Recent advances in optimization-based simultaneous process and control design

In this work, we first discuss recent advances towards the integration of process design, process control and process operability from the open literature and then we focus on techniques towards this endeavor that were developed within our group at Imperial College. While most of the approaches employ controllability measures to achieve this goal, our developments can be classified as a simultaneous process and control design methodology. Based on novel mixed integer dynamic optimization algorithms, our strategy features high fidelity process dynamic models, conventional PI control schemes, explicit consideration of structural process and control design aspects (such as number of trays, pairing of manipulated and controlled variables) through the introduction of 0–1 variables, and explicit consideration of time-varying disturbances and time-invariant uncertainties. The application of this strategy to a typical distillation system is discussed. In the second part of this chapter we present an extension of the process and control design framework that incorporates advanced model-based predictive controllers. Parametric programming is used for the controller derivation giving rise to a closed-form controller structure and removing the need for solving an optimization problem on-line. The resulting parametric controller is readily incorporated in the design optimization framework bringing about significant economic and operability benefits. The key features and advantages of this approach are highlighted via a simple binary distillation example.