Development of Strategic Data-Driven Approaches To Sustainable Bioprocess Modelling and Optimisation

The development of sustainable bio-manufacturing routes for both platform and high-value chemicals (e.g. biofuels, food supplement, healthcare relevant materials) is a high priority area to establish a low carbon economy and achieve energy/food security. Bioprocesses (e.g. fermentation and photo-production) exploit the metabolic reaction pathways of different types of microorganisms, such as bacteria, yeast, and microalgae, to convert raw materials into target products. Due to the complex interaction between metabolic activities and culture fluid dynamics (industrial production systems), bioprocesses are highly sensitive to changes in operating conditions (e.g. pH, nutrient supply, culture mixing). Therefore, to maximise the process profitability and material/energy conversion efficiency, it is essential to maintain suitable operating conditions for microorganism growth and bioproduct synthesis. However, for generic large-scale bio-manufacturing systems, the amount of available information (e.g. measureable data) is often scarce and highly noisy due to the limitation on current online monitoring equipment, impeding the decision making for real-time process control and optimisation.

This PhD project aims to develop state-of-the-art hybrid modelling tools to simulate and optimise complex biochemical processes. This will be achieved by coupling machine learning technologies and physically-driven methodologies to represent and recreate industrial bio-production scenarios. The models will then be combined with cutting-edge online optimisation strategies to construct real-time dynamic optimisation frameworks to predict and intensify the performance of existing bioprocesses at different scales. Particular attention of this project will be placed on the development of advanced data-driven tools through adoption and exploration of a variety of machine learning methodologies such as different types of neural networks, Gaussian Processes, ensemble learning, and reinforcement learning. The strategies will be tested for both bacterial fermentation processes and microalgal photo-production systems through extensive collaborations with research groups and industrial partners in the UK, China, and Mexico.