasification, air preheating, and steam generation), see Fig. 1. The following implementations or assumptions were made:

The overall mass balance for the biomass gasification process is outline in Fig. 1. Gasification sub-processes can be described with the equations: Drying: i

Yi, biomass, waf + YA

drying

i

Yi, biomass, daf + YM , wb + YA

(1)

where i Yi, biomass, waf is the mass fraction of the ith element (carbon, hydrogen, oxygen, nitrogen) in wet biomass on an ash free basis, YA is the ash content in biomass on dry basis, i Yi, biomass, daf is the mass fraction of the ith element (carbon, hydrogen, oxygen, nitrogen) in dry biomass on an ash free basis, YM , wb is the moisture content of biomass in dry ash free basis. Pyrolysis [34]:

1) Adding a drying unit, that predicts the removal of moisture from raw biomass. The percentage of removed moisture can alternatively be set by the user. 2) Adding a pyrolysis unit that, using empirical correlations, predicts the formation of pyrolysis products (charcoal and volatiles, including tar). 3) The tar and charcoal were considered as products of the gasification process. The maximum tar content was limited to 6 g/Nm3 (the concentration of tar, for downdraft gasifiers, ranges from 0.01 g/ Nm3 to 6 g/Nm3 [32]). 4) Particles leaving the gasifier are set by the user as mg/Nm3 in the producer gas. These particles are considered to consist only of carbon. 5) Biomass-bound nitrogen, is during the gasification process converted into diatomic nitrogen (N2). Nitrogen generally comprises a relatively small portion of the overall makeup of the biomass (typically 0.05 to 2 wt% for most biomass) [33]. The major gas-phase nitrogenous species, generated by biomass gasification, mainly include diatomic nitrogen (N2), ammonia (NH3), and hydrogen cyanide (HCN). According to experimental results found in literature, except in extreme circumstances, NH3 and HCN concentrations in the producer gas can be expected to fall below 1% [33]. With the exception of N2, it is normally not necessary to measure/ model NH3, HCN in order to complete a full mass balance, as their mass fraction is usually smaller than measurement/modelling errors [33]. A further detailed explanation of this assumption is

i

Yi, biomass, daf + YM , wb + YA

pyrolysis

j

Y jp, products, daf + YHp 2 O + YA

(2)

where j Y jp, products, daf is the mass fraction of jth pyrolysis product (charcoal, tar and gas) on a dry ash free basis, YHp 2 O is the moisture content in gas obtained in the process of pyrolysis. Gasification: j

Y jp, products, daf + YHp 2 O + YA + YAIR + YSTEAM

gasification

j

Y jg, products, daf + Y gH2 O + YA

(3)

where YAIR is the mass fraction of air, YSTEAM is the mass fraction of steam, j Y jg, products, daf is the mass fraction of jth gasification product g (charcoal, tar and gas) on a dry ash free basis, Y H is the moisture 2O content in gas obtained in the process of gasification. Independent solution of each zone of the model is coupled with each other to yield an overall solution of the model. In other words, output of the drying zone becomes input to the pyrolysis zone; output of the pyrolysis zone becomes input to the gasification zone. 4

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Fig. 2. Flow diagram of the model for the downdraft bed gasifier.

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2.2. Pyrolysis

According to Basu [10], the water–gas reaction is dominant when steam is the gasifying medium. In the absence of steam, when air or oxygen is the gasifying medium, the Boudouard reaction is dominant [10]. Another important gasification reaction is the water–gas shift and steam reforming reaction, which takes place in the gas phase. The water–gas and Boudouard reactions are coupled by the water–gas shift reaction, and therefore, only two of these reactions can be considered to be truly independent [38]. The equilibrium constant for the water–gas shift reaction is:

The use of consistent data from pyrolysis, valid over wide temperature ranges and for different materials, is particularly important in a gasification process because, contrary to coal gasification where the devolatilization stage accounts only for 20–40% of the total mass conversion, in biomass gasification this contribution increases up to 60–80% [35]. On the other hand, the pyrolysis characteristics influence the predictions of both the producer gas quality and activity of gasification reactions, through hydrogen, carbon dioxide, and steam concentrations [35–37]. In this regard, the main objective of this modelling unit is to determine the yields of charcoal, tar and volatiles produced during pyrolysis and to determine the composition of the light gas. The pyrolysis process is represented by Eq. (2). The pyrolysis products and gas composition can be represented by Eqs. (4) and (5) [34]: j

YYpj, products,daf = YCC + YT + YG

YG = YCO2 + YCO + YCH 4 + YH 2

K1 =

YT =

1.38T 2

(4) (5)

K1 = e { (

10

YG = 1.12T 2 10

0.125 T + 68.87

(6)

+ 0.12 T + 12.64

(7)

0.058 T + 30.77

(8)

5 4

4

2.65T 2 10

4

YCO2 =

2.85T 2 10

5

+ 0.27 T

K2 =

0.029 T + 70.89

YCH 4 = 6.69T 2 10

5

YH2 = 7T 2 10

0.0371T + 5.1117

5

0.037 T + 4.28

=

K3 =

YG = Y

CO2

+Y

CO

+Y

+Y CH 4

T

+Y

+Y

H2

N2

7082.842 7.467 6.567lnT + T10 T 2 0.702 10 6 + + 32.541 2T2

3

2.167 2 T 6 (18)

nCO nH2 3 nCH4 nH2O

(19)

(11)

K3 = 1.198(1013)e

26830 T

(20)

In addition to these correlations, the energy balance, and mass and molar balances for each element (C, H, O, and N) are set and used to calculate the gasification products. Detailed energy, mass and molar balances for each element considered in gasification process are presented in Supplementary Material in Section S2. An initial gasification temperature is assumed in the iterative solution procedure. 2.4. Solution algorithm Fig. 2 shows a schematic diagram of the simulation. One iteration is concerned with yield of CH4 in the producer gas after gasification. Input parameters that consist of air/steam flow rate, biomass flow rate, drying temperature, pyrolysis temperature, air temperature, gasification temperature, ultimate and proximate analysis, charcoal, tar and particle yield after gasification were provided to start simulations. Yield of CH4 was given as an initial guess. After solving Eqs. (1)–(20), the new CH4 was obtained and compared with the initial guess. Once the CH4 iteration finished, the gas yield, composition of gas etc. is determined.

(13)

+Y

(17)

The equilibrium constant K3 is evaluated from the relation proposed by Bottino et al. [40]:

(12)

G

nCH4 nCn2H2

(10)

The gasification process is represented by Eq. (3). The gasification products and gas composition can be represented by Eqs. (13) and (14): CC

(16)

The equilibrium constant for the methane - steam reforming reaction are as follows:

2.3. Gasification

YYgj, products,daf = Y

}

3.961

lnK2

In addition to these correlations, the energy balance, and mass and molar balances for each element are set and used to calculate pyrolysis products. The elements considered are C, H, O, and N. S is neglected due to its small amount. Other elements are lumped as ash. Detailed energy, mass and molar balances for each element considered in pyrolysis process are presented in Supplementary Material in Section S2.

j

)

where nCandnCH 4 represent the corresponding mole fraction of the individual species carbon and methane. The equilibrium constant K2 is evaluated from the relation proposed by Zainal et al. [22]:

(9)

32.71

4276 T

The equilibrium constant for the methanation reaction is:

Dependence of gas yield on pyrolysis temperature is described by:

YCO =

(15)

where nCO2, nCO,nH 2 and nH2O represent the corresponding mole fraction of the individual species carbon dioxide, carbon monoxide, hydrogen and water vapour. The equilibrium constant K1 is evaluated from the following relation published by Pedroso et al. [39]:

were YCC , YT , YG are the mass fraction of pyrolysis products (charcoal, tar and gas) on dry ash free basis, YCO2, YCO, YCH 4, YH 2 are the mass fraction of different gases (CO2, CO, CH4 and H2) on dry basis. The assumption of this model is that, due to lower pyrolysis temperature, biomass-N is mainly converted into the tar-N part and the charcoal-N. For prediction of pyrolysis products, empirical relationships between the product yield and pyrolysis temperature are used (Eqs. (6)–(12)). The determination of empirical relationships between the product yields and pyrolysis temperature are explained in detail by Trninic et al. [34]. Temperature dependent charcoal, tar and gas yields are given by [34].

Ycc = 7.97T 2 10

nCO2 nH2 . nCO nH2O

(14)

where Y CC , Y T , Y G are the mass fraction of charcoal, tar and gas on a dry ash free basis, while Y CO2 , Y CO , Y CH 4 , Y H 2 , Y N 2 are the mass fraction of the different gases (CO2, CO, CH4, H2 and N2) on dry basis produced after the gasification process. The three most common gas–solid reactions that occur in the gasification zone considered in modelling are: the water–gas or steam reaction, the Boudouard reaction and the methanation reaction.

3. Results and discussion 3.1. Validation of the model The “Engineering Equation Solver (EES)” has been found to be very 6

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suitable for modelling this kind of system, because it contains all of the necessary thermodynamic functions and it is possible for the model builder to make a user interface, which can make the model userfriendly [3]. Model operating parameters (biomass characteristics - proximate analyses and the elemental compositions of biomass), drying temperature, percentage of removed moisture, pyrolysis temperature, air inlet temperature, steam inlet temperature, gasification temperature and percentage of charcoal, tar and particles leaving the gasifier can be directly introduced by the user. The results obtained with the model are validated with those obtained experimentally from different kinds of biomass, as reported in literature. The ultimate and proximate analysis of the biomass given in Table 1 were used in the model. Predicted results from the present modified equilibrium model are presented in Table 2. In general, the present model gives smaller volume fractions of CH4 and slightly higher volume fractions of H2 than the experimental results given by da Silva [41], Masmoudi et al. [42], Arun et al. [43], Altafini et al. [24] and Yucel et al. [44]. Amount of CH4 was set by the user (initial guess value was 2 vol%). Further, the present model gives higher volume fractions of CH4 and slightly lower volume fractions of H2 than the experimental results given by Barrio [45]. Knowing that with increasing CH4 the amount of H2 decreases (Equations (17) and (19)), the results obtained from the model are in good agreement. Also, model results are in good agreement with experimental data given by Perez et al. [46] and Jayah et al. [25]. Although the experimental results from the related literature are given as a range for a considered gas composition, the already observed trends with model results could be confirmed. Further comparison with previously published models and experimental results used for their validation is presented in Fig. 3. It can be observed that the model gives results with higher accuracy than the models developed by Giltrap et al. [47], Perez et al. [46], Arun et al. [43] and Altafini et al. [24]. The model is further validated by comparison of results given by variation of different parameters with the experimental results from Plis and Wilk [48], Mathieu and Dubuisson [49], and Baratieri et al. [17], explained in paragraph Sensitivity Analysis.

3.2.1. Effect of equivalence ratio (λ) and gasification temperature on producer gas composition Variation in the amount of air used have a significant effect on the composition and quality of the gas produced [51]. Bilbao and GarciaBacaicoa [52] concluded that air flow rate was one of the most important variables influencing gasifier performance as it determined biomass consumption and product distribution. The quantity of air entering the gasifier can be expressed as a factor of stoichiometric air requirement, which is the mass of dry air required to burn a unit mass of dry fuel. The equivalence ratio (λ) is the ratio of actual air–fuel ratio to the stoichiometric air–fuel ratio. The theoretical gasification occurs for λ values of 0.19–0.43 [53]. The theoretical optimum λ for gasification is near 0.25 [54]. Below 0.25, charcoal remains with corresponding energy losses, and at higher λ, some gas is burned and the temperature inside the gasifier increases [54]. In other words, below 0.25 pyrolysis takes place, while if the λ is increased too much the process becomes a partial combustion [55]. In an adiabatic gasifier the gasification temperature depends on the amount of air fed to the gasifier (Fig. 4) and as a result, varying λ or gasification temperature will have the same effect on producer gas composition, heating value, and gasification efficiency. For this reason, only gasification temperature is plotted against producer gas composition and LHV. The variation of the composition of producer gas and the heating value as a function of the gasification temperature in an adiabatic gasifier using corn cob with a moisture content of 5% is presented in Fig. 5. These results were compared with the ones published by Plis and Wilk [48], Mathieu and Dubuisson [49] and Puig et al. [3]. The three models and the presented model give the same qualitative as well as similar quantitative predictions. The gas heating value decreased when the λ increased since the resulting proportion of non-combustible components (N2 and CO2) in the product gas increased while that of combustible components (CO, H2) decreased. A similar behaviour was observed by Plis and Wilk [48] and Puig et al. [3]. The percentage of CH4 remains very low and decreases when the λ increases (the same behaviour was observed by Plis and Wilk [48], Mathieu and Dubuisson [49] and Puig et al. [3]). The H2 percentage decreases from 22 to 12% when the equivalence ratio increases from 0.25 to 0.35; the same behaviour was observed by Mathieu and Dubuisson [49], with a decrease for the same range of λ values from 22 to 5%. The CO2 percentage increases slightly from 8.6 to 9% (from 8.5 to 11.3% for Plis and Wilk [48] and from 9 to 11% for Mathieu and Dubuisson [49]) and the CO percentage decreases from 23 to 20.5% in this model (from 25.9 to 16.6% for Plis and Wilk [48], from 27 to 15% for Mathieu and Dubuisson [49] and from 22.5 to 18% for Puig et al. [3]).

3.2. Sensitivity analysis It is of great interest to have a model sensitive enough to predict the effect of the operational variables on the producer gas quality. For this reason, the developed model has been used to study the influence of different parameters on property of gas product: equivalence ratio (λ), air-preheating, steam injection, oxygen enrichment. Since the influence of different biomass types is taken into account through proximate and ultimate compositions, the influence of moisture content in biomass is also taken into consideration. The results from sensitivity analysis are compared to those reported by other authors. In this way, the model is further validated.

3.2.2. Effect of air preheating on producer gas composition Air preheating is an efficient way to increase general conversion efficiency of the gasification process. The sensible heat in the air causes a rise in the gasification temperature, which in turn influences the product gas composition, causing an increase in the production of the

Table 1 Ultimate and proximate analysis of feedstock (db). C

H

Biomass wt% wt% corn cob 45.80 5.88 corn cob 44.70 6.30 pine bark 55.49 5.56 rubber wood 50.60 6.50 1 almond shell 45.64 6.19 wood pellets2 50.67 6.18 sawdust 52.00 6.07 wood pellets 50.70 6.90 wood chips 48.56 5.78 1 – with MC of 7 wt%; 2 – with MC of 4.18 wt%

O

N

S

FC

VM

Ash

HHV

Ref.

wt% 46.45 45.20 37.74 42.00 45.43 40.97 41.55 42.20 44.25

wt% 1.20 1.20 0.17 0.20 0.50 2.00 0.25 0.30 0.30

wt% 0.10 0.09 0.09 0.00 0.05 0.18 0 0 –

wt% 18.10 16.60 28.87 19.20 19.06 – – – –

wt% 80.30 66.30 70.18 80.10 78.23 – – – –

wt% 1.60 14.70 0.95 0.70 2.71 1.00 0.10 0.39 1.10

MJ/kg 19.25 15.45 19.99 19.60 – – 20.41 18.86 19.70

da Silva [41] Arun et al. [43] Perez et al. [46] Jayah et al. [25] Masmoudi et al. [42] Yucel et al. [44] Altafini et al. [44] Barrio [45] Chee [50]

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Table 2 Comparison of gas composition given by the downdraft gasification model and results from literature review.

o

Tgasification, C λ Air/Biomass biomass CO, % vol CO2, % vol H2, % vol CH4,% vol N2,% vol Unit

Model

da Silva [41]

930 0.25 1.54 corn cob 21.77 10.73 16.10 1.89 49.41

930 0.25 –

Model o

Tgasification C λ Air/Biomass biomass CO, % vol CO2, % vol H2, % vol CH4,% vol N2,% vol

19.00 10.30 15.90 3.00 49.51

δx (%)

Model

Arun et al. [43] 750 0.30 –

14.60 4.20 1.30 37.00 0.20

750 0.30 1.18 corn cob 17.51 14.9 17.71 1.90 47.86

Masmoudi et al. [42]

1078 1078 0.37 0.37 2.01 2.06 almond shell 16.74 14.48 11.86 11.50 12.99 8.58 1.98 3.85 56.42 60.94

15.62 14.37 15.62 1.95 52.42

δx (%)

Model

15.6 3.1 51.4 48.6 7.4

827 827 0.30 – 1.90 1.2 wood pellets 23.92 26.07 9.021 10.40 16.85 14.71 1.96 3.42 48.24 45.4

δx (%)

Model

Perez et al. [46] 813 – 3.07

12.10 3.80 13.34 4.60 8.70

813 0.33 2.24 pine bark 22.06 10.26 13.79 1.97 51.89

Yucel et al. [44]

16.17–26.98 6.92–15.28 12.76–20.75 1.16–3.70 43.86–51.80

δx (%)

Model

Barrio [45]

8.25 13.26 14.55 42.69 6.26

986 986 0.30 0.30 2.16 wood pellets 22.56 24.7 8.81 9.70 15.02 16.1 1.98 1.60 51.63 47.9

δx (%)

Model

Jayah et al. [25]

2.22 8.19 21.46 23.35 7.82

800 800 0.30 – 3.0 – rubber wood 21.55 19.10–22.10 8.65 8.50–10.80 18.54 12.50–18.30 1.82 1.10–1.40 49.44 50.70–59.10

δx (%)

Model

Altafini et al. [44] 800 – 1.82

8.66 9.18 6.71 23.75 7.79

800 0.30 1.95 sawdust 19.57 12.4 19.23 1.79 47.01

20.14 12.06 14.00 2.31 50.79

δx (%)

9.05 24.86 11.54 23.08 4.98 δx (%)

2.83 2.81 37.35 22.51 7.44

Note, λ can be set in the model; Air/Biomass is calculated by the model

combustible gases H2 and CO [3]. Air preheating offers an alternative and more economical approach than oxygen blown systems [3]. The overall efficiency of the process on a thermal basis would be increased if the heat required for air preheating is recovered from the gas cooling section of the plant. Sugiyama et al. [56] suggested that the use of high temperature air as an oxidant achieves downsizing of the plant since a smaller volume of air is needed to bring the gasifier to the required operating temperature; which in turn reduces the size of the reactor and gas clean-up system needed [3]. The influence of air preheating on the gasification is presented in Fig. 6, for gasification of corn cob with moisture content of 5% and λ = 0.3. The gasification temperature increases almost linearly with the air temperature (Fig. 7). The rising temperature promotes the products of endothermic reactions and simultaneously the reactants in exothermic reactions [3]. Another important consideration is that the air temperature has evident influence on the composition of the producer gas. With air temperature increase, contents of CO and H2 increase while CO2 decreases (the same as in Puig et al. [3]). Heating value of producer gas increases due to increase of the fraction of combustible gas in the product gas

producer gas and decreases the CO content, while CO2 remains more or less constant. The producer gas LHV decreased slightly from 5.55 to 5.40 MJ/ Nm3. The steam injection causes a rise in H2O content, which results in a lower producer gas LHV. Also, as in the case of Doherty et al. [58], CO and CH4 are shifted and reformed respectively with the additional H2O, decreasing their contents and producing CO2. The important effect of steam injection is the rise in H2 content, in this case H2 increases by 2% (from 16.86 to18.87%) over the range of steam injection. However, according to Doherty et al. [58] increasing steam injection decreases the gasifier temperature because of highly endothermic reforming and water − gas reactions, unless heat is supplied from an external source [58]. A decrease in the temperature is undesirable because this would degrade the performance of the gasifier and could lead to a high tar yield. For this reason, air preheating should be taken into account when using high moisture fuels and/or steam injection [58]. 3.2.5. Effect of biomass moisture content on producer gas composition The effect of initial moisture content of the corn cob on the producer gas composition at 800 °C and λ of 0.31 is presented in Fig. 11. The percentage of CO2 increases with the moisture content, while CO decreases. A similar trend is also observed for the H2 in the producer gas, which increases continuously with the moisture content. The heating value of the producer gas decreases, due to the additional air flow required when increasing the moisture content in order to generate the heat required to keep the desired temperature. The same tendencies were observed by Puig et al. [3] and Plis and Wilk [48]. Also, Walawender et al. [59] reported a linear decrease in dry feed rate, product gas heating value, gas-to-feed ratio, air-to-feed ratio, and cold gas efficiency, with increasing wood chip moisture content in a downdraft gasifier. In the literature, for gasification it is recommended to dry the biomass if the moisture content in the biomass exceeds 15–20 wt% [60].

3.2.3. Effect of oxygen enrichment on producer gas composition Fig. 8 shows the variation of the producer gas composition with changes of oxygen fraction in the air for corn cob gasification. The N2 yield decreases with increasing oxygen fraction in the air as expected. The percentage of H2 in the producer gas increases continuously with the increase in oxygen fraction, from 19.32 to 22.77%, for an increase in the oxygen fraction from 25 to 35%. The modelling results agree with results reported by Puig et al. [3]. A similar trend is also observed for CO, while CO2 remains more or less constant (around 10%). Similar results were obtained by Puig et al. [3] and Babu and Sheth [57]. Fig. 9 shows the change in the volume of air required for the gasification according to the volume of oxygen. Increasing the oxygen content enhances the conversion of carbon in biomass, increase the heating value, and hence the efficiency of gasification.

4. Conclusion

3.2.4. Effect of steam injection on producer gas composition The influence of steam injection on the gasifier performance was analysed (Fig. 10) for λ of 0.3 and compared to the results presented by Doherty et al. [58] and Puig et al. [3]. The steam injection rate was varied from 0 to 30 kg/h. Steam injection in biomass gasification increases the H2 content of

A gasification model is established containing a series of submodules that models individual sub-steps during gasification of biomass (drying, pyrolysis and gasification). The developed model is based on thermodynamic equilibrium calculations and includes modifications to be more adapted to a real process, in which only thermodynamic 8

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Fig. 3. Predicted results from the model compared with different models and experimental results: a) da Silva [41], b) Arun et al [43], c) Perez et al. [46], d) Jayah et al. [25], e) Masmoudi et al. [42], f) Yucel et al. [44], g) Barrio [45], h) Altafini et al. [24], i) Giltrap [47] and Chee [50].

equilibrium is partially achieved. The model predictions were significantly closer to the experimental results than the commonly applied conventional single stage equilibrium models. The developed model is able to predict phenomena in a wide range

of experimental conditions (air and steam gasification, temperature, equivalence ratio etc.) and for different types of biomass material with a defined ultimate composition and moisture content. The model can be used to predict the final producer gas composition and its heating value. The proposed model is a useful tool for preliminary calculations, 9

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Fig. 4. Influence of the gasification temperature on λ at adiabatic conditions.

Fig. 8. Influence of oxygen amount on gas composition and LHV, for gasification of corn cob with moisture content of 5% and at λ = 0.3.

Fig. 5. Influence of equivalence ratio (λ) on gas composition and LHV, for corn cob gasification with a moisture content of 5%.

Fig. 9. Influence of oxygen amount on air consumption.

Fig. 10. Influence of steam injection on producer gas composition and LHV.

Fig. 6. Influence of inlet air temperature on gas composition and LHV, for gasification of corn cob with moisture content of 5% and at λ = 0.3.

Fig. 7. Effect of air preheating on the gasification temperature for corn cob gasification with a moisture content of 5% and at λ = 0.3.

Fig. 11. Influence of biomass moisture content on gas composition and LHV.

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design, and operation of biomass gasifiers. Also, this model can be used as input to a combustion model or as an input model for a whole biomass cogeneration plant.

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CRediT authorship contribution statement Marta Trninić: Writing - original draft, Conceptualization, Methodology, Investigation, Resources, Data curation, Visualization, Writing review & editing. Dragoslava Stojiljković: Conceptualization, Methodology, Writing - review & editing, Supervision. Nebojsa Manić: Conceptualization, Methodology, Writing - review & editing, Software, Visualization, Supervision. Øyvind Skreiberg: Writing - original draft, Conceptualization, Methodology, Writing - review & editing. Liang Wang: Writing - original draft, Methodology. Aleksandar Jovović: Conceptualization, Methodology, Writing - review & editing, Supervision. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements The authors are very grateful to the Norwegian University of Science and Technology, Trondheim, especially to Professor Dr Vojislav Novakovic and Dr Morten Grønli, for the opportunity to make experimental researches (biomass characterizations, biomass pyrolysis, etc.). The authors would like to express upmost gratitude to early deceased Dr Jerko Labus (Instituto Nacional de Eficiencia Energética y Energías Renovables – INER), who was the initiator of this research work. In the same vein, we owe a special debt debts of gratitude to the late Professor Dr Michael J. Antal, Jr. He was a world leading person in biomass research, a great colleague and sincere friend through decades of many of us. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.fuel.2019.116867. References [1] Bridgwater A. Thermal biomass conversion and utilization Biomass information system. Brussels, Luxembourg: Office for Official Publications of the European Communities; 1996. [2] Sürmen Y. The necessity of biomass energy for the Turkish economy. Energy Sources 2003;25(2):83–92. [3] Puig-Arnavat M, Bruno JC, Coronas A. Modified thermodynamic equilibrium model for biomass gasification: a study of the influence of operating conditions. Energy Fuels 2012;26(2):1385–94. [4] Arnavat MP. Performance modelling and validation of biomass gasifiers for trigeneration plants. Doctoral Thesis. Tarragona: Universitat Rovira i Virgili; 2011:227. [5] Chanphavong L, Zainal ZA. Characterization and challenge of development of producer gas fuel combustor: a review. J Energy Inst 2019;92(5):1577–90. [6] Gautam G. Parametric study of a commercial-scale biomass Downdraft Gasifier: Experiments and Equilibrium Modeling. Master Thesis. Alabama: Auburn University; 2010. [7] Higman Chris, van der Burgt Maarten. Chapter 5 – Gasification processes. In: Higman C, Burgt Mvd, editors. Gasification. Burlington: Gulf Professional Publishing; 2003, p. 85–170. [8] Sharma AK. Modeling and simulation of a downdraft biomass gasifier 1. Model development and validation. Energ Convers Manage 2011;52(2). [9] Puig-Arnavat M, Hernández JA, Bruno JC, Coronas A. Artificial neural network models for biomass gasification in fluidized bed gasifiers. Biomass Bioenergy 2013;49:279–89. [10] Basu P. Biomass gasification and pyrolysis: practical design and theory. Oxford, UK: Elsevier Inc.; 2010. [11] Baruah D, Baruah DC. Modeling of biomass gasification: a review. Renew Sustain

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