Viscosity estimation of mixed oil using RBF-ANN approach

Diluting the bitumen and heavy oil with a liquid solvent such as tetradecane is one way to decrease the viscosity. The accurate estimation for the viscosity of the aforesaid mixture is serious due to the sensitivity of enhanced oil recovery method. The main aim of this study was to propose an impressive relation between the viscosity of heavy n-alkane and Athabasca bitumen mixtures based on pressure, temperature, and the weight percentage of n-tetradecane using radial basis function artificial neural network (RBF-ANN). Also, this model has been compared with previous equations and its major accuracy was evidenced to estimate the viscosity. The amounts of mean relative error (MRE %) and R-squared received 0.32 and 1.00, respectively. The endeavors confirmed amazing forecasting skill of RBF-ANN for the approximation of the viscosity as a function of temperature, pressure, and the weight percentage of n-tetradecane.     

Ni_2P/Al_2O_3-SAPO-11催化剂的原位磷化法制备及其加氢异构化反应性能

以三苯基膦为磷化剂,采用原位磷化法制备具有较高分散性的Ni_2P/Al_2O_3-SAPO-11催化剂,通过低温N2吸附-脱附、XRD、HRTEM、NH_3-TPD和Py-IR等对催化剂晶相结构、微观形貌和酸性质进行表征。以正十四烷为模型化合物,考察SAPO-11含量及工艺条件对正十四烷加氢异构化性能的影响。结果表明,最佳SAPO-11质量分数为40%,优化的工艺条件为:反应温度360℃,反应压力2 MPa,体积空速1. 5 h-1,氢烃体积比300,该条件下,正十四烷转化率为78. 1%,异构化选择性为89. 2%。     

微波法合成含磷MCM-41分子筛及催化性能研究

采用微波法合成了含磷MCM-41分子筛。运用XRD、SEM、NH3-TPD、BET、NMR手段对合成的分子筛进行表征。结果表明,该分子筛主要有弱酸和中强酸中心,无强酸中心,酸量为0.349 ~0.553 mmol/g;磷原子已经成功进入了分子筛的骨架结构。分别选用1,3,5-三异丙基苯和正十四烷作为反应物,使用自动微反装置,对分子筛进行催化活性的评价。结果表明,采用含磷MCM-41分子筛为催化剂,1,3,5-三异丙基苯的转化率为87.1%,正十四烷的转化率为56.2%,均高于MCM-41分子筛为催化剂时的转化率。     

Modeling of viscosity for mixtures of Athabasca bitumen and heavy n-alkane with LSSVM algorithm

The resources of heavy oil and bitumen are more than those of conventional light crude oil in the world. Diluting the bitumen with liquid solvent can decrease viscosity and increase the empty space between molecules. Tetradecane is a candidate as liquid solvent to dilute the bitumen. Owning to the sensitivity of enhanced oil recovery process, the accurate approximation for the viscosity of aforementioned mixture is important to decrease uncertainty. The aim of this study was to develop an effective relation between the viscosity of Athabasca bitumen and heavy n-alkane mixtures based on temperature, pressure, and weight percentage of n-tetradecane using the least square support vector machine. This computational model was compared with the previous developed correlation and its accuracy was confirmed. The value of R² and MSE obtained 1.00 and 1.02 for this model, respectively. This developed predictive tool can be applied as an accurate estimation for any mixture of heavy oil with liquid solvent.     

Molecular dynamics simulation of phase transformation process of n-tetradecane

采用分子动力学模拟方法，应用COMPASS力场模拟正十四烷烃无定形体系结构。将体系在NPT系综下从270 K逐渐升温至290 K，分析了该温度区间中正十四烷烃分子的扩散性随着温度的变化情况，并得出了体系的相变温度在278.5 K，通过将模拟得出的密度值、相变温度值与实验比较，密度误差为0.8%，熔点误差为0.23%。通过对正十四烷烃分子链在相变前后的末端距分布情况以及径向分布函数的分析，发现正十四烷烃在固态时分子链构象随着温度的升高逐渐由直链趋向于弯曲状态，而液态时随着温度的升高分子链的构象逐渐由弯曲状态趋向于直链。原子周围1.11 Å（1 Å=0.1 nm，余同）处呈现其他分子或者原子的可能性是最大的，原子周围0.99 Å内没有其他原子。研究结果可以为后续研究其他添加剂对于该分子体系微观结构的改变进行对比，为寻找更高效实用的相变储能材料提供一定的引导。     

Pt基催化剂上正十四烷的加氢异构反应性能

采用常规水热法合成了SAPO-11,ZSM-22,ZSM-23,β分子筛,并负载Pt制备了Pt/SAPO-11,Pt/ZSM-22,Pt/ZSM-23,Pt/β加氢催化剂;采用XRD、SEM、N2吸附-脱附、NH3-TPD和吡啶吸附FTIR表征了4种催化剂的结构和酸性;以正十四烷为模型化合物,采用固定床反应器研究了4种催化剂对正十四烷加氢异构反应的催化性能。实验结果表明,正十四烷加氢异构反应遵循孔口-锁钥机理和β断裂机理,催化剂的活性和选择性主要取决于催化剂的结构、酸量、酸强度及其分布,不同催化剂上正十四烷加氢异构反应活性按下列顺序递减:Pt/βPt/ZSM-23Pt/ZSM-22Pt/SAPO-11;较弱的酸性和较小的孔径更有利于正构烷烃的加氢异构化反应,减少了二次裂化反应。     

PSO-ANFIS modeling of viscosity for mixtures of Athabasca bitumen and a high-boiling n-alkane

The bitumen and heavy oil reservoirs are more in number than light crude oil reservoirs in the world. To increase the empty space between molecules and decrease viscosity, the bitumen was diluted with a liquid solvent such as tetradecane. Due to the sensitivity of enhanced oil recovery process, the accurate approximation for the viscosity of mentioned mixture is important. The purpose of this study was to develop an effective relation between the viscosity of Athabasca bitumen and heavy n-alkane mixtures based on pressure, temperature, and the weight percentage of n-tetradecane using the adaptive neuro-fuzzy inference system method. For this model, the value of MRE and R² was obtained as 0.34% and 1.00, respectively; so this model can be applied as an accurate approximation for any mixture of heavy oil with a liquid solvent.