Abstract In this study we bridge traditional standalone data-driven and knowledge-driven process monitoring approaches by proposing a novel hybrid framework that exploits the advantages of both simultaneously. Namely, we design a process monitoring system based on a data-driven model that includes two different data types: i) “actual” data coming from sensor measurements, and ii) “virtual” data coming from a state estimator, based on a first-principles model of the system under investigation. We test the proposed approach on two simulated case studies: a continuous polycondensation process for the synthesis of poly-ethylene terephthalate, and a fed-batch fermentation process for the manufacturing of penicillin. The hybrid monitoring model shows superior fault detection and diagnosis performances with respect to conventional monitoring techniques, even when the first-principles model is relatively simple and process/model mismatch exists.

中文翻译：

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过程监控的混合框架：通过状态和参数估计增强数据驱动的方法

摘要 在这项研究中，我们通过提出一种新颖的混合框架，将传统的独立数据驱动和知识驱动的过程监控方法连接起来，同时利用两者的优点。也就是说，我们设计了一个基于数据驱动模型的过程监控系统，该模型包括两种不同的数据类型：i) 来自传感器测量的“实际”数据，以及 ii) 来自状态估计器的“虚拟”数据，基于第一个-被调查系统的原理模型。我们在两个模拟案例研究中测试了所提出的方法：用于合成聚对苯二甲酸乙二醇酯的连续缩聚工艺，以及用于制造青霉素的分批补料发酵工艺。混合监控模型相对于传统监控技术显示出优越的故障检测和诊断性能，