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进行工艺发现和开发。对于多相催化过程的开发来说尤其如此，这些过程通常仍然是通过艰苦的试错方法来优化的。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，以实现适用于使用多相催化剂从生物质原料合成重要化学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剂反应器。该反应器允许我们使用多相沸石催化剂，在真正的连续操作条件下，通过执行operando光学光谱，实时跟踪葡萄糖转化为各种具有工业意义的生物基化学物质。特别是，与催化剂活性位点和化学过程的所有反应途径相关的独特光信号被识别出来，包括期望的反应途径和不希望的副反应途径。此外，我们可以将每个光信号的强度与形成的产物的数量联系起来，通过离线方法（HPLC和1H-13C HSQC NMR）进行验证，从而获得每种催化剂在活性，选择性和稳定性方面的实时性能信息。由于反应途径信号是与催化剂激活底物相关的电荷转移带（而不是反应物和生成物的发色团），因此它们也提供了对各种反应途径过渡状态的直接了解。这些突破在催化研究中是前所未有的，也是这个项目的起点。该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算法的发展，这些算法在各种操作条件下独特地考虑了活动、选择性和稳定性。我们将验证这一假设，然后使用智能光学反应器（IOR）技术通过开发自优化反应器技术来加速生物基化学制造。在后期阶段，我们将尝试使用反应器提供的过渡态见解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.