RSM AND ANN MODELLING FOR COAG-FLOCCULATION OF PARTICULATE OIL FROM SIMULATED OILY NON-PROCESS EFFLU

In this study, Chitosan a polycationic composite was extracted from periwinkle shell by deproteinisation, demineralization and deacetylation to be used as agent with clay and wood as coagulant aids at a combined magnitude relation of zero.375:0.375:1 within the redress of oily non-process effluent water. The coag-flocculation performance was investigated employing a jar check equipment at temperature at 3 operational parameters: pH (2-12), indefinite quantity (3-11) g/l and sinking time (3-30) minutes. Particulate oil removal potency was monitored as method response. Experiments were conducted as per the central composite style and therefore the information was used for model fitting, using the response surface methodology (RSM) and therefore the artificial neural network (ANN). most particulate oil removal potency of eighty four.04% and 84.81% were obtained for RSM and ANN severally whereas Optimum murkiness removal potency of eighty five.40% was obtained that resulted within the creation of nondominated best points giving AN insight relating to the optimal operational conditions of the method.

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