Handling Multi-rate and Missing Data in Variable Duration Economic Model Predictive Control of Batch Processes with Application to Electric Arc Furnace Operation

Abstract

In this Chapter, the problem of variable duration economic model predictive control (EMPC) of batch processes subject to multi-rate and missing data is considered. To this end, a recently developed subspace-based model identification approach for batch processes is generalized 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.