Integrated Data-Driven Process Monitoring and Explicit Fault-Tolerant Multiparametric Control. Academic Article uri icon

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.

published proceedings

  • Ind Eng Chem Res

altmetric score

  • 1

author list (cited authors)

  • Onel, M., Burnak, B., & Pistikopoulos, E. N.

citation count

  • 6

complete list of authors

  • Onel, Melis||Burnak, Baris||Pistikopoulos, Efstratios N

publication date

  • February 2020