Publication | Open Access
Comparison of Derivative‐Free Algorithms for their Applicability in Self‐Optimization of Chemical Processes
16
Citations
33
References
2022
Year
Numerical AnalysisSearch OptimizationProcess IntegrationEngineeringMultidisciplinary Design OptimizationDerivative‐free AlgorithmsComputational ChemistryUnconstrained OptimizationChemical ProcessesChemical EngineeringComputer-aided EngineeringChemical Process OptimizationSystems EngineeringDerivative-free OptimizationAutomated Reaction SetupProcess OptimizationContinuous FlowLinear OptimizationProcess DesignFlow ChemistryFlow SynthesisProcess Systems EngineeringReaction EngineeringSelf-optimizationChemical Kinetics
Abstract In this work, several implementations of different derivative‐free optimization algorithms are compared for the usage in chemical process optimization. As such, a benchmarking process is carried out, using optimization problems of different types to compare reliability, accuracy, and performance. Finally, using an automated reaction setup and a bespoke Python‐based script featuring a graphical user interface, all algorithms are tested in an optimization of a Suzuki‐Miyaura cross‐coupling reaction in continuous flow. To increase the scope of comparison, a model function based on the reaction is also used, to allow for a more in‐depth comparison without the use of physical resources.
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