矿井突水水源识别的RS-LSSVM模型

为了对矿井突水水源进行准确、高效的判别,综合考虑水化学特征,选取Ca~(2+),Mg~(2+),K~++Na~+,HCO-3,SO2-4,Cl~-和总硬度7个指标的质量浓度(mg/L)作为矿井突水水源的最初判别指标。利用粗糙集(RS)理论的属性约简来筛选水化学特征指标,用以作为水源识别的核心判别指标,建立基于RS的矿井突水水源识别的最小二乘支持向量机(LSSVM)模型。选用约简处理后的13组煤矿数据对模型进行训练,再用训练好的模型对另外12组突水数据进行水源判别,并与未进行属性约简的LSSVM模型及Fisher判别分析法、随机森林方法进行对比。结果表明,利用属性约简方法可以很好地排除原始数据中的冗余信息干扰,因而能有效判别矿井突水水源,使矿井突水水源模型的误判率降低至0;而且指标约简过程可以降低LSSVM运算的复杂度,也能够提高判别效率。     

湿法脱硫入口SO_2浓度对脱硫效率影响的非线性回归分析

通过对太原二电厂11号FGD现场168运行数据采用一元非线性回归分析和基于距离判别法的多元非线性回归分析两种不同的分析方法,利用最小二乘曲线拟合法建立了不同出口SO2浓度范围内的入口SO2浓度与脱硫效率的非线性回归方程,为实际运行过程中脱硫效率的预测和分析提供理论依据。此外,分析了入口SO2浓度对脱硫效率的影响,为运行人员在实际运行过程中提高脱硫效率而提供控制思路。     

Integrated leakage detection and localization model for gas pipelines based on the acoustic wave method

With the development of natural gas transportation systems, major accidents can result from internal gas leaks in pipelines that transport high-pressure gases. Leaks in pipelines that carry natural gas result in enormous financial loss to the industry and affect public health. Hence, leak detection and localization is a major concern for researchers studying pipeline systems. To ensure the safety and improve the efficiency of pipeline emergency repair, a high-pressure and long-distance circular pipe leakage simulation platform is designed and established by similarity analysis with a field transmission pipeline, and an integrated leakage detection and localization model for gas pipelines is proposed. Given that the spread velocity of acoustic waves in pipelines is related to the properties of the medium, such as pressure, density, specific heat, and so on, this paper proposes a modified acoustic velocity and location formula. An improved wavelet double-threshold de-noising optimization method is also proposed to address the original acoustic wave signal collected by the test platform. Finally, the least squares support vector machine (LS-SVM) method is applied to determine the leakage degree and operation condition. Experimental results show that the integrated model can enhance the accuracy and precision of pipeline leakage detection and localization.     

煤与瓦斯突出预测的QGA-LSSVM模型

为快速、有效地对煤与瓦斯突出类型作出预测,运用灰色关联和因子分析模型对所选主要的判别指标进行分析提取,利用量子遗传算法(QGA)对最小二乘支持向量机(LSSVM)的参数作寻优处理,最终建立QGA-LSSVM煤与瓦斯突出预测模型。选取从砚石台矿区历史实测的数据,以96∶20的比例对该模型进行训练与测试,并将预测结果与其他预测模型的预测效果进行了比较。研究结果表明:对判别指标进行灰色关联分析可以有效去除对煤与瓦斯突出影响作用小的指标;用因子分析进行公共因子提取,可以有效减少数据信息冗余;利用QGA优化的LSSVM模型能使结果避免陷入局部最优解,用该模型可以有效预测煤与瓦斯突出类型,误判率为0。     

A COMPARISON OF GEOSTATISTICAL METHODOLOGIES USED TO ESTIMATE SNOW WATER EQUIVALENT1

ABSTRACT: The need to monitor and forecast water resources accurately, particularly in the western United States, is becoming increasingly critical as the demand for water continues to escalate. Consequently, the National Weather Service (NWS) has developed a geostatistical model that is used to obtain areal estimates of snow water equivalent (the thtal water content in all phases of the snowpack), a major source of water in the West. The areal snow water equivalent estimates are used to update the hydrologic simulation models maintained by the NWS and designed to produce extended streamflow forecasts for river systems throughout the United States. An alternative geostatistical technique has been proposed to estimate snow water equivalent. In this research, we describe the two methodologies and compare the accuracy of the estimates produced by each technique. We illustrate their application and compare their estimation accuracy using snow data collected in the North Fork Clearwater River basin in Idaho.     

酸雨pH预测的偏最小二乘回归模型

酸雨pH值受酸性离子[SO4^2-]、[NO3^-]和碱性离子[Ca^2 ]、[NH4^ ]等的影响。这些影响因素之间存在多重相关性。用一般最小二乘回归法建模预测pH值，估计参数存在着很大的误差且物理意义明显不足。应用偏最小二乘回归技术建立pH值预测模型，克服了自变量之间的多重相关性的问题，因而更具有先进性，计算结果更为可靠。以我国17个城市pH值预测为例，探讨偏最小二乘法的优势，并与最小二乘回归法进行了比较。     

ERRORS IN ESTIMATED STREAMFLOW PARAMETERS AND STORAGES FOR UNGAUGED CATCHMENTS1

ABSTRACT: For a set of 81 catchments in southeast Victoria, Australia, predictive equations were developed by least squares regression of the mean and coefficient of variation of annual Streamflow against a variety of rainfall and physiographic parameters. The data were also divided into subsets according to catchment size, subregion, or record length of investigate if the relationships differed significantly between subsets. Only the catchment area and some rainfall statistical parameters were found to be significant. Streamflow parameters predicted by the regression equations were used to estimate storage requirements in ungauged catchments. The influence of errors in the Streamflow parameters on the storage error was examined.     

PROCESS-ORIENTED ESTIMATION OF SUSPENDED SEDIMENT CONCENTRATION1

ABSTRACT: Least squares regression and ARIMA models were developed from suspended sediment data for the Ausable River, Southern Ontario, Canada. A poor correlation between discharge and suspended sediment concentration results from the dynamics of the physical system, including seasonality, antecedent conditions, and hysteresis. Regression model results were significantly improved by the division of the data set into seasons and the addition of simple. but physically meaningful variables. Misleading improvements obtained from the regression of sediment load and discharge are discussed. ARIMA models provided accurate forecasts of sediment concentration on a real-time basis, but the rigorous data requirements limit their use in modeling suspended sediment concentrations in Canadian rivers.     

Corrosion rules for ordinary concrete exposed to sulfur dioxide-containing environments

Sulfur dioxide (SO₂) significantly influences the durability of concrete. Concrete corrosion in SO₂-containing environments is a serious concern. In this study, an accelerated test was devised to examine the change rules of concrete corrosion in SO₂-containing environments. Experiments were conducted to explore changes in mass, strength, and sulfatized depth with different water–cement ratios and a series of SO₂ concentrations. Data indicated that the water–cement ratio and SO₂ levels all impact the durability of concrete.     

The Importance of the Chosen Technique to Estimate Diffuse Solar Radiation by Means of Regression

The Ordinary Least Squares (OLS) is one of the widely used methods which is used for estimating the diffuse solar radiation. However, in order to use the OLS method in the estimation, the dataset must provide certain assumptions. In this study, alternative robust methods have been described and they were compared with the OLS method, which is used for estimating diffuse radiation frequently in an application. At the end of the analysis, the R² value obtained by the OLS method is less than the values obtained by M regression models. In other words, the explanation of the dependent value is weak when the OLS method is used. Finally, it can be said that the most appropriate method is Andrews for estimating the diffuse solar radiation.