Artificial neural network based modeling to evaluate methane yield from biogas in a laboratory-scale anaerobic bioreactor

Vijay V. Nair; Hiya Dhar; Sunil Kumar; Arun Kumar Thalla; Somnath Mukherjee; Jonathan W C WONG

doi:10.1016/j.biortech.2016.03.046

Artificial neural network based modeling to evaluate methane yield from biogas in a laboratory-scale anaerobic bioreactor

Vijay V. Nair, Hiya Dhar, Sunil Kumar^*, Arun Kumar Thalla, Somnath Mukherjee, Jonathan W C WONG

^*Corresponding author for this work

Department of Biology

Research output: Contribution to journal › Journal article › peer-review

109 Citations (Scopus)

Abstract

The performance of a laboratory-scale anaerobic bioreactor was investigated in the present study to determine methane (CH₄) content in biogas yield from digestion of organic fraction of municipal solid waste (OFMSW). OFMSW consists of food waste, vegetable waste and yard trimming. An organic loading between 40 and 120 kg VS/m³ was applied in different runs of the bioreactor. The study was aimed to focus on the effects of various factors, such as pH, moisture content (MC), total volatile solids (TVS), volatile fatty acids (VFAs), and CH₄ fraction on biogas production. OFMSW witnessed high CH₄ yield as 346.65 L CH₄/kg VS added. A target of 60–70% of CH₄ fraction in biogas was set as an optimized condition. The experimental results were statistically optimized by application of ANN model using free forward back propagation in MATLAB environment.

Original language	English
Pages (from-to)	90-99
Number of pages	10
Journal	Bioresource Technology
Volume	217
DOIs	https://doi.org/10.1016/j.biortech.2016.03.046
Publication status	Published - 1 Oct 2016

Scopus Subject Areas

Bioengineering
Environmental Engineering
Renewable Energy, Sustainability and the Environment
Waste Management and Disposal

User-Defined Keywords

Anaerobic bioreactor
Artificial neural network
Methane
Optimization
Total volatile solid

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.biortech.2016.03.046

Cite this

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title = "Artificial neural network based modeling to evaluate methane yield from biogas in a laboratory-scale anaerobic bioreactor",

abstract = "The performance of a laboratory-scale anaerobic bioreactor was investigated in the present study to determine methane (CH4) content in biogas yield from digestion of organic fraction of municipal solid waste (OFMSW). OFMSW consists of food waste, vegetable waste and yard trimming. An organic loading between 40 and 120 kg VS/m3 was applied in different runs of the bioreactor. The study was aimed to focus on the effects of various factors, such as pH, moisture content (MC), total volatile solids (TVS), volatile fatty acids (VFAs), and CH4 fraction on biogas production. OFMSW witnessed high CH4 yield as 346.65 L CH4/kg VS added. A target of 60–70% of CH4 fraction in biogas was set as an optimized condition. The experimental results were statistically optimized by application of ANN model using free forward back propagation in MATLAB environment.",

keywords = "Anaerobic bioreactor, Artificial neural network, Methane, Optimization, Total volatile solid",

author = "Nair, {Vijay V.} and Hiya Dhar and Sunil Kumar and Thalla, {Arun Kumar} and Somnath Mukherjee and WONG, {Jonathan W C}",

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T1 - Artificial neural network based modeling to evaluate methane yield from biogas in a laboratory-scale anaerobic bioreactor

AU - Nair, Vijay V.

AU - Dhar, Hiya

AU - Kumar, Sunil

AU - Thalla, Arun Kumar

AU - Mukherjee, Somnath

AU - WONG, Jonathan W C

PY - 2016/10/1

Y1 - 2016/10/1

N2 - The performance of a laboratory-scale anaerobic bioreactor was investigated in the present study to determine methane (CH4) content in biogas yield from digestion of organic fraction of municipal solid waste (OFMSW). OFMSW consists of food waste, vegetable waste and yard trimming. An organic loading between 40 and 120 kg VS/m3 was applied in different runs of the bioreactor. The study was aimed to focus on the effects of various factors, such as pH, moisture content (MC), total volatile solids (TVS), volatile fatty acids (VFAs), and CH4 fraction on biogas production. OFMSW witnessed high CH4 yield as 346.65 L CH4/kg VS added. A target of 60–70% of CH4 fraction in biogas was set as an optimized condition. The experimental results were statistically optimized by application of ANN model using free forward back propagation in MATLAB environment.

AB - The performance of a laboratory-scale anaerobic bioreactor was investigated in the present study to determine methane (CH4) content in biogas yield from digestion of organic fraction of municipal solid waste (OFMSW). OFMSW consists of food waste, vegetable waste and yard trimming. An organic loading between 40 and 120 kg VS/m3 was applied in different runs of the bioreactor. The study was aimed to focus on the effects of various factors, such as pH, moisture content (MC), total volatile solids (TVS), volatile fatty acids (VFAs), and CH4 fraction on biogas production. OFMSW witnessed high CH4 yield as 346.65 L CH4/kg VS added. A target of 60–70% of CH4 fraction in biogas was set as an optimized condition. The experimental results were statistically optimized by application of ANN model using free forward back propagation in MATLAB environment.

KW - Anaerobic bioreactor

KW - Artificial neural network

KW - Methane

KW - Optimization

KW - Total volatile solid

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U2 - 10.1016/j.biortech.2016.03.046

DO - 10.1016/j.biortech.2016.03.046

M3 - Journal article

C2 - 27005793

AN - SCOPUS:84961233699

SN - 0960-8524

VL - 217

SP - 90

EP - 99

JO - Bioresource Technology

JF - Bioresource Technology

ER -

Artificial neural network based modeling to evaluate methane yield from biogas in a laboratory-scale anaerobic bioreactor

Abstract

Scopus Subject Areas

User-Defined Keywords

UN SDGs

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