Machine Learning for Polymer Design to Enhance Pervaporation-Based Organic Recovery.

Journal: Environmental science & technology
Published Date: May 14, 2024

Abstract

Pervaporation (PV) is an effective membrane separation process for organic dehydration, recovery, and upgrading. However, it is crucial to improve membrane materials beyond the current permeability-selectivity trade-off. In this research, we introduce machine learning (ML) models to identify high-potential polymers, greatly improving the efficiency and reducing cost compared to conventional trial-and-error approach. We utilized the largest PV data set to date and incorporated polymer fingerprints and features, including membrane structure, operating conditions, and solute properties. Dimensionality reduction, missing data treatment, seed randomness, and data leakage management were employed to ensure model robustness. The optimized LightGBM models achieved RMSE of 0.447 and 0.360 for separation factor and total flux, respectively (logarithmic scale). Screening approximately 1 million hypothetical polymers with ML models resulted in identifying polymers with a predicted permeation separation index >30 and synthetic accessibility score <3.7 for acetic acid extraction. This study demonstrates the promise of ML to accelerate tailored membrane designs.

Authors

  • Meiqi Yang
    Department of Civil and Environmental Engineering and Andlinger Center for Energy and the Environment, Princeton University, Princeton, New Jersey 08544, United States.
  • Jun-Jie Zhu
    State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.
  • Allyson L McGaughey
    Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey 08544, United States.
  • Rodney D Priestley
    Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08544, United States.
  • Eric M V Hoek
    Department of Civil & Environmental Engineering, University of California Los Angeles, Los Angeles, California 90095, United States.
  • David Jassby
    Department of Civil & Environmental Engineering, University of California Los Angeles, Los Angeles, California 90095, United States.
  • Zhiyong Jason Ren
    Department of Civil and Environmental Engineering and Andlinger Center for Energy and the Environment, Princeton University, Princeton, New Jersey 08544, United States.

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