磷改性β沸石催化剂上催化裂化轻汽油的醚化

在小型固定床反应装置上,考察了负载磷的β沸石催化剂的制备条件对其催化性能的影响以及醚化反应温度、压力、醇烯摩尔比、LHSV对醚化反应的影响。实验结果表明,浸渍磷的质量分数为2%的Hβ催化剂具有较好的醚化活性。在最佳反应条件(温度70 ℃,压力0.8 MPa,LHSV 1.0 h-1,醇烯摩尔比1.0)下,该催化剂的叔碳烯烃的总转化率可达到56.91%。负载磷后的催化剂具有活性好、选择性高、不易失活等优点。反应性能均优于Hβ催化剂。     

催化裂化轻汽油及其醚化产品的分析

采用气相色谱-质谱、气相色谱-红外、色谱保留值法、化学法、转化率计算法等技术对催化裂化轻汽油中的组分特别是活性烯烃及其醚化产物进行了结构确定,用色谱峰面积校正归一法测定醚化产品中醚的含量;方法被用于催化裂化轻汽油的醚化催化剂评价和工艺研究。     

In situ FT-IR spectroscopy investigations of carbon nanotubes supported Co-Mo catalysts for selective hydrodesulfurization of FCC gasoline

To better understand the nature of carbon nanotubes supported Co-Mo catalysts （Co-Mo/CNTs） for selective hydrodesulfurization （HDS） of fluid catalytic cracking （FCC） gasoline, studies are carried out using in situ Fourier transform infrared spectroscopy （FT-IR）. The catalytic performances of Co-Mo/CNTs catalysts were evaluated with a mixture of cyclohexane, diisobutylene, cyclohexene, 1-octene （60 ： 30 ： 5 ： 5, volume ratio） and thiophene （0.5%, ratio of total weight） as model compounds to simulate FCC gasoline. The HDS experimental results suggested that the HDS activity and selectivity of Co-Mo/CNTs catalysts were affected by Co/Mo ratio; the optimal Co/Mo atomic ratio is about 0.4, and the optimum reaction temperature is 260 ℃. The in situ FT-IR studies revealed that 1-octene can be completely saturated at 200 ℃. In the FT-IR spectra of diisobutylene, the characteristic absorption peak around 3081 cm^-1 for the stretching vibration peak of =C-H bond was still clear at 320 ℃ indicating that diisobutylene is difficult to be hydrogenated. As for the thiophene, no characteristic absorption peak could be found around 3092 cm^-1 and 835 cm^-1 when the reaction temperature was raised to 280 ℃, indicating that thiophene had been completely hydrodesulfurized. On the basis of FT-IR results, it can be deduced that thiophene HDS reaction occurred mainly through direct hydrogenolysis route, whereas thiophene HDS and diisobutylene hydrogenation reaction over Co-Mo/CNTs catalysts might occur on two different kinds of active sites.     

焦化汽油中硫化物类型分布的气相色谱-硫化学发光检测方法

建立了焦化汽油中硫化物类型分布的气相色谱-硫化学发光检测分析方法。考察了色谱条件对焦化汽油中各种硫化物分离的影响,定性了某焦化汽油中的74个硫化物。以硫化氢、乙硫醇、正丙硫醇、噻吩、2-甲基噻吩、2-乙基噻吩、2-丙基噻吩、碳四噻吩(tR=40.28 min)、苯并噻吩、甲基苯并噻吩(tR=58.13 min)的保留时间为尺度,计算了焦化汽油中各种硫化物的保留指数,并可推广到其他类型的汽油馏分中各种硫化物保留指数的计算,为仅能提供硫化物信息的仪器提供了可靠的定性依据。焦化汽油中几种主要硫化物(异丙硫醇、正丙硫醇、正丁硫醇、2-甲基噻吩、3-甲基噻吩、2,4-二甲基噻吩、2,3,4-三甲基噻吩)含量测定值的相对标准偏差均小于5%。当信噪比为3时,测得硫的检测限为0.05 mg/L。研究发现:同其他类型的汽油相比,焦化汽油的硫含量较高且所含硫醇比例明显偏高,2-甲基噻吩和3-甲基噻吩的含量差别较大。该法可为加氢脱硫催化剂和工艺的研究提供数据。     

ZnO/θ-Al2O3催化剂上全馏分FCC汽油的选择性加氢脱硫

研究了三种氧化铝载体和一种ZnO/θ-Al2O3催化剂对全馏分FCC汽油的选择性加氢脱硫性能.结果表明,θ-Al2O3对于全馏分FCC汽油的加氢脱硫选择性因子高于γ-Al2O3和在900℃下焙烧过的γ-Al2O3.负载ZnO能进一步提高θ-Al2O3的加氢脱硫活性和选择性.通过吡啶吸附傅里叶变换红外光谱、氢气程序升温还原和X射线衍射表征发现,θ-Al2O3表面只有微弱的L酸性,其吡啶吸附红外光谱明显不同于其他氧化铝.负载ZnO导致θ-Al2O3载体中出现了尖晶石结构,并且使催化剂的高温H2还原峰位向低温方向移动.     

Probing the sulfur content in gasoline quantitatively with terahertz time-domain spectroscopy

Terahertz time-domain spectroscopy(THz-TDS)was used for the quantitative detection of sulfur content in gasoline.Models of chemo metrics methods and partial least squares(PLS)were built to measure THz-TDS and the sulfur content.All of the samples were divided into two parts.One part was used for calibration and the other one for validation.In order to evaluate the quality of the models,the correlation coefficient(R)and root-mean-square errors(RMSE)of calibration and validation models were calculated.The value of R and RMSE were close to 1 and 0 within acceptable levels,respectively,indicating that the combination of THz-TDS and PLS is a potential method for further quantitative detection.     

中红外光谱技术对乙醇汽油乙醇含量的检测

乙醇汽油是一种新型清洁燃料,燃料乙醇在乙醇汽油中的含量会影响发动机的性能。为了确保发动机的工作可靠性,需要对乙醇汽油中的乙醇含量进行快速精准检测。本文使用中红外光谱技术对采集到的乙醇汽油的光谱数据进行定量分析。首先对原始光谱数据使用多元散射校正、基线校正、一阶导数、二阶导数等预处理方法进行预处理。然后利用ELM、LSSVM、PLS对乙醇汽油中的乙醇含量建立预测模型,通过比较3种建模方法对乙醇含量的预测能力发现,PLS方法的精度比其余两种方法更高。模型决定因子R2为0.958,预测均方误差RMSEP为1.479%(V/V,体积比)。中红外光谱技术对乙醇汽油乙醇含量的快速准确检测提供了新的思路。     

Advanced gasoline engine development using optical diagnostics and numerical modeling

Twenty years ago, homogeneous-charge spark-ignition gasoline engines (using carburetion, throttle-body-, or port-fuel-injection) were the dominant automotive engines. Advanced automotive engine development remained largely empirical, and stratified-charge direct-injection gasoline-engine production was blocked by lack of robustness in its combustion process [W.G. Agnew, Proc. Combust. Inst. 20 (1984) 1-17]. Today, a wide range of direct-injection gasoline engines are in (or near) production, and combustion science is playing a direct role in advanced gasoline-engine development through the simultaneous application of advanced optical diagnostics, three-dimensional computational fluid dynamics (CFD) modeling, and traditional combustion diagnostics. This paper discusses the use of optical diagnostics and CFD in five gasoline-engine combustion systems: homogeneous spark-ignition port-fuel-injection (PFI), homogeneous spark-ignition direct-injection (DI), stratified wall-guided spark-ignition direct-injection (WG-SIDI), stratified spray-guided spark-ignition direct-injection (SG-SIDI), and homogeneous-charge compression-ignition (HCCI). The emphasis is on WG-SIDI, SG-SIDI, and HCCI engines. Key in-cylinder physical processes (e.g., sprays and vaporization, turbulent fuel-air mixing, wall wetting, ignition and early flame development, turbulent partially premixed flame propagation, and emissions formation) can be visualized, quantified, and optimized through optical engine experiments and CFD-based engine modeling. Outstanding issues for stratified engines include reducing piston wall-wetting, pool fires and smoke in WG-SIDI engines, eliminating intermittent misfires in SG-SIDI engines, and optimizing lean NO_x after-treatment systems. HCCI engines require better control of combustion timing and heat-release rate over wide speed/load operating ranges, smooth transitions between operating modes, and individual cylinder sensors and controls. Future directions in optical diagnostics and modeling are suggested to improve our fundamental understanding of important in-cylinder processes and to enhance CFD modeling capabilities.     

Screening Brazilian commercial gasoline quality by hydrogen nuclear magnetic resonance spectroscopic fingerprintings and pattern-recognition multivariate chemometric analysis

The identification of gasoline adulteration by organic solvents is not an easy task, because compounds that constitute the solvents are already in gasoline composition. In this work, the combination of Hydrogen Nuclear Magnetic Resonance (¹H NMR) spectroscopic fingerprintings with pattern-recognition multivariate Soft Independent Modeling of Class Analogy (SIMCA) chemometric analysis provides an original and alternative approach to screening Brazilian commercial gasoline quality in a Monitoring Program for Quality Control of Automotive Fuels. SIMCA was performed on spectroscopic fingerprints to classify the quality of representative commercial gasoline samples selected by Hierarchical Cluster Analysis (HCA) and collected over a 6-month period from different gas stations in the São Paulo state, Brazil. Following optimized the ¹H NMR-SIMCA algorithm, it was possible to correctly classify 92.0% of commercial gasoline samples, which is considered acceptable. The chemometric method is recommended for routine applications in Quality-Control Monitoring Programs, since its measurements are fast and can be easily automated. Also, police laboratories could employ this method for rapid screening analysis to discourage adulteration practices.     

Rapid detection of gasoline by a portable Raman spectrometer and chemometrics

Identification of the gasoline purity is important for quality control and detection of gasoline adulteration. Principal component analysis and Raman spectroscopy were used to authenticate gasoline adulterated with methyl tert‐butyl ether (MTBE) and benzene. Gasoline could be clearly distinguished from gasoline adulterated with MTBE and benzene by a plot of the first principal component (x‐axis) against the second principal component (y‐axis). And the radial basis function neural network was used for quantitative prediction of the volume percentages of MTBE and benzene in gasoline based on Raman Spectra. The correlation coefficient (r) and mean absolute percentage error between predictive values and spiked values were 0.9907 and 0.9934 and 15.73 and 8.19%, respectively. Moreover, the Raman spectra of the samples were obtained with a portable Raman spectrometer. Therefore, the method is simple, effective, fast, does not require sample pre‐processing, and is promising for rapid gasoline detection. Copyright © 2012 John Wiley & Sons, Ltd.