Müller, DavidEsche, ErikLopez C., Diana C.Wozny, Günter2021-02-042021-02-042014-07-14978-0-444-63433-71570-7946https://depositonce.tu-berlin.de/handle/11303/12554http://dx.doi.org/10.14279/depositonce-11374Considering 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.en660 Chemische Verfahrenstechnikuncertaintyparameter estimationoptimizationMonte Carlo simulationBook Part