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Operable adaptive sparse identification of systems: Application to chemical processes
Author(s) -
Bhadriraju Bhavana,
Bangi Mohammed Saad Faizan,
Narasingam Abhinav,
Kwon Joseph SangIl
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.16980
Subject(s) - identification (biology) , computer science , system identification , process (computing) , nonlinear system , process modeling , artificial intelligence , system dynamics , artificial neural network , nonlinear system identification , machine learning , control engineering , work in process , data mining , engineering , operations management , physics , quantum mechanics , biology , measure (data warehouse) , operating system , botany
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.

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