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