Predicting Extraction Selectivity of Acetic Acid in Pervaporation by Machine Learning Models with Data Leakage Management

Meiqi Yang, Jun Jie Zhu, Allyson McGaughey, Sunxiang Zheng, Rodney D. Priestley, Zhiyong Jason Ren

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

The extraction of acetic acid and other carboxylic acids from water is an emerging separation need as they are increasingly produced from waste organics and CO2 during carbon valorization. However, the traditional experimental approach can be slow and expensive, and machine learning (ML) may provide new insights and guidance in membrane development for organic acid extraction. In this study, we collected extensive literature data and developed the first ML models for predicting separation factors between acetic acid and water in pervaporation with polymers’ properties, membrane morphology, fabrication parameters, and operating conditions. Importantly, we assessed seed randomness and data leakage problems during model development, which have been overlooked in ML studies but will result in over-optimistic results and misinterpreted variable importance. With proper data leakage management, we established a robust model and achieved a root-mean-square error of 0.515 using the CatBoost regression model. In addition, the prediction model was interpreted to elucidate the variables’ importance, where the mass ratio was the topmost significant variable in predicting separation factors. In addition, polymers’ concentration and membranes’ effective area contributed to information leakage. These results demonstrate ML models’ advances in membrane design and fabrication and the importance of vigorous model validation.

Original languageEnglish (US)
Pages (from-to)5934-5946
Number of pages13
JournalEnvironmental Science and Technology
Volume57
Issue number14
DOIs
StatePublished - Apr 11 2023

All Science Journal Classification (ASJC) codes

  • General Chemistry
  • Environmental Chemistry

Keywords

  • acetic acid
  • data leakage management
  • machine learning
  • pervaporation
  • separation factor

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