In this paper, simultaneous maximization of generated ozone concentration and minimization of electrical energy consumption is investigated in a laboratory-scale electrochemical ozone production system (EOP). Neural network simulation of EOP was carried out for generated ozone concentration prediction by Abbasi et al. (Chem Eng Res Des 92(11):2618–2625, 2014). In this study, neural network models (as black box models) were developed to predict both generated ozone concentration and electrical energy consumption. The models then were used for optimization. Altruistic non-dominated sorting genetic algorithm with jumping gene variant and termination criterion was used for MOO. Generational distance and spread were used in the termination criterion in order to stop algorithm after the right number of generations. Moreover, several optimal solutions from the Pareto-optimal set are chosen and then validated experimentally.
