An intelligent approach of controlled variable selection for constrained process self-optimizing control

Self-optimizing control (SOC) is a technique for selecting appropriate controlled variables (CVs) and maintaining them constant such that the plant runs at its best. Some tough challenges in this subject, such as how to select CVs when the active constraint set changes remains unsolved since the notion of SOC was presented. Previous work had some drawbacks such as structural complexity and control inaccuracy when dealing with constrained SOC problems due to the elaborate control structures or the limitation of local SOC. In order to overcome the deficiency of previous methods, this paper developed a constrained global SOC (cgSOC) approach to implement self-optimizing controlled variable selection and control structure design. The constrained variables that may change between inactive and active are represented as a nonlinear function of available measurement variables under optimal operations. The unknown function is then intelligently learnt over the whole operating region through neural network training. The difference between the nonlinear function and the actual constrained variables measured in real-time is then used as CVs. When the CVs are controlled at zero in real-time, near-optimal operation can be ensured globally whenever active constraint changes. The efficacy of the proposed approach is demonstrated through an evaporator case study.