Optimization of operational conditions in continuous electrodeionization method for maximizing Strontium and Cesium removal from aqueous solutions using artificial neural network

Abstract Strontium (Sr) and Cesium (Cs) are two important nuclear fission products which are present in the radioactive wastewater resulting from nuclear power plants. They should be treated by considering environmental and economic aspects. In this study, artificial neural network (ANN) was implemented to evaluate the optimal experimental conditions in continuous electrodeionization method in order to achieve the highest removal percentage of Sr and Ce from aqueous solutions. Three control factors at three levels were tested in experiments for Sr and Cs: Feed concentration (10, 50 and 100 mg/L), flow rate (2.5, 3.75 and 5 mL/min) and voltage (5, 7.5 and 10 V). The obtained data from the experiments were used to train two ANNs. The three control factors were utilized as the inputs of ANNs and two quality responses were used as the outputs, separately (each ANN for one quality response). After training the ANNs, 1024 different control factor levels with various quality responses were predicted and finally the optimum control factor levels were obtained. Results demonstrated that the optimum levels of the control factors for maximum removing of Sr (97.6%) had an applied voltage of 10 V, a flow rate of 2.5 mL/min and a feed concentration of 10 mg/L. As for Cs (67.8%) they were 10 V, 2.55 mL/min and 50 mg/L, respectively.