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

Meiqi Yang; Jun-Jie Zhu; Allyson McGaughey

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

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

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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.

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《Environmental Science & Technology: ES&T》 |2023年第14期|5934-5946|共13页
作者
Meiqi Yang; Jun-Jie Zhu; Allyson McGaughey;
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作者单位

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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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
原文格式 PDF
正文语种英语
中图分类环境科学、安全科学;
关键词
machine learning; data leakage management; pervaporation; acetic acid; separation factor;

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

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