 Modeling and optimization of biogas production from a waste digester using artificial neural network and genetic algorithmH. Abu Qdais, K. Bani Hani and N. Shatnawi Resources, Conservation & Recycling, 2010, vol. 54, issue 6, 359-363 Abstract: Artificial neural networks (ANNs) and genetic algorithms (GA) are considered among the latest tools that are used to solve complicated problems that cannot be solved by conventional solutions. The present study utilizes the ANN and GA as tools for simulating and optimizing of biogas production process from the digester of Russaifah biogas plant in Jordan. Operational data of the plant for a period of 177 days were collected and employed in the analysis. The study considered the effect of digester operational parameters, such as temperature (T), total solids (TS), total volatile solids (TVS), and pH on the biogas yield. A multi-layer ANN model with two hidden layers was trained to simulate the digester operation and to predict the methane production. The performance of the ANN model is verified and demonstrated the effectiveness of the model to predict the methane production accurately with correlation coefficient of 0.87. Keywords: Biogas; Digester; Waste; Artificial neural networks; Optimization; Genetic algorithms; Jordan (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:recore:v:54:y:2010:i:6:p:359-363 DOI: 10.1016/j.resconrec.2009.08.012 Access Statistics for this article Resources, Conservation & Recycling is currently edited by Ming Xu More articles in Resources, Conservation & Recycling from Elsevier |
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