Modeling and optimization of turbidity removal from produced water using response surface methodology and artificial neural network

dc.creatorEzemagu, I.G.
dc.creatorEjimofor, M.I.
dc.creatorMenkiti, M.C.
dc.creatorNwobi-Okoye, C.C.
dc.date.accessioned2022-06-08T13:56:40Z
dc.date.available2022-06-08T13:56:40Z
dc.date.issued2021
dc.descriptionAttribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)en_US
dc.description.abstractIn this study, results of parametric effects and optimization of turbidity removal from produced water using response surface methodology (RSM) and artificial neural network (ANN) based on a statistically designed experimentation via the Box–Behnken design (BBD) are reported. A three-level, three-factor BBD was employed using dosage (), time () and temperature () as process variables. A quadratic polynomial model was obtained to predict turbidity removal efficiency. The RSM model predicted an optimal turbidity removal efficiency of 83% at conditions of (1 g/L), (16.5 min) and (45 °C) and validated experimentally as 82.73% with low model lack of fit F value of 0.6 and CV value of 8.22%. The ANN model predicted optimal turbidity removal of 83.01% at conditions of (1 g/L), (16.5 min) and (45 °C) and validated as 82.98%. Both models showed to be effective in describing the parametric effect of the considered operating variables on the turbidity removal from produced water. However, the ANN described the parametric effect more accurately when compared with the RSM model, with a smaller PRE (percentage relative error) and AAD (absolute average deviation) of ±0.0241% and ±0.0139%, respectively.en_US
dc.identifier.citationEzemagu, I. G., Ejimofor, M. I., Menkiti, M. C., & Nwobi-Okoye, C. C. (2020, December 2). Modeling and optimization of turbidity removal from produced water using response surface methodology and artificial neural network. South African Journal of Chemical Engineering. Retrieved June 8, 2022, from https://www.sciencedirect.com/science/article/pii/S102691852030069X?via%3Dihuben_US
dc.identifier.urihttps://doi.org/10.1016/j.sajce.2020.11.007
dc.identifier.urihttps://hdl.handle.net/2346/89489
dc.language.isoengen_US
dc.subjectCoagulationen_US
dc.subjectTurbidity removalen_US
dc.subjectBox–Behnken designen_US
dc.subjectModeling and optimizationen_US
dc.subjectANNen_US
dc.subjectRSMen_US
dc.titleModeling and optimization of turbidity removal from produced water using response surface methodology and artificial neural networken_US
dc.typeArticleen_US

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