Data-Driven Optimization of an Industrial Batch Polymerization Process Using the Design of Dynamic Experiments Methodology-Reference-Cited by-同舟云学术

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Data-Driven Optimization of an Industrial Batch Polymerization Process Using the Design of Dynamic Experiments Methodology

Published:2020-07-09 Issue:33 Volume:59 Page:14868-14880
ISSN:0888-5885
Container-title:Industrial & Engineering Chemistry Research
language:en
Short-container-title:Ind. Eng. Chem. Res.

Author:

Georgakis Christos¹²^ORCID,Chin Swee-Teng³,Wang Zhenyu³^ORCID,Hayot Philippe⁴,Chiang Leo³,Wassick John⁵,Castillo Ivan³

Affiliation:

1. Process Cybernetics LLC, Waban, Massachusetts 02468, United States

2. Systems Research Institute, Tufts University, Medford, Massachusetts 02155, United States

3. Chemometrics and AI, Dow Inc., Lake Jackson, Texas 77566, United States

4. PU/PS&F Tech. Center, Dow Inc., Terneuzen 4542, Netherlands

5. Digital Fulfillment Center, Dow Inc., Midland, Michigan 48642, United States

Publisher

American Chemical Society (ACS)

Subject

Industrial and Manufacturing Engineering,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.iecr.0c01952

Reference22 articles.

1. Modelling of the liquid phase polymerization of olefins in loop reactors

2. Modeling and simulation of propylene polymerization in nonideal loop reactors

3. Linking Models and Experiments

4. Optimal design of dynamic experiments in the development of cybernetic models for bioreactors

5. Reactor modeling and recipe optimization of polyether polyol processes: Polypropylene glycol

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1. Data-driven quasi-convex method for hit rate optimization of process product quality in digital twin;Journal of Industrial Information Integration;2024-09

2. Machine learning techniques for the prediction of polymerization kinetics and polymer properties;The Canadian Journal of Chemical Engineering;2023-12-20

3. Hierarchical‐linked batch‐to‐batch optimization based on transfer learning of synthesis process;The Canadian Journal of Chemical Engineering;2023-04-12

4. Direct prediction of the batch time and process variable profiles using batch process data based on different batch times;Computers & Chemical Engineering;2023-01

5. Multi-step lookahead Bayesian optimization with active learning using reinforcement learning and its application to data-driven batch-to-batch optimization;Computers & Chemical Engineering;2022-11

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