Identification of the reaction network for the synthesis of adipic acid using machine learning coupling with target factor analysis

Abstract

This study investigated the reaction network of the oxidation of cyclohexanone with nitric acid through machine learning (ML) model coupling with target factor analysis (TFA). Experiments for the synthesis of adipic acid (AA) were carefully designed and carried out in a microreactor system. An artificial neural network (ANN) model was applied and optimized by training on experimental data. To assess the established ANN model, a comparison between the predicted concentrations of the products and those calculated with the power law kinetic model was implemented. TFA was then performed on both the experimental data and the data simulated by ANN for identifying the candidate reactions and finding out the temperature boundaries. Based on the identification results of the reaction network, the kinetic characteristics of this oxidation process under various operational conditions were further researched through the incremental approach and simultaneous approach.

Computational fluid dynamics modeling and mass transfer study of an in situ gas production process

“…The kinetics developed in our previous work is utilized in this research 39 : where is the pre‐exponential factor, is the activation energy, kJ/mol, R is the universal gas constant, 8.314 J/(mol·K), T is the reaction temperature, K, is the concentration of cyclohexanone, mol/L, and is the concentration of nitric acid, mol/L.…”