Application of Bayesian Approach in the Parameter Estimation of Continuous Lumping Kinetic Model of Hydrocracking Process

Abstract

Hydrocracking is a catalytic reaction process in the petroleum refineries for converting the higher boiling temperature residue of crude oil into a lighter fraction of hydrocarbons such as gasoline and diesel. In this study, a modified continuous lumping kinetic approach is applied to model the hydrocracking of vacuum gas oil. The model is modified to take into consideration the reactor temperature on the reaction yield distribution. The model is calibrated by maximizing the likelihood function between the modeled and measured data at four different reactor temperatures. Bayesian approach parameter estimation is also applied to obtain the confidence interval of model parameters by considering the uncertainty associated with the measured errors and the model structural errors. Then Monte Carlo simulation is applied to the posterior range of the model parameters to obtain the 95% confidence interval of the model outputs for each individual fraction of the hydrocracking products. A good agreement is observed between the output of the calibrated model and the measured data points. The Bayesian approach based on the Markov Chain Monte Carlo simulation is shown to be efficient to quantify the uncertainty associated with the parameter values of the continuous lumping model.

Progress in Accurate Chemical Kinetic Modeling, Simulations, and Parameter Estimation for Heterogeneous Catalysis

“…The primary advantage of the Bayesian statistics approach, as described here, is that both experimental and theoretical errors can be included. There are also other useful applications of Bayesian theory for kinetics studies. ,− A significant impediment to more widespread utilization of the approach presented here is that the uncertainty in experimental values is often not reported and often difficult to estimate: not only the errors in measurements such as mass spectrometry signals but also those of the number of active sites. Thus, comparisons between experiment and theory have frequently focused on parameters and trends for which the uncertainties are less influential such as the apparent activation energies reaction orders. ,,, This approach of looking at trends should be continued when applying Bayesian parameter estimation, in order to work on the easiest and most computationally accessible problems first.…”

Effect of Heating Period and Temperature on the Synthesis of Nano-Beta Zeolite Assisted by Microwaves

“…Now-a-days, zeolites have been used in many petroleum and petrochemical processes such as petroleum refinery, reactions, separation, hydrocracking and adsorption processes [1][2][3]. They have attracted more attention due to good thermal and chemical stability, strong acid sites, molecular sieve properties, and wide range of pore sizes.…”

Modeling and Optimizing a Vacuum Gas Oil Hydrocracking Plant using an Artificial Neural Network

“…The presence of hydrogen, which improves the combustion properties in hydrocracking products, enables this technology to provide the features needed for present and future fuels. Due to the important role of converting heavy oil cuts into light desirable products, the hydrocracking process has always been of interest to craftsmen and researchers for the production of high-quality middle distillates (Boosari et al, 2017). Reactions that occur in the hydrocracking reactor include breaking down (cracking) and converting the high molecular weight compounds into lower molecular weight ones and hydrogenating olefins, aromatics, sulfur, nitrogen, and some oxygen compounds.…”