Design of artificial neural networks using a genetic algorithm to predict saturates of vacuum gas oil

Accurate prediction of chemical composition of vacuum gas oil （VGO） is essential for the routine operation of refineries. In this work, a new approach for auto-design of artificial neural networks （ANN） based on a genetic algorithm （GA） is developed for predicting VGO saturates. The number of neurons in the hidden layer, the momentum and the learning rates are determined by using the genetic algorithm. The inputs for the artificial neural networks model are five physical properties, namely, average boiling point, density, molecular weight, viscosity and refractive index. It is verified that the genetic algorithm could find the optimal structural parameters and training parameters of ANN. In addition, an artificial neural networks model based on a genetic algorithm was tested and the results indicated that the VGO saturates can be efficiently predicted. Compared with conventional artificial neural networks models, this approach can improve the prediction accuracy.

Application of Discrete Lumped Kinetic Modeling on Vacuum Gas Oil Hydrocracking

The kinetic model of vacuum gas oil （VGO） hydrocracking based on discrete lumped approach was investigated, and some improvement was put forward at the same time in this article. A parallel reaction scheme to describe the conver- sion of VGO into products （gases, gasoline, and diesel） proposed by Orochko was used. The different experimental data were analyzed statistically and then the product distribution and kinetic parameters were simulated by available data. Fur- thermore, the kinetic parameters were correlated based on the feed property, reaction temperature, and catalyst activity. An optimization code in Matlab 2011b was written to fine-me these parameters. The model had a favorable ability to predict the product distribution and there was a good agreement between the model predictions and experiment data. Hence, the ki- netic parameters indeed had something to do with feed properties, reaction temperature and catalyst activity.

Egyptian heavy vacuum gas oil hydrotreating over Co-Mo/CNT and Co-Mo/γ-Al2O3 catalysts

The catalytic activity of CoMoS /CNT towards the Egyptian heavy vacuum gas oil hydrotreating was studied. The delivered CNT was functionalized with 6 mol /L HNO_3. The CNT were loaded with 12% MoO_3( by weight) and 0.7 Co /Mo atomic ratio with impregnation methods. The γ-Al_2O_3 catalyst was also prepared by impregnation method to compare both catalysts activities.The analysis tools such XRD,Raman spectroscopy,TEM,and BET were used to characterize the catalysts. The autoclave reactor was used to operate the hydrotreating experiments. The hydrotreating reactions were tested at various operating conditions of temperature 325-375 ℃,pressure 2-6 MPa,time 2-6 h,and catalyst /oil ratio( by weight) of 1 ∶75,1 ∶33 and 1 ∶10. The results revealed that the CoMoS /CNT was highly efficient for the hydrotreating more than the CoMoS /γ-Al_2O_3. Also, the hydrodesulfurization( HDS) increased with increasing catalyst /oil ratio. Additionally,results showed that the optimum condition was temperature 350℃,pressure 4 MPa,catalyst /oil ratio of 1 ∶75 for 2 h. Furthermore,even at low CoMoS /CNT catalyst /oil ratio of 1 ∶75,an acceptable HDS of 77.1% was achieved.

Molecular reconstruction of vacuum gas oils using a general molecule library through entropy maximization

Vacuum gas oil(VGO)is the most important feedstock for hydrocracking processes in refineries,but its molecular composition cannot be fully acquired by current analysis techniques owing to its complexity.In order to build an accurate and reliable molecular-level kinetic model for reactor design and process optimization,the molecular composition of VGO has to be reconstructed based on limited measurements.In this study,a modified stochastic reconstruction-entropy maximization(SR-REM)algorithm was applied to reconstruct VGOs,with generation of a general molecule library once and for all via the SR method at the first step and adjustment of the molecular abundance of various VGOs via the REM method at the second step.The universality of the molecule library and the effectiveness of the modified SR-REM method were validated by fifteen VGOs(three from the literature)from different geographic regions of the world and with different properties.The simulated properties(density,elemental composition,paraffin-naphthene-aromatics distribution,boiling point distribution,detailed composition of naphthenes and aromatics in terms of ring number as well as composition of S-heterocycles)are in good agreement with the measured counterparts,showing average absolute relative errors of below 10%for each property.

Adsorption of Naphthenic Acids from Dewaxed Vacuum Gas Oil by Activated Clay: Kinetics, Equilibrium and Thermodynamic Studies

This work was mainly concentrated on the removal of naphthenic acids(NAs) from dewaxed vacuum gas oil(VGO) by adsorption using a commercial grade activated clay(AC) adsorption during lube base oil refining. The NAs in dewaxed VGO cut-4 were identified by negative-ion electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry(ESI FT-ICR MS). The AC sample from a refinery was characterized by XRD, BET, TG/DTA, and SEM. A series of experiments were carried out to investigate the performance of NAs adsorption by AC using a batch adsorption technique, in which some key experimental parameters such as temperature, contact time, initial concentration of NA in oil sample as well as the dosage of adsorbent were investigated. Equilibrium isotherms were analyzed using the Langmuir, Freundlich, Tempkin and Dubinin-Radushkevich(D-R) adsorption models. The pseudo-first order, the pseudo-second order, and intraparticle diffusion models were employed to describe the kinetics data. The results revealed that the D-R isotherm provided a better fit to the experimental data than other isotherms, and the adsorption kinetics followed the pseudo-first order kinetic equation. The thermodynamic data indicated that the adsorption process was feasible and spontaneous as an endothermic process. The results could provide a clear understanding of the NAs adsorption by AC during lube base oil processing at refineries.

FCC Study of Canadian Heavy Gas Oils—Comparisons of Product Yields and Qualities between Reactors

Several series of cracking tests in a comprehensive study were conducted on separate occasions involving all or parts of ten Canadian vacuum gas oils (VGOs) and two catalysts with bottoms-cracking or octane-barrel capability. VGOs were cracked in fixed- and/or fluid-bed microactivity test (MAT) units, in an Advanced Cracking Evaluation (ACE) unit, and in a modified ARCO riser reactor. Individual yields of gas, liquid, and coke from the MATs at 55, 65, 70, and 81 wt% conversion levels were compared with their respective pilot plant data. Good linear correlations could be established between MAT and riser yields except for liquefied petroleum gas (LPG) and light cycle oil (LCO). At a given conversion, correlations existed among the fixed- and fluid-bed MAT units and the ACE for each product yield. Liquid products from the fixed or fluid-bed MAT were analyzed for hydrocarbon types, sulfur, nitrogen and density, most of which showed good agreement with those obtained from the riser study. When cracking Canadian oil-sands-derived VGOs, the bottoms-cracking catalyst containing a large-pore active matrix was found to be more suitable than the octane-barrel catalyst with smaller pores to produce higher yields of valuable distillates, but with less superior qualities (in terms of sulfur and nitrogen contents). The advantages of hydrotreating some poor feeds to improve product yields and qualities were demonstrated and discussed.