ChemEngML/MP_FO_ML: MP_FO Scripts
AI-assisted Prediction & Optimization of Micropollutants Removal with Forward Osmosis Membranes — this repo provides the curated dataset (642 experiments across 17 commercial/lab-fabricated FO membranes and 102 micropollutants, with standardized chemical, membrane, and process descriptors in Dataset.csv) plus the Python scripts used to train, tune, and interpret the GBR and ANN models for water flux and rejection-rate prediction.
Cite this dataset as:
Jafari, M.,
2025.
ChemEngML/MP_FO_ML: MP_FO Scripts.
Version 3.
Zenodo.
Available from: https://doi.org/10.5281/zenodo.15748192.
Export
Creators
Mehryar Jafari
University of Bath
Contributors
Bernardo Castro Dominguez
Supervisor
University of Bath
Ali Molaei Aghdam
Researcher
Auburn University
University of Bath
Rights Holder
Funders
University of Bath
https://doi.org/10.13039/501100000835
UK Research and Innovation
https://doi.org/10.13039/100014013
Engineering and Physical Sciences Research Council
https://doi.org/10.13039/501100000266
DTP 2022-2024 University of Bath
EP/W524712/1
Publication details
Publication date: 13 June 2025
by: Zenodo
Version: 3
DOI: https://doi.org/10.5281/zenodo.15748192
URL for this record: https://researchdata.bath.ac.uk/1560
Related papers and books
Jafari, M., Tzirtzipi, C., Aghdam, A. M., Chahartagh, N. M., and Dominguez, B. C., 2025. AI-assisted prediction and optimization of micropollutants removal with forward osmosis membranes. Journal of Membrane Science, 733, 124346. Available from: https://doi.org/10.1016/j.memsci.2025.124346.
Contact information
Please contact the Research Data Service in the first instance for all matters concerning this item.
Contact person: Mehryar Jafari
Faculty of Engineering & Design
Chemical Engineering
Research Centres & Institutes
Centre for Digital, Manufacturing & Design (dMaDe)
Centre for Integrated Materials, Processes & Structures (IMPS)
Institute for Advanced Automotive Propulsion Systems (IAAPS)
Institute for Sustainability
