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Abstract:
Kinetic process models are widely applied in science and engineering, including atmospheric, physiological and technical chemistry, reactor design, or process optimization. These models rely on numerous kinetic parameters such as reaction rate, diffusion or partitioning coefficients. Determining these parameters by experiments can be challenging, especially for multiphase systems, and researchers often face the task of intuitively selecting experimental conditions to obtain insightful results. We developed a kinetic compass (KC) method that integrates kinetic models, global optimization, ensemble methods, and machine learning to identify experimental conditions with the greatest potential to constrain kinetic parameters. The approach is based on the quantification of model output variance in an ensemble of solutions that agree with experimental data. The utility of the KC method is demonstrated for the kinetic parameters in a multi-layer model describing the heterogeneous ozonolysis of oleic acid aerosols. We show how neural network surrogate models of the multiphase chemical reaction system can be used to accelerate the application of the kinetic compass for a comprehensive mapping and analysis of experimental conditions. The code is openly available and can be adapted to various types of process models.
