Estimation of biogas and methane yields in an UASB treating potato starch processing wastewater with backpropagation artificial neural network. (March 2017)

• Record Type: Journal Article
• Authors: Antwi, Philip; Li, Jianzheng; Boadi, Portia Opoku; Meng, Jia; Shi, En; Deng, Kaiwen; Bondinuba, Francis Kwesi
• Is Part Of: Bioresource technology. Volume 228(2017)
• Page Start: 106
• Page End: 115
• Publication Date: 2017-03
• Subjects: Potato starch processing wastewater -- Upflow anaerobic sludge blanket -- Methane yield -- Optimized -- Artificial neural networks; Biomass -- Periodicals; Biomass energy -- Periodicals; Bioremediation -- Periodicals; Agricultural wastes -- Periodicals; Factory and trade waste -- Periodicals; Organic wastes -- Periodicals; Bioénergie -- Périodiques; Déchets agricoles -- Périodiques; Déchets industriels -- Périodiques; Déchets organiques -- Périodiques; Déchets (Combustible) -- Périodiques; 662.88
• DOI: 10.1016/j.biortech.2016.12.045
• Languages: English
• ISSNs: 0960-8524

Abstract

Highlights: Estimation of CH4 and biogas yield from a UASB with BP-ANN and MnLR. Evaluation and selection of optimum algorithm from eleven training algorithms. Optimization of anaerobic parameters to identify their effects on methanation. BP-ANN models predictions were more reliable compared to MnLR. 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 ( R 2 ) 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.