Accelerating Net Zero Manufacturing with Intelligent Optical Reactor Technology

快速过渡到净零制造要求研究人员使用现代方法来发现和开发工艺。对于多相催化过程的开发尤其如此，这些过程通常仍通过艰苦的反复试验方法进行优化。因此，该项目的总体目标是将开创性的光谱和反应器技术与机器学习和实时优化相结合，实现适用于以生物质为原料使用非均相催化剂合成重要化工产品的自优化反应器技术。我们最近率先开发了一种配备光纤技术的新型多相催化反应器。该反应器使我们能够使用非均相沸石催化剂，在真正连续的操作条件下，通过进行Operando光学光谱分析，实时跟踪葡萄糖转化为各种工业上感兴趣的生物基化学品的过程。特别是，识别了与催化剂的活性中心和化学过程的所有反应途径有关的独特的光学信号，包括所需的反应途径和不希望发生的副反应的反应途径。此外，我们可以将每个光信号的强度与生成的产物的数量联系起来，通过离线方法(高效液相色谱和1H-13C HSQC核磁共振)进行验证，从而实时了解每个催化剂在活性、选择性和稳定性方面的性能。由于反应途径信号是与催化剂激活底物有关的电荷转移带(而不是反应物和产物的生色团)，它们还提供了对各种反应途径的过渡态的直接洞察。这些突破在催化研究中是前所未有的，也是本项目的起点。本项目将利用这一突破，将机器学习应用于多相催化和生物质转化。特别是，我们将通过Operando光学技术将快速数据生成与在线学习算法相结合，通过时间序列建模实时构建数据驱动模型。这将促进ML算法的开发，该算法在不同的操作条件下唯一地考虑活性、选择性和稳定性。我们将验证这一假设，然后利用智能光学反应器(IOR)技术，通过开发自优化反应器技术来加速生物化工制造。在以后的阶段，我们将尝试使用反应器提供的转变状态洞察来生成与未来催化剂设计相关的高级结构活性关系。

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.