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首页> 外文期刊>Advances in Chemical Engineering and Science >Application of Bayesian Approach in the Parameter Estimation of Continuous Lumping Kinetic Model of Hydrocracking Process
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Application of Bayesian Approach in the Parameter Estimation of Continuous Lumping Kinetic Model of Hydrocracking Process

机译：贝叶斯方法在加氢裂化过程连续拉伸动力学模型中的应用

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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.
机译：加氢裂化是石油炼油厂中的催化反应过程，用于将原油的较高沸腾温度残留转换成较轻的烃类碳氢化合物和柴油。在该研究中，应用了一种改进的连续延长动力学方法来模拟真空瓦斯油的水力裂。修改模型以考虑对反应产率分布的反应器温度。通过在四个不同的反应堆温度下最大化建模和测量数据之间的似然函数来校准该模型。贝叶斯方法参数估计也应用于通过考虑与测量误差和模型结构误差相关的不确定性来获得模型参数的置信区间。然后将蒙特卡罗模拟应用于模型参数的后部范围，以获得用于加氢裂化产品的每个零件的模型输出的95％置信区间。在校准模型的输出和测量数据点之间观察到良好的一致性。基于Markov链蒙特卡罗模拟的贝叶斯方法被证明是有效的，以量化与连续列表模型的参数值相关的不确定性。

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