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Updated: Feb 13, 2026

Development of Sulfidogenic Sludge from Marine Sediments and Trichloroethylene Reduction in an Upflow Anaerobic Sludge Blanket Reactor
Published on: October 15, 2015
Feedforward neural network model estimating pollutant removal process within mesophilic upflow anaerobic sludge
Philip Antwi1, Jianzheng Li2, Jia Meng2
1Jiangxi Key Laboratory of Mining & Metallurgy Environmental Pollution Control, School of Resources and Environmental Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, PR China; Department for Management of Science and Technology Development, Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Viet Nam; State Key Laboratory of Urban Water Resource and Environment, School of Environmental, Harbin Institute of Technology, 73 Huanghe Road, Harbin 150090, PR China.
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 (R2), the BPANN model demonstrated significant performance with R2 reaching 87%. The study revealed that, control and optimization of an anaerobic digestion process with BPANN model was feasible.
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