AI-assisted Prediction and Optimization of Micropollutants Removal with Forward Osmosis Membranes

Mehryar Jafari, Christina Tzirtzipi, Ali Molaei Aghdam, Nima Mikaeili Chahartagh, Bernardo Castro Dominguez

Research output: Contribution to journalArticlepeer-review

Abstract

Membrane technology is a simple, energy-saving, and high-performance separation process that can satisfy the high demand for water purification and separation of high-value products, such as dyes, pharmaceuticals, and therapeutical proteins. Despite all the recent progress in this field, serious issues remain unaddressed. High energy requirement and fouling in Reverse Osmosis (RO) membranes, concentration polarization, and reverse solute flux (RSF) for Forward Osmosis (FO) are among these. Recently, a number of Artificial Intelligence (AI) techniques, have been increasingly applied to optimize membrane performance by predicting and simulating the filtration process for a broad range of membranes and feed material. During this project, we try to harness the capabilities of different AI techniques namely Artificial Neural Networks (ANN) and Gradient Boosting Regressor (GBR) to first predict the performance of commercial FO membranes in removing various micropollutants with high accuracy and then use the best model available to develop a web-application accessible to public and researchers in order to estimate the water flux (Jw) and rejection rate (R%) they can obtain from a certain FO membrane removing a certain micropollutant without the need for costly and tedious experimental campaigns.
Original languageEnglish
Article number124346
JournalJournal of Membrane Science
Volume733
Early online date20 Jun 2025
DOIs
Publication statusE-pub ahead of print - 20 Jun 2025

Data Availability Statement

Dataset used in this study has been made available in the supplementary documents (Dataset.csv). Furthermore, the python scripts of AI models used can be accessed through following link: https://github.com/ChemEngML/MP_FO_ML.git.
Data attached in CSV file.

Funding

The authors wish to thank the financial support provided by the University of Bath, United Kingdom Research and Innovation (UKRI) and the Engineering and Physical Science Research Council (EPSRC), grant number: EP/W524712/1.

FundersFunder number
Engineering and Physical Sciences Research CouncilEP/W524712/1

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