Machine learning-guided performance prediction of forward osmosis polymeric membranes for boron recovery

Abstract

Efficient recovery of boron is one of the crucial strategies of sustainably extracting valuable resource from water. It however still remains a key technological challenge to efficiently predict boron recovery from unconventional water resources such as underground water, geothermal water and seawater, which are still few concerned in open literature. To effectively address this issue, herein we propose an efficient strategy to precisely predict boron recovery performance and then explore mechanism in forward osmosis process via advanced machine learning techniques with better model performance. Specifically, to explore the complex relationships among various boron recovery factors, we compare different advanced machine learning regression models to provide valuable insights into how these key factors impact system performance. We find that three key driving factors (i.e., pH, boron concentration, and membrane orientation) significantly affect boron recovery performance in the forward osmosis process. The best prediction accuracy with a high r-square (R², 95.4 %) is achieved via the XGBoost model combined with the particle swarm optimization algorithm, demonstrating its remarkable ability for precise boron recovery prediction. By employing this hybrid model to optimize the search space, the overall performance of forward osmosis system was significantly enhanced, with a predicted boron rejection rate as high as 98.28 %, outperforming the reported values. Our work demonstrates the powerful potential of advanced machine learning for efficiently predicting boron recovery for water quality improvement and resource recovery applications.