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Computer-aided design of formulated products: A bridge design of experiments for ingredient selection

Computer-aided design of formulated products: A bridge design of experiments for ingredient selection
Computer-aided design of formulated products: A bridge design of experiments for ingredient selection
Formulations are ubiquitous in many industries. As formulations are being modified and re-developed to includemore renewable and recyclable ingredients, the speed of formulations development becomes important. Thisstudy expands on the previous work demonstrating successful application of multi-objective Bayesian optimi-zation to design of formulations within a restricted set of the available ingredients. Here we develop an approachthat resolves the un-solved to date problem in algorithmic formulations development, when a subset of in-gredients should be chosen from a larger available pool of suitable ingredients. The new DoE algorithm wasdemonstrated in a workflow making use of a ’make and test’ formulation robots. The developed new DoEprocedure demonstrated an efficient selection of a subset of ingredients from a larger number of the availableones, optimizing their concentration and allowing assignment of differential priorities to the optimizationobjectives.
Design of Experiments, Bayesian optimization, Product design, Gaussian processes, Machine Learning
0098-1354
Cao, Liwei
969e7662-d2ae-4c70-8cb2-11f38e3c68e2
Russo, Danilo
30ac2467-6f46-4842-8dce-0f368e3212dc
Matthews, Emily S
aaab52c6-3e01-44c9-ae35-649e07ab79d5
Lapkin, Alexei
9ef6f6a0-3802-43a4-8425-ffaf03711102
Woods, David
ae21f7e2-29d9-4f55-98a2-639c5e44c79c
Cao, Liwei
969e7662-d2ae-4c70-8cb2-11f38e3c68e2
Russo, Danilo
30ac2467-6f46-4842-8dce-0f368e3212dc
Matthews, Emily S
aaab52c6-3e01-44c9-ae35-649e07ab79d5
Lapkin, Alexei
9ef6f6a0-3802-43a4-8425-ffaf03711102
Woods, David
ae21f7e2-29d9-4f55-98a2-639c5e44c79c

Cao, Liwei, Russo, Danilo, Matthews, Emily S, Lapkin, Alexei and Woods, David (2022) Computer-aided design of formulated products: A bridge design of experiments for ingredient selection. Computers and Chemical Engineering, 169, [108083]. (doi:10.1016/j.compchemeng.2022.108083).

Record type: Article

Abstract

Formulations are ubiquitous in many industries. As formulations are being modified and re-developed to includemore renewable and recyclable ingredients, the speed of formulations development becomes important. Thisstudy expands on the previous work demonstrating successful application of multi-objective Bayesian optimi-zation to design of formulations within a restricted set of the available ingredients. Here we develop an approachthat resolves the un-solved to date problem in algorithmic formulations development, when a subset of in-gredients should be chosen from a larger available pool of suitable ingredients. The new DoE algorithm wasdemonstrated in a workflow making use of a ’make and test’ formulation robots. The developed new DoEprocedure demonstrated an efficient selection of a subset of ingredients from a larger number of the availableones, optimizing their concentration and allowing assignment of differential priorities to the optimizationobjectives.

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More information

Accepted/In Press date: 25 November 2022
e-pub ahead of print date: 28 November 2022
Published date: 7 December 2022
Keywords: Design of Experiments, Bayesian optimization, Product design, Gaussian processes, Machine Learning

Identifiers

Local EPrints ID: 473470
URI: http://eprints.soton.ac.uk/id/eprint/473470
ISSN: 0098-1354
PURE UUID: 862e5b21-7358-434e-b620-3532f19eb980
ORCID for David Woods: ORCID iD orcid.org/0000-0001-7648-429X

Catalogue record

Date deposited: 19 Jan 2023 17:36
Last modified: 17 Mar 2024 02:51

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Contributors

Author: Liwei Cao
Author: Danilo Russo
Author: Emily S Matthews
Author: Alexei Lapkin
Author: David Woods ORCID iD

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