Hybrid modeling has attracted increasing attention in order to take advantage of the additional data to improve process understanding. Current practice often adopts mechanistic models to predict process behaviors. These mechanistic models are based on physical understandings and experimental studies, but they sometimes lead to plant‐model mismatch (PMM) as they may be inaccurate to fully describe real processes. Black‐box models can serve as an alternative, but they often suffer from poor generalization and interpretability. To combine the two techniques, hybrid models are developed to make use of process data while maintaining a degree of physical understanding. In this work, we implement a framework of identification of PMM using partial correlation coefficient and mutual information, followed by introducing and comparing serial, parallel, and combined structures of hybrid models. The framework is applied and tested with a simulated reactor model and two pharmaceutical unit operation case studies.

中文翻译：

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识别植物模型不匹配的混合模型开发框架

混合建模吸引了越来越多的关注，以便利用附加数据来提高对过程的理解。当前的实践通常采用机械模型来预测过程行为。这些机械模型基于物理理解和实验研究，但是有时它们可​​能导致工厂模型不匹配（PMM），因为它们可能不足以完全描述真实的过程。黑匣子模型可以作为替代方案，但它们通常遭受泛化和可解释性差的困扰。为了结合这两种技术，开发了混合模型以利用过程数据，同时保持一定程度的物理理解。在这项工作中，我们使用部分相关系数和互信息来实现PMM识别的框架，然后介绍和比较串行，并行，混合模型的组合结构。该框架通过模拟反应器模型和两个制药单元操作案例研究进行了应用和测试。