Skip to main content
3.128.172.168

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

By Chen, Yingjie; Ierapetritou, Marianthi

Published on CMKC

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.

Journal

AIChE Journal. Volume 66, 10, 2020, e16996-

DOI

10.1002/aic.16996

Type of publication

Peer-reviewed journal

Affiliations

  • Rutgers, The State University of New Jersey

Article Classification

Research Article

Classification Areas

  • Modelling

Tags