# Systematic parameter selection for optimization under uncertainty

## FG Dynamik und Betrieb technischer Anlagen

Considering uncertainty is crucial for the decision making process in chemical engineering. However, when working in optimization under uncertainty a systematic selection of relevant uncertain parameters is required. In this contribution, an algorithm is presented in which uncertain parameters are selected based on their linear- independence to one another, their sensitivity towards state variables, and their sensitivity towards a user-defined process objective function. This workflow is applied in a case study. To analyze the information loss due to the reduction of uncertain parameters, Monte Carlo simulations are performed.