Efficient calculation of constraint back-offs for optimization under uncertainty: A case study on maleic anhydride synthesis

Abstract In the present work we propose an efficient and general algorithm for optimization under uncertainty based on the work of Srinivasan et al. (2003). We use specialized cubature rules to speed up the uncertainty propagation step which results in a significant reduction of the overall computational effort. The approach is illustrated by studying the optimal design of a fixed bed reactor for the synthesis of maleic anhydride from raffinate II feedstock, where the amount of n -butane and n -butenes in the feed is assumed to be uncertain. Applying the algorithm results in a robustified reactor design which shows significantly less temperature constraint violations and runaway conditions while still satisfying reactor performance criteria such as minimally required conversion and maximum allowable pressure drop.

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