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

开发可持续生物过程建模和优化的战略数据驱动方法

• 批准号: 2323695
• 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: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2323695
• 关键词: Development; Strategic; Data; Driven; Approaches

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.

为平台和高价值化学品（例如生物燃料，食品补充，医疗保健相关材料）开发可持续生物制造路线是建立低碳经济并实现能源/粮食安全的高优先领域。生物过程（例如发酵和照相生产）利用了不同类型的微生物（例如细菌，酵母和微藻）的代谢反应途径，将原材料转化为靶产品。由于代谢活动与培养流体动力学（工业生产系统）之间的复杂相互作用，生物过程对工作条件的变化（例如pH，营养供应，培养混合）高度敏感。因此，为了最大程度地提高工艺盈利能力和材料/能量转化效率，必须维持微生物生长和生物产品合成的合适工作条件。但是，对于通用的大规模生物制造系统，由于当前在线监控设备的限制，可用信息的量（例如，可衡量的数据）通常很稀少，而且高度嘈杂，这阻碍了实时过程控制和优化的决策。该博士学位项目旨在开发最先进的混合建模工具，以模拟和优化复杂的生化过程。这将通过耦合机学习技术和物理驱动的方法来实现，以代表和重现工业生物生产的情况。然后，模型将与尖端的在线优化策略结合使用，以构建实时动态优化框架，以预测和增强不同尺度上现有生物过程的性能。通过采用和探索各种机器学习方法，例如不同类型的神经网络，高斯流程，集合学习和增强学习，将特别关注该项目对高级数据驱动工具的开发。这些策略将通过与英国，中国和墨西哥的研究小组和工业合作伙伴进行广泛的合作，对细菌发酵过程和微藻照相系统进行测试。