Generating Data-Driven Models from Molecular-Level Kinetic Models: A Kinetic Model Speedup Strategy

Strategies to reduce the computer time to access the information in molecular-level kinetic models (MLKMs) were evaluated. A triglyceride hydroprocessing MLKM was used to generate data sets for small ranges of input parameters simulating three output parameters. The data sets were used to generate multilinear regression, polynomial regression, decision tree regression, gradient boosting regression, and artificial neural network data-driven model (DDM) representations of the MLKM. All of the DDMs were able to predict results very quickly (≪1 s). The predictive accuracy for the DDMs was compared to the polynomial regression, gradient boosting regression, and artificial neural network models, providing the best models over the entire range of the input parameters selected. However, in narrow input parameter ranges, multiple multilinear models and decision tree models also provide good accuracy, with the added benefit of easily understood parameters and faster solution times. Additionally, multilinear regress...