Monitoring, Multi-rate Modeling, and Economic Model Predictive Control of Chemical Processes

Abstract

In this work we develop methods and strategies for the design of advanced data-driven monitoring, control, and optimization algorithms. In the initial phase of this research, in an effort to better detect and identify process faults, we propose a novel pattern matching based process monitoring approach. In this regard, a novel pattern analysis driven dissimilarity approach is developed by integrating multidimensional mutual information with independent component analysis in order to quantitatively evaluate the statistical dependency between the independent component subspaces of the normal benchmark and monitored data sets. The process monitoring results of the proposed method are demonstrated to be superior to those of the traditional monitoring approaches. Next, we address the problem of the unavailability of reliable and computationally manageable first-principles-based models by developing a data-based multi-rate modeling and control approach. To this end, we consider the problem of multi-rate modeling and economic model predictive control (EMPC) of batch processes. First, multi-rate models are identified that include predictions for both the infrequently and frequently measured process variables. The resulting models are integrated into a two-tiered predictive controller that enables the target end-point to be achieved while minimizing the associated cost. The EMPC is implemented on the electric arc furnace (EAF) process and the closed-loop simulation results subject to the limited availability of process measurements and noise illustrate the improvement in economic performance over existing trajectory-tracking approaches. Finally, 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 EAF process demonstrate the efficacy of the proposed approach compared to a traditional trajectory tracking approach subject to the limited availability of process measurements, missing data, measurement noise and constraints.