Multi-objective Bayesian optimisation of a two-step synthesis of p-cymene from crude sulphate turpentine

Perman Jorayev; Danilo Russo; Joshua D. Tibbetts; Artur M. Schweidtmann; Paul Deutsch; Steven D. Bull; Alexei A. Lapkin

doi:10.1016/j.ces.2021.116938

Multi-objective Bayesian optimisation of a two-step synthesis of p-cymene from crude sulphate turpentine

Perman Jorayev, Danilo Russo^*, Joshua D. Tibbetts, Artur M. Schweidtmann, Paul Deutsch, Steven D. Bull, Alexei A. Lapkin^*

^*Corresponding author for this work

Chemistry

Research output: Contribution to journal › Article › peer-review

Abstract

Production of functional molecules from renewable bio-feedstocks and bio-waste has the potential to significantly reduce the greenhouse gas emissions. However, the development of such processes commonly requires invention and scale-up of highly selective and robust chemistry for complex reaction networks in bio-waste mixtures. We demonstrate an approach to optimising a chemical route for multiple objectives starting from a mixture derived from bio-waste. We optimise the recently developed route from a mixture of waste terpenes to p-cymene. In the first reaction step it was not feasible to build a detailed kinetic model. A Bayesian multiple objectives optimisation algorithm TS-EMO was used to optimise the first two steps of reaction for maximum conversion and selectivity. The model suggests a set of very different conditions that result in simultaneous high values of the two outputs.

Original language	English
Article number	116938
Number of pages	10
Journal	Chemical Engineering Science
Volume	247
Early online date	8 Jul 2021
DOIs	https://doi.org/10.1016/j.ces.2021.116938
Publication status	Published - 16 Jan 2022

Bibliographical note

Funding Information:
PJ is grateful to UCB Pharma for PhD scholarship. This project was co-funded by the UKRI project “Combining Chemical Robotics and Statistical Methods to Discover Complex Functional Products” (EP/R009902/1), UKRI project “Terpene-based Manufacturing for Sustainable Chemical Feedstocks” EP/K014889 and the National Research Foundation, Prime Minister’s Office, Singapore under its CREATE programme, project “Cambridge Centre for Carbon Reductions in Chemical Technology”. AMS is supported by the TU Delft AI Labs Programme.

Keywords

Bayesian optimisation
Bio-based chemicals
Biowaste
Circular economy
Crude sulphate turpentine
Reaction development

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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title = "Multi-objective Bayesian optimisation of a two-step synthesis of p-cymene from crude sulphate turpentine",

abstract = "Production of functional molecules from renewable bio-feedstocks and bio-waste has the potential to significantly reduce the greenhouse gas emissions. However, the development of such processes commonly requires invention and scale-up of highly selective and robust chemistry for complex reaction networks in bio-waste mixtures. We demonstrate an approach to optimising a chemical route for multiple objectives starting from a mixture derived from bio-waste. We optimise the recently developed route from a mixture of waste terpenes to p-cymene. In the first reaction step it was not feasible to build a detailed kinetic model. A Bayesian multiple objectives optimisation algorithm TS-EMO was used to optimise the first two steps of reaction for maximum conversion and selectivity. The model suggests a set of very different conditions that result in simultaneous high values of the two outputs.",

keywords = "Bayesian optimisation, Bio-based chemicals, Biowaste, Circular economy, Crude sulphate turpentine, Reaction development",

author = "Perman Jorayev and Danilo Russo and Tibbetts, {Joshua D.} and Schweidtmann, {Artur M.} and Paul Deutsch and Bull, {Steven D.} and Lapkin, {Alexei A.}",

note = "Funding Information: PJ is grateful to UCB Pharma for PhD scholarship. This project was co-funded by the UKRI project “Combining Chemical Robotics and Statistical Methods to Discover Complex Functional Products” (EP/R009902/1), UKRI project “Terpene-based Manufacturing for Sustainable Chemical Feedstocks” EP/K014889 and the National Research Foundation, Prime Minister{\textquoteright}s Office, Singapore under its CREATE programme, project “Cambridge Centre for Carbon Reductions in Chemical Technology”. AMS is supported by the TU Delft AI Labs Programme.",

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doi = "10.1016/j.ces.2021.116938",

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AU - Jorayev, Perman

AU - Russo, Danilo

AU - Tibbetts, Joshua D.

AU - Schweidtmann, Artur M.

AU - Deutsch, Paul

AU - Bull, Steven D.

AU - Lapkin, Alexei A.

N1 - Funding Information: PJ is grateful to UCB Pharma for PhD scholarship. This project was co-funded by the UKRI project “Combining Chemical Robotics and Statistical Methods to Discover Complex Functional Products” (EP/R009902/1), UKRI project “Terpene-based Manufacturing for Sustainable Chemical Feedstocks” EP/K014889 and the National Research Foundation, Prime Minister’s Office, Singapore under its CREATE programme, project “Cambridge Centre for Carbon Reductions in Chemical Technology”. AMS is supported by the TU Delft AI Labs Programme.

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N2 - Production of functional molecules from renewable bio-feedstocks and bio-waste has the potential to significantly reduce the greenhouse gas emissions. However, the development of such processes commonly requires invention and scale-up of highly selective and robust chemistry for complex reaction networks in bio-waste mixtures. We demonstrate an approach to optimising a chemical route for multiple objectives starting from a mixture derived from bio-waste. We optimise the recently developed route from a mixture of waste terpenes to p-cymene. In the first reaction step it was not feasible to build a detailed kinetic model. A Bayesian multiple objectives optimisation algorithm TS-EMO was used to optimise the first two steps of reaction for maximum conversion and selectivity. The model suggests a set of very different conditions that result in simultaneous high values of the two outputs.

AB - Production of functional molecules from renewable bio-feedstocks and bio-waste has the potential to significantly reduce the greenhouse gas emissions. However, the development of such processes commonly requires invention and scale-up of highly selective and robust chemistry for complex reaction networks in bio-waste mixtures. We demonstrate an approach to optimising a chemical route for multiple objectives starting from a mixture derived from bio-waste. We optimise the recently developed route from a mixture of waste terpenes to p-cymene. In the first reaction step it was not feasible to build a detailed kinetic model. A Bayesian multiple objectives optimisation algorithm TS-EMO was used to optimise the first two steps of reaction for maximum conversion and selectivity. The model suggests a set of very different conditions that result in simultaneous high values of the two outputs.

KW - Bayesian optimisation

KW - Bio-based chemicals

KW - Biowaste

KW - Circular economy

KW - Crude sulphate turpentine

KW - Reaction development

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DO - 10.1016/j.ces.2021.116938

M3 - Article

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VL - 247

JO - Chemical Engineering Science

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M1 - 116938

ER -

Multi-objective Bayesian optimisation of a two-step synthesis of p-cymene from crude sulphate turpentine

Abstract

Bibliographical note

Keywords

UN SDGs

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