Designing plant experiments for real-time optimization systems

This paper presents a new profit-based criterion to design plant experiments for model-based real-time optimization (RTO) systems. Experiments are designed by maximizing expected profit over a future horizon. The objective function for designing plant experiments includes the gain in profit after reducing the offset and variability by experimentation, and loss in profit during experimentation. Therefore, large plant movement from the optimum during experimentation and a large number of experiments are penalized by the profit loss term. By solving the experimental design problem, the time to start the experiments after a disturbance change, the number of experiments and the experimental operating conditions are determined. The proposed experimental design approach is applied to the simulated Williams-Otto reactor, and it yields higher profit by improved tracking of the optimum than achieved by standard RTO without experimentation.