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

Research output: Contribution to journalArticlepeer-review

51 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 languageEnglish
Pages (from-to)90-99
Number of pages10
JournalBioresource Technology
Volume217
DOIs
Publication statusPublished - 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

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