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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Nair, V.V. | |
| dc.contributor.author | Dhar, H. | |
| dc.contributor.author | Kumar, S. | |
| dc.contributor.author | Thalla, A.K. | |
| dc.contributor.author | Mukherjee, S. | |
| dc.contributor.author | Wong, J.W.C. | |
| dc.date.accessioned | 2020-03-31T08:19:12Z | - |
| dc.date.available | 2020-03-31T08:19:12Z | - |
| dc.date.issued | 2016 | |
| dc.identifier.citation | Bioresource Technology, 2016, Vol.217, , pp.90-99 | en_US |
| dc.identifier.uri | http://idr.nitk.ac.in/jspui/handle/123456789/10445 | - |
| dc.description.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. 2016 Elsevier Ltd | en_US |
| dc.title | Artificial neural network based modeling to evaluate methane yield from biogas in a laboratory-scale anaerobic bioreactor | en_US |
| dc.type | Article | en_US |
| Appears in Collections: | 1. Journal Articles | |
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