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Performance prediction of fluidised bed gasification of biomass using experimental data-based simulation models

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Abstract

Wood sawdust is gasified in air-fluidized bed with steam injection for the enrichment of product gas with hydrogen. A gasification experimental setup with sand as bed material is designed and developed for this purpose with a biomass feed rate of 10.3 kg/h. Air and steam flow rates are varied between 0.0042–0.0063 and 0–0.0072 m3/h, respectively. Axial variations of temperature and pressure inside the reactor shell are investigated. The data for the product gas composition from the experiment are utilised to develop two models. One is a feedforward artificial neural network (ANN) model for the prediction of gasification temperature and product gas composition. The second is a Redlich–Kwong real gas equilibrium correction model incorporating tar (aromatic hydrocarbons) and unconverted char to predict the product gas composition, heating value and thermodynamic efficiencies. Good accuracy of ANN prediction with experimental results is achieved based on the computed statistical parameters of comparison such as coefficient of correlation, root mean square error (RMSE), average percentage error and covariance. The corrected equilibrium model developed by introducing correction factors for real gas equilibrium constants shows satisfactory agreement (RMSE = 5.96) with the experimental values. Maximum concentration of hydrogen achieved in the experiments is 29.1 % at the equivalence ratio (ER) = 0.277 and steam to biomass ratio (SBR) = 2.53. The corresponding predicted values are 28.2 % for ANN model and 31.6 % for corrected equilibrium model. The corrected equilibrium model for wood sawdust is validated with major air–steam gasification experimental results of other biomass materials and is found to be 95.1 % accurate on average. It is revealed from the study that the ANN model (RMSE = 2.64) is a better predictor for the product gas composition than the corrected real gas equilibrium model (RMSE = 5.96). The study proposes a more comprehensive ANN model capable of simulating various process conditions in fluidised bed gasification applicable to variety of biomass feedstocks.

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Abbreviations

ANN:

Artificial neural network

CC:

Coefficient of correlation

C d :

Coefficient of discharge

d o :

Diameter of the air orifice in distributor plate (in meter)

d p :

Diameter of sand particles (in meter)

ER:

Equivalence ratio

Exb :

Exergy rate of biomass (in megajoule per hour)

Exsteam :

Exergy rate of steam (in megajoule per hour)

FA:

Fluidising agent

FB:

Fluidised bed

GA:

Gasifying agent

HHVdg :

Higher heating value of dry gas (in megajoule per normal cubic meter)

H steam :

Enthalpy of supplied steam (in megajoule per kilogram)

H :

Height of the bubbling fluidised bed (in meter)

H mf :

Bed height at minimum fluidisation condition (in meter)

LHVdg :

Lower heating value of dry gas (in megajoule per normal cubic meter)

LHVb :

Lower heating value of biomass (in megajoule per kilogram)

MAPE:

Mean average percentage error

MS:

Mild steel

m N,a :

Mass flow rate of nitrogen in feed air (in kilogram per hour)

m N,b :

Mass flow rate of nitrogen in biomass (in kilogram per hour)

m b :

Mass flow rate of biomass (in kilogram per hour)

m steam :

Mass flow rate of supplied steam (in kilogram per hour)

M N :

Molecular weight of nitrogen (in kilogram per kilomole)

N :

Number of data points

RMSE:

Root mean square error

Remf :

Reynolds number at minimum fluidisation velocity

SBR:

Steam to biomass ratio (in kilogram per kilogram)

TExdg :

Total exergy rate of dry gas (in megajoule per hour)

U f :

Fluidisation velocity during gasification (in meter per second)

U mf :

Minimum fluidisation velocity (in meter per second)

U t :

Terminal velocity (in meter per second)

V dg :

Volume flow rate of dry gas (in normal cubic meter per hour)

V m :

Molar volume of ideal gas (in normal cubic meter per kilomole)

wt%:

Percentage by weight

X i :

Experimental data

Y :

Mole fraction of gas

Y i :

ANN data

ρ g :

Density of air (in kilogram per cubic meter)

μ :

Dynamic viscosity of air (in kilogram per meter per second)

ρ p :

Density of sand particles (in kilogram per cubic meter)

∆H :

Heat of reaction (in kilojoule per mole)

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Acknowledgments

The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper.

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Authors and Affiliations

  1. Department of Mechanical Engineering, National Institute of Technology Calicut, Calicut, India

    C. C. Sreejith, C. Muraleedharan & P. Arun

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  1. C. C. Sreejith
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Correspondence to C. C. Sreejith.

Appendix

Appendix

A1: Minimum fluidisation velocity, U mf is calculated by Eq. (A1) [38].

$$ {\mathrm{Re}}_{\operatorname{mf}}=\frac{U_{mf}{d}_{prg}}{m}={\left[{c}_1^2+{C}_2\operatorname{Ar}\right]}^0.5-{c}_1 $$
(A1)

Where, Archimedes number,

$$ \mathrm{Ar}=\frac{\rho_g\left({\rho}_p-{\rho}_g\right)g\kern0.5em {d}_p^3}{m^2} $$
(A2)

The values of the empirical constants C1 and C2 are 27.2 and 0.0408, respectively. The relevant property values of the fluidising agent (air) and bed material (sand) are given in Table 12. The computed value of Umf is 0.155 m/s. For spherical particles, the terminal velocity based on the corresponding Reynolds number obtained from Eq. (A3) is 3.245 m/s. For 0.4 < Re < 500: Intermediate law [38]

Table 12 Properties of fluidising agent (air) and bed material (sand)
Full size table
$$ \frac{d_p{U}_t{\rho}_g}{\mu }={\left[\frac{ Ar}{7}.5\right]}^0.666 $$
(A3)

The superficial velocity of the gas to be used during the gasifier operation was established considering the relation (Eq. (A4)) between the expanded and minimum heights of the fluidised bed suggested by Basu [38]. The superficial velocity is computed as 0.732 m/s.

$$ \frac{H}{H_{mf}}=1+\frac{10.978.{\left({U}_f-{U}_{mf}\right)}^{0.738}{\rho}_p^0.376\kern0.5em {d}_p^1.006}{U_{mf}^0.937\kern0.5em {\rho}_g^0.126} $$
(A4)

By fixing diameter of the orifice (d o ) as 2 mm, the number of orifices required per unit area (in square meter) of the plate is arrived as 1,695. For a distributor plate of diameter 280 mm, the required number of air flow orifices is 104.31 ≈ 105.

A2: Extracts of the equilibrium model formulation reported by the authors elsewhere [37] is presented here. The global chemical equation for air–steam gasification is given by Eq. (A5). Reactant side is contributed by biomass, steam, moisture in biomass, and air. At the product side, in addition to dry gases, tar (modelled as mixture of benzene, toluene and naphthalene in 1:2.5:6.5 proportions by weight [45, 46]), steam and unconverted char (modelled as carbon) are present.

$$ {n}_{\mathrm{biomass}}{C}_x{H}_y{O}_z+{n}_{steam}{H}_2O+w{H}_2O+{n}_{air}\left({O}_2+3.76{N}_2\right)\to {n}_{co} CO+{n}_{c{o}_2}C{O}_2+{n}_{H_2}{H}_2+{n}_{c{H}_4}C{H}_4+{n}_{H_2O}{H}_2O(g)+{n}_{tar} tar+{n}_{uncon}C $$
(A5)

Where, x, y and z are the atoms present in the chemical formula of biomass, n biomass, n steam and n air are the number of moles of biomass, steam and air respectively, ‘w’ is the weight of moisture present in the biomass, n co, n co2, \( {n}_{{\mathrm{H}}_2} \), \( {n}_{{\mathrm{CH}}_4} \), \( {n}_{{\mathrm{H}}_2\mathrm{O}} \), n tar and n uncon are the number of moles of the respective species at the product side.

The yield (mole number) of the unconverted char is dictated by the carbon conversion efficiency, which is defined as the ratio of mass of total carbon between product and reactant (biomass) streams. Thus, by fixing carbon conversion efficiency, three elemental balance equations (Eqs. (A6)–(A8)) can be solved in conjunction with the equations for equilibrium constants of methane hydrogenation and WGS reactions.

$$ \mathrm{Carbon}:{n}_{biomass} x={n}_{co}+{n}_{c{o}_2}+{n}_{C{H}_4}+{n}_{uncon}+8.85\kern0.5em {n}_{tar} $$
(A6)
$$ \mathrm{Hydrogen}:{n}_{biomass}y+2{n}_{steam}+2w=2{n}_{{\operatorname{H}}_2}+4{n}_{C{H}_4}+2{n}_{{\operatorname{H}}_2O}+7.8 $$
(A7)
$$ \mathrm{Oxygen}:{n}_{biomass}\kern0.5em z+{n}_{steam}+w+2{n}_{air}={n}_{co}+2{n}_{c{o}_2}+{n}_{H_20} $$
(A8)

The model is simulated by fixing the carbon conversion at various levels (60, 70 % etc.) for parametric representation of the results. Tar yield is specified by Corella’s [42] correlation (Eq. A9) in terms of gasification temperature (T), for its mass fraction in the product mixture. Mass balance is applied between the reactant and product sides of Eq. (A5) to estimate the mass of product mixture. The yield of tar is estimated using Eq. (A10).

$$ {\operatorname{tar}}_{wt.\%}=35.98 \exp \left(-,0.0029,T\right) $$
(A9)
$$ {n}_{tar}=\frac{{\operatorname{tar}}_{wt.\%}}{100}\left(\frac{\mathrm{biomass}\kern0.5em feed+ SBR\left( biomass\kern0.5em feed\right)+ moisture\kern0.5em in\kern0.5em biomass}{\mathrm{m} olecular\kern0.5em weight\kern0.5em of\kern0.5em tar}\right) $$
(A10)

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Sreejith, C.C., Muraleedharan, C. & Arun, P. Performance prediction of fluidised bed gasification of biomass using experimental data-based simulation models. Biomass Conv. Bioref. 3, 283–304 (2013). https://doi.org/10.1007/s13399-013-0083-5

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