The optimal solution in dynamic optimization of batch processes often exhibits active path constraints. The goal of this work is the robust satisfaction of path constraints in the presence of parametric uncertainties based on known worst-case formulations. These formulations are interpreted as semi-infinite programs (SIP). Two known SIP algorithms are extended to the dynamic case and assessed. One is a discretization approach and the other a local reduction approach. With these presented concepts, robust path constraint satisfaction is in principle guaranteed. In this work, however, local methods are used to approximate the global solution of the lower-level problem with local solvers thus allowing for (rather unlikely) constraint violations. Finally, the penicillin fermentation is introduced as a well-known case study with uncertainties, which is modified in this work by adding further dependencies. The adaptation of the SIP concepts to dynamic optimization problems are shown to be successful for this case study.

动态优化批处理过程中的最佳解决方案通常表现出活动路径约束。这项工作的目标是基于已知的最坏情况公式，在存在参数不确定性的情况下完全满足路径约束。这些公式被解释为半无限程序（SIP）。将两种已知的SIP算法扩展到动态情况并进行评估。一种是离散化方法，另一种是局部约简方法。利用这些提出的概念，原则上保证了鲁棒的路径约束满足。但是，在这项工作中，使用局部方法来近似使用局部求解器解决较低级别问题的全局解决方案，从而允许（不太可能）违反约束。最后，介绍了青霉素发酵作为一个不确定性的著名案例研究，在此工作中，通过添加其他依赖项对其进行了修改。对于此案例研究，表明SIP概念适应动态优化问题是成功的。