Handling multi‐rate and missing data in variable duration economic model predictive control of batch processes

• Published in: AIChE journal 2017-07, Vol.63 (7), p.2705-2718
• Main Authors: Rashid, Mudassir M., Mhaskar, Prashant, Swartz, Christopher L. E.
• Format: Article
• Language: English
• Subjects: batch process control; Batch processes; Batch processing; Computer simulation; Data processing; economic model predictive control; Economic models; Economics; Effectiveness; electric arc furnace; Electric arc furnaces; Energy; Mathematical models; Missing data; multi‐rate system identification; Noise measurement; Noise prediction; Predictive control; Singular value decomposition; subspace identification; Tracking; Trajectories
• ISSN: 0001-1541; 1547-5905
• DOI: 10.1002/aic.15619

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