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

Research output: Contribution to journalArticlepeer-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 languageEnglish
Article number116938
JournalChemical Engineering Science
Volume247
Early online date8 Jul 2021
DOIs
Publication statusE-pub ahead of print - 8 Jul 2021

Keywords

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

ASJC Scopus subject areas

  • Chemistry(all)
  • Chemical Engineering(all)
  • Industrial and Manufacturing Engineering

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