Modelling and optimisation of biomass fluidised bed gasifier

Jarungthammachote and Dutta [14] presented a thermodynamic model based on equilibrium constant for predicting the syngas composition of a downdraft waste gasifier without considering the

Modelling and optimisation of biomass fluidised bed gasifier

Rex T.L Ng, Douglas H.S Tay, Wan Azlina Wan Ab Karim Ghani, Denny K.S Ng

PII: S1359-4311(13)00226-3

DOI: 10.1016/j.applthermaleng.2013.03.048

Reference: ATE 4717

To appear in: Applied Thermal Engineering

Received Date: 14 November 2012

Accepted Date: 26 March 2013

Please cite this article as: R.T.L Ng, D.H.S Tay, W.A Wan Ab Karim Ghani, D.K.S Ng, Modellingand optimisation of biomass fluidised bed gasifier, Applied Thermal Engineering (2013), doi: 10.1016/j.applthermaleng.2013.03.048

This is a PDF file of an unedited manuscript that has been accepted for publication As a service toour customers we are providing this early version of the manuscript The manuscript will undergocopyediting, typesetting, and review of the resulting proof before it is published in its final form Pleasenote that during the production process errors may be discovered which could affect the content, and alllegal disclaimers that apply to the journal pertain

MODELLING AND OPTIMISATION OF BIOMASS FLUIDISED BED GASIFIER

Rex T L Nga,b, Douglas H S Taya, Wan Azlina Wan Ab Karim Ghanic, Denny K S Nga*

a

Department of Chemical and Environmental Engineering/

Centre of Excellence for Green Technologies, University of Nottingham, Malaysia,

Broga Road, 43500 Semenyih, Selangor, Malaysia

b

GGS Eco Solutions Sdn Bhd., Wisma Zelan, Suite G.12A & 1.12B, Ground Floor, No 1, Jalan

Tasik Permaisuri 2,Bandar Tun Razak, Cheras, 56000 Kuala Lumpur, Malaysia

c

Department of Chemical & Environmental Engineering, Faculty of Engineering, Universiti

Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia

Email: ngtonglip@gmail.com; taydouglas@yahoo.com; wanaz@eng.upm.edu.my;

proposed model is also reformulated for different types of biomass feedstock (e.g., rice husk, coconut shell, etc.) Based on the developed models, the operating condition of the gasifier can

be optimised and the composition of the syngas can also be determined

Keywords: Biomass; Gasification; Modelling; Optimisation; Biorefinery

Biomass has been distinguished as one of the promising renewable energy sources for

power generation and production of chemicals It could be converted into various types of

value-added products via various conversion technologies (e.g., biological, thermochemical,

physical, etc.) Biorefinery, a facility which has a similar concept to petroleum refinery, has

been proposed to integrate the biomass conversion processes and equipment to produce

various biochemicals, biofuels and bioenergy with minimum waste generation [1] The

concept of integrated biorefinery, which integrates multiple platforms as a whole, has been

proposed to have more flexibility in product generation [2] In integrated biorefinery, the

overall energy consumption is lower compared to technology that operates independently [3]

Various works have been presented in systematic design of integrated biorefineries based on

mathematical optimisation models, such as modular optimisation [4], fuzzy optimisation [5],

etc Recently, Martín and Grossmann [6] reviewed the recent works on integration of

production processes, including first, second and third generation of biofuels

Note that gasification process is recognised as one of the most promising technologies

to convert biomass into energy and value-added products in an integrated biorefinery [7]

Therefore, it is essential to further analyse and optimise the gasification process in order to

increase the overall performance of the integrated biorefinery Gasification typically operates

in a temperature range of 600˚C – 1400˚C [8], with a controlled supply of oxygen and/or

steam to convert biomass into a gaseous mixture which is commonly known as synthesis gas

or syngas These gas mixtures consist of carbon dioxide (CO2), steam (H2O), methane (CH4),

carbon monoxide (CO) and hydrogen (H2) Other by-products include gaseous hydrocarbons

(CHs), tars, char, inorganic constituents, and ashes were also produced from the gasification

process other than production of syngas [9] Syngas produced from biomass gasification

through the robust thermal conversion can be used as feedstock for the production of liquid

fuels and chemicals as well as the generation of heat and power [10] Waste heat generated in

the biomass gasification system can be utilised in utility systems through heat integration

[11]

Generally, gasification modelling is predominantly divided into two categories:

kinetic modelling and thermodynamic equilibrium modelling [12] As shown in literature

[12], there are two methods for thermodynamic equilibrium modelling which are

stoichiometric and non-stoichiometric equilibrium The stoichiometric method is based on

stoichiometric reactions while non-stoichiometric method is based on minimising the total

Gibbs free energy in the system It is noted that equilibrium models with or without char

formation were widely studied and presented for predicting syngas composition, coal, and

wastes However, in the actual gasification system, equilibrium chemical reactions are hardly

entertained due to kinetic limitations [13] Jarungthammachote and Dutta [14] presented a

thermodynamic model based on equilibrium constant for predicting the syngas composition

of a downdraft waste gasifier without considering the char The model was also enhanced by

multiplying equilibrium constants with equilibrium constant Later, Huang and Ramaswamy

[15] further enhanced the prediction of gas composition by considering and without

considering char formation via the same approach However, the prediction of syngas

composition via the equilibrium model with char formation does not match well with

experimental results Recently, Barman et al [16] presented an equilibrium model for fixed

bed downdraft biomass gasifier by considering tar production Modification to the model was

made to upgrade their equilibrium model to match experimental data.

Conceptual designs of gasification-based biorefineries using thermodynamic

equilibrium optimisation mathematical model [10] and graphical targeting approach via

O ternary diagram [17] was presented Oliveira and Silva [18] developed kinetic

mathematical model in predicting the temperature and mole fraction of syngas A

non-stoichiometric equilibrium model for a downdraft gasifier was developed in order to predict

syngas composition from miscanthus, olive wood and cardoon gasification [19] On the other

hand, process modelling of biomass gasification combined heat and power plant by

considering tars in syngas was simulated via ASPEN Plus [20] More recently,

Computational Fluid Dynamics (CFD) modelling was extended to predict characteristics of

fuels in combustion and gasification processes of fluidized bed gasifier [21] A steady state

mathematical model of circulating fluidized bed biomass gasifier had also been presented by

integrating the hydrodynamics, gasification reactions, as well as heat and mass balances [22]

As shown in the previous works done by Jarungthammachote and Dutta [14], Huang

and Ramaswamy [15] and Barman et al [16], the gasification models were improved by

introducing correction factors to modify the equilibrium constants in each reaction However,

the proposed models are limited to single temperature None of the abovementioned

modelling works developed for gasification process at different temperatures based on

experimental data Most recently, Ng et al [23] proposed a modelling of biomass gasifier for

palm kernel shell (PKS) with integrating a function of temperature However, the presented

gasification modelling was limited to one type of biomass feedstock

This paper describes a systematic approach of gasifier modelling which can be used to

predict syngas composition for different types of biomass To illustrate the proposed

approach, a stoichiometric equilibrium model of biomass fluidised bed gasifier is first

developed Experimental results of bagasse are used to validate the developed model Note

that the proposed approach can be reformulated easily for different biomass feedstock (rice

husk and coconut shell) by changing its empirical formula into proposed model The

developed model is then further improved by including correction factors to the equilibrium

constants with a function of temperature In order to ensure the accuracy of the model,

predicted syngas composition is then validated with the experimental results (e.g., bagasse

[24], rice husk [25] and coconut shell [24]) Based on the developed models, the gasifier for

different biomass feedstocks can be optimised to achieve various objectives, such as

maximum hydrogen production A case study with different biomass feedstock was solved to

illustrate the approach

2 Biomass Gasification Modelling

As shown in the literature [10], biomass can be generally defined as CaHbOcNd which

can be determined from the ultimate analysis of biomass Ultimate analysis gives the

compositions of the biomass in weight percentage of carbon (C), hydrogen (H), oxygen (O)

and nitrogen (N) The overall gasification reaction with air (79% N2 and 21% O2), steam

(H2O), and CO2 can be written as [10]:

CaHbOcNd + (w + v) H2O + hO2 + (79/21)hN2 + jCO2 →

n1H2 + n2CO + n3CO2 + n4H2O + n5CH4 + n6N2 + n7C

where a, b, c, and d represent the number of atoms of carbon (C), hydrogen (H), oxygen (O)

and nitrogen (N) of biomass; w, is stoichiometric coefficient (per mole of biomass feedstock)

of biomass moisture, h is stoichiometric coefficient (per mole of biomass feedstock) of

oxygen and nitrogen which supplied as gasification agent from air, v and j are stoichiometric

coefficients (per mole of biomass feedstock) of steam and carbon dioxide as gasification

agent; n

1 – n

7 are the stoichiometric coefficients of H2, CO, CO2, H2O, CH4, N2 and solid carbon (C)

Based on the experiment results [24], other than syngas and solid carbon, by-products

such hydrocarbons (CHs), tars, inorganic constituents, and ash are obtained throughout the

experiment More details on the experiments can be found in [24] Since the results show

additional products are generated, the gasification reaction [10] presented previously is

modified by introducing an additional term, CxHyOz to represent the formation of

hydrocarbons The modified overall gasification reaction is written as below:

where x, y, and z represent the number of atoms of C, H, and O of hydrocarbons; n8 are the

stoichiometric coefficient of hydrocarbons (CxHyOz)

Note that the overall gasification reaction can be governed by the overall mass

balance, enthalpy balance and thermodynamic equilibrium equations As the main objective

of this work is to predict and optimise the syngas composition without considering additional

heat transferred into the gasifier, therefore, the enthalpy balance can be neglected In this

study, only mass balance and thermodynamic equilibrium equations are taken into account in

modelling work To further simplify the modelling efforts, the gasifier is assumed to operate

under steady state conditions and atmospheric pressure [26] Other than that, syngas behaves

as an ideal gas, whereas ash and N2 are assumed to be inert at high temperature [10] With

such assumptions, the complexity of gasification model can be significantly reduced

Furthermore, the computation time for solving the model can also be reduced However, the

proposed model is able to generate reasonable prediction of the syngas composition as

compared with the experimental results

Based on modified gasification reaction, the atomic balances of C, H, O and N in

biomass gasification are expressed as followed:

where f i is the molar flowrate of the biomass i

In a thermodynamic equilibrium model of gasification, five reactions that involve all

chemical species are considered Boudouard equilibrium, methane decomposition and

heterogeneous water-gas shift reactions are endothermic reactions (positive value of heat of

reaction); while hydrogenating gasification and water-gas shift reactions are exothermic

reactions (negative value of heat of reaction) [27]

As shown in previous work [28], two independent reactions of five reactions need to

be considered in the case where no solid carbon remains in the equilibrium state (n7= 0)

Nevertheless, in the case where solid carbon remains as a gasification product because of an

oxidant deficit, three independent reactions are needed for the equilibrium calculations In

this study, since solid carbon (i.e., ash) remains as a significant gasification product obtained

in the experiment; thus, the formation of solid carbon cannot be neglected Three independent

reactions are needed for the equilibrium calculations Methane decomposition, water-gas shift

reaction and heterogeneous water gas-shift reactions are selected to represent the interaction

of all components

In order to determine the syngas composition, equilibrium constant of three selected

reactions are required to be included in the model Although all gaseous components are

assumed to be ideal gases, the reactions might not interact ideally The models can be further

altered by multiplying the equilibrium constants with correction factors (α1 – α3) to yield a

corrected activity coefficient of reactants and products [14-16] The corrected equilibrium

constants corresponding to stoichiometric coefficients of components are shown as below:

2 5 4 2

3 1 MD

m m

m m

4 2

3 1 WGS

2

m m

m m

P m

m m K

4

2 1 HWGS

where KMD, KWGS and KHWGS are equilibrium constants for methane decomposition,

water-gas shift reaction and heterogeneous water water-gas-shift reaction; m

1 – m

5 is the molar fraction of

H2, CO, CO2, H2O, and CH4; P is the operating pressure of the gasifier

To determine the equilibrium constants by reaction temperature T, the standard free

energy formation and the thermodynamic relation is expressed as:

where kH2O, kCH4, kCO and kCO2are the thermodynamic equilibrium constants for the formation

reaction of H2O, CH4, CO and CO2 at the reaction temperature T

Based on the work of Baron and co-workers [29], the kH2O, kCH4, kCO and kCO2 can be

expressed by the following equations:

The modified models are solved to predict the composition of syngas at different

operating condition (temperature) and amount of oxidants (steam and air) To compare the

predicted syngas composition from the modified model and experimental results, summation

the number of syngas speces considered in determining RMS

3 Model Modification, Analysis and Validation

As mentioned earlier, the experimental results of bagasse reported in [24] are used to

validate the developed model Based on the ultimate analysis, the empirical formula of

bagasse is given as CH1.452O0.807N0.023. As shown in the literature, the experiments were

conducted at different temperature (1,073 K – 1,373 K) and the results are also summarised

in Table 1 Note that the results are presented in mole basis In this work, Lingo 13.0 with

Global solver is used as a platform to develop the thermodynamic equilibrium model It uses

a branch-and-bound (B&B) algorithm that combined with linearization to find globally

optimal solutions to non-linear programming (NLP) [30]

[Table 1]

3.1 Determination of Correction Factor

In order to enhance the prediction of syngas composition for a specific type of

gasifier, correction factor can be included As mentioned previously, the gasification is hardly

entertained equilibrium reaction; therefore, with introducing the correction factors, the extent

of reactions can be determined Note also that in this works, the correction factors are

modelled as a function of temperature instead of single temperature as shown in [14 - 16]

With such improvement, the model of the gasifier can be optimised based on the given

temperature range The optimum gasification temperature can then be determined

In this work, a NLP model is solved by inserting operating conditions and syngas

composition as shown in Table 1 with the constraints in Equations 1 – 14 via Lingo 13.0 with

Global solver to determine the correction factors (α1, α2 and α3) The relationships between

operating temperature and correction factors (α1, α2 and α3) of bagasse is plotted as shown in

are not equal to one This indicates that in the actual gasification system, the chemical

reactions do not achieve equilibrium This is due to kinetic limitations (including mass/heat

transfer) [13] Therefore, modification of model is needed in order to predict the actual

performance of the gasifier

3.2 Modification of Model

In order to further improve the accuracy of the model, the correction factors are taken

as a function of temperature Via such modification, the model can predict the syngas

composition more accurately in the given operating temperature range as shown in Figure 1

Next, regression is conducted to determine the relationships between the correction factors

with operating temperatures The outliers are removed for the analysis to simplify the

complexity of model The regression analysis is performed via multiple regression in

Microsoft Office Excel which performs both linear and exponential multiple regression

analysis An equation with higher value of R2 is chosen to represent the relationship between

two variables

Based on Figure 1, high values of R2 of 0.9621, 0.9713 and 0.9772 are obtained the

regression analysis It shows clearly that correction factors are changing with temperatures

instead of a constant as in previous works [14-16] The relationships of temperature and

correction factors for bagasse are written in Equations 16 – 18 Unitless correction factors

(α1, α2 and α3) can be determined with given gasifier operating temperature (T) via Equations

16 – 18 Better estimation of syngas composition in actual gasification system can be made

by multiplying correction factors (α

36.299exp

5.1252exp

17.612exp

Note that there are a total of eleven unknowns (n1 – n8) and only seven equations

(Equations 1 – 7) are available Based on the degree of freedom analysis, an additional

equation is needed to solve coefficient of hydrocarbons, n8 Therefore, additional mass

relationship and ratio of mass of hydrocarbons to mass of biomass(R/B) is included in the

modified model which considers the hydrocarbons formation The relationship of temperature

and R/B for bagasse is determined and plotted as illustrated in Figure 2 Based on the

regression in Figure 2, the relationship of temperature and R/B for bagasse is determined in

Equation 19

58.806

71.641/ =T−

B

[Figure 2]

3.3 Analysis of Model

The actual equilibrium constants are determined by multiplying the ideal equilibrium

constant (KMD, KWGS and KHWGS) with the correction factors (α1, α2 and α3) The actual

equilibrium performance for different biomasses is plotted in Figure 3 Based on Figure 3, the

actual equilibrium constants of methane decomposition (MD) and heterogeneous water

gas-shift (HWGS) reactions increase linearly with increasing temperature, which is also in

agreement with Van’t Hoff equation [31] Both MD and HWGS reactions are endothermic in

which continuous input of energy is needed for the reaction to occur These lead to a negative

enthalpy change On the contrary, the water gas-shift (WGS) reaction is exothermic reaction

and equilibrium constant is decreasing exponentially with the increase in temperature

However, as shown in Figure 3, the actual equilibrium constants of WGS reaction are

increasing with temperature According to Le Chatelier’s principle, an increase in

temperature drives an exothermic reaction toward the reactants [32] Based on the reaction in

Equation 10, an increase of temperature, the WGS reaction tends to shift towards reactants

side Therefore, CO cannot be totally converted into CO2 in the reformer [33] and significant

amount of CO is observed As shown in Table 1, the compositions of CO2 in syngas produced

from different biomass decrease with the increase in temperature

The models with different temperatures are solved based on the constraints in

Equations 1 – 19 via Lingo 13.0 with Global solver at same operating conditions in the

experiments The prediction of major syngas (H2, CO, CO2, and CH4) and solid carbon (C)

compositions are summarised in Table 2 In addition, RMS error is also presented to compare

the modified model result with the experimental results These major syngas and carbon

compositions (H2, CO, CO2, CH4 and C) are considered in determining the RMS of each

biomass Thus, D term in Equation 22 is taken as 5 in this work

[Table 2]

Table 2 shows that the modified model is relatively accurate to predict the

composition of syngas and solid carbon The RMS values of bagasse are 0.141, 0.433, 0.251,

0.148 and 0.262 for the syngas prediction at temperature of 1,073 K, 1,123 K, 1,173 K, 1,223

K and 1,273 K Thus, the average RMS of bagasse at these five temperatures is determined as

0.247 The above proposed approach can be repeated by modelling biomass gasification of

other biomass feedstocks (e.g., rice husk, coconut shell and PKS [23]) As shown in Table 2,

the average RMS of rice husk, coconut shell, and PKS at different temperature are

determined as 0.319, 0.451 and 0.306 It is worth mentioning that the proposed approach for

modelling of gasification is more accurate as compared with the previous work [14] Note

that previous work presented RMS errors of 0.882 and 3.917 for 16.0 MC% and 14.0 MC%

of rubber wood chips [14] Based on the proposed approach, RMS errors for bagasse, rice

Based on the above developed model, the gasifier can be further optimised to achieve

various objectives, for example, maximising hydrogen production Due to limitations of

gasifier available in laboratory, additional process constraints are included in the model In

this work, the gasifier is set to be operated at a pressure (P) of 1 atm, maximum air flowrate

(f i) of 1.1 Nm3/h, and an equivalence ratio (ER) range of 0.23 – 0.27

Meanwhile, the gasification temperature (T) is set in the conventional range of 1,073

– 1,273 K Based on the abovementioned limitations of the gasifier, additional constraints

(Equations 20 – 23) are included in the model to provide better representation of the gasifier

Since hydrogen fuel from the biomass waste is the best supersede for fossil fuels, it

has a great potential to be used as an energy carrier such as fuel cell that can be applied to

power cars, factories and also for home usages in the future [24] Therefore, in order to

maximise the hydrogen production in gasifier, the optimisation objective is set as

Maximise n1

As mentioned previously, different types of feedstock would be able to generate

different compositions of syngas Therefore, in this work, different types of biomass

(bagasse, rice husk, coconut shell and PKS) are also analysed The presented gasification

models are solved with the optimisation objective with additional constraints in Equations 20

– 23 The optimised results for different feedstocks are summarised in Table 3

[Table 3]

As shown in Table 3, total H2 production of 9.76 mol is targeted in bagasse

gasification Note that CO, CO2, CH4 and solid carbon ash compositions are targeted 2.04

mol, 0.90 mol, 2.49 mol, and 2.34 mol For rice husk gasification, maximum H2 production is

targeted as 10.07 mol, and the composition of CO, CO2, CH4 and solid carbon ash are 2.73

mol, 1.26 mol, 3.21 mol, and 11.35 mol Meanwhile, for coconut shell gasification, the

composition of syngas CO, CO2, CH4 and solid carbon ash are determined as 2.77 mol, 0.94

mol, 2.09 mol, and 4.73 mol with maximum H2 production of 11.54 mol Based on Ng et al

[23], maximum H2 production of 13.26 mol can be produced from PKS and compositions of

syngas CO, CO2, CH4 and solid carbon ash in PKS gasification are located as 3.18 mol, 1.02

mol, 2.82 mol, and 6.86 mol, respectively

As shown in Table 3, the optimised temperature and ER are targeted as 1,273 K and

0.27 for all types of biomass The gasification of PKS achieves the highest yield of hydrogen

production (28.48 g H2/kg PKS) followed by coconut shell, rice husk, and bagasse Note that

gasification of bagasse produces the lowest ash (0.04 kg C/kg bagasse) as compared with the

other biomass feedstocks From the optimisation models, the produced gas quality and

performance with PKS seems to be more efficient than with other biomass However, the