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

16 Citations (SciVal)
19 Downloads (Pure)


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
Early online date8 Jul 2021
Publication statusPublished - 16 Jan 2022


  • 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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