The industrialization of catalytic processes requires reliable kinetic models for their design, optimization and control. Mechanistic models require significant domain knowledge, while data-driven and hybrid models lack interpretability. Automated knowledge discovery methods, such as ALAMO (Automated Learning of Algebraic Models for Optimization), SINDy (Sparse Identification of Nonlinear Dynamics), and genetic programming, have gained popularity but suffer from limitations such as needing model structure assumptions, exhibiting poor scalability, and displaying sensitivity to noise. To overcome these challenges, we propose two methodological frameworks, ADoK-S and ADoK-W (Automated Discovery of Kinetic rate models using a Strong/Weak formulation of symbolic regression), for the automated generation of catalytic kinetic models using a robust criterion for model selection. We leverage genetic programming for model generation and a sequential optimization routine for model refinement. The frameworks are tested against three case studies of increasing complexity, demonstrating their ability to retrieve the underlying kinetic rate model with limited noisy data from the catalytic systems, showcasing their potential for chemical reaction engineering applications.

翻译：催化过程的工业化需要可靠的动力学模型进行设计、优化与控制。机理模型依赖大量领域知识，而数据驱动模型和混合模型缺乏可解释性。诸如ALAMO（用于优化的代数模型自动学习）、SINDy（非线性动力学稀疏辨识）及遗传编程等自动化知识发现方法虽已获得广泛应用，但存在需预设模型结构假设、可扩展性差及对噪声敏感等局限性。为克服这些挑战，我们提出两个方法框架：ADoK-S和ADoK-W（基于强/弱形式符号回归的催化动力学速率模型自动发现），采用稳健的模型选择准则自动生成催化动力学模型。我们利用遗传编程进行模型生成，并采用序贯优化流程进行模型精炼。通过三个复杂度递增的案例研究，该框架展现出有限噪声数据下恢复催化系统潜在动力学速率模型的能力，凸显其在化学反应工程应用中的潜力。