Modeling and predicting of water production by capacitive deionization method using artificial neural networks

Water desalination is a method to deal with water shortage that today considered as a way to meet the growing human demand. Obtaining freshwater at low cost guarantees the stability of the desalination method. Capacitive Deionization (CDI) method is a new technology for deionization of water with saline properties, the least energy, and environmental pollution. In this paper, an Artificial Neural Network (ANN) model was developed to estimate the amount of water produced by the CDI method based on experimental data. The backpropagation Multi-Layer Perceptron (MLP) model with Adam (Adaptive Moment Estimation) learning algorithm with two hidden layers was used. To evaluate performance, RMSE, MAE, MSE and R² mathematical indices were used. The input layer had three variables that include the amount of initial solution concentration, the flow rate, and the amount of voltage applied to the cell. The output layer had a neuron as the amount of percentage of water desalination. For estimating the amount of salt removal percentage, the ANN modeling method was acceptable. The amount of correlation coefficient between experimental laboratory data and data estimated by ANN for training data was equal to 0.972. The overall correlation coefficient was 0.90 that for estimating the amount of salt removal percentage by CDI method is acceptable. The RMSE values for testing and training data were 0.008 and 0.003, respectively. In the statistical study of the effect of variables, it was found that by CDI method the initial concentration of salt has an inverse relationship with the amount of salt removed from water. Also, the amount of applied voltage has a direct relationship and the amount of feed rate has an inverse relationship with the amount of salt removed from water.