Sequential treatment of surfactant-laden wastewater using low-cost rice husk ash coagulant and activated carbon: Modeling, optimization, characterization, and techno-economic analysis
Treating surfactant-laden wastewater by a sequential coagulation/flocculation (C/F) and adsorption process was investigated. The treatment process performance was modeled and optimized using response surface methodology (RSM) and artificial neural network (ANN). C/F using rice husk ash (RHA) coagulant provided 42.3 % surfactant removal at 3.79 g/L RHA dosage, pH 3.6, and 23 min slow-mixing time. For adsorption onto granular activated carbon (GAC), 96.5 % surfactant removal was maintained at 10 g/L GAC and pH = 4.5. The ANN model showed better predictive capabilities for C/F and adsorption than RSM. By applying real wastewater, the combined system effluent contained 367 ± 2.4, 18.4 ± 1.2, 4.2 ± 0.2, and 0.95 ± 0.06 mg/L for TDS, COD, oil-grease, and surfactants, respectively, with almost complete removals of turbidity and TSS. The integrated system exhibited a profitability scenario with 6.3 years payback period. The study output is beneficial for designing full-scale systems of surfactant-laden wastewater treatment and predicting the associated performance under various operating conditions.
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