Forecasting and analysis of biogas-based power production using extremal neural network. Issue 8 (3rd August 2017)

• Record Type: Journal Article
• Authors: Thomas, Paul; Debnath, Tapas; Soren, Nirmala
• Is Part Of: Energy sources. Volume 12:Issue 8(2017)
• Page Start: 730
• Page End: 739
• Publication Date: 2017-08-03
• Subjects: ENN -- neural network -- OF-MSW -- power production -- sludge; Natural resources -- Periodicals; Energy consumption -- Periodicals; Energy consumption -- Climatic factors -- Periodicals; Energy conversion -- Periodicals; Energy conversion -- Environment aspects -- Periodicals; 333.7905
• DOI: 10.1080/15567249.2017.1295116
• Languages: English
• ISSNs: 1556-7249

Abstract

ABSTRACT: The primary objective of this study is to design an efficient and robust method to predict the power production from the anaerobic digester by taking into consideration various factors and feedstocks (organic fraction municipal solid waste, wastewater sludge, and co-digestion). This study focuses on the influence of primary factors such as temperature, pH, and nitrogen concentration on power production. Extremal neural network tool was developed with the assistance of MATLAB programming to predict the power production. The results showed that this approach is trustworthy enough to predict the energy output with respect to the primary variables mentioned above. It is expected that this new prediction model will improve the biogas productivity and contribute significantly to energy production. Moreover, this design has an economic advantage in processing.