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Alikhani, J., Deinhart, A.L., Visser, A., Bibby, R.K., Purtschert, R., Moran, J.E., Massoudieh, A. and Esser, B.K. (2016) Nitrate Vulnerability Projections from Bayesian Inference of Multiple Groundwater Age Tracers. Journal of Hydrology, 543, 167-181.
https://doi.org/10.1016/j.jhydrol.2016.04.028

has been cited by the following article:

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

  AUTHORS: S. Sina Hosseini Boosari, Neda Makouei, Philip Stewart

  KEYWORDS: Hydrocracking, Continuous Lumping Kinetic Model, Bayesian Approach, Parameter Estimation, Markov Chain Monte Carlo

  JOURNAL NAME: Advances in Chemical Engineering and Science, Vol.7 No.3, May 18, 2017

  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 hydro-cracking 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.