Please use this identifier to cite or link to this item: http://hdl.handle.net/11189/8634
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dc.contributor.authorAkinpelu, Enoch Akinbiyien_US
dc.contributor.authorNtwampe, Seteno Karabo Obeden_US
dc.contributor.authorTaiwo, Abiola Ezekielen_US
dc.contributor.authorNchu, Felixen_US
dc.date.accessioned2022-07-14T11:59:58Z-
dc.date.available2022-07-14T11:59:58Z-
dc.date.issued2020-
dc.identifier.citationAkinpelu, E.A., Ntwampe, S.K.O., Taiwo, A.E. & Nchu, F. 2020. Optimising brewery-wastewater-supported acid mine drainage treatment vis-à-vis response surface methodology and artificial neural network. Processes. 8(11): [https://doi.org/10.3390/pr8111485]en_US
dc.identifier.issn2227-9717-
dc.identifier.urihttp://hdl.handle.net/11189/8634-
dc.description.abstractThis 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 challengesen_US
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.relation.ispartofProcessesen_US
dc.subjectAcid Mine Drainageen_US
dc.subjectartificial neural networken_US
dc.subjectbrewing wastewateren_US
dc.subjectresponse surface methodologyen_US
dc.subjectsulphate reductionen_US
dc.subjectoptimisationen_US
dc.titleOptimising brewery-wastewater-supported acid mine drainage treatment vis-à-vis response surface methodology and artificial neural networken_US
dc.identifier.doihttps://doi.org/10.3390/pr8111485-
dc.typeArticleen_US
Appears in Collections:Eng - Journal articles (DHET subsidised)
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