Enhanced process development using automated continuous reactors by self-optimisation algorithms and statistical empirical modelling

Jeraal, Mohammed I. and Holmes, Nicholas and Akien, Geoffrey R. and Bourne, Richard A. (2018) Enhanced process development using automated continuous reactors by self-optimisation algorithms and statistical empirical modelling. Tetrahedron, 74 (25). pp. 3158-3164. ISSN 0040-4020

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Abstract

Reaction optimisation and understanding is fundamental for process development and is achieved using a variety of techniques. This paper explores the use of self-optimisation and experimental design as a tandem approach to reaction optimisation. A Claisen-Schmidt condensation was optimised using a branch and fit minimising algorithm, with the resulting data being used to fit a response surface model. The model was then applied to find new responses for different metrics, highlighting the most important for process development purposes.

Item Type:
Journal Article
Journal or Publication Title:
Tetrahedron
Additional Information:
This is the author’s version of a work that was accepted for publication in Tetrahedron. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Tetrahedron, 74, (25) 2018 DOI: 10.1016/j.tet2018.02.061
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/3000/3002
Subjects:
ID Code:
123822
Deposited By:
Deposited On:
01 Mar 2018 14:00
Refereed?:
Yes
Published?:
Published
Last Modified:
05 Jul 2020 06:14