Artificial neural network and response surface design for modeling the competitive biosorption of pentachlorophenol and 2,4,6-trichlorophenol to Canna indica L and analyzed by UV-Vis spectrometry in Aquaponia

The continuous exposure of the environment to carcinogenic wastes and toxic chlorophenols such as pentachlorophenol (PCP) and 2,4,6-trichlorophenol (TCP) resulting from industrial production activities has become a great concern to research scientists and environmental policymakers. The search for a cost-efficient and eco-friendly approach to the phytoremediation of water will guarantee sustainability. The present research concerns the cost-benefit evaluation and the optimization modeling of the competitive biosorption of PCP and TCP from aqueous solution to Cana indica. L (CiL-plant) using response surface methodology (RSM) , artificial neural network (ANN) model, and UV-Vis Spectrometry. The predictive performances of the ANN model and the RSM were compared based on their statistical metrics. The antagonistic and synergetic effects of significant biosorption variables (pH, initial concentration, and exposure time) on biosorption were studied at p-values ≤0.005. The findings from the phytoremediation process confirmed that PCP and TCP removal rate reached equilibrium at the optimum conditions corresponding to predominantly acidic pH (4), required initial concentration of 50 mg L-1, and exposure time of 25 days in aquaponia. The optimized output transcends to PCP and TCP removal rates of 90% and 87.99% efficiencies at predicted r-squared ≤0.9999 and a 95% confidence interval. The cost-benefit evaluation established that at the optimum conditions, the cost of operating the removal of TCP from the aqueous solution would save $ 7.72 compared to PCP. The optimization model’s reliability based on the experiment’s (DoE) design was more sustainable than the one-factor at-a-time (OFAT) methodologies reported in previous research.