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A framework of hybrid model development with identification of plant-model mismatch

机译：混合模型开发框架，鉴定植物模型不匹配

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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.
机译：混合造型吸引了越来越多的关注，以利用额外的数据来改善过程理解。目前的实践通常采用机械模型来预测过程行为。这些机制模型基于物理谅解和实验研究，但它们有时会导致植物模型不匹配（PMM），因为它们可能不准确地完全描述真实过程。黑匣子型号可以作为替代方案，但它们经常遭受较差的泛化和可解释性。为了组合这两种技术，开发了混合模型以在保持物理理解程度的同时利用过程数据。在这项工作中，我们利用部分相关系数和互信息来实现PMM的识别框架，然后引入和比较混合模型的串行，并行和组合结构。用模拟的反应器模型和两种制药单元操作案例研究应用并测试框架。

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