Optimising Brewery-Wastewater-Supported Acid Mine Drainage Treatment vis-à-vis Response Surface Methodology and Artificial Neural Network

Abstract

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.

Comparative evaluation of bioethanol fermentation process parameters using RSM, ANN and ANFIS

“…ANN is a smart modeling technique that imitates the biological processing power of the human brain. It is capable of handling the simulation and modeling of very complex non‐linear systems 33 . Once a neural network has been successfully trained using the experimental dataset, it can be used to predict the results of a new set of input data by identifying the trained datasets.…”

Investigating the microwave parameters correlating effects on total recovery of bioactive alkaloids from sesame leaves using orthogonal matrix and artificial neural network integration

“…Studies have shown the preference for ANN above other optimization techniques like one‐variable‐at‐a‐time (OVAT) and response surface methodology (RSM), due to its inherent ability to predict complex and non‐linear relationships among the chosen process variables in optimization studies (Sewsynker‐Sukai et al, 2017). ANNs can adapt and simulate non‐linear and complicated interactions, which is crucial since many of the correlations between inputs and outputs, in reality, are non‐linear and multidimensional (Akinpelu et al, 2020). It can infer unobserved correlations after learning from the original inputs and their correlations, thereby allowing the model to generalize and predict the new dataset.…”

“…ANNs can adapt and simulate non-linear and complicated interactions, which is crucial since many of the correlations between inputs and outputs, in reality, are non-linear and multidimensional (Akinpelu et al, 2020). It can infer unobserved correlations after learning from the original inputs and their correlations, thereby allowing the model to generalize and predict the new dataset.…”