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

通过使用数字建模技术加速生物废物增值技术

• 批准号: 2627301
• 金额: --
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2627301
• 关键词: Accelerating; biowaste; valorisation; technology; through

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.

开发可持续的生物制造路线，用于平台和高价值化学品的工业生产，是建立低碳经济的优先事项。生物质废弃物，如菜籽粕(RSM)，可以作为可持续的原料来生产一系列有价值的化合物，包括蛋白质、生物聚合物和酚类化合物。为了在更大的范围内实现生物废弃物的有价化和提高基于生物的过程的性能，通过全系统的方法来研究潜在生物过程的每个步骤的生物学和动力学机制是至关重要的。特别是，为了加速对生物废弃物衍生的工业生物技术的理解和推广，一个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)来有效地分析生物过程数据，以发现未知的过程知识和指导实验设计(DOE)。这种数据驱动的方法将极大地促进生物过程知识的产生，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。我们还收集了RSM定价化过程中的大量实验数据。结合这些以前的成果，这个博士项目旨在研究RSM酚类提取、蛋白质提取、水解和发酵生产生物聚合物的潜在过程机理，确定每个生物处理步骤的最佳操作条件，并通过反向实验设计(逆向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，这个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提纯，潜在地有助于进一步减少产量和改善环境影响。该项目将在目前获得资金的基础上，与行业合作者合作，开发一种可控、可扩展的综合生物工艺。