System Identification and Process Modelling of Dynamic Systems Using Machine Learning

Inapakurthi, Ravi kiran and Mitra, Kishalay (2022) System Identification and Process Modelling of Dynamic Systems Using Machine Learning. In: 26th International Conference on System Theory, Control and Computing, ICSTCC 2022, 19-21 October 2022, Sinaia.

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Nonlinear system identification of complex and nonlinear unit operations and unit processes requires accurate modelling approaches. For this, first-principles based models were initially explored as they enable the causal explanation available among variables. However, the numerical integration issues along with the availability of voluminous data for developing data-based models has resulted in the shift from the conventional modelling approach to Machine Learning (ML) based modelling. In this study, Support Vector Regression (SVR) is used to model complex Industrial Grinding Circuit (IGC). To aid the accurate model requirement in process systems engineering domain, the tunable parameters of SVR are optimized using a novel multi-objective optimization formulation, which helps in minimizing the chances of over-fitting while simultaneously ensuring accurate models for IGC. The formulation is optimized using evolutionary algorithm to track and retain the most accurate models. The Pareto optimal SVR models have a minimum accuracy of 99. 786% and the prediction performance of the best model selected using knee point from the Pareto optimal set is compared with a model selected using arbitrary approach to show the competitiveness of the proposed technique. © 2022 IEEE.

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IITH Creators:
IITH CreatorsORCiD
Mitra, Kishalay
Item Type: Conference or Workshop Item (Paper)
Additional Information: Authors acknowledge the financial support received by the project grant # ST/NSM/R&D_HPC_Applications/2021/23 received from the Department of Science and Technology, Government of India, towards the execution of this work.
Uncontrolled Keywords: evolutionary algorithms; machine learning; multi-objective optimization; process modelling; system identification
Subjects: Chemical Engineering
Divisions: Department of Chemical Engineering
Depositing User: Ms Palak Jain
Date Deposited: 22 May 2023 09:31
Last Modified: 22 May 2023 09:31
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