A framework of hybrid model development with identification of plant‐model mismatch. Issue 10 (15th August 2020)

• Record Type: Journal Article
• Authors: Chen, Yingjie; Ierapetritou, Marianthi
• Is Part Of: AIChE journal. Volume 66:Issue 10(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-08-15
• Subjects: continuous pharmaceutical manufacturing -- data‐driven model -- Grey‐box model -- hybrid model -- mechanistic model; Chemical engineering -- Periodicals; Génie chimique -- Périodiques; 660.28
• DOI: 10.1002/aic.16996
• Languages: English
• ISSNs: 0001-1541

Abstract

Abstract: 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.