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Operating biopharmaceutical manufacturing processes at optimal con-ditions is critical to maximize productivity and product quality, while minimizingmanufacturing cost and environmental impact. However, the experimental budgetfor the search of optimal bioprocess parameters is limited. Thus, it is essential toemploy efficient and effective optimization strategies such as Design of Experimentsor Gaussian Process optmization. In practice, these optimization strategies have tobe parameterized. It is unclear under which circumstances what optimzation strat-gey performs best and how to paramterize it effectively. The aim of this study is tocompare the performance and robustness of multiple optimization strategies acrossa diverse range of parameter and process configurations of a simulated ion exchangechromatography process. The outcome of this study is to provide guidance navigatingthe large decision space encountered when performing (bio)process optimization.

Parameter optimization, Design of Experiments, Gaussian Process op- timization, Biorprocess