Feedforward neural network model estimating pollutant removal process within mesophilic upflow anaerobic sludge blanket bioreactor treating industrial starch processing wastewater. (June 2018) - Antwi, Philip; Li, Jianzheng; Meng, Jia; Deng, Kaiwen; Koblah Quashie, Frank; Li, Jiuling; Opoku Boadi, Portia
Graphical abstract: Schematic diagram of: (A) an UASB operated under mesophilic condition; (B) flowchart of the feedforward BPANN architecture. Highlights: COD removal process was predicted with feedforward artificial neural networks. Training algorithm and model building parameters were optimized before employed. Model performance suggested feasibility to control and optimize AD process with BPANN. Microbial communities coexisted without evidence of inhibition on the AD process. Abstract: In this a, three-layered feedforward-backpropagation artificial neural network (BPANN) model was developed and employed to evaluate COD removal an upflow anaerobic sludge blanket (UASB) reactor treating industrial starch processing wastewater. At the end of UASB operation, microbial community characterization revealed satisfactory composition of microbes whereas morphology depicted rod-shaped archaea. pH, COD, NH4 +, VFA, OLR and biogas yield were selected by principal component analysis and used as input variables. Whilst tangent sigmoid function ( tansig ) and linear function ( purelin ) were assigned as activation functions at the hidden-layer and output-layer, respectively, optimum BPANN architecture was achieved with Levenberg-Marquardt algorithm ( trainlm ) after eleven training algorithms had been tested. Based on performance indicators such the mean squared errors, fractional variance, index of agreement and coefficient of determination (R 2 ), the BPANN model demonstrated significant performance with R 2 reaching 87%. The study revealed that, control and optimization of an anaerobic digestion process with BPANN model was feasible.