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.

Highlights

  • Of the numerous wastewaters from different food and beverage industries, malting and brewing wastewaters are especially nutrient-rich

  • A recent example was when brewing wastewater (BW) was used as a fertiliser treatment for crop production, resulting in yields that resembled those under inorganic fertiliser [10]

  • It was further suggested that BW, when at ambient temperature, has high biodegradability; this assertion was made for aerobic conditions [6]

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Summary

Methodology and Artificial Neural Network

Water Pollution Monitoring and Remediation Initiatives Research Group, School of Chemical and Minerals. Received: 23 October 2020; Accepted: 9 November 2020; Published: 18 November 2020

Introduction
Chemical Reagents
Bacterial Inoculum
Carbon Source Limiting Growth Test
Experimental Set-Up
Design of Experiment—Box–Behnken
Appraisal of Artificial Neural Network Predictability
Effect of Carbon Substrate Limitation on the SRB Consortium
RSM Modelling
Graphical Representation of the Model
Optimum Comparison ofmodel
Overall Effect of Individual Parameters
Conclusions
Full Text
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