An integrated approach for parameter estimation of a propyl propionate synthesis model

Abstract

This paper introduces an innovative approach to the ProPro synthesis process, aiming to improve the modeling and parameter estimation by integrating and optimizing existing methodologies. It combines well-established models available in the literature with a hybrid solution approach, emphasizing computational efficiency. Additionally, it assesses and enhance the modeling results by calculating confidence regions and uncertainties in both kinetic and thermodynamic modeling. To this end, a batch reactor with Amberlyst 46 catalyst is modeled. The parameter estimation approach combines Particles Swarm Optimization and Gradient Method to estimate both thermodynamic and kinetic parameters, sequentially and simultaneously. Confidence regions and uncertainties are determined for the sequential approach, guiding the search space for the simultaneous estimation. By utilizing PSO, the parameters are estimated, and their confidence regions are determined, providing a comprehensive understanding of their uncertainty. The uncertainty is propagated through the model predictions of the batch reactor, allowing for a comprehensive analysis of the system's behavior. The results demonstrate a good agreement between the model predictions and experimental data, with the expanded uncertainty adequately reflecting the statistical variability of the experimental observations. Furthermore, the heterogeneous Langmuir-Hinshelwood model yields improved representation of the experimental data in both transient and steady states compared to the pseudo-homogeneous model. The Root Mean Square Deviation (RMSD) for parameter estimation shows significant improvement compared to literature values. The simultaneous parameter estimation approach exhibits the best fit performance for the model predictions, confirming its effectiveness in accurately representing the system.