Accelerating Net Zero Manufacturing with Intelligent Optical Reactor Technology

快速过渡到净零制造需要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尤其如此，这些过程通常仍然通过艰苦的试错法进行优化。因此，该项目的总体目标是将联合收割机先进的光谱学和反应器技术与机器学习和实时优化相结合，实现自优化反应器技术，适用于使用非均相催化剂从生物质原料合成重要化学产品。我们最近率先开发了配备光纤技术的新型非均相催化剂反应器。该反应器使我们能够在真正的连续操作条件下和在真实的时间内，通过进行操作光谱学，使用非均相沸石催化剂，跟踪葡萄糖向各种工业感兴趣的生物基化学品的转化。特别是，确定了与催化剂的活性位点和化学过程的所有反应途径相关的独特光学信号，包括期望的反应途径和不期望的副反应的那些。此外，我们可以将每个光信号的强度与形成的产物的量相关联，如通过离线方法（HPLC和1H-13 C HSQC NMR）所验证的，从而得到每个催化剂在活性、选择性和稳定性方面的性能的真实的时间信息。由于反应途径信号是与催化剂活化底物有关的电荷转移带（而不是反应物和产物的发色团），它们还提供了对各种反应途径过渡态的直接洞察。这些突破在催化研究中是前所未有的，代表了本项目的起点。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，我们将联合收割机通过operando光学技术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，这些算法在各种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，然后使用智能光反应器（IOR）技术，通过开发自优化反应器技术来加速生物基化学品的生产。在后面的阶段，我们将尝试使用过渡态的反应器提供的见解，以产生先进的结构活性关系的相关性，未来的催化剂设计。

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.