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Handling multi‐rate and missing data in variable duration economic model predictive control of batch processes
Author(s) -
Rashid Mudassir M.,
Mhaskar Prashant,
Swartz Christopher L. E.
Publication year - 2017
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.15619
Subject(s) - missing data , model predictive control , process (computing) , subspace topology , computer science , trajectory , identification (biology) , batch processing , variable (mathematics) , duration (music) , control variable , control theory (sociology) , mathematical optimization , control (management) , mathematics , machine learning , artificial intelligence , art , physics , botany , biology , programming language , operating system , mathematical analysis , literature , astronomy
In the present work, we consider the problem of variable duration economic model predictive control of batch processes subject to multi‐rate and missing data. To this end, we first generalize a recently developed subspace‐based model identification approach for batch processes to handle multi‐rate and missing data by utilizing the incremental singular value decomposition technique. Exploiting the fact that the proposed identification approach is capable of handling inconsistent batch lengths, the resulting dynamic model is integrated into a tiered EMPC formulation that optimizes process economics (including batch duration). Simulation case studies involving application to the energy intensive electric arc furnace process demonstrate the efficacy of the proposed approach compared to a traditional trajectory tracking approach subject to limited availability of process measurements, missing data, measurement noise, and constraints. © 2017 American Institute of Chemical Engineers AIChE J , 63: 2705–2718, 2017