A new optimization framework for dynamic systems under uncertainty

In industrial practice, uncertainties are usually compensated for by using conservative, decisions like an over-design of process equipment or an overestimation of operational parameters caused by worst case assumptions of the uncertain parameters. which leads to significant deterioration of the objective function in an optimization problem. In other deterministic optimization approaches, the expected values are used, which most likely leads to violations of the constraints when the decision variables are implemented on site. Thus, several studies on systematic approaches taking these uncertainties into consideration have been made recently. In this work. novel algorithms for nonlinear chance constrained optimization are proposed specially for such stochastic optimization problems where no monotone relation between constrained output and uncertain input variables exists. This is necessary, especially for those processes which involve chemical chain reactions or other complex reaction systems where the decision variables are strongly critical to the question of whether there is a monotony or not.