Modeling of deionization of aqueous compounds using electrodialysis process (data collection for enhanced clarity)

Abstract

ABSTRACT The deionization process is increasingly recognized as a solution to global water shortages. This study aims to investigate the impact of various parameters on the deionization efficiency. Specifically, volume flow rates (F) were tested (1, 2.5, 5, 10, 15, and 20 ml min−1), solute concentrations (C) (200, 500, and 1000 mg l−1), temperatures (T) (50, 60, and 70°C), voltages (V) (10, 20, and 30 V), and pressures (P) (200, 400, and 800 Pa). In another experiment examined F (100, 250, and 500 mol l−1 m2), V (0.1, 0.5, and 1 V), C (100, 200, 300, 400, and 500 moles m3), and time durations (D) (16, 32, 48, 64, 80, and 96 min). These experiments aimed to determine the separation percentage of potassium chloride cations and anions and were based on data extracted from scientific articles. In the subsequent step, the ANFIS model with the FCM network creation method and the error backpropagation training algorithm was employed to evaluate the impact of each cell input on the separation percentage, output flux, and the separation percentage of potassium chloride cations and anions. This evaluation was conducted through 31 and 15 iterations, respectively. Based on the evaluation criteria, it was observed that the volume flow rate as the sole input yielded the highest prediction accuracy for the separation percentage and output flux. Additionally, the FCTVP model results indicated a slight reduction in the error criteria compared to the FCTV, FCT, FC, and single models. Consequently, it can be concluded that the cell pressure factor may play a decisive role, either reducing or increasing the separation process (R2 = 0.93,MBE = −0.3, and RMSE = 6.4), as well as the output flux (R2 = 0.85, MBE = 0.0003, and RMSE = 0.07). Furthermore, the statistical criteria values of the FVCT model for the separation percentage of potassium chloride cations and anions demonstrated a proper correlation (R2 = 0.96 and 0.95), slight underestimation, and low error MBE = −0.57 and −1.2) (RMSE = 15.2 and 18.5, respectively).