Global self-optimizing control for uncertain constrained process systems

Abstract

Self-optimizing control is a promising control strategy to achieve real-time optimization (RTO) for uncertain process systems. Recently, a global self-optimizing control (gSOC) approach has been developed to extend the economic performance to be globally acceptable in the entire uncertain space spanned by disturbances and measurement noise. Nevertheless, the gSOC approach was derived based on the assumption of no change in active constraints, which limits the applicability of the approach. To address this deficiency, this paper proposes a new CV selection approach to handle active constraint changes. It ensures that all constraints are within their feasible regions when the selected CVs are maintained at constant setpoints for all expected uncertainties. In particular, constraints of interest are linearized at multiple operating conditions to get better estimates of their values and then incorporated into the optimization formulation when solving the globally self-optimizing CVs. The new CV selection approach is able to ensure an improved operational economic performance without potential constraint violations, as illustrated in an evaporator case study.