In this study, we critically examine the performance of hybrid and purely data-driven models in reactor systems using catalytic methanation in a continuously stirred tank reactor as a representative case. Our comparative analysis includes four models: one purely data-driven model and three hybrid models. These hybrid models blend data-driven and mechanistic approaches, using data-driven submodels for specific process parts and data correction for mechanistic inaccuracies. The models are evaluated on simulated data to assess their accuracy, training effort, and reliability. Our results show that hybrid models do not consistently outperform the purely data-driven model. This highlights the need for careful model selection, taking into account the specifics of the problem. The choice between hybrid and pure data-driven models requires a balanced evaluation of effort and potential benefits, emphasizing the importance of systematic analysis in model selection.

中文翻译：

---

基于数据的反应堆建模面临的挑战：CSTR 案例研究的纯数据驱动模型和混合模型的批判性分析

在这项研究中，我们以连续搅拌釜反应器中的催化甲烷化为代表案例，批判性地研究了反应器系统中混合模型和纯数据驱动模型的性能。我们的比较分析包括四种模型：一种纯数据驱动模型和三种混合模型。这些混合模型融合了数据驱动和机械方法，对特定过程部件使用数据驱动子模型，并针对机械误差进行数据校正。根据模拟数据对模型进行评估，以评估其准确性、训练工作量和可靠性。我们的结果表明，混合模型并不总是优于纯数据驱动模型。这凸显了需要仔细选择模型，并考虑到问题的具体情况。混合模型和纯数据驱动模型之间的选择需要对工作量和潜在收益进行平衡评估，强调系统分析在模型选择中的重要性。