Model‐based design of experiments for polyether production from bio‐based 1,3‐propanediol

Sequential model‐based design of experiments (MDBOE) accounts for information from previous experiments when selecting conditions for new experiments. In the current study, sequential MBDOE is used to select operating conditions for experiments in a batch‐reactor that produces bio‐based polytrimethy...

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Bibliographic Details
Published inAIChE journal Vol. 67; no. 11
Main Authors Vo, Anh‐Duong Dieu, Shahmohammadi, Ali, McAuley, Kimberley B.
Format Journal Article
LanguageEnglish
Published Hoboken, USA John Wiley & Sons, Inc 01.11.2021
American Institute of Chemical Engineers
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Summary:Sequential model‐based design of experiments (MDBOE) accounts for information from previous experiments when selecting conditions for new experiments. In the current study, sequential MBDOE is used to select operating conditions for experiments in a batch‐reactor that produces bio‐based polytrimethylene ether glycol (PO3G). These Bayesian A‐optimal experiments are designed to obtain improved estimates of 70 fundamental‐model parameters, while accounting for industrial data from eight previous runs. Settings are selected for three decision variables: reactor temperature, initial catalyst level, and initial water concentration. If only one new experiment is conducted, it should be run at high temperature, with relatively high concentrations of catalyst and initial water. When two new runs are conducted, one should use an intermediate catalyst concentration. The effectiveness of the proposed MBDOE approach is tested using Monte‐Carlo simulations, revealing that the selected experiments are superior compared to experiments selected randomly from corners of the permissible design space.
Bibliography:Funding information
Natural Sciences and Engineering Research Council of Canada, Grant/Award Number: RGPIN‐2020‐03901; Mitacs Globalink
ISSN:0001-1541
1547-5905
DOI:10.1002/aic.17394