Premium
A framework of hybrid model development with identification of plant‐model mismatch
Author(s) -
Chen Yingjie,
Ierapetritou Marianthi
Publication year - 2020
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.16996
Subject(s) - interpretability , generalization , identification (biology) , computer science , process (computing) , black box , process modeling , machine learning , system identification , data mining , artificial intelligence , work in process , measure (data warehouse) , mathematics , engineering , mathematical analysis , operations management , botany , biology , operating system
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.