(189h) Chance-Constrained MINLP Optimization for the Process Synthesis of the Oxidative Coupling of Methane
In this contribution, a comparison of the results of the chance-constrained MINLP framework to deterministic, robust, and other sampling-based methods (e.g. Monte Carlo sampling) for the introduction of uncertainty into the optimal process synthesis task is carried out. The oxidative coupling of methane process is employed as an exemplary process synthesis task. The process concept considers choices on different reactor feeding policies and various downstreaming options. The latter consist of combined pressure and temperature swing adsorption, combinations of gas permeation membranes, and absorption desorption processes using different scrubbing liquids for the removal of carbon dioxide from the product stream.
In addition to the comparison of the results, options for the extension of the chance-constrained optimization framework for larger scale systems, greater numbers of uncertain variables and chance constraints, as well as the interfacing with additional solvers is discussed.
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