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Publication Detail

Title: Integrated Data-Driven Process Monitoring and Explicit Fault-Tolerant Multiparametric Control.

Authors: Onel, Melis; Burnak, Baris; Pistikopoulos, Efstratios N

Published In Ind Eng Chem Res, (2020 Feb 12)

Abstract: We propose a novel active fault-tolerant control strategy that combines machine learning based process monitoring and explicit/multiparametric model predictive control (mp-MPC). The strategy features (i) data-driven fault detection and diagnosis models by using the support vector machine (SVM) algorithm, (ii) ranking via a nonlinear, kernel-dependent, SVM-based feature selection algorithm, (iii) data-driven regression models for fault magnitude estimation via the random forest algorithm, and (iv) a parametric optimization and control (PAROC) framework for the design of the explicit/multiparametric model predictive controller. The resulting explicit control strategies correspond to affine functions of the system states and the magnitude of the detected fault. A semibatch process, an example for penicillin production, is presented to demonstrate how the proposed framework ensures smart operation for which rapid switches between a priori computed explicit control action strategies are enabled by continuous process monitoring information.

PubMed ID: 32549652 Exiting the NIEHS site

MeSH Terms: No MeSH terms associated with this publication

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