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

[Display omitted] •Multi-objective surrogate-model-based optimization was applied to a complex reaction.•Several non-obvious optima were obtained in agreement with physical interpretation.•Most influential variables were identified.•Results suggested future investigations to efficiently adopt different feedstocks.•The method is promising for complex biowaste valorization for circular economy. 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.