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

为平台和高价值化学品的工业生产开发可持续的生物制造路线是建立低碳经济的重中之重。油菜籽粕（RSM）等生物质废弃物可作为可持续原料，用于生产一系列有价值的化合物，包括蛋白质、生物聚合物和酚类物质。为了在更大范围内实现生物废物的增值和提高生物基过程的性能，通过全系统方法研究每一步潜在生物过程的生物学和动力学机制至关重要。特别是，为了加速对生物废物衍生的工业生物技术的理解和升级，一种创新的方法是应用前沿的数字建模技术（机器学习，动力学建模，数据分析）来有效分析生物过程数据，以发现未确定的过程知识并指导实验设计（DoE）。这种数据驱动的方法将极大地促进生物工艺知识的产生，并促进生物科学在工业规模上转化为新的生物技术。我们已经开发了许多用于生物过程多尺度建模、代谢通量分析、优化和放大的数字工具。我们还从RSM的增值过程中收集了大量的实验数据。结合这些先前的成果，本博士项目旨在研究生物聚合物生产中RSM酚提取、蛋白质提取、水解和发酵的潜在工艺机制，确定每个生物处理步骤的最佳操作条件，并通过反设计实验（逆DoE）验证预测。这将使用我们的数字工具来筛选我们的数据集并提取必要的生物过程知识。此外，该博士项目将加强发酵和分离技术，用于有价值的生物可再生能源的合成和纯化，可能有助于进一步减少产量和改善环境影响。该项目将以目前资助的与工业合作者合作的工作为基础，开发一种可控的、可扩展的综合生物工艺。

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.