Designing plant experiments for real-time optimization systems

Abstract

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