Chae, Sung Ho Hong, Seok Won Son, Moon Cho, Kyung Hwa 2024-07-18T07:30:10Z 2024-07-18T07:30:10Z 2024-07-18 2024-07 https://pubs.kist.re.kr/handle/201004/150269 Water (A) and solute permeability (B) are the key parameters characterizing the performance of reverse osmosis (RO) membranes. However, determining A and B requires multiple times of experiments that are timeconsuming. Thus, developing a method to estimate A and B quickly has been encouraged. This study introduces a data-driven machine learning (ML) pipeline designed to predict A and B for RO membranes. The ML pipeline addresses all stages for estimating A and B, from data preprocessing to practical guidance on developed models. In particular, the membrane composition data, an overlooked essential component detailing A and B, was employed as features for the ML models. Sixteen ML models were tested, and categorical boost (CatBoost) and extremely randomized trees (ET) were selected as the best models for A and B, respectively. The selected ML models were retrained using the minimum yet effective features identified through feature importance analysis. Consequently, the retrained ML models exhibited high accuracy in predicting A and B (R2adj,test = 0.925/MAEtest = 0.246 for A; R2adj,test = 0.986/MAEtest = 0.069 for B), demonstrating the viability of a data-driven approach for parameter estimation. Moreover, this study highlights that RO membrane performance can be gauged effectively using membrane composition data and a limited set of operating conditions. English Elsevier Limited Determining water and solute permeability of reverse osmosis membrane using a data-driven machine learning pipeline Article 10.1016/j.jwpe.2024.105634 1 Journal of Water Process Engineering, v.64 Journal of Water Process Engineering 64 Y scie scopus 001258668200001 2-s2.0-85195876837 Engineering, Environmental Engineering, Chemical Water Resources Engineering Water Resources Article DESALINATION MODEL PERFORMANCE REJECTION Parameters Reverse osmosis membrane Solute permeability Water permeability Machine learning