This thesis discusses recent advances towards the grand unification of process design, con- trol and operational optimization via multi-parametric programming techniques. First the PARametric Optimization and Control (PAROC) framework is presented together with a prototype implementation for the development of advanced receding horizon policies via multi-parametric programming and their closed-loop implementation. It is shown how state- of-the-art multi-parametric programming algorithms are utilized for the development of explicit optimization policies for a variety of problems (including control, estimation and scheduling) based on high-fidelity models and model approximation steps.
Two developments are presented towards the integration of design, control and opera- tional optimization within PAROC; (i) the integration of design and control and (ii) the integration of scheduling and control. For (i) we develop receding horizon optimization for- mulations where the design variables are simultaneously considered, resulting in explicit, design–dependent policies which are then included within a Mixed-Integer Dynamic Opti- mization (MIDO) algorithm minimizing operational and investment costs. For (ii), we de- velop scheduling strategies where the process dynamics and corresponding controller designs are simultaneously considered, resulting in explicit control–dependent scheduling schemes. Surrogate/approximate models are proposed to address the time–scale mismatch between the mid–term schedule and the short–term control optimization problem. Finally, the inte- gration of (i) and (ii) is shown within an overall dynamic optimization problem.
The developments are presented via a domestic cogeneration heat and power (CHP) system example and case studies of a tank, a continuously stirred tank reactor and a binary distillation column.