Multi-Parametric Optimization & Control in Multi-Scale Energy Systems

Systems Conversation with Stratos Pistikopoulos, Texas A&M University

Model based multi-parametric optimization provides a complete map of solutions of an optimization problem as a function of, unknown but bounded, parameters in the model, in a computationally efficient manner, without exhaustively enumerating the entire parameter space. In a Model-based Predictive Control (MPC) framework, multi-parametric optimization can be used to obtain the governing control laws—the optimal control variables as an explicit function of the state variables. The main advantage of this approach is that it reduces repetitive on-line control and optimization to simple function evaluations, which can be implemented on simple computational hardware, such as a microchip, thereby opening avenues for many applications in chemical, energy, automotive, and biomedical equipment, devices, and systems.

In this presentation, we will first provide a historical progress report of the key developments in multi-parametric optimization and control. We will then describe PAROC, a systematic framework and prototype software system which allows for the representation, modelling, and solution of integrated design, operation, and advanced control problems. Algorithms that enable the integration capabilities for design, scheduling, and control are presented along with applications in sustainable energy systems, smart manufacturing, and process intensification.