RSM AND ANN MODELLING FOR COAG-FLOCCULATION OF PARTICULATE OIL FROM SIMULATED OILY NON-PROCESS EFFLUENT WATER USING BENTONITE, CHITOSAN AND SAWDUST FORMULATION

In this study, Chitosan a polycationic composite was extracted from periwinkle shell by deproteinisation, demineralization and deacetylation for use as coagulant with bentonite and sawdust as coagulant aids at a combined ratio of 0.375:0.375:1 in the remediation of oily non-process effluent water. The coag-flocculation performance was investigated using a jar test apparatus at room temperature at three operational parameters: pH (2-12), Dosage (3-11) g/l and settling time (3-30) minutes. Particulate oil removal efficiency was monitored as process response. Experiments were conducted as per the central composite design and the data was used for model fitting, employing the response surface methodology (RSM) and the artificial neural network (ANN). Maximum particulate oil removal efficiency of 84.04% and 84.81% were obtained for RSM and ANN respectively while Optimum turbidity removal efficiency of 85.40% was obtained which resulted in the creation of nondominated optimal points giving an insight regarding the optimal operating conditions of the process.