Experimental and artificial neural network application on the optimization of paint effluent (PE) coagulation using novel Achatinoidea shell extract (ASE)

Abstract

This study investigated the potentials of Achatinoidea shell extract (ASE) as a coagulant for the pretreatment of paint wastewater. ASE preparation was adapted from the modified method of Fernandez-Kim, with effective deacetylation of precursor by NaOH at 65°C for 2h. Bench scale jar test experiment was carried out using ASE for the removal of TDSP (total dissolved and suspended particles). The influence of operating parameters on the process efficiency was examined using both one factor at a time (OFAT) and central composite design (CCD) methods. Results indicated the process followed second order reaction with rate constant of 0.0001 (l/g.s) and period of 7.432s. Optimum performance of 99.22% was recorded at pH 4, 4g/l dosage and 45°C. There was significant agreement between results obtained by OFAT and CCD, which was validated by experimental and artificial neural network (ANN) methods. The process efficiency showed that paint effluents (PE) could effectively be treated at the conditions of the experiment.