Estimation of biogas and methane yields in an UASB treating potato starch processing wastewater with backpropagation artificial neural network

Philip Antwi, Jianzheng Li, Portia Opoku Boadi, Jia Meng, En Shi, Kaiwen Deng, Francis Kwesi Bondinuba

Research output: Contribution to journalArticle

33 Citations (Scopus)
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Abstract

Three-layered feedforward backpropagation (BP) artificial neural networks (ANN) and multiple nonlinear regression (MnLR) models were developed to estimate biogas and methane yield in an upflow anaerobic sludge blanket (UASB) reactor treating potato starch processing wastewater (PSPW). Anaerobic process parameters were optimized to identify their importance on methanation. pH, total chemical oxygen demand, ammonium, alkalinity, total Kjeldahl nitrogen, total phosphorus, volatile fatty acids and hydraulic retention time selected based on principal component analysis were used as input variables, whiles biogas and methane yield were employed as target variables. Quasi-Newton method and conjugate gradient backpropagation algorithms were best among eleven training algorithms. Coefficient of determination (R2) of the BP-ANN reached 98.72% and 97.93% whiles MnLR model attained 93.9% and 91.08% for biogas and methane yield, respectively. Compared with the MnLR model, BP-ANN model demonstrated significant performance, suggesting possible control of the anaerobic digestion process with the BP-ANN model.
Original languageEnglish
Pages (from-to)106–115
Number of pages10
JournalBioresource Technology
Volume228
Early online date16 Dec 2016
DOIs
Publication statusPublished - Mar 2017

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