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

Chemistry

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1-s2.0-S0040402018302217-main
Rights statement: 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
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https://doi.org/10.1016/j.tet.2018.02.061
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Keywords

Self-optimisation, Design of experiments, Clasien-schmidt condensation, Reaction metrics, Process development, Flow chemistry

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Enhanced process development using automated continuous reactors by self-optimisation algorithms and statistical empirical modelling. / Jeraal, Mohammed I.; Holmes, Nicholas; Akien, Geoffrey R. et al.
In: Tetrahedron, Vol. 74, No. 25, 21.06.2018, p. 3158-3164.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Bibtex

@article{c59f79d73d464aec8a252225516bd929,

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

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.",

keywords = "Self-optimisation, Design of experiments, Clasien-schmidt condensation, Reaction metrics, Process development, Flow chemistry",

author = "Jeraal, {Mohammed I.} and Nicholas Holmes and Akien, {Geoffrey R.} and Bourne, {Richard A.}",

note = "This is the author{\textquoteright}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",

year = "2018",

month = jun,

day = "21",

doi = "10.1016/j.tet.2018.02.061",

language = "English",

volume = "74",

pages = "3158--3164",

journal = "Tetrahedron",

issn = "0040-4020",

publisher = "Elsevier Limited",

number = "25",

}

RIS

TY - JOUR

T1 - Enhanced process development using automated continuous reactors by self-optimisation algorithms and statistical empirical modelling

AU - Jeraal, Mohammed I.

AU - Holmes, Nicholas

AU - Akien, Geoffrey R.

AU - Bourne, Richard A.

N1 - 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

PY - 2018/6/21

Y1 - 2018/6/21

N2 - 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.

AB - 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.

KW - Self-optimisation

KW - Design of experiments

KW - Clasien-schmidt condensation

KW - Reaction metrics

KW - Process development

KW - Flow chemistry

U2 - 10.1016/j.tet.2018.02.061

DO - 10.1016/j.tet.2018.02.061

M3 - Journal article

VL - 74

SP - 3158

EP - 3164

JO - Tetrahedron

JF - Tetrahedron

SN - 0040-4020

IS - 25

ER -

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