Evaluation of extract of Ipomoea batatas leaves as a green coagulant–flocculant for turbid water treatment: Parametric modelling and optimization using response surface methodology and artificial neural networks

Wastewater produced from many industrial processes is characterized by high levels of turbidity which is usually an indication of the level of pollution. Coagulation coupled with flocculation has been reported to be one of the efficient ways of treating turbid wastewater. Thus, this work evaluated the potential use of Ipomoea batatas leaves extract as a green and novel coagulating and flocculating agent for treating turbid water. The process was modelled and optimized using response surface methodology (RSM) and artificial neural networks coupled with genetic algorithm (ANN-GA). The FTIR results showed that the coagulant–flocculant contained beneficial functional groups which facilitated turbidity removal. The FESEM results indicated a porous matrix while the EDS results showed that carbon (61.2%) and oxygen (29.7%) were the main components with some lesser amounts of potassium (4.7%), phosphorus (2%) and aluminium (1.4%) which had a synergistic effect on the process. Although RSM and ANN modelled the treatment process with relatively high accuracy, the ANN model was however found to perform better than RSM as seen in the better statistical metrics. The ANN model predicted a maximum turbidity removal of 96% with a corresponding initial turbidity, coagulant dosage, rapid mixing time, rapid mixing speed, slow mixing time and slow mixing speed values of 250 NTU, 10 g/L, 2 min, 150 rpm, 10 min and 70 rpm, respectively. Thus, based on these findings, Ipomoea batatas leaves extract could serve as a replacement for the conventional chemical coagulants currently in the market.