Start-up and Shut-down Conditions for Iterative Real-Time Optimization Methods

Iterative real-time optimization methods are able to identify the real process optimum in the presence of structural and parametric plant-model mismatch. However, upon converging to the process optimum they may suffer from generating ongoing process perturbations in response to measurement noise which are inefficient. In this paper, we propose a strategy for the shut-down of the iterative optimization schemes upon convergence to the plant optimum and a strategy for the start-up of the iterative optimization when a change in the process behavior occurs, in order to avoid a loss of performance. We employ techniques from statistical process monitoring to formulate appropriate conditions to detect a change in the process. The performance of the proposed start-up and shut-down strategies in combination with a powerful real-time optimization method namely, modifier adaptation with quadratic approximation (MAWQA), is analyzed using a chemical engineering case study.