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

Journal article

Meiqi Yang, Jun‐Jie Zhu, Allyson L. McGaughey, Sunxiang Zheng, Rodney D. Priestley, Z. Ren
Environmental Science and Technology, 2023

DOI: 10.1021/acs.est.2c06382

Semantic Scholar DOI PubMed

Cite

APA Click to copy
Yang, M., Zhu, J. J., McGaughey, A. L., Zheng, S., Priestley, R. D., & Ren, Z. (2023). Predicting Extraction Selectivity of Acetic Acid in Pervaporation by Machine Learning Models with Data Leakage Management. Environmental Science and Technology. https://doi.org/10.1021/acs.est.2c06382

Chicago/Turabian Click to copy
Yang, Meiqi, Jun‐Jie Zhu, Allyson L. McGaughey, Sunxiang Zheng, Rodney D. Priestley, and Z. Ren. “Predicting Extraction Selectivity of Acetic Acid in Pervaporation by Machine Learning Models with Data Leakage Management.” Environmental Science and Technology (2023).

MLA Click to copy
Yang, Meiqi, et al. “Predicting Extraction Selectivity of Acetic Acid in Pervaporation by Machine Learning Models with Data Leakage Management.” Environmental Science and Technology, 2023, doi:10.1021/acs.est.2c06382.

BibTeX Click to copy

@article{meiqi2023a,
  title = {Predicting Extraction Selectivity of Acetic Acid in Pervaporation by Machine Learning Models with Data Leakage Management.},
  year = {2023},
  journal = {Environmental Science and Technology},
  doi = {10.1021/acs.est.2c06382},
  author = {Yang, Meiqi and Zhu, Jun‐Jie and McGaughey, Allyson L. and Zheng, Sunxiang and Priestley, Rodney D. and Ren, Z.}
}

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.