Accelerating Net Zero Manufacturing with Intelligent Optical Reactor Technology

利用智能光反应堆技术加速净零制造

• 批准号: 2896327
• 金额: --
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2896327
• 关键词: Accelerating; Net; Zero; Manufacturing; Intelligent

Quickly transitioning to net zero manufacturing requires researchers to use modern methods for process discovery and development. This is especially true for the development of heterogeneously catalysed processes, which are still typically optimised by painstaking trial-and-error methods. As such, the overall objective of this project is to combine pioneering spectroscopy and reactor technology with machine learning and real-time optimisation to achieve self-optimising reactor technology applicable to the synthesis of important chemical products from biomass feedstock using heterogeneous catalysts.We recently pioneered development of a novel heterogeneous catalyst reactor equipped with fiber optic technology. This reactor allowed us to follow the conversion of glucose to various bio-based chemicals of industrial interest using heterogeneous zeolite catalysts, at true continuous operational conditions and in real time, by performing operando optical spectroscopy. In particular, unique optical signals related to the active sites of the catalyst and all of the reaction pathways of the chemical process were identified, including the desired reaction pathway and those of undesired side reactions. Moreover, we could relate the intensity of each optical signal to the quantity of products formed, as verified by offline methods (HPLC and 1H-13C HSQC NMR), resulting in real time information of the performance of each catalyst in terms of activity, selectivity, and stability. As the reaction pathway signals are charge transfer bands associated with activation of the substrate by the catalyst (as opposed to chromophores of reactants and products), they also provide direct insight into the transition states of the various reaction pathways.These breakthroughs are unprecedented in catalysis research, and represent the starting point for this project. This project will use this breakthrough to target the application of machine learning to heterogeneous catalysis and biomass conversion. In particular, we will combine fast data generation through operando optical technology with online learning algorithms to construct data-driven models in real-time through time series modelling. This will facilitate the development of ML algorithms that uniquely account for activity, selectivity and stability, at various operational conditions. We will validate this hypothesis, and then use the Intelligent Optical Reactor (IOR) technology to accelerate bio-based chemical manufacture by developing self-optimising reactor technology. In later stages, we will attempt to use the transition state insights provided by the reactor to generate advanced structure activity relationships of relevance to future catalyst design.

快速过渡到净零制造业要求研究人员使用现代方法进行过程发现和开发。对于异质催化过程的开发尤其如此，这些过程通常通过艰苦的试验和错误方法来优化。因此，该项目的总体目标是将开拓性光谱和反应堆技术与机器学习和实时优化相结合，以实现适用于生物质原料中的重要化学产品的自动化反应堆技术。我们最近使用异质性催化剂从生物质原料中进行了新型化学产品。该反应器使我们能够在真正的连续操作条件下，实时执行Operando光学光谱法，遵循葡萄糖转化为葡萄糖，使用异质的沸石催化剂转化为工业兴趣的各种生物化学物质。特别是，鉴定了与催化剂的活性位点和化学过程的所有反应途径有关的独特光学信号，包括所需的反应途径和不希望的侧反应。此外，我们可以将每个光学信号的强度与形成的产物数量（通过离线方法（HPLC和1H-13C HSQC NMR）验证的产物数量相关联，从而在活动，选择性和稳定性方面提供了每种催化剂性能的实时信息。由于反应途径信号是与催化剂激活底物相关的电荷传递带（与反应物和产品的发色团相反），它们还直接洞悉了各种反应途径的过渡状态。这些突破在催化研究中是前所未有的，并代表了该项目的起点。该项目将利用这一突破来定位机器学习到异质催化和生物量转化的应用。特别是，我们将通过Operando光学技术与在线学习算法进行快速数据生成，以实时构建数据驱动的模型，从而实时通过时间序列建模。这将促进在各种操作条件下独特地解释活动，选择性和稳定性的ML算法的发展。我们将验证这一假设，然后使用智能光反应堆（IOR）技术来通过开发自优化反应器技术来加速基于生物的化学制造。在以后的阶段，我们将尝试使用反应堆提供的过渡状态洞察力来生成与未来催化剂设计相关性的高级结构活动关系。