Accelerating Kinetic Parameter Identification by Extracting Information from Transient Data: A Hydroprocessing Study Case

Abstract

Hydroprocessing reactions require several days to reach steady-state, leading to long experimentation times for collecting sufficient data for kinetic modeling purposes. The information contained in the transient data during the evolution toward the steady-state is, at present, not used for kinetic modeling since the stabilization behavior is not well understood. The present work aims at accelerating kinetic model construction by employing these transient data, provided that the stabilization can be adequately accounted for. A comparison between the model obtained against the steady-state data and the one after accounting for the transient information was carried out. It was demonstrated that by accounting for the stabilization, combined with an experimental design algorithm, a more robust and faster manner was obtained to identify kinetic parameters, which saves time and cost. An application was presented in hydrodenitrogenation, but the proposed methodology can be extended to any hydroprocessing reaction.

Reducing the Number of Experiments Required for Modelling the Hydrocracking Process with Kriging Through Bayesian Transfer Learning

“…We have applied this method for the case of diesel density prediction, but it can be applied for any type of problem for which the kriging or linear models are efficient, and therefore for any type of product quality. A possibility is to extend this Bayesian approach to kinetic models, consisting of solving a differential equation system, which are widely used to simulate the hydrocracking process (Ancheyta et al., 2005; Becker et al., 2017; Cao et al., 2020). Further work will also focus on the design of experiments.…”

Special Issue on Catalyst Deactivation and Regeneration

“…The instabilities and transient state happenings in catalytic processes, particularly at the beginning of the reaction, are valuable information from the kinetic point of view. In line with this, Cao et al [2] have used these transient data to accelerate kinetic modeling discovery and fitting. This interesting avenue calls for a reconsideration of the features that make the selection of the catalysts for a given process, such as the one selected: hydrotreating.…”

“…Department of Chemical Engineering, University of the Basque Country (UPV/EHU), 48080 Bilbao, Spain; pedro.castano@ehu.eus or pedro.castano@kaust.edu.sa2 Multiscale Reaction Engineering, KAUST Catalysis Center, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi ArabiaCatalyst deactivation causes major losses in the chemical industry worldwide every year. The deactivation phenomenon does not only drop the catalytic activity, but forces an allocation of time and a regeneration of resources, which, at the same time, involves wasting more feedstock and materials, and emitting more CO2.…”

Reducing the Number of Experimental Points to Fit Kinetic Models: A Bayesian Approach

“…The variable of interest is the nitrogen content (N) after the hydrotreating stage (Nslip). In order to model its value, an ODE-based kinetic model with a structure shown to be efficient for the modeling of hydrodenitrogenation 14 is used, and the influential features are presented in Table 1. This model is very simple yet complex enough to show the methodology.…”