The industrialization of catalytic processes is of far more importance today than it has ever been before and kinetic models are essential tools for their industrialization. Kinetic models affect the design, the optimization and the control of catalytic processes, but they are not easy to obtain. Classical paradigms, such as mechanistic modeling require substantial domain knowledge, while data-driven and hybrid modeling lack interpretability. Consequently, a different approach called automated knowledge discovery has recently gained popularity. Many methods under this paradigm have been developed, where ALAMO, SINDy and genetic programming are notable examples. However, these methods suffer from important drawbacks: they require assumptions about model structures, scale poorly, lack robust and well-founded model selection routines, and they are sensitive to noise. To overcome these challenges, the present work constructs two methodological frameworks, Automated Discovery of Kinetics using a Strong/Weak formulation of symbolic regression, ADoK-S and ADoK-W, for the automated generation of catalytic kinetic models. We leverage genetic programming for model generation, a sequential optimization routine for model refinement, and a robust criterion for model selection. Both frameworks are tested against three computational case studies of increasing complexity. We showcase their ability to retrieve the underlying kinetic rate model with a limited amount of noisy data from the catalytic system, indicating a strong potential for chemical reaction engineering applications.

翻译：催化过程的工业化如今比以往任何时候都更为重要，而动力学模型是其工业化的关键工具。动力学模型影响催化过程的设计、优化和控制，但其获取并不容易。经典范式（如机理建模）需要大量领域知识，而数据驱动和混合建模则缺乏可解释性。因此，一种被称为自动知识发现的新方法近年来逐渐受到关注。在该范式下已发展出多种方法，其中ALAMO、SINDy和遗传编程是典型代表。然而，这些方法存在重要缺陷：它们需要对模型结构做出假设、可扩展性差、缺乏稳健且有充分依据的模型选择程序，且对噪声敏感。为克服这些挑战，本研究构建了两种方法论框架——利用符号回归的强形式/弱形式自动发现动力学（ADoK-S和ADoK-W），用于自动生成催化动力学模型。我们采用遗传编程进行模型生成、序列优化程序进行模型精炼，并基于稳健准则进行模型选择。两个框架均在三个计算案例研究中进行了测试，案例复杂度递增。我们展示了它们利用有限且含噪的催化系统数据恢复潜在动力学速率模型的能力，表明其在化学反应工程应用中具有巨大潜力。