汽油与水和氧混合重整制氢气

 以LiLaNiO/γ-Al2O3为催化剂,自配模拟汽油为原料,在常规固定床反应器中进行了汽油与水和氧混合重整制氢气的研究. 考察了反应温度、空速和水碳比对催化剂性能的影响,确定了最佳反应条件,并在优化条件下进行了长时间的运行. 反应后的催化剂用XRD,TG和NH3-TPD等手段进行了表征. 研究结果表明,LiLaNiO/γ-Al2O3催化剂对汽油水氧混合重整具有优异的催化性能, 优化条件下模拟汽油的转化率为100%,H2的选择性为96%,并且反应具有良好的稳定性.     

应用ICP-MS分析汽油中微量的砷、汞、铅

采用电感耦合等离子体质谱（ICP-MS）,建立了分析汽油中的砷、汞、铅的方法．该方法是将汽油样品经过乙醇的稀释处理后直接进样,用内标法定量,消除了样品基底带来的影响．测定汽油中微量的砷、汞、铅元素含量,检出限为0．05μg／kg,各元素的回收率为92％-104％．     

组合改性对纳米HZSM-5催化剂降低汽油烯烃性能的影响

 纳米HZSM-5(20～50 nm)沸石晶粒经碱性介质水热处理、负载稀土和锌氧化物组合改性后,制备成降低FCC汽油烯烃的催化剂,并用TEM,XRF,NH3-TPD及IR等手段对其进行了表征. 在温度为370 ℃, 压力3 MPa, 质量空速(WHSV)3 h-1和氢油比(V(H2)/V(oil))为600的反应条件下, 在固定床反应器上对催化剂的降烯烃性能进行了评价. 结果表明,组合改性后的纳米HZSM-5催化剂表现出很强的降烯烃能力. 全馏分FCC汽油中烯烃含量(φ,下同)从49.6%降至15.4%, 芳烃(主要是C7～C9)含量从11.4%增加到33.5%, 异构烷烃(主要是C4～C6)含量从17.4%增加到31.1%, 异构烷烃与正构烷烃比从3.3增加到8.1, 在烯烃大量降低的同时,汽油的辛烷值(RON)有所增加. 同时汽油中苯的含量从2.84%降低到0.66%, 而硫的含量从0.02%(w)降低到0.01%. 催化剂具有降烯、除苯和脱硫的综合性能,且连续反应320 h仍保持性能稳定.     

La改性HZSM-5分子筛催化剂甲醇转化制汽油反应性能研究

采用浸渍法制备了La改性HZSM-5分子筛催化剂,利用X射线衍射（XRD）、氮气吸附-脱附、氨气程序升温脱附（NH₃-TPD）以及吡啶吸附红外光谱（Py-FTIR）技术对催化剂进行表征,并在固定床微型反应评价装置上,在反应温度350℃,系统压力0.1 MPa,甲醇质量空速4.74 h^-1的条件下,考察La改性HZSM-5分子筛催化剂的甲醇转化制汽油反应性能。结果表明,La改性HZSM-5分子筛催化剂的酸量降低,比表面积和孔容减小。La负载量为4%时,其MTG反应性能最佳,催化剂的寿命和汽油收率分别由改性前的12 h和52.69%增加到16 h和59.28%。此外,随着La负载量的增加,汽油中芳烃含量显著降低,降幅达18%。     

Al2O3孔结构对纳米HZSM-5基催化剂改质FCC汽油性能的影响

采用NH₃-TPD、FT-IR、N₂吸附-脱附等手段对两种不同来源的氧化铝样品进行了表征。结果表明,两种Al₂O₃的总酸量及酸强度没有明显差别,酸类型均以Lewis酸为主,其中,Al₂O₃ (b)的平均孔径及孔体积较大。在固定床微型反应装置上考察了以两种Al₂O₃为载体制备的纳米HZSM-5基催化剂改质全馏分FCC汽油的性能。实验结果表明,以大孔Al₂O₃为载体的HZSM-5基催化剂具有较好的降烯烃、芳构化、异构化活性及稳定性。改性纳米HZSM-5负载的LaNiMo催化剂对FCC汽油的300 h评价结果表明,烯烃饱和率为83%,脱硫率为87%,同时维持了油品的辛烷值。     

Ethanol Fuel Production from Cassava as a Substitute for Gasoline

The potential in alcohols as fuel had for long being recognized by the early inventors of machines and engines, even before gasoline and the hydrocarbons became popular. In fact, Henry Ford, one of the pioneers in automobile manufacture, designed his equipment to run on ethanol. But since then, time has seen gasoline and other conventional fuels take the front seat in engine application. This article is an insight into the experiment carried out to produce fuel from cassava starch and the characterization to determine some of the fuel properties in comparison to gasoline. The choice of cassava for the production was based on its availability and ability to grow in almost all geographical regions in Nigeria. Experimental production gave a sample concentration of 87% corresponding to a yield of 0.534 cm³ of ethanol per gram of starch hydrolyzed while the sample characteristics for the latent heat of vaporization, heat of combustion, flash point, and density are 950 kJ/kg, 22133.7 kJ/kg, 17–20°C, and 0.825, respectively. The results obtained compared favorably with those of gasoline that the sample concentration.     

Biodiesel classification by base stock type (vegetable oil) using near infrared spectroscopy data

The use of biofuels, such as bioethanol or biodiesel, has rapidly increased in the last few years. Near infrared (near-IR, NIR, or NIRS) spectroscopy (>4000 cm⁻¹) has previously been reported as a cheap and fast alternative for biodiesel quality control when compared with infrared, Raman, or nuclear magnetic resonance (NMR) methods; in addition, NIR can easily be done in real time (on-line). In this proof-of-principle paper, we attempt to find a correlation between the near infrared spectrum of a biodiesel sample and its base stock. This correlation is used to classify fuel samples into 10 groups according to their origin (vegetable oil): sunflower, coconut, palm, soy/soya, cottonseed, castor, Jatropha, etc. Principal component analysis (PCA) is used for outlier detection and dimensionality reduction of the NIR spectral data. Four different multivariate data analysis techniques are used to solve the classification problem, including regularized discriminant analysis (RDA), partial least squares method/projection on latent structures (PLS-DA), K-nearest neighbors (KNN) technique, and support vector machines (SVMs). Classifying biodiesel by feedstock (base stock) type can be successfully solved with modern machine learning techniques and NIR spectroscopy data. KNN and SVM methods were found to be highly effective for biodiesel classification by feedstock oil type. A classification error (E) of less than 5% can be reached using an SVM-based approach. If computational time is an important consideration, the KNN technique (E = 6.2%) can be recommended for practical (industrial) implementation. Comparison with gasoline and motor oil data shows the relative simplicity of this methodology for biodiesel classification.     

燃油燃料含硫化合物形态分布剖析技术研究进展

介绍了目前用于燃油、燃料(如汽油、柴油、煤油、喷气燃料)的硫化物形态分布剖析的多种分析技术,包括电化学方法和气相色谱与多种选择性检测器联用的方法(如GC - FPD、GC - AED、GC - SCD、GC - MS等),并对各种分析技术的研究现状进行了概述.     

汽油储罐火灾燃烧特性的实验研究

本文通过对直径为1 m、 1.5 m和2.7 m的汽油储罐进行火灾实验,分析了汽油罐燃烧时不同阶段的燃烧特 性。本文重点讨论了描述燃烧特性的几个重要参数的变化规律,包括火焰脉动频率、火焰高度、倾斜角度以及火焰的温度 分布等,并将结果拟合为相应的实验关联式,以揭示油罐火灾的内在规律和预测其发展趋势。     

F改性Hβ对Mo-Ni/F-Hβ催化剂的FCC汽油硫转移反应催化性能的影响

通过添加不同含量的F对Hβ分子筛进行改性,制备Mo-Ni/F-Hβ催化剂,采用N₂吸附-脱附、NH₃-TPD、XRD、Py-FTIR和SEM等方法对该催化剂进行了表征,研究了F改性对该Mo-Ni/F-Hβ催化剂在FCC汽油中硫醇醚化和噻吩烷基化等硫转移反应中催化性能的影响。结果表明,以0.5%含量F修饰的β分子筛制备的催化剂对硫醚化反应和噻吩烷基化反应具有明显的促进作用,并能提高对二烯烃选择性加氢的选择性。F的引入可增强Hβ分子筛的中强酸量,降低强酸量,并提高了L/B酸中心比例,这些变化对催化性能改善起到重要作用。