Bayesian Optimization of Crude Sulphate Turpentine Conversion to p-Cymene

The identification of novel routes to synthetize functional molecules from bio-waste feedstock is one of the open challenges for a sustainable development of the chemical industry. However, most of the available feedstocks are complex mixtures of different molecules, which would ideally be processed without tedious and costly purification. The chemical complexity of such mixtures and their chemical transformations makes it difficult to build accurate mechanistic models for the optimization of the process conditions. To efficiently tackle this problem, we combine recent developments in machine learning (ML) and closed-loop optimization. The use of data-driven surrogate models coupled with design of experiments (DoE) algorithms have the potential to significantly speed up optimization of processes and suggest optimal conditions for synthetic routes, when no physical models are available.