Machine Learning-Based Adaptive Regression to Identify Nonlinear Dynamics of Biochemical Systems: A Case Study on Bio 2,3-Butanediol Distillation Process-Reference-Cited by-同舟云学术

Machine Learning-Based Adaptive Regression to Identify Nonlinear Dynamics of Biochemical Systems: A Case Study on Bio 2,3-Butanediol Distillation Process

Published:2024-07-09 Issue:29 Volume:12 Page:10869-10879
ISSN:2168-0485
Container-title:ACS Sustainable Chemistry & Engineering
language:en
Short-container-title:ACS Sustainable Chem. Eng.

Author:

Choi Yeongryeol¹²,Bhadriraju Bhavana³,Kwon Hyukwon¹²,Lim Jongkoo⁴,Kwon Joseph Sang-Il³^ORCID,Kim Junghwan²^ORCID

Affiliation:

1. Green Materials and Processes R&D Group, Korea Institute of Industrial Technology, Ulsan 44413, Republic of Korea

2. Department of Chemical and Biomolecular Engineering, Yonsei University, Seoul 03722, Republic of Korea

3. Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77845, United States

4. Research and Development Center, GS Caltex Corporation, Daejeon 34122, Republic of Korea

Funder

Energy Institute, Texas A and M University

Artie McFerrin Department of Chemical Engineering, Texas A and M University

Korea Institute for Advancement of Technology

Korea Institute of Industrial Technology

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acssuschemeng.4c02570

Reference47 articles.

1. Modeling and Control of Nonlinear Processes Using Sparse Identification: Using Dropout to Handle Noisy Data

2. Multi-objective optimization for sustainable and economical polycarbonate production with reaction kinetics inference for real-world industrial process

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4. Data-driven discovery of coordinates and governing equations

5. Interpretable machine learning framework for catalyst performance prediction and validation with dry reforming of methane