Data-Based Modeling, Multi-Objective Optimization and Multi-Criteria Decision Making of a Catalytic Ozonation Process for Degradation of a Colored Effluent

Abstract

In the catalytic ozonation process (COP), the reactions are complex, and it is very difficult to determine the effect of different operating parameters on the degradation rate of pollutants. Data-based modeling tools, such as the multilayer perceptron (MLP) neural network, can be useful in establishing the complex relationship of degradation efficiency with the operating variables. In this work, the COP of acid red 88 (AR88) with Fe3O4 nano catalyst was investigated in a semi-batch reactor and a MLP model was developed to predict the degradation efficiency (%DE) of AR88 in the range of 25 to 96%. The MLP model was trained using 78 experimental data having five input variables, namely, AR88 initial concentration, catalyst concentration, pH, inlet air flow rate and batch time (in the ranges of 150–400 mg L−1, 0.04–0.4 g L−1, 4.5–8.5, 0.5–1.90 mg min−1 and 5–30 min, respectively). Its optimal topology was obtained by changing the number of neurons in the hidden layer, the momentum and the learning rates to 7, 0.075 and 0.025, respectively. A high correlation coefficient (R2 > 0.98) was found between the experimental and predicted values by the MLP model. Simultaneous maximization of %DE and minimization of Fe3O4 concentration was carried out by multi-objective particle swarm optimization (MOPSO) and the Pareto-optimal solutions were successfully obtained. The trade-off was analyzed through multi-criteria decision making, and one Pareto-optimal solution was selected. The developed model and optimal points are useful for treatment of AR88 wastewater.