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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Published in | AIChE journal Vol. 67; no. 11 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
Hoboken, USA
John Wiley & Sons, Inc
01.11.2021
American Institute of Chemical Engineers |
Subjects | |
Online Access | Get full text |
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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. |
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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 |