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.
Bibliographical noteFunding 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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- Bayesian optimisation
- Bio-based chemicals
- Circular economy
- Crude sulphate turpentine
- Reaction development