Handling multi‐rate and missing data in variable duration economic model predictive control of batch processes. Issue 7 (2nd February 2017)

• Record Type: Journal Article
• Authors: Rashid, Mudassir M.; Mhaskar, Prashant; Swartz, Christopher L. E.
• Is Part Of: AIChE journal. Volume 63:Issue 7(2017)
• Page Start: 2705
• Page End: 2718
• Publication Date: 2017-02-02
• Subjects: multi‐rate system identification -- subspace identification -- economic model predictive control -- batch process control -- electric arc furnace; Chemical engineering -- Periodicals; Génie chimique -- Périodiques; 660.28
• DOI: 10.1002/aic.15619
• Languages: English
• ISSNs: 0001-1541

Abstract

Abstract : 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