Dynamic Chance-Constrained Optimization under Uncertainty on Reduced Parameter Sets

Uncertainty is a crucial topic for the decision making process in almost every scientific field. Therefore, the correct implementation into optimization problems is vital. Herein, the chance-constrained optimization approach is applied and compared with a standard Monte Carlo optimization on a CSTR model. The two approaches are expanded by limiting the number of uncertain parameters in the system with according subset selection strategies from parameter estimation studies. The idea here is that a high number of uncertain parameters does not add to a better description of a system. The uncertainty can be represented by a subset of uncertain parameters, which suffice to describe the system behavior. In this contribution, it is shown that the results, both for the chance constrained and Monte Carlo optimization approaches, are improved regarding result stability and control action indication. Additionally, it is discussed how the chance-constrained approach yields even better results regarding the objective function of the optimization problem and it is shown that the solution time is drastically reduced.