Reinforcement Learning for Process Optimisation and Control

自20世纪末以来，微藻可持续生产生物能源受到了相当大的关注，因为它可以直接利用太阳能和CO2来合成生物可再生能源。然而，由于缺乏工艺效率，生物工艺在经济上仍然是不可行的。强化学习（RL）是人工智能（AI）的一个子领域，它训练“机器”做出最佳决策。RL已经取得了巨大的成就，例如“机器”可以自我学习玩棋盘游戏或视频游戏。我们将利用这种快速发展的技术来解决生物过程面临的挑战，通过采用RL来优化设计，控制和操作生物过程生产。在2019年新型冠状病毒肺炎疫情之后，拟议的研究非常重要，而且相当热门。事实上，对RNA疫苗的研究可以将应对疾病爆发的时间从数年缩短到数周。该博士项目将开发工具和技术，以克服在疫苗工艺工程中实施物理模型的主要障碍，例如缺乏用于准确测量的生物分析工具。

Sustainable production of bioenergy by microalgae has received considerable attention since the end of the 20th century as it can directly utilise solar energy and CO2 to synthesise biorenewables. However, due to the lack of process efficiency, bioprocesses are still economically inviable. Reinforcement Learning (RL) is a subfield of Artificial Intelligence (AI) which trains "machines" to make optimal decisions. RL has made great achievements, such as "machines" self-learning to play board games or videogames. We will make use of this rapidly-growing technology to address current challenges faced by bioprocesses, by adopting RL to optimally design, control and operate bioprocess production. The proposed research is highly significant and rather topical in the wake of the Covid-2019 pandemic. Indeed research into RNA vaccines can reduce the time to respond to disease outbreaks from years to weeks. This PhD project will develop the tools and techniques to overcome major hurdles to the implementation of physical models in the process engineering of vaccines, such as the lack of bioanalytical tools for accurate measurements.