Economic Model Predictive Control Theory: Computational Efficiency and Application to Smart Manufacturing

The chemical industry is a vital sector of the US economy. Maintaining optimal chemical process operation is critical to the future success of the US chemical industry on a global market. Traditionally, economic optimization of chemical processes has been addressed in a two-layer hierarchical architecture. In the upper layer, real-time optimization carries out economic process optimization by computing optimal process operation set-points using detailed nonlinear steady-state process models. These set-points are used by the lower layer feedback control systems to force the process to operate on these set-points. While this paradigm has been successful, we are witnessing an increasing need for dynamic market and demand-driven operations for more efficient process operation, increasing response capability to changing customer demand, and achieving real-time energy management. To enable next-generation market-driven operation, economic model predictive control (EMPC), which is an model predictive control scheme formulated with a stage cost that represents the process economics, has been proposed to integrate dynamic economic optimization of processes with feedback control.Motivated by these considerations, novel theory and methods needed for the design of computationally tractable economic model predictive control systems for nonlinear processes are developed in this dissertation. Specifically, the following considerations are addressed: a) EMPC structures for nonlinear systems which address: infinite-time and finite-time closed-loop economic performance and time-varying economic considerations such as changing energy pricing; b) two-layer (hierarchical) dynamic economic process optimization and feedback control frameworks that incorporate EMPC with other control strategies allowing for computational efficiency; and c) EMPC schemes that account for real-time computation requirements. The EMPC schemes and methodologies are applied to chemical process applications. The application studies demonstrate the effectiveness of the EMPC schemes to maintain process stability and improve economic performance under dynamic operation as well as to increase efficiency, reliability and profitability of processes, thereby contributing to the vision of Smart Manufacturing