In the sampling procedure of the stochastic reconstruction method,the number of samples for each structural attribute is unequal and varying.To use Latin hypercube sampling to reduce the variance of the random reconstruction model,based on the characteristics of the sampling process of the stochastic reconstruction method and the principle of Latin hypercube sampling,a new Latin hypercube sampling method suitable for the stochastic reconstruction method was proposed.In the novel Latin hypercube sampling,the sampling number for each structural attribute is determined by the values of the preorder structural attribute.A Latin hypercube sampling-based stochastic reconstruction model for a vacuum gas oil sample was developed.The determination of the sampling number for each structural attribute was introduced in detail with the building diagram.Multiple cases with different predefined molecular numbers were designed to investigate the effects of the novel Latin hypercube sampling on the variance and accuracy of the stochastic reconstruction model.The results showed that the novel Latin hypercube sampling method can significantly reduce the variance and objective function value of the model.As the molecular number ranging from 1000 to 50000,the standard deviations of the new model are 71.36%—74.53%lower than the traditional model,and the objective function values are 1.69%—13.82%lower than the traditional model.Based on the model accuracy and the computational cost of the simulation process,4000-6000 was selected as the optimal molecule numbers for the new model.By comparing the values of objective function,bulk properties and mass fraction distributions in saturates and aromatics,it was found that the performance of the new model when the molecular number is 2000 is consistent with the performance of the traditional model when the molecular number is 10000,and the computing time of the new model is only 22.54%of that of the traditional model.
stochastic reconstruction methodnovel Latin hypercube samplingvariancesimulationMonte Carlo simulation