Operable adaptive sparse identification of systems: Application to chemical processes. Issue 11 (3rd September 2020)

• Record Type: Journal Article
• Authors: Bhadriraju, Bhavana; Bangi, Mohammed Saad Faizan; Narasingam, Abhinav; Kwon, Joseph Sang‐Il
• Is Part Of: AIChE journal. Volume 66:Issue 11(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-09-03
• Subjects: data‐driven system identification -- deep neural networks -- model predictive control -- online model identification -- sparse regression; Chemical engineering -- Periodicals; Génie chimique -- Périodiques; 660.28
• DOI: 10.1002/aic.16980
• Languages: English
• ISSNs: 0001-1541

Abstract

Abstract: Over the past few decades, several data‐driven methods have been developed for identifying a model that accurately describes the process dynamics. Lately, sparse identification of nonlinear dynamics (SINDy) has delivered promising results for various nonlinear processes. However, at any instance of plant‐model mismatch or process upset, retraining the model using SINDy is computationally expensive and cannot guarantee to catch up with rapidly changing dynamics. Hence, we propose operable adaptive sparse identification of systems (OASIS) framework that extends the capabilities of SINDy for accurate, automatic, and adaptive approximation of process models. First, we use SINDy to obtain multiple models from historical data for varying input settings. Next, using these models and their training data, we build a deep neural network that is incorporated in a model predictive control framework for closed‐loop operation. We demonstrate the OASIS methodology on the identification and control of a continuous stirred tank reactor.