Experimental and artificial intelligence optimization of paint wastewater (PWW) coagulation using novel seed extract (PVSE).

This study investigated the application of artificial intelligence algorithms (AIA) in the coagulation treatment of paint wastewater anchored by novel seed extract (PVSE). Untreated wastewater discharge harms the ecosystem, and therefore harmful industrial effluent, such as paint wastewater, must be brought to safe discharge levels before being released into the environment. In addition to AIA, comprehensive characterization tests, coagulation kinetics, and process optimization were also executed. Characterization results revealed that total solid in the PWW was above allowable standard, justifying the need for effective particle decontamination. The XRD and FTIR characterization indicated that PVSE structure is amorphous with abundant amine groups. Results of analysis of variance (ANOVA) obtained from process modeling indicated that the coagulation-flocculation process was a nonlinear quadratic system (F-value = 45.51) which was mostly influenced by PVSE coagulant dosage (F-value = 222.48; standardized effect = 14.85). Artificial intelligence indicated that neural network training effectively captured the nonlinear nature of the system in ANN (RMSE = 0.00040194; R = 0.98497), and ANFIS (RMSE = 0.003961) algorithms. Regression coefficient obtained from process modeling highlighted the suitability of RSM (0.9662), ANN (0.9739), and ANFIS (0.9718) in forecasting the coagulation-flocculation process, while comparative statistical appraisal authenticated the superiority of ANN model over RSM and ANFIS models. The coagulation kinetics experiment, which used a coagulation kinetic model, revealed a constant flocculation constant (Kf-value) for all jar test batches and a strong association between the Menkonu coagulation-flocculation constant (Km) and Kf values. Best removal efficiency of 97.01 % was obtained using ANN coupled genetic algorithm optimization (ANN-GA) at PVSE dosage of 4 g/L, coagulation time of 29 min and temperature of 25.1C.