A decision support system for coagulation and flocculation processes using the adaptive neuro-fuzzy inference system

Decision support system (DSS) is an approach to have a smart and sustainable management of facilities for monitoring, predicting and controlling sections. The mentioned platform can be useful in operation of complex facilities like the water treatment plant (WTP). This study proposes an adaptive neuro-fuzzy inference system (ANFIS) for prediction of energy consumption and outlet turbidity according to inlet turbidity and ferric chloride as coagulant in coagulation and flocculation unit process of WTP. The outcomes of ANFIS model are used in the Petri Net modeling as a smart conceptual control system. Therefore, the main purpose of this research is the development of a DSS model for coagulation and flocculation processes in WTP. The results of quantitative data analysis showed that the correlation coefficients of ANFIS model are more than 80% meaning that it can reliably predict the outlet turbidity and energy consumption's variables. With regards to our findings, the first one is to provide a smart and sustainable control system to be implemented in operations of coagulation and flocculation processes in WTPs. It goes without saying that, our DSS model confirms that the variation of 15 +/- 5% for turbidity values and the additive coagulant materials (ferric chloride) should be set, on 60-85 and 40-60 kg/day, respectively, for controlling energy consumption and outlet turbidity. At last but not least, the main benefit from our DSS model is to manage the operation of WTP with a high efficiency and low human-based errors.