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Predicting Extraction Selectivity of Acetic Acid in Pervaporation by Machine Learning Models with Data Leakage Management by Meiqi Yang; Jun-Jie Zhu; Allyson McGaughey; Sunxiang Zheng; Rodney D. Priestley; Zhiyong Jason Ren is a Environmental Science article available to read on EtoBox.
What is Predicting Extraction Selectivity of Acetic Acid in Pervaporation by Machine Learning Models with Data Leakage Management about?
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 CO 2 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
Who reads Predicting Extraction Selectivity of Acetic Acid in Pervaporation by Machine Learning Models with Data Leakage Management?
It is typically read by researchers, students, and practitioners in Environmental Science.
- Author
- Meiqi Yang; Jun-Jie Zhu; Allyson McGaughey; Sunxiang Zheng; Rodney D. Priestley; Zhiyong Jason Ren
- Publisher
- American Chemical Society (ACS)
- Published
- 2023
- Language
- EN
- Field
- Environmental Science (Physical Sciences)