Kim, Hoo Hugo Choi, Byeongwook Ullah, Zahid Jeong, Nahyeon Cho, Kyung Hwa Park, Sanghun Baek, Sang-Soo Son, Moon 2024-08-08T01:30:12Z 2024-08-08T01:30:12Z 2024-08-08 2024-09 0043-1354 https://pubs.kist.re.kr/handle/201004/150377 Owing to its simplicity of measurement, effluent conductivity is one of the most studied factors in evaluations of desalination performance based on the ion concentrations in various ion adsorption processes such as capacitive deionization (CDI) or battery electrode deionization (BDI). However, this simple conversion from effluent conductivity to ion concentration is often incorrect, thereby necessitating a more congruent method for performing real-time measurements of effluent ion concentrations. In this study, a random forest (RF)-based artificial intelligence (AI) model was developed to address this shortcoming. The proposed RF model showed an excellent prediction accuracy when it was first validated in predicting the effluent conductivity for both CDI (R-2 = 0.86) and BDI (R-2 = 0.95) data. Moreover, the RF model successfully predicted the concentration of each ion (Na+, K+, Ca2+, and Cl-) from the conductivity values. The accuracy of the ion concentration prediction was even higher than that of the effluent conductivity prediction, likely owing to the linear correlation between the input and output variables of the dataset. The effect of the sampling interval was also evaluated for conductivity and ion concentrations, and there was no significant difference up to sampling intervals of <80 s based on the error value of the model. These findings suggest that an RF model can be used to predict ion concentrations in CDI/BDI, which may be used as core indicators in evaluating desalination performance. English Elsevier BV Decoupling ion concentrations from effluent conductivity profiles in capacitive and battery electrode deionizations using an artificial intelligence model Article 10.1016/j.watres.2024.122092 1 Water Research, v.262 Water Research 262 Y scie scopus 001275834200001 2-s2.0-85198937872 Engineering, Environmental Environmental Sciences Water Resources Engineering Environmental Sciences & Ecology Water Resources Article DESALINATION MEMBRANE AMMONIUM Membrane capacitive deionization Effluent conductivity Ion concentration prediction Artificial intelligence Random forest