Biomass gasification models for downdraft gasifier: A state-of-the-art review

The gasifier reactor needs to be designed either based on experi-mental data on similar fuel fed into a gasifier of similar size or by using mathematical models of the gasification process

Biomass gasi fication models for downdraft gasifier:

A state-of-the-art review

Chemical Engineering Department, Birla Institute of Technology and Science – Pilani, Pilani Campus, Pilani 333031, Rajasthan, India

a r t i c l e i n f o

Article history:

Received 9 February 2015

Accepted 6 May 2015

Keywords:

Biomass

Modeling and simulation

Gasification

Downdraft gasifier

Equilibrium model

Transport and kinetic model

a b s t r a c t

Among the different methods of energy production from biomass, gasification is considered as the most suitable option as it is a simple and economically viable process to produce thermal energy or decentralized electricity generation Downdraft gasifiers are typically small-scale units having maximum power production capacity up to 5 MW This feature makes it more suitable for decentralized power generation and distribution to the remote villages/islands deprived of grid electricity Mathematical models can be helpful for the design of gasifiers, prediction of operational behavior, emissions during normal conditions, startup, shutdown, change of fuel, change of loading, and to alleviate the type of problems mentioned above It has been observed that although many researchers have developed models of various types and degrees of complexity, reviews of these modeling and simulation studies are scarce Largely, it is observed that the review articles reported in the literature fail to address the basic understanding of each model types and their applicability to design different gasifiers for a certain feedstock and variation of operating parameters This review article discusses different models available for downdraft gasifiers such as thermodynamic equilibrium, kinetic, CFD, ANN and ASPEN Plus models A comparative analysis of each model and its output

is carried out A critical analysis of the effect of different modeling parameters andfinally the advantages and disadvantages of each modeling technique is outlined

& 2015 Elsevier Ltd All rights reserved

Contents

1 Introduction 583

2 Gasification process and gasifier types 585

2.1 Gasification process 585

2.2 Types of gasifiers 585

2.2.1 Fixed-bed gasification 585

2.2.2 Fluidized-bed gasification 586

2.2.3 Advantages/disadvantages of different gasifying reactors 586

3 Biomass gasification models 586

3.1 Equilibrium models 586

3.2 Combined transport and kinetic modeling 588

3.3 CFD models 590

3.4 ANNs model 591

3.5 ASPEN Plus models 591

4 Conclusions 592

Acknowledgment 592

References 592

1 Introduction India has massive energy needs and difficulty to meet those needs through conventional power generation technologies is increasing day

Contents lists available atScienceDirect

journal homepage:www.elsevier.com/locate/rser

Renewable and Sustainable Energy Reviews

http://dx.doi.org/10.1016/j.rser.2015.05.012

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n Corresponding author Tel.: þ91 1596 515636 (office); þ91 9799212070

(mobile); fax: þ91 1596 244183.

E-mail address: pratik@pilani.bits-pilani.ac.in (P.N Sheth).

by day The Central Electricity Authority (CEA), Government of India,

anticipated a base load energy deficit of 5.1% for the fiscal year 2014–

15 in their Load Generation Balance report[1] Based on the progress

report on village electrification by CEA, 25894 villages are not

elec-trified[2] Apart from developing domestic energy sources to satisfy

the growing demand, increasing amounts of fossil fuels are imported

that is exacerbating the trade deficit and can be harmful to the

environment as well Coal imports hit a record high during the last

fiscal year and will likely rise further over the next five years since

India aims to expand its power-generation capacity by 44% (Ministry

of coal) According to data reported by Ministry of Coal, Government of

India, the total import of coal and products, i.e coke, for the year 2013–

14 is 154.55 million tones[3] There is a rise of 6.4% in the coal import

from the previous year It is observed that there is a hike in power

tariff rates continually in both categories: domestic and non-domestic

Decentralized electricity generation is also rapidly growing by taking

advantage of abundantly available renewable energy sources like solar,

wind, hydropower, biomass, biogas, geothermal and hydrogen energy,

and fuel cells Power generation from renewable sources is on the rise

in India, with the share of renewable energy in the country's total

energy mix rising from 7.8% in 2008 to 12.3% in 2013 Wind accounts

for 68% of the capacity, with 19.1 GW of installed capacity, making

India the world's fifth largest wind energy producer As shown in

Table 1, small hydropower (3.6 GW), bio-energy (3.6 GW) and solar

energy (1.7 GW) constitute the remaining capacity

For the regions deprived of grid electricity supply, remote villages

in states such as Assam, Odisha, Meghalaya, Andaman and Nicobar

Islands, and Arunachal Pradesh, there is an urgent need to utilize and

promote renewable energy sources in order to make them

indepen-dent of grid supply Until these remote villages are connected to the

national grid, projects based on solar energy, biomass gasifiers and

small hydropower plants are suitable options The Government of

India provides substantialfinancial assistance for decentralized

elec-tricity generation from renewable sources such as biomass gasi

fica-tion, solar, wind and small hydropower projects

However, biomass gasification is the most preferable alternative

in India for various reasons: (1) availability and uniform distribution

of biomass in the country, (2) it is available throughout the year at

cheap rates, (3) capital investments for gasifier, duel fuel or 100%

producer gas generator, gas cleaning system and other accessories

are quite low, and (4) technology is simple and

unskilled/semi-skilled labor can handle operation and maintenance of the plant[4]

Today, biomass gasification is able to provide a solution to mitigate

environmental pollution as well as heath issues arising due to the

inefficient cooking method adopted by the rural people in India It

also fulfils the power requirements of the remote areas by providing

them an affordable and sustainable source of energy from biomass

It is a carbon-neutral process that reduces global warming and

climate change effects as well

Gasification is a process that converts solid or liquid

hydro-carbon into synthesis gases It proved to be a successful option for

the waste management, chemical production, and energy

produc-tion from non-convenproduc-tional feeds like forest waste, agricultural

waste, poultry waste, municipal refuge and sewage Gasification

adds value to low- or negative-value feedstocks by converting them into marketable fuels and products This conversion process

is considerably more complex than combustion, and is influenced

by a number of factors, including amount of oxidant, feedstock composition, gasifier temperature, reactor geometry and mode of gas–solid contact Thus, the size of a gasifier could not be based on criteria like volumetric energy release rates as it is done at times for combustors[5] Various types of gasification systems have been developed and some of them are commercialized Fixed-bed gasification is the most common technology for the energy use

of biomass and solid municipal wastes

The gasifier reactor needs to be designed either based on experi-mental data on similar fuel fed into a gasifier of similar size or by using mathematical models of the gasification process in the reactor The first approach, though most reliable, is not always practical, leaving modeling as the next best option Besides sizing of the reactor, modeling is also very effective in optimizing the operation of an existing gasifier, and in exploring operational limits A good model could help in identifying the sensitivity of the gasifier performance, to the variation in different operating and design parameters[6] Models can be helpful for the design of gasifiers, prediction of operational behavior, emissions during normal conditions, startup, shutdown, change of fuel, change of loading, and to alleviate the type of problems mentioned above The modeling may be under-taken with different aims: thefield of interest ranges from pre-liminary design of an industrial process to complex simulation of a unit Experiments, especially at a large scale, are often expensive and complicated; modeling can save time and money, and it can support preparation and optimization of experiments to be under-taken in a real system

Mathematical models and simulations are being practiced excee-dingly in thefield of research and development work Simulations provide a less-expensive means of evaluating the benefits and associated risk with appliedfield Gasification is a complex mechan-ism, which incorporates thermochemical conversion of carbon-based feedstock Therefore, simulation of gasification provides a better comprehension of physical and chemical mechanisms inside the gasifier than general conjecture and assists in optimizing the yield Considerable research has been done in modeling the different types

of gasifiers

Current interest in using fixed bed as an attractive means especially for gasification of biomass, underlines a need for summarization of the work done in modeling offixed-bed gasi-fiers It has been observed that although many researchers have developed the models of various types and degrees of complexity, reviews of these modeling and simulation studies are scarce Puig

et al.[7] reviewed briefly different biomass gasification models including thefixed and fluidized bed The article covers the review

of few research articles pertaining to mathematical modeling of each type of gasification However, the model development over the years in terms of modeling complexity is not discussed and it leads to inconclusive information They have also not discussed CFD models related to gasification and detailed mechanism of each modeling technique is not covered Ahmed et al.[8]discussed the mathematical and computational approaches for hydrogen pro-duction from biomass They have divided the models into two broad categories, i.e mathematical models and simulation models Mathematical models include equilibrium, kinetic and ANN mod-els whereas regarding simulation modmod-els they have only discussed CFD models However, they have not covered ASPEN Plus models

In thefinal section, they have mentioned about process optimiza-tion and heat integraoptimiza-tion effects Recently, Baruah and Baruah[9]

contributed a review article on biomass gasification modeling The authors have explained the importance of modeling for complex processes like biomass gasification The paper largely discusses equilibrium models for bothfluidized bed and downdraft gasifiers

Table 1

Total renewable energy installed capacity (May 2014) [4]

However, other models, i.e kinetic, CFD and ANN, are discussed

briefly Like Ahmed et.al.[8], the authors did not discuss ASPEN

Plus models Largely, it is observed that these review articles fail to

address the basic understanding of each model type and their

applicability for designing different gasifiers for a certain feedstock

and variation of operating parameters

In this article, we have tried sincerely to cover all these aspects

in our article to provide a better understanding on the modeling of

downdraft biomass gasification process This article reviews the

current state of the art of modeling of biomass gasification in fixed

beds A brief review of individual processes involved in gasification

is presented to set the stage for the description of models of the

process

2 Gasification process and gasifier types

2.1 Gasification process

Biomass gasification process usually involves the reactions

pertaining to various phenomena such as drying, pyrolysis,

oxida-tion, and reduction In the drying stage, moisture content of the

biomass is reduced It occurs at 100–200 1C and decreases the

moisture content of the biomass as low as 5% In general, the

moisture content of raw biomass ranges from 5% to 35% In the

pyrolysis stage, the thermal decomposition of biomass occurs in

the absence of oxygen or air and volatile matter is released as a

consequence of the thermal breakdown of biomass As a result, the

mixture of gases containing carbon monoxide, hydrogen, carbon

dioxide and hydrocarbon gases from the biomass is released and

biomass is reduced to solid charcoal The hydrocarbon gases

condense at a low temperature to generate liquid tars The gases

released from drying and pyrolysis zones may or may not pass

through the oxidation zone depending upon the type of gasifier

Combustion is a reaction between solid carbonized biomass and

oxygen in the air, resulting in the formation of CO2 Hydrogen

present in the biomass is also oxidized to generate water An

excessive amount of heat is released with the oxidation of carbon

and hydrogen The heat released is utilized for drying, pyrolysis

and gasification reactions In the gasification, several reduction

reactions occur and the temperature ranges between 800 and

10001C These reactions are mostly endothermic in nature The

main reactions in this zone are as follows:

Water–gas reaction:

Boudouard reaction:

Shift reaction:

Methane reaction:

2.2 Types of gasifiers

Gasifiers can be divided into two principal types: fixed beds

andfluidized beds A third type, the entrained suspension gasifier,

has been developed for finely divided coal gasification (o0.1–

0.4 mm)[10] This design is not recommended forfibrous

materi-als such as wood[11]

2.2.1 Fixed-bed gasification Fixed-bed gasifiers are the oldest and most common reactors employed to synthesize syngas Large-scale (higher than 10 MW) fixed-bed gasifiers are losing the interests of industrial units due to scale-up issues [12] However, small-scale (lower than 10 MW) fixed-bed gasifiers with high thermal efficiency are in use for decentralized power generation and for thermal applications in many industries[13] Due to easy construction and simple opera-tion, fixed-bed gasifiers are widely used and studied Depending upon the direction and entry of airflow, the gasifiers are classified as updraft, downdraft, or cross-draft[14] The positioning of reaction distribution regions, i.e drying, pyrolysis, combustion and reduc-tion, in afixed-bed reactor differ depending on the type of gasifier 2.2.1.1 Updraft gasifier In an updraft gasifier, the biomass is fed from top of the gasifier, while air is supplied at the bottom of the gasifier At the top of the gasifier, the fed biomass gets dried and it passes through the pyrolysis zone, where the feed is decomposed to volatiles, tar and char Volatile-free biomass moves downward towards the combustion zone and released volatile combine with the gas stream leaving the reduction zone located above the bottom-most zone, i.e combustion zone In the combustion zone, the biomass gets oxidized and flue gases are generated It passes through the reduction zone containing charcoal, produced by pyrolysis of the biomass, and gets converted into producer gas The producer gas leaving the reduction zone passes through the pyrolysis and subsequently the drying zone It provides its sensible heat to the biomass resting in the respective zone and partially meets the energy requirement of pyrolysis and drying The heat generated in the combustion zone is utilized by reduction, pyrolysis and drying zones The producer gas leaving from top of the gasifier is accompanied by a high amount of tar and moisture 2.2.1.2 Downdraft gasifier In a downdraft gasifier, both biomass and air move in the downward direction in the lower section of the gasifier unit The downdraft gasifier has four distinct zones: (1) drying zone, (2) pyrolysis zone, (3) oxidation zone, and (4) reduction zone The product gases leave at a point just below the grate of the gasifier, which enables partial cracking of the formed tars and hence a gas with low tar content is produced The product gas contains a low concentration of particulates and tars (approximately 1 g/Nm3) as most of the tars are combusted in the gasifier The downdraft gasifier is ideal when clean gas is desired

[15] The disadvantages of this type of gasifier include a relatively low overall thermal efficiency and difficulties in handling biomass with high moisture and ash content

2.2.1.3 Cross-flow gasifier In a cross-flow gasifier, the biomass fed

at the top of the unit moves downward, while the air enters from the side of the gasifier Product gas leaves from the upper side of the unit at about the same level that the biomass is fed A hot combustion/gasification zone forms around the air entrance and pyrolysis and drying zones get formed in the vessel Ash is removed from the bottom of the unit and the temperature of the gas leaving the unit is about 800–900 1C As a result, low overall energy efficiency with a gas having high tar content is expected in cross-flow gasifier units

In general,fixed-bed gasifiers have the advantage of involving simple designs but have the shortcoming of producing a low gas calorific value with high tar content The product gas composition

is typically 40–50% N2, 15–20% H2, 10–15% CO, 10–15% CO2and 3– 5% CH4, with a net CV of 4–6 MJ/Nm3[16] To obtain a high gas calorific value, the moisture content of the feed should remain below 15–20 wt% Fixed-bed gasifiers generally produce outlet gases with a lower particulate loading (e.g ash, tar, char) than fluidized-bed gasifiers

2.2.2 Fluidized-bed gasification

Among the technologies that can be used for biomass

combus-tion,fluidized beds are emerging as the best due to their flexibility

in terms of type of fuel and high efficiency Fluidized bed (FB)

gasification is used extensively for coal gasification for many years

Its advantage overfixed-bed gasifiers is the uniform temperature

distribution in the reduction zone This temperature uniformity is

accomplished using a bed offine granular material (e.g sand) into

which air is circulated,fluidizing the bed Fluidized beds are used

for a broad variety of fuels Loss of adequate fluidization or

defluidization due to bed agglomeration is a major problem in

fluidized-bed gasifiers However, there are successful solutions

that have been reported for other biomass feedstocks[17] These

solutions are mainly based on lowering and controlling the bed

temperature Two main types of fluidized-bed gasifiers are in

current use: (a) circulatingfluidized bed and (b) bubbling bed A

third type of FB gasifier, an internally circulating bed, which is

based on the design features of the other two types, is being

investigated at the pilot plant scale

2.2.2.1 Circulatingfluidized beds Circulating fluidized-bed gasifier

is based on the mechanism of continuous circulation of the bed

material between the reaction vessel and a cyclone separator,

where the ash is separated and the bed material and char return

back to the reaction vessel These types of gasifiers are able to cope

with high-capacity biomass throughputs Circulatingfluidized-bed

gasifiers can be operated at high pressures Output gases produced

are delivered at gas turbine operating pressure without requiring

further compression

2.2.2.2 Bubbling bed In a bubbling-bed FB gasifier, the air is fed

from the bottom of the reactor through the grate The fine bed

material is placed above the grate into which the biomass feed is

introduced The bed temperature is maintained between 700 and

9001C by controlling the air/biomass ratio The biomass is

pyrolyzed in the hot bed forming char, gaseous compounds and

tar The high molecular weight tar reacts with the hot bed

material, to give a product gas with lower tar content (o1–3 g/

Nm3)

2.2.3 Advantages/disadvantages of different gasifying reactors

A reported comparison between fixed-bed and fluidized-bed

reactors based on technology, size restriction of material, energy

requirement, environment and economy shows that there is no

significant advantage between these two systems[18] Selection of

a particular gasifier type and its design will require however a

close scrutiny of a number of other factors such as the properties

of the feedstock (both chemical and physical), the quality of

product gas required, the heating method and the various

opera-tional variables involved [19] The features of a fluidized-bed

gasifier that make it appear less attractive are a more complex

design and operation and energy expenses in biomass particle size

reduction Particle size reduction as well entails the formation of

dust unsuitable forfluidization The product gas contains as well a

higher tar content requiring extensive external gas cleaning High

plant costs make fluidized-bed gasification economical at the 5–

10 MW scale In comparison tofluidized-bed gasifiers, the

fixed-bed gasifier appears the most adaptable for the production of low

calorific value gas in small-scale power generation stations with

gas turbines The fixed-bed gasifier plant is simpler in this

application and has no or very few moving parts[20]

3 Biomass gasification models 3.1 Equilibrium models

The thermodynamic equilibrium model is a tool to calculate the maximum yield that can be attained for a desired product in a reacting system Practically it is impossible to attain chemical or thermodynamic equilibrium within the gasifier However, this model provides the designer with a reasonable prediction of maximum achievable yield of a desired product The model calculations are independent of gasifier design and hence helpful for studying the influence of fuel and process parameters only Chemical equilibrium is determined by either of the following:

The equilibrium constant

Minimization of the Gibbs free energy

For a given reaction condition thermodynamic equilibrium state gives the maximum conversion of the reactants Normally equilibrium is achieved at higher temperatures (41500 K), where the effect of variation in operating parameters can be observed There are two following methods for equilibrium modeling:

Stoichiometric method

Non-stoichiometric method

A detailed specification of all the chemical reactions and species involved in the model are required for the stoichiometric approach whereas the non-stoichiometric method is based on Gibbs free energy minimization[21]

Chern et al.[22]developed an equilibrium model to evaluate the degree of approximation in predicting the performance of an air-blown wood downdraft gasifier over wide ranges of operating parameters The experimental parameters such as char yield, exit temperature and gas composition were simulated and the results were compared with comprehensive experimental data The basic assumptions and simplifications used in the model are: (a) the dry and ash-free feed material, which is represented by CHaObNc, (b) air is composed of oxygen and nitrogen only; their molar ratio

is 21/79, (c) the char comprises pure solid carbon, (d) the product gas consists of N2, H2, CO, CH4, H2O and CO2, as only these gas species are thermodynamically significant under the gasification conditions, and (e) exiting char and wet product gas are at thermodynamic equilibrium

The air-blown gasification process was represented by the overall stoichiometric reaction (Eq.5):

CHaObNcþnwH2Oþna (0.21O2þ0.79N2)-ncCþnG

(y1H2þy2COþy3CO2þy4H2Oþy5CH4þy6N2) (5)

In addition to stoichiometric equation, four elemental balances (C, H, O and N) and energy balance were used as described by Eqs

(6)–(10)

Chern et al [22]considered homogeneous and heterogeneous equilibrium reactions [Eqs.(11)–(15)] tofind the equilibrium gas compositions

CþCO2-2CO (12)

In their simulation, the elemental and energy balance

equa-tions are solved simultaneously with the relation of mole fracequa-tions

of gaseous species with equilibrium constants for a particular

reaction in order to find the product composition The model

predicts the temperature, gas composition and char yield at the

exit of the gasifier for a specified set of heat loss and input

conditions A parametric study was also conducted through

simulations forfinding the influences of the air-to-feed mass ratio

and the moisture-to-feed mass ratio on the performance of the

gasifier The model predictions were compared with a

compre-hensive set of experimental data obtained from the gasification of

wood in a commercial-scale downdraft gasifier; the air-to-feed

ratios range from 1.1 to 2.1 and the moisture to feed ratios range

from 0.05 to 0.3 The predicted trends for variations in the

operating parameters were in general in good agreement with

the experimental data

Zainal et al.[23]proposed an equilibrium model for the gasi

fica-tion of biomass in a downdraft biomass gasifier for the prediction of

product gas composition and its calorific value The model proposed

is a modified version of the model developed by Chern et al.[22] The

model assumes that all reactions are in thermodynamic equilibrium

All the pyrolysis products burn completely in the reduction zone of

the gasifier The chemical formula for the wood does not contain

nitrogen and sulfur It was assumed that global gasification reaction

[Eq.(5)] does not yield any solid carbon In the model, three elemental

balances (C, H and O), two equilibrium constant relationships [Eqs

(11) and (14)] and energy balance are used to solve six unknowns

(molar fractions of H2, CO, CO2, H2O, CH4and oxygen content for the

reaction) These sets of equations were converged to a set of three

equations, one linear and two nonlinear equations These above set of

equations were solved using the Newton–Raphson method This

model predicted the calorific value and composition of the producer

gas using wood as a raw material for the downdraft gasifier It also

determined the predictions for paddy husk, paper and municipal

waste The predicted value closely matches with the experimental

values available in the literature for wood By knowing the

composi-tion and calorific value of any biomass, this model can accurately

predict the composition and calorific value of the producer gas

The equilibrium model proposed by Melgar et al.[24]

incorpo-rates the mass fraction of sulfur in the biomass formula along with

C, H, O and N The global gasification reaction [Eq.(5)] is modified

by incorporating the production of SO2and release of unconverted

O2in the product gas Thefive atomic balances (C, H, O, N and S),

two equilibrium constant relationships [Eqs.(11) and (14)], and

energy balance constitute the model equations The proposed

equilibrium model also takes care of the water dissociation for

the hydrogen production The Newton–Raphson method has been

employed to solve the set of nonlinear equations In each iteration,

partial correction (δ/5) is performed to guarantee the stability of

the algorithm The reported model is validated with the

experi-mental data of Jayah et al.[25]

Jarungthammachote and Dutta[26]modified the equilibrium

model proposed by Zainal et al.[23]by incorporating the nitrogen

in the biomass formula and multiplying the equilibrium constants

with a coefficient Experimental data reported at Zainal et al.[23],

Altafini et al.[27]and Jayah et al.[25]were used to modify the

model This combination gives a total of eleven cases to use as

experimental data A coefficient of 11.28 was used to multiply with

the equilibrium constant of Eq.(14)in the calculation procedure in

order to improve the performance of the model This coefficient came from the average value of the ratio of CH4from the eleven experimental data and CH4 calculated from the model A value 0.91 was defined to be the coefficient for modifying the equili-brium constant of Eq.(11) After modifying the model, the amount

of H2 significantly reduced as compared to the predicted value from the unmodified model The amount of CH4 dramatically increased and was found closer to the experimental values It is reported that the predicted results of the modified model were better compared to unmodified model The results from the modified model are satisfactorily close to the experimental value The modified model was employed to simulate the gasification of Thailand MSW and to study the effect of moisture content on the temperature and producer gas composition

Vaezi et al [28]used the thermodynamic equilibrium model reported by Zainal[23]and subsequently modified by Jarungth-ammachote and Dutta [26] The model is used to find the suitability of a particular biomass for certain applications The authors have reported the range of variations of oxygen content and C/H ratio for 55 different biomass materials from the ultimate analysis data The influence of such variation on the syngas composition is analyzed The results are plotted in a generalized format, which can be used for a variety of biomass materials The variation of higher heating value (HHV) of the produced syngas with respect to oxygen content and C/H ratio is depicted by a contour plot It is reported that the influence of C/H ratio on HHV

is much higher than that of the oxygen content Forfixed oxygen content, an increase in C/H ratio to about 8.2 results in an increase

in HHV and beyond that value the reduction of HHV is reported Sharma[29]reported a brief review of the historic equilibrium models developed in the past He has proposed the global gasification reaction based on the heterogeneous model including char formation He has incorporated the three heterogeneous reactions [Eqs (11)–(13)] and methane reforming reaction [Eq

(16)] in his model

Four atomic balances (C, H, O and N), four equilibrium constant relationships [Eqs.(11)–(13) and (16)], energy balance, and equa-tion based on Dalton's law of partial pressure constitute the model proposed by Sharma[29] This model predicts the unreacted char

at various thermodynamic conditions prevailed in downdraft gasifier over and above the producer gas composition The proposed model is validated with the experimental data of Jayah

et al.[25] Ratnadhariya and Channiwala [30] proposed a three-zone equilibrium and kinetic free model of biomass gasifier The first zone of the model was drying and pyrolysis combined together; the second and third zones were oxidation and reduction, respec-tively Each zone has been formulated with: (i) reaction stoichio-metry; (ii) constituent balance; and (iii) energy balance along with

a few empirical relationships In the drying and pyrolysis zone equilibrium model, the species considered are C, CH4, CO, CO2,

C2H2, H2 and H2O The empirical relation such as 50% of the available hydrogen in the biomass releases as hydrogen and the rest releases as C2H2and CH4is considered The ratios of moles of

CO and CO2and the same of CH4and C2H2were inversely related

to their molecular masses, respectively It is also assumed that 80%

of the available oxygen produces H2O and the rest associated with fuel carbon to release CO and CO2 on decomposition In the oxidation zone model, it is assumed that all the hydrogen coming from pyrolysis zone gets combusted to release water The char oxidation releases CO and CO2and their distribution was assumed

to be inversely proportional to the exothermicity of their reactions Moreover, it is assumed that CO, CO, CH, and CH are assumed

to be carried forward to the reduction zone without reacting with

oxygen In the reduction zone, Boudouard and water–gas shift

reactions are incorporated It is observed the proposed model does

not use any thermodynamic equilibrium constant relationships

Moreover, many assumptions used in this model lack justification

such as no heat transfer across zones, no reaction of CH4and C2H2

in oxidation and reduction zones, etc The model can be named as

stoichiometric kinetic free model rather than equilibrium kinetic

free model

Barman et al [31] incorporated the species tar in the global

gasification reaction The tar composition used in the model was

taken from the literature The model is constituted of three atomic

balances (C, H, and O), three equilibrium constant relationships

[Eqs.(11), (14) and (16)], and the energy balance equation Tofit

the data better with the experimental data of Jayah et al [25],

similar to the modification proposed by Jarungthammachote and

Dutta[26]regarding the equilibrium constant multiplication with

some coefficient, Barman et al.[31]also modified the model They

reported that the modified model with the coefficient of 3.5 for

equilibrium constant of Eq.(11)better predicts the experimental

producer gas composition Barman et al.[31]also validated their

model with the experimental data of Ptasinski et al.[32], Dogru

et al.[33]and Pedroso et al.[34]

Silva and Rouboa [35] presented a realistic gasification model

based on the carbon boundary point concept developed by Ptasinski

et al [32] by discussing the effect of oxygen enrichment air The

equilibrium model considered both homogeneous (above the CBP

where all the compositions are in the gaseous state) and

hetero-geneous equilibrium (below and at the CBP, with the presence of

unconverted solid carbon) Both elemental mass balance and energy

balance were satisfied in the model, leading to the prediction of exit

gas temperature and gas composition Silva and Rouboa[35]also used

the modified equilibrium constant values proposed by

Jarungth-ammachote and Dutta [26] The proposed model determines the

temperature at the carbon boundary point, i.e optimum gasification

point With increase in oxygen content, the temperature at the CBP

increases and it decreases with the increase in the moisture content

of biomass It was also observed that the molar fractions of hydrogen

and carbon monoxide decrease as oxygen content increases and the

carbon dioxide shows the opposite trend The methane molar fraction

increment was only minor The oxygen content increment leads to

increased energetic and exergetic efficiencies

The thermodynamic equilibrium model discussed above is also

used by other researchers such as Balu and Chung[36], Koroneos

and Lykidou[37]Azzone et al.[38]and Bhavanam and Sastry[39]

Considering little contribution in terms of model development/

upgradation, these models are not discussed in the present article

3.2 Combined transport and kinetic modeling

The inadequacy of the equilibrium model to correlate the

reactor design parameter with thefinal product gas composition

leads to the development of kinetic models to evaluate and imitate

the gasifier behavior A kinetic model involves parameters such as

reaction rate, residence time, reactor hydrodynamics (superficial

velocity, diffusion rate) and length of reactor Thus, the kinetic

model provides a wide dimension to investigate the behavior of a

gasifier via simulation and they are more accurate but

computa-tionally intensive As biomass gasification is quite an extensive

process that it is difficult to formulate the exact reaction pathways

and difficult to simulate Most of the models account for modeling

for reduction reaction and often separate sub-models for pyrolysis,

oxidation and reduction Separating the overall process into

sub-models of pyrolysis, oxidation and reduction zones help in

simplifying the model and provide better understanding of the

downdraft gasifier behavior

Blasi[40]proposed a one-dimensional unsteady state model for biomass gasification in a stratified downdraft gasifier The model proposes the generalized set of equations for all zones of the biomass gasifier The proposed model includes mass and energy balances of both solid and gaseous phases separately The model incorporates the reactions of various processes such as drying, biomass pyrolysis, combustion and gasification of char, combustion of the gases and tar cracking The species considered are oxygen, nitrogen, hydrogen, steam, carbon dioxide, carbon monoxide, methane and hydrocar-bons Moisture evaporation was considered as a diffusion-limited process and represented by an empirical expression for the vapor pressure The author neglected the bed porosity considering it had very little effect on devolatilization of biomass based on their earlier study[41] Pyrolysis was represented as a one-step global reaction, where the fractions of gases, tars and chars are produced [42] Secondary tar cracking occurs in the voids of the bed to produce secondary gases For the proposed kinetic scheme, the kinetic constants for tar cracking are taken from Liden et al [43] The composition of the secondary gas has been estimated based on literature data obtained for wood [44] For the simulation of gasification process, the composition of the gases produced from pyrolysis is required Three sets of devolatilization data have been generated by performing the experimental study at a surface temperature of 8501C Similarly for the combustion of volatiles the method proposed by Bryden and Ragland[45] for the“fixed-bed” combustion of biomass was used Combustion and gasification reactions of char are heterogeneous and were described by the unreacted core, shrinking particle model In their study, chars were assumed to consist of pure carbon and those of heterogeneous combustion products were taken as only carbon dioxide Literature correlations are used for the effective thermal conductivity of the bed

[46], the effective bed-to-wall heat transfer coefficients [47], the solid/gas heat transfer and the mass transfer coefficients [48] However, as a consequence of unsteady heat transfer the solid/gas heat transfer coefficient is multiplied by empirical factors (ξ) with values in the range 0.02–1[49,50] The operator splitting procedures and “finite-differences” approximations were used to solve the modeling equations Blasi [40] has not validated the developed model due to non-availability of enough experimental data From the qualitative point of view, the model predictions match well the dynamic behavior of downdraft wood gasifiers and also the depen-dence of the air/fuel feed rate on steady-state configurations Blasi

[40] discussed the effect of various parameters such as model parameters, the physico-chemical properties of feedstock and the plant size, single-particle effects, and char reactivity on the product gas compositions It was concluded that more reliable input data are required in relation to both transport coefficients and intrinsic reaction kinetics to simulate the biomass gasification process Giltrap et al [51] proposed a steady-state kinetic model for predicting the product gas composition and temperature inside a downdraft biomass gasifier using the reaction kinetics parameters obtained by Wang and Kinoshita[52] The model was developed specifically for reduction zone of the downdraft biomass gasifier only It was assumed that all the pyrolysis products get completely cracked and complete combustion occurs in the combustion zone The pyrolysis and tar cracking reactions were not included in the developed model The reaction scheme used was the same as that proposed by Wang and Kinoshita[52] The reaction rates were all considered to have an Arrhenius-type temperature dependence and to be proportional to the difference between the actual reactant/product ratio and the corresponding equilibrium ratio The values for the activation energies in the rate equations were taken as reported by Wang and Kinoshita [52] However, the frequency factor values in this model are not used exactly as reported by Wang and Kinoshita [52] In fact a multiplication factor, i.e.“Char Reactivity Factor” (C), that represents the relative

reactivity of different char types is incorporated in the model A set

of sevenfirst-order ordinary differential equations was obtained

by applying mass and energy balances to the system Shell mass

and energy balance was applied to the system of cylindrical

gasifier with uniform cross-sectional area with negligible radial

variation Two more equations (empirical equation for pressure

drop and velocity variation equation based on differentiation of

ideal gas law equation) were added in the model The nine

ordi-nary differential model equations were solved using the ODE45

function in MATLAB The model predicts the output gas

composi-tion for a particular set of input parameters The gas composicomposi-tion

predicted by the model was in reasonable agreement with the

experimental results apart from over-prediction of the CH4

con-centration The model produced reasonable agreement with the

experimental results of Chee [53] and Senelwa [54] for all

components except CH4 It is reported that the model could be

improved with more data on the initial gas concentrations at the

top of the reduction region, the relationship between the amount

of pyrolysis products produced and temperature, and the variation

of the char reactivity factor along the length of the gasifier bed

Jayah et al.[25]proposed a kinetic model which consists of two

sub-models, namely, theflaming pyrolysis and gasification zones

Theflaming pyrolysis zone sub-model is used to determine the

maximum temperature and the product concentration of gas

leaving that zone The concepts of equilibrium in chemical

reac-tions with mass and energy balance principles are used in the

model development The concentrations and temperatures

calcu-lated by theflaming pyrolysis zone sub-model are used as inputs

to the gasification zone model The gasification zone

sub-model represents a single-particle one-dimensional sub-model along

the vertical axis of the gasifier This sub-model includes a

descrip-tion of the physical and chemical processes, flow equations,

transport phenomena and conservation principles Jayah et al

[25] also carried out an experimental study to validate the

proposed model The model was calibrated using the

experimen-tally determined gas compositions The gas compositions

pre-dicted by the gasification zone sub-model are within 75.8% of the

measured values The gasification zone sub-model predicts the

gasification temperature as well with reasonable accuracy They

have also performed the computer simulations to investigate the

effects of various operating parameters on conversion efficiency It

was concluded that moisture content and heat loss have greater

effects on reactor temperature and hence on the conversion

efficiency It was found that the design with smaller throat angle

increases the conversion efficiency provided the gasification zone

length is extended From the above study it can be concluded that

the length of the gasification zone is an important design

para-meter for downdraft gasifiers The optimum gasification zone

length has to be selected for maximum output for a given range

of operating parameters Another parameter studied is the

tem-perature of the inlet air It was reported that the high inlet air

temperatures are improving the gasifier performance but not to

the extent that it can compensate the heating cost involved

Tinaut et al [55] developed a one-dimensional steady-state

model for the gasification process in a fixed-bed downdraft

biomass gasifier The model takes into account almost all the

phenomena that occur during the gasification process such as

moisture evaporation and biomass devolatilization;

heteroge-neous reactions of the char with water vapor, carbon dioxide,

hydrogen and oxygen; combustion of the volatile matter;

homo-geneous reactions such as water–gas shift reaction and reforming

reactions of methane and tars The model is developed by

incorporating mass and heat transfer along the bed, heat transfer

between solid–gas, solid-walls and gas-walls, heat transfer by

radiation in the solid phase, variation of the bed void fraction

throughout the length of the gasifier, variation of the transversal

section of the gasifier (geometry), variation of the biomass particles diameter, and pressure losses in the bed The gaseous phase includes the species H2O, H2, CO2, CO, CH4, C6H6.2O0.2, O2

and N2 and the solid phase includes biomass (CnHmOp), vegetal char and ash The different mass and energy interchanges between the gaseous phase, the solid phase and the reactor wall are considered in the model development Tinaut et al.[55] applied the shell balance approach to develop the differential equations of conservation of species, energy, and pressure losses in the bed along the reactor length The equations of energy conservation in each phase consider the heat transfer by convection between the phases and the gasifier wall, by conduction in the axial direction and by radiation The pressure losses along the bed are described

by the equations proposed by Ergun The source terms of the conservation equations such as convection between the gaseous and solid phases, and between each of these phases and the gasifier wall are calculated using the equations reported by Di Blasi

[40,56] To account for the energy losses, the equations proposed

by Hobbs et al.[47,57]have been adapted The correlations for the Nusselt and Sherwood numbers for mass and energy transfer in a packed bed reported by Wakao and Kaguei[58]are integrated in the model The model equations are solved iteratively considering temperature profile as an iteration variable The model has been validated with biomasses of different size and varying air super-ficial velocity They have found a reasonable agreement between the experimental and calculated results

Sharma [59] developed a 1-dimensional steady state kinetic model to predict the performance of a downdraft biomass gasifier The packed bed of the biomass gasifier was assumed to be porous

in nature Hence, thefluid flow rate increases in the direction of flow due to the shrinkage of solid particles constituting the bed The thermo-chemical processes were described by five separate zones, i.e preheating zone, drying, pyrolysis, combustion and reduction In the developed model, biomass drying has been described via thermal equilibrium, where mass transfer deter-mines the rate of moisture removal from wet biomass particles The flow of air and biomass consumption in the gasifier was related by the phenomena offluid flow, heat transfer, and thermo chemical processes In the drying and preheating zones, shrinkage

in particle size has not been considered But in pyrolysis, oxidation and reduction zones, as different chemical reactions occur which leads to reduction in particle size, hence particle shrinkage is incorporated in the modeling equations Moving porous bed of suction gasifier was modeled as one-dimensional (1-D) with finite control volumes (CVs) These modules were solved using the tri-diagonal matrix algorithm (TDMA) A steady state kinetic model for reduction reactions as described by Sharma[60,61]is used The kinetic model predicts the un-reacted char andfinal gas composi-tion Kinetic modeling approach for the reduction zone constitutes

an efficient algorithm allowing rapid convergence with adequate fidelity A constant value of 1000 for the char reactivity factor (CRF) as recommended by Giltrap et al.[51]is included in order to account for the active sites present on char surface A 20 kWe open top downdraft biomass gasifier developed in Indian Institute of Science, Bangalore, was chosen The experimental data of Sharma

[61], generated on the same configuration, have been used for validation or testing of various modules and overall gasifier model The fluid flow module, mass transfer model for biomass drying and the equilibrium based oxidation model all were validated and found to be robust and adequate for the prediction of product composition Finally, the gasifier model was validated against the experimental data with good agreement

Gordillo and Belghit [62] developed a numerical model of a solar downdraft gasifier of biomass char (biochar) with steam based on the systems kinetics The model simulates the gasifying process of biochar The pyrolysis and cracking reactions were not

considered The model uses the reactions kinetics proposed by

Wang and Kinoshita[52]

Simone et al.[63]proposed a mathematical model, based on the

literature kinetic, mass transfer and heat transfer sub-models The

gasifier is represented with a 1D domain The model is for the

reduction zone of the gasifier The model treats the gas and the

solid phase separately, which is similar to that followed by authors

Shin and Choi[64], Blasi[40]and Tinaut et al.[55] The two phases

are correlated by mass and energyfluxes The two phases exchange

heat via radiation and conduction The gasifier is divided into

several small cells of thickness dz Each cell incorporates all the

chemical and physical phenomena along with source terms

Bio-mass drying is represented with an Arrhenius-type relationship In

this work biomass devolatilization is represented with a global

devolatilization reaction generating gas, tar and char according to

the assigned coefficient for the macro-products distribution[40]

Tar and its decomposition into CO, CO2, and CH4are represented as

in Blasi[40] Char is assumed to be composed of pure carbon

Model equations were solved using the software gPROMS

(Process System Enterprise) The domain is meshed with a

variable-length grid with a total number of intervals of 460 The

system of differential equation is solved with afirst-order

back-wardfinite difference method To simplify the simulation

execu-tion, heat and mass transfer coefficients are imposed constant on

different sections of the gasifier To validate the model, the syngas

composition and the temperature profiles calculated by the model

were compared to the experimental values The discrepancy

between the model and experimental results was minimized by

adjusting the parameter a and thus the char reactivity The model

satisfactorily represents the gasifier behavior and can be used for

evaluating the effect of the operating parameters In particular, the

modeling approach is able to catch whether stable operating

conditions can be reached or not

Blasi [65] proposed a mathematical model for gasification of

wood pellets in an open-core downdraft gasifier, with dual air

entry The authors have carried out a parametric analysis on the

influences of the quantity and position of secondary air on the

temperature profile and the conversion of both tar and char for a

pilot-scale reactor developed by Barrio and coworkers [66–68]

The data reported by these authors are also validated with the

experimental data The conservation equations for the solid and

the gas phase are written for a one-dimensional, unsteady packed

bed The assumptions of the model were no spatial variation of

temperature within the particle, uniform size and (spherical)

shape of the particles and constant bed porosity The main

processes modeled include: (1) moisture evaporation, (2)

finite-rate kinetics of wood pyrolysis, (3) primary tar cracking, (4)

gasi-fication of steam, carbon dioxide and hydrogen, (5) combustion of

char, (6) combustion of volatile species and refractory tar, (7) steam

reforming of methane and refractory tar, (8)finite-rate water–gas

shift, (9) heat and mass transfer across the bed due to convection

and diffusion, (10) absence of thermal equilibrium (different solid

and gas temperatures), (11) solid and gas-phase heat transfer with

the reactor walls, (12) radiative heat transfer through the porous

bed, and (13) variable solid and gasflow rates

A one-step global reaction is considered for wood

devolatiliza-tion, where the fractions of gas, primary tar and char produced are

included The solution of the model equations is carried out using

operator splitting procedure andfinite-differences approximations

The entire solution process was split into three segments, thefirst

one corresponds to chemical reaction processes, the subsequent

steps were heat exchange (between phases and with the reactor

wall) and transport phenomena For each time step, in thefirst two

stages, the ordinary differential equations were solved by the

first-order implicit Euler method In the third step the transport

equations are solved using a semi-implicit procedure The model

is experimentally validated using the measurements reported by Barrio et al [66–68] The predicted temperatures are in good quantitative agreement with the measured ones, although the latter miss the maximum values There is also agreement between the predictions and the measurements for the changes in the shape of the temperature profile from the case of no secondary air injection

to the case of the forced, center-stabilized front configuration It is observed that the actual temperature values predicted along the char bed are highly dependent on the wall heat losses However, it has been found that only a very small portion of the bed (approxi-mately 0.01 m thick) is affected by the bottom heat losses The comparison between predicted and measured composition of the producer gas shows a good agreement except for the higher predictions in the yields of methane

3.3 CFD models Computationalfluid dynamics (CFD) play an important role in the modeling of bothfluidized-bed gasifier and fixed-bed down-draft gasifier A CFD model implicates a solution of conservation of mass, momentum of species, energy flow, hydro-dynamics and turbulence over a defined region Solutions of such a sophisticated approach can be achieved with commercial software such as ANSYS, Fluent, Phoenics and CFD2000 CFD appears to be a cost-effective option to explore the various configurations and operat-ing conditions at any scale to identify the optimal configuration depending on the project specification

Fig 1exposes the several sub-models that can be incorporated within the CFD model CFD modeling involves advanced numerical methods for accounting solid phase description, gas phase cou-pling and also focuses on the mixing of the solid and gas phase The turbulent mixing may be modeled by the application of several equations such as Direct Numerical Simulation (DNS), Large-eddy simulation (LES) and Reynolds-averaged Navier– Stokes (RANS) equations Furthermore, complex parameters such

as drag force, porosity of the biomass and turbulence attenuation are mostly taken into consideration Theflow phase is modeled using either the Two-fluid model or the Discrete particle model Moreover, the heterogeneous chemistry of biomass gasification including devolatilization, char combustion and gas phase chem-istry also required to be modeled simultaneously considering the heat, mass and momentum change at each phase

Comprehensive CFD simulations for biomass gasification are scarce, mainly due to lack of broad computational resources and the anisotropic nature of biomass However, some simplified CFD models had been established to simulate the gasification behavior

by Fletcher et al.[69], Yu et al.[70]and Janajreh et al.[71] The CFD models reveal promising results that indeed are beneficial for further investigation on hydrodynamic inside the gasifier How-ever, modeling of tar is quite challenging even in CFD modeling There are very less number of articles on the modeling of

GAS PHASE Turbulent mixing Direct Numerical Simulation (DNS) Large-eddy simulation (LES) Reynolds-averaged Navier-Stokes (RANS)

CHEMISTRY Heterogeneous chemistry Biomass devolatilization Char combustion Gas phase chemistry Primary tar decomposition Secondary tar formation

Heat mass and

momentum exchanges

Fig 1 Modeling scheme of biomass gasification by the CFD approach.

downdraft biomass gasification by the CFD approach A few of

them are discussed below

Rogel and Aguillon[72]formulated a hybrid“1-Dþ2-D”

numer-ical model to simulate the gasification of pine wood pellets in a

stratified downdraft gasifier The model incorporates reactions for

drying, primary pyrolysis of biomass, secondary tar cracking,

com-bustion, gasification and particle shrinkage The particle model for

the stratified gasifier is based on intraparticle mass and energy

balances and is written in spherical coordinates for a

one-dimen-sional unsteady system However, the gas phase model incorporates

mass, energy and momentum balances for two-dimensional

unst-eady system in cylindrical polar coordinates PHOENICS, a

commer-cially available CFD code, was used to solve the model numerically As

the bed permeability was very high, it was assumed that the pressure

inside the reactor remains constant The pressure drop model was

based on modified Ergun equation All transport equations were

solved numerically andfinite rate kinetics was used for all reactions

For the transport coefficients and chemical kinetics, correlations

available in the literature were used A finite volume has been

adopted to simulate the gasification process The model predictions

were reported to be in good agreement with the experimental data

in terms of syngas composition, gas temperature profile, biomass

temperature profile and biomass particle shrinkage

3.4 ANNs model

Artificial neural networks (ANNs) modeling may be considered

as a computational paradigm in which a dense distribution of

simple processing element is supplied to provide a representation

of complex process including nonlinear and discrete systems ANN

is a standard modeling tool consisting of multilayer perceptron

(MLP) paradigm [33] MLP further consists of an input layer, a

hidden layer and an output layer of neurons

The neurons in the input layer, consisting of inputs and weights,

simply forward the signals to the hidden neurons However, each

neuron in the hidden and output layers has a threshold parameter

known as bias ANN models are mostly characterized as

non-mechanistic, non-equilibrium and non-analytical model However,

it can produce numerical results that can be used to predict the

composition of product gas from the gasifier

The neural network simulation of downdraft gasifier requires

an extensive set of database, which consists of a large amount of

experimental downdraft biomass gasification data Thus, collected

data is used as input in artificial neural network modeling The

next step involves the training of the network and its validation

that can be successfully achieved with the help of Statistical Neural

Networks– SNN (Statsofts) software

Because of its mechanistic, equilibrium and

non-analytical behavior, ANNs have many limitations in terms of dynamic

modeling, despite its accuracy in composition prediction The

per-formance of ANNs solely depends on its training and, in addition,

training requires a large set of experimental data to calibrate and

evaluate the constant parameters of the neural network

ANN is widely used for signal processing, function

approxima-tion and simulaapproxima-tion and recogniapproxima-tion of patterns However, the use

of ANN for biomass gasification is rare ANN is a useful tool

especially when the primary aim is to optimize the process

parameters and output of a complex system It does not require

any information on the mathematical description of the process,

the only input required is the inlet data sets Therefore ANNs are

best suited for simulation and scaling-up of a process Thus, ANNs

modeling may not be the viable option for a new technology such

as biomass gasification as the number of experimental data sets is

limited Even any kind of open literature describing the ANNs

modeling for downdraft biomass gasification was not found

However, MaurÃcio Bezerra et al.[73]proposed an artificial neural

network model for circulatingfluidized-bed gasifier and described the methods, results and validation in reference[33]

3.5 ASPEN Plus models ASPEN Plus is a chemical process optimization software, which was developed at Massachusetts Institute of Technology (MIT) It uses unit operation blocks, such as reactors, heaters, pumps, etc These blocks are joined using material and energy streams to create aflow sheet for the process The simulation calculations are performed using the in-built physical properties database The program uses a sequential modular (SM) approach, i.e solves the process scheme module by module, calculating the outlet stream properties using the inlet stream properties for each block This simulation package has been used for modeling coal and biomass power generation systems in many research projects Non-conventional fuels, e.g biomass, municipal solid waste (MSW), and specific coals, can be used by ASPEN Plus by incorporating a user-defined Fortran code User models can be created in Excel or written using Fortran and can be fully integrated into the ASPEN Plusflow sheet

To model a gasifier using ASPEN Plus, the overall process must

be broken down into a number of sub-processes For example a model may include the following zones: drying and pyrolysis, partial oxidation, and gasification Each zone may be represented

by a reactor/separator The mass and energy transfer across these zones can be incorporated in such a way that all unit operations’ combination represent the entire biomass gasifier

Many researchers have developed gasification models for coal and biomass using Aspen Plus De Kam et al [74] studied the potential of co‐products of the dry grind ethanol process and Corn Stover to generate combined heat and power (CHP) using Aspen Plus Mansaray et al.[75–77]developed and analyzed a model for gasification of rice husks using a fluidized-bed gasifier Ersoz et al

[78]developed a model by integrating fuel cell with coal or biomass gasification and simulated for the generation of electricity Aspen Plus contains built‐in models for common (conven-tional) downstream equipment and processes such as cyclone separators, heaters, and gas turbines, but it lacks a gasification model Validation of the model predictions with the experimental data is essential, because the downstream processing of syngas is largely dependent on the final syngas composition Since Aspen Plus database lacks the properties of the biomass, gasification models developed by many authors (Nikoo and Mahinpey [79], Sharma[60]; Shen et al.[80]; De Kam et al.[74]) incorporated an Ryield reactor, which decomposed the biomass into its individual components before feeding them into the gasification reactor (RGibbs) for further reactions to take place

Ramzan et al.[81]developed a steady state computer model for hybrid biomass gasifier using commercial simulation software ASPEN Plus The model used gasification of three different biomass feedstocks, i.e food waste (FW), municipal solid waste (MSW) and poultry waste (PW) The gasification process has been modeled in three stages In the first stage moisture content of the fuel is reduced before feeding to the reactor In second stage biomass is decomposed into volatile components and char The yield distribu-tion for this stage has been specified by using a FORTRAN statement

in calculator block The third stage models the partial oxidation and gasification reactions by minimizing Gibbs free energy

The Peng–Robinson equation of state with Boston–Mathias alpha function (PR–BM) has been used to estimate all physical properties of the conventional components in the gasification process For the estimation of the enthalpy and density for both biomass and ash, which are non-conventional components, HCOAL-GEN and DCOALIGT models were used Four ASPEN Plus blocks have been used to simulate the gasifier The “RStoic” block has been used

to model the drying of the biomass whereas the drying operation is

controlled by writing the FORTRAN statement in the calculator

block The RGibbs model is used to simulate the gasification of

biomass The RGibbs models chemical equilibrium by minimizing

Gibbs free energy Before feeding the biomass into the RGibbs block,

it was fed to the RYield reactor, which decomposes biomass into its

elements (C, H, O, N, S, etc.) This is based on the ultimate analysis of

the biomass compound

The simulationed model was validated with the experimental

data obtained by the authors from gasification of three wastes in a

lab-scale hybrid gasifier They have observed that the model results

were in good agreement with the experimental results for food

waste and municipal solid waste However, there is considerable

difference between the experimental and simulation results for

poultry waste The authors predict that the deviation may be due to

the specific composition of poultry waste

Kuo et al.[82]developed an Aspen Plus-based model to evaluate

the gasification potentials of raw bamboo, torrefied bamboo at 250 1C

(TB250), and torrefied bamboo at 300 1C (TB300) in a downdraft

fixed-bed gasifier using thermodynamic analysis The stream classes

were used to define the structure of simulation streams The

MCINCPSD stream class was used since biomass and ash properties

are not available in the standard Aspen Plus component database In

this study, the Peng–Robinson equation of state was utilized to

estimate the physical properties The enthalpies of nonconventional

components such as biomass and ash were calculated by the

HCOALGEN model, which includes a number of empirical correlations

for heat of combustion, heat of formation, and heat capacity For the

calculation of the density of biomass the DCOALIGT model was used

The authors have used the Gibbs energy minimization

approach in the gasification rector, i.e the RGibbs reactor, to

predict the equilibrium composition of the produced gas The

output compositions from water–gas shift reaction at various

operating conditions like steam/CO ratios and reaction

tempera-tures were compared with the experimental data of Chen et al

[83] It was reported that the predictions from the RGibbs reactor

in Aspen Plus closely matches with the results of Chen et al.[83]

The developed model of gasification was also validated with the

experimental data of Jayah et al.[25]

4 Conclusions

Modeling of biomass gasifiers is one of the important areas of

research that needs more attention In order to study complex

processes like gasification, without relying on the experimental

method of verification, which is time consuming and expensive,

modeling and simulation studies may prove to be helpful It has

been found that most of the modeling studies focus on

thermo-dynamic equilibrium modeling because it is simple and easy to

develop However, equilibrium modeling provides the maximum

yield achievable under equilibrium conditions which are not the

real conditions inside a gasifier Hence, the results produced are less

reliable and we should focus on more accurate modeling techniques

like kinetic modeling A very few researchers have developed

kinetic models for downdraft gasifier; some of them have

devel-oped only for reduction zone of the gasifier The complete transport

and kinetic model including the particle model for all zones for the

whole gasification process is yet to be developed There is a need to

develop both gasification bed model as well as a model for

individual particles in the bed ANN and ASPEN Plus models are

used to study the effect of inlet parameters, which need a large

number of experimental data input The models are optimization

tools to achieve the desired product composition However they do

not correlate with the actual operating conditions CFD models are

also one of the tools to develop 2D and 3D models with better

accuracy but it requires lot of kinetic and design data from the literature

Acknowledgment The authors acknowledge thefinancial assistance received from Department of Science and Technology, Government of India, New Delhi, for carrying out the present work under the fast-track scheme for young scientists (Grant no SB/FTP/ETA-213/2012) References

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