Predicting Extraction Selectivity of Acetic Acid in Pervaporation by Machine Learning Models with Data Leakage Management-Reference-Cited by-同舟云学术

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

Published:2023-03-27 Issue:14 Volume:57 Page:5934-5946
ISSN:0013-936X
Container-title:Environmental Science & Technology
language:en
Short-container-title:Environ. Sci. Technol.

Author:

Yang Meiqi¹^ORCID,Zhu Jun-Jie¹^ORCID,McGaughey Allyson¹²^ORCID,Zheng Sunxiang¹,Priestley Rodney D.²^ORCID,Ren Zhiyong Jason¹^ORCID

Affiliation:

1. Department of Civil and Environmental Engineering and Andlinger Center for Energy and the Environment, Princeton University, Princeton, New Jersey08544, United States

2. Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey08544, United States

Funder

Princeton University

Andlinger Center For Energy And Environment, Princeton University

Publisher

American Chemical Society (ACS)

Subject

Environmental Chemistry,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.est.2c06382

Reference70 articles.

1. Acetic Acid Production and Purification: Critical Review Towards Process Intensification

2. Progress in pervaporation membranes for dehydration of acetic acid

3. Recent advances in the methanol carbonylation reaction into acetic acid

4. Production and separation of acetic acid from pyrolysis oil of lignocellulosic biomass: a review

5. Unprecedented Use of NHC Gold (I) Complexes as Catalysts for the Selective Oxidation of Ethane to Acetic Acid

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