Model prediction of coagulation by magnetised rice starch for wastewater treatment using response surface methodology (RSM) with artificial neural network (ANN)
Nomthandazo Precious Sibiya, Gloria Amo‐Duodu, Emmanuel Kweinor Tetteh, Sudesh Rathilal
Abstract
This study synthesized magnetized rice starch (MRS) with recoverability benefits as an alternative coagulant for the coagulation treatment of industrial wastewater. The MRS was characterization by scanning electron microscopy and energy dispersive X-ray (SEM/EDX) and Brunauer–Emmett–Teller (BET). The engineered rice starch with BET surface area of 31.44 m2/g and cationic elementals showed good agglomeration behavior. Three process variables (coagulant dose, settling time, and mixing rate) were model and optimized for the coagulation removal efficiency of turbidity, color, and phosphate. Using the response surface methodology (RSM), Box-Behnken design (BBD) with 17 experimental runs was used to model and optimized the experimental parameters. At optimal conditions of coagulant dosage of 4 g, settling time of 15 mins, and mixing rate of 50 rpm resulted in removal efficiency of 72 %, 53.2 %, and 56.5 %, respectively for turbidity, color, and phosphate. This infers a desirability efficiency of 82.6% was attained via analysis of variance (ANOVA) at a 95% confidence level. The experimental results showed close agreement with the RSM and artificial neural network (ANN) model prediction. Results of the model's prediction significance (P <0.05) were compared in terms of coefficient of determination (R2 = 0.994) for the ANN model and RSM model (R2 = 0.97). Inclusively, the use of RSM and ANN for coagulation process optimization using magnetized rice starch is viable for wastewater management for reuse. Therefore, exploring the potency of the magnetized rice starch in real world application warrants the model adaptability to estimate its cost benefit.