Abstract

Petroleum refinery effluent (PRE) containing a high concentration of colloidal particles causing turbidity is a point source pollutant. There is currently no baseline for the residual concentration of colloids in industrial effluent. In the present study, the performance of land snail shells (LSS) characterized using FTIR and XRD techniques used for the treatment of PRE was investigated. The effluent collected from the outlet train of the industrial facility contains 220 NTU of turbidity corresponding to 520 mg/L of colloidal particles. Analysis of the industrial effluent yielded a COD to BOD ratio > 3.5 eliminating the option of a biological method of treatment. Coagulation-flocculation treatment of the PRE was carried-out following a standard nephelometric test. To clarify the applicability of LSS beyond removal efficiency, machine learning (ML), adsorption, and coag-flocculation kinetics were applied to investigate the treatment process. The predictive capacities of the ML models were compared using statistical metrics. The synergetic effects of operating variables were equally studied. The predicted optimum operating conditions of the treatment process were pH 6, dosage of 0.1 g/L, and a settling time of 30 minutes. The pseudo-second-order and coag-flocculation kinetics result confirmed the reduction of the colloidal particles that occurred via adsorption and inter-particle bridging mechanism. The flocculation outcome proved that the mixing regime 20 s−1≤ G≤120 s−1 promoted aggregation rate over breakage coefficient transcending to 90% removal efficiency. The finding shows that the stability of the finished water corresponds to the 23 mg/L threshold of residual colloidal particles, and 10NTU, which satisfied the EPA guideline for environmentally sustainable recovery.

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