Optimising microbial protein biotechnology using machine learning and mathematical optimisation for protein sustainability

This project draws upon cross-disciplinary expertise from King's College London (KCL) and well supported by Quorn Foods. Prof Rob Johnson from Quorn and Miao Guo from KCL have developed strong collaboration to drive exciting and timely research. They have been collaborating on lignocellulosic mycoprotein research and co-supervising 3 PhDs. The lab and modelling research in Guo's lab and Lam's intelligent control lab form the foundation for this programme.

The plant-sourced and animal-sourced proteins are carbon-intensive, resource-demanding, and vulnerable to pandemic impacts due to long-production cycles. This combined with increasing protein demands and pandemic of hunger highlight the challenges on providing sustainable protein solutions. Microbial proteins produced under controlled fermentation with advanced optimisation- aided design enable rapid biotechnology advancement for future protein security and sustainability.

This project aims to couple machine learning and advanced optimisation approach to enable step- change in microbial protein biotechnologies to achieve zero-waste, zero-emissions, where sustainable Mycoprotein innovation will be tested as a representative study. The research objectives (Obj) are -

Obj-1 Novel integration of machine learning with model predictive control (MPC) to enable optimised and autonomous Mycoprotein biotechnology.
This objective, underpinned by process control, optimisation theory and machine learning techniques, will develop a learning-based controller to harness real-time data and optimise mycoprotein fermentation to achieve maximised resource efficiency and minimised waste; such fermentation represents an advanced industrial biotechnology with multivariable dynamics, nonlinearities, and constraints. The data-driven machine learning techniques will play a significant role to deal with the highly-nonlinear and complex process of mycoprotein biotechnology that the analytical methods are difficult to be applied. The data-driven approach offers a feasibale alternative to reveal undercovered characteristics through machine learning. The developed learning-based MPC approach will advance microbial protein technologies and enable autonomous fermentation with waste recovery and optimised resource utilisation.

Obj-2 Advanced optimiation to enable precision decision on net-zero Mycoprotein supply chains.

Coupling data value chains and advanced computational modelling has the potential to bring tremendous opportunities to protein biotechnology to enable real-time data acquisition, analysis and data-informed responsive optimisation to achieve sustainability across supply chains. Underpinned by this new concept, Obj-2 will couple simulation, life cycle sustainability, mathematical optimiation and learning algorithms to develop a novel data-driven optimisation with hybrid solution search algorithms to bring real-time supply chain data (e.g. sensor data) to precision decision-support; the optimisation approach will be tested on Mycorpotein supply chains to achieve zero-emissions. Obj-2 approach is expected to catalyse protein sector transformation by shifting isolated technologies towards performance-optimised and machinery-interconnected ecosystem