Self-optimizing Control and NCO tracking in the Context of Real-Time Optimization

This paper reviews the role of self-optimizing control (SOC) and necessary conditions of optimality tracking (NCO tracking). We argue that self-optimizing control is not an alternative to real-time optimization (RTO), NCO tracking or model predictive control (MPC), but is to be seen as complementary. In self-optimizing control we determine controlled variables (CV), that keep the process close to the optimum when a disturbance enters the process. These CVs are controlled at their setpoints using PID or model predictive controllers. Preferably, the setpoints are kept constant, but they may also be adjusted using RTO or NCO tracking. In any case, a good choice of CVs will reduce the frequency of setpoint changes by RTO or NCO tracking. When selecting self-optimizing CVs, a set of disturbances has to be assumed, as unexpected disturbances are not rejected in SOC. On the other hand, RTO and NCO tracking adapt the inputs at given sample times without any assumptions on what disturbances occur. It is only assumed that they occur on a slower time scale than the sampling. Disturbances with high frequencies or which which do not lead to a steady state are not rejected optimally. By using NCO tracking in the optimization layer and SOC in the control layer below, we demonstrate that the advantages of both methods complement each other. This combination allows fast optimal action for the expected disturbances, while other disturbances are compensated by NCO tracking on a slower time scale.