A new leak location method based on leakage acoustic waves for oil and gas pipelines

In order to study a new leak detection and location method for oil and natural gas pipelines based on acoustic waves, the propagation model is established and modified. Firstly, the propagation law in theory is obtained by analyzing the damping impact factors which cause the attenuation. Then, the dominant-energy frequency bands of leakage acoustic waves are obtained through experiments by wavelet transform analysis. Thirdly, the actual propagation model is modified by the correction factor based on the dominant-energy frequency bands. Then a new leak detection and location method is proposed based on the propagation law which is validated by the experiments for oil pipelines. Finally, the conclusions and the method are applied to the gas pipelines in experiments. The results indicate: the modified propagation model can be established by the experimental method; the new leak location method is effective and can be applied to both oil and gas pipelines and it has advantages over the traditional location method based on the velocity and the time difference. Conclusions can be drawn that the new leak detection and location method can effectively and accurately detect and locate the leakages in oil and natural gas pipelines.     

Analysis of the Trend and Seasonal Cycle of Carbon Monoxide Concentrations in an Urban Area (4 pp)

Background, Aim and Scope Air quality is an field of major concern in large cities. This problem has led administrations to introduce plans and regulations to reduce pollutant emissions. The analysis of variations in the concentration of pollutants is useful when evaluating the effectiveness of these plans. However, such an analysis cannot be undertaken using standard statistical techniques, due to the fact that concentrations of atmospheric pollutants often exhibit a lack of normality and are autocorrelated. On the other hand, if long-term trends of any pollutant’s emissions are to be detected, meteorological effects must be removed from the time series analysed, due to their strong masking effects. Materials and Methods The application of statistical methods to analyse temporal variations is illustrated using monthly carbon monoxide (CO) concentrations observed at an urban site. The sampling site is located at a street intersection in central Valencia (Spain) with a high traffic density. Valencia is the third largest city in Spain. It is a typical Mediterranean city in terms of its urban structure and climatology. The sampling site started operation in January 1994 and monitored CO ground level concentrations until February 2002. Its geographic coordinates are W0°22′52″ N39°28′05″ and its altitude is 11 m. Two nonparametric trend tests are applied. One of these is robust against serial correlation with regards to the false rejection rate, when observations have a strong persistence or when the sample size per month is small. A nonparametric analysis of the homogeneity of trends between seasons is also discussed. A multiple linear regression model is used with the transformed data, including the effect of meteorological variables. The method of generalized least squares is applied to estimate the model parameters to take into account the serial dependence of the residuals of this model. This study also assesses temporal changes using the Kolmogorov-Zurbenko (KZ) filter. The KZ filter has been shown to be an effective way to remove the influence of meteorological conditions on O₃ and PM to examine underlying trends. Results The nonparametric tests indicate a decreasing, significant trend in the sampled site. The application of the linear model yields a significant decrease every twelve months of 15.8% for the average monthly CO concentration. The 95% confidence interval for the trend ranges from 13.9% to 17.7%. The seasonal cycle also provides significant results. There are no differences in trends throughout the months. The percentage of CO variance explained by the linear model is 90.3%. The KZ filter separates out long, short-term and seasonal variations in the CO series. The estimated, significant, long-term trend every year results in 10.3% with this method. The 95% confidence interval ranges from 8.8% to 11.9%. This approach explains 89.9% of the CO temporal variations. Discussion The differences between the linear model and KZ filter trend estimations are due to the fact that the KZ filter performs the analysis on the smoothed data rather than the original data. In the KZ filter trend estimation, the effect of meteorological conditions has been removed. The CO short-term componentis attributable to weather and short-term fluctuations in emissions. There is a significant seasonal cycle. This component is a result of changes in the traffic, the yearly meteorological cycle and the interactions between these two factors. There are peaks during the autumn and winter months, which have more traffic density in the sampled site. There is a minimum during the month of August, reflecting the very low level of vehicle emissions which is a direct consequence of the holiday period. Conclusions The significant, decreasing trend implies to a certain extent that the urban environment in the area is improving. This trend results from changes in overall emissions, pollutant transport, climate, policy and economics. It is also due to the effect of introducing reformulated gasoline. The additives enable vehicles to burn fuel with a higher air/fuel ratio, thereby lowering the emission of CO. The KZ filter has been the most effective method to separate the CO series components and to obtain an estimate of the long-term trend due to changes in emissions, removing the effect of meteorological conditions. Recommendations and Perspectives Air quality managers and policy-makers must understand the link between climate and pollutants to select optimal pollutant reduction strategies and avoid exceeding emission directives. This paper analyses eight years of ambient CO data at a site with a high traffic density, and provides results that are useful for decision-making. The assessment of long-term changes in air pollutants to evaluate reduction strategies has to be done while taking into account meteorological variability     

铜川新区建设生态城市发展模式研究

以可持续发展为指导思想，结合生态城市的内涵，提出城乡一体化和生态生产的城市发展思路，建立了土地容量与人口适宜容量数学模型。根据铜川新区具有的自然优势，从自然—社会—经济复合生态角度，针对铜川新区的土地容量与人口适宜容量，探讨了将铜川新区建设成为一个环境优美、人口适宜的生态城市的发展模式。     

水质模拟及趋势分析

根据洛河、伊河河流及流经地区特征 ,选择具有代表性的断面 ,选用一维河流有限差分水质模型 ,计算洛河、伊河 1 995～2 0 0 0年主要断面污染物浓度 ,并对其进行评价。采用季节性肯达尔检验法 ,对计算出的洛河、伊河 1 995～ 2 0 0 0年主要断面污染物浓度进行趋势分析 ,并分析其变化特征及形成原因     

从动力地质作用原理探讨沙漠化成因

大规模沙漠化从岩石记录中可以追溯到中生代中期，第四纪以来南北两半球各存在一条中纬度沙漠带，中国的沙漠化是这个全球沙漠化进程的一个组成部分。沙漠化是在地质演化过程中受内、外动力地质作用联合控制的地质事件。是岩石圈与大气圈、生物圈及水圈强烈作用在地壳表层形成的一种特殊地质现象。这种现象的形成与演化是漫长的和具有周期性的，不会因为局部的条件变化而发生整体意义上的突变。人类活动是局部的，在整个地球沙漠化进程中只是起到一个加速剂的作用。沙漠化过程可分为3个阶段，即物理风化与沙源积聚阶段、风沙作用阶段和沙漠化阶段。沙漠期后沙丘沙经过固化生草、胶结成岩阶段后即形成风成砂岩。     

长江,嘉陵江重庆城区段二维水质对流扩散数学模型

对二水质对流扩散数学模型的有限差分解作了较详细的叙述，同时对距离步长，时间步长计算，网络内水深，流速，横向扩散系数的求取也作了简要介绍。     

人工神经网络模型和公路复垦土地适宜性评价

提出了人工神经网络用于公路临时用地复垦土地适宜性评价，利用该模型对京大高速公路山西段复垦山西段复垦土地进行了适宜性评价，探讨了人工神经网络在土地复垦适宜性评价时的特点和注意问题。     

灰色关联分析在城市污水处理厂规划改造决策中的应用

应用灰色系统理论中的灰色关联度定义 ,建立了城市污水处理厂改造的灰色决策模型 ,并对 1 1座污水处理厂进行了改造决策的综合评价 ,结果表明 ,其评价结果符合实际。     

Game theory approach to optimal capital cost allocation in pollution control

ＧａｍｅｔｈｅｏｒｙａｐｐｒｏａｃｈｔｏｏｐｔｉｍａｌｃａｐｉｔａｌｃｏｓｔａｌｏｃａｔｉｏｎｉｎｐｏｌｕｔｉｏｎｃｏｎｔｒｏｌＣｈｅｎＷｅｎｙｉｎｇＩｎｓｔｉｔｕｔｅｏｆＮｕｃｌｅａｒＥｎｅｒｇｙＴｅｃｈｎｏｌｏｇｙ，ＴｓｉｎｇｈｕａＵｎｉｖｅｒ...     

铁路运输安全事故灰色预测方法研究

近年来 ,我国铁路运输发展迅猛 ,尤其是列车运行多次提速 ,列车运行密度加大 ,对铁路运输安全管理不断提出了新的要求。依靠科技进步 ,尽量防止或减少安全事故的发生 ,要应用安全信息技术 ,使安全管理人员对铁路运输安全现状及未来事故发展趋势有所了解 ,提高安全防范意识及事故处理能力 ,同时还要不断提高员工的安全意识和安全文化素质 ,这些方法对于提高安全管理水平 ,降低事故的发生 ,保护人民的生命安全与健康具有重要意义。铁路运输安全事故预测技术就可很好解决上述问题。笔者应用灰色预测理论 ,建立了铁路运输安全事故预测模型 ,开发了安全事故预测软件系统 ,并以某铁路运输企业为例 ,介绍了研制的灰色预测系统软件的应用效果