LAPSE:2021.0485
Published Article
LAPSE:2021.0485
Optimising Brewery-Wastewater-Supported Acid Mine Drainage Treatment vis-à-vis Response Surface Methodology and Artificial Neural Network
Enoch A. Akinpelu, Seteno K. O. Ntwampe, Abiola E. Taiwo, Felix Nchu
May 28, 2021
This study investigated the use of brewing wastewater (BW) as the primary carbon source in the Postgate medium for the optimisation of sulphate reduction in acid mine drainage (AMD). The results showed that the sulphate-reducing bacteria (SRB) consortium was able to utilise BW for sulphate reduction. The response surface methodology (RSM)/Box−Behnken design optimum conditions found for sulphate reduction were a pH of 6.99, COD/SO42− of 2.87, and BW concentration of 200.24 mg/L with predicted sulphate reduction of 91.58%. Furthermore, by using an artificial neural network (ANN), a multilayer full feedforward (MFFF) connection with an incremental backpropagation network and hyperbolic tangent as the transfer function gave the best predictive model for sulphate reduction. The ANN optimum conditions were a pH of 6.99, COD/SO42− of 0.50, and BW concentration of 200.31 mg/L with predicted sulphate reduction of 89.56%. The coefficient of determination (R2) and absolute average deviation (AAD) were estimated as 0.97 and 0.046, respectively, for RSM and 0.99 and 0.011, respectively, for ANN. Consequently, ANN was a better predictor than RSM. This study revealed that the exclusive use of BW without supplementation with refined carbon sources in the Postgate medium is feasible and could ensure the economic sustainability of biological sulphate reduction in the South African environment, or in any semi-arid country with significant brewing activity and AMD challenges.
Keywords
acid mine drainage, artificial neural network, brewing wastewater, optimisation, response surface methodology, sulphate reduction
Suggested Citation
Akinpelu EA, Ntwampe SKO, Taiwo AE, Nchu F. Optimising Brewery-Wastewater-Supported Acid Mine Drainage Treatment vis-à-vis Response Surface Methodology and Artificial Neural Network. (2021). LAPSE:2021.0485
Author Affiliations
Akinpelu EA: Bioresource Engineering Research Group (BioERG), Cape Peninsula University of Technology, P.O. Box 652, Cape Town 8000, South Africa
Ntwampe SKO: Water Pollution Monitoring and Remediation Initiatives Research Group, School of Chemical and Minerals Engineering, North-West University, P. Bag X60001, Potchefstroom 2520, South Africa
Taiwo AE: Department of Chemical Engineering, College of Engineering, Landmark University, PMB 1001, Omu Aran 240243, Nigeria [ORCID]
Nchu F: Bioresource Engineering Research Group (BioERG), Cape Peninsula University of Technology, P.O. Box 652, Cape Town 8000, South Africa; Department of Horticultural Sciences, Bellville Campus, Cape Peninsula University of Technology, Symphony Way, PO Box 190
Journal Name
Processes
Volume
8
Issue
11
Article Number
E1485
Year
2020
Publication Date
2020-11-18
Published Version
ISSN
2227-9717
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Original Submission
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PII: pr8111485, Publication Type: Journal Article
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LAPSE:2021.0485
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doi:10.3390/pr8111485
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May 28, 2021
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May 28, 2021
 
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Calvin Tsay
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