Accelerating a biowaste valorisation technology through the use of digital modelling techniques

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Developing sustainable bio-manufacturing routes for industrial production of both platform and high-value chemicals is a high priority in establishing a low carbon economy. Biomass waste such as rapeseed meal (RSM) can be used as a sustainable feedstock to produce a range of valuable compounds including proteins, biopolymers and phenolics. To enable biowaste valorisation and improve performance of bio-based processes at larger scales, it is of critical importance to investigate the biological and kinetic mechanisms of the underlying bioprocess at each step through a whole-systems approach.In particular, to accelerate the understanding and upscaling of biowaste derived industrial biotechnologies, an innovative approach is to apply frontier digital modelling techniques (machine learning, kinetic modelling, data analytics) to efficiently analyse bioprocess data to discover undetermined process knowledge and guide design of experiments (DoE). This data-driven approach will greatly facilitate bioprocess knowledge generation and promote the translation of bioscience into novel biotechnologies at industrial scales.We have developed a number of digital tools for bioprocess multiscale modelling, metabolic flux analysis, optimisation, and scale-up. We have also collected substantial experimental data from the RSM valorisation process. Together with these previous achievements, this PhD project aims to investigate the underlying process mechanisms of RSM phenolic extraction, protein extraction, hydrolysis, and fermentation for biopolymer production, to identify the optimal operating conditions for each bioprocessing step and to verify the predictions through inverse design of experiments (inverse DoE). This will be carried out using our digital tools to screen our dataset and extract essential bioprocess knowledge. In addition, this PhD project will enhance fermentation and separation techniques for valuable biorenewables synthesis and purification, potentially facilitating further decreases in production and improvements in environmental impact. The project will build on currently funded work with industrial collaborators to develop a controllable, scalable integrated bioprocess.