水电厂设备设施风险的动态分级研究

为保障水电厂安全运行,根据水电厂设备设施构成要素,系统分析其风险,建立涵盖水轮机及其辅助设备、发电机及其辅助设备、计算机监控系统及自动装置、电气一次设备、电气二次设备、水工建筑物及金属结构、机具与防护设施等因素在内的评价指标体系。采用欧氏距离定义样本差异,标准化处理属性特征值,通过聚类迭代,开发水电厂设备设施风险的动态分级方法。研究结果表明:水电厂设备设施故障风险在每年的第2,3季度明显大于第1,4季度,并且随着时间推移,各季度设备设施故障风险同比有增加的趋势,风险分级结果与设备设施故障的变化趋势基本吻合。     

页岩气压裂作业人因失误结构化辨识方法研究

为提高页岩气压裂作业人因失误辨识结果的全面性和针对性,建立一种结构化的人因失误辨识方法。通过概括辨识阶段的作业流程,建立作业人员行为模型,概述作业人员的行为阶段。利用筛选出的引导词辨识行为阶段人因失误。将该方法应用于页岩气压裂过程的替液阶段。结果表明,用引导词能明确辨识方向,减少头脑风暴时间以及对专家知识的依赖;辨识过程的动态调整能改善认知可靠性,使更多人因失误模式(与传统CREAM方法相比)被辨识出来。     

Modelling and Prediction of Diesel Engine Performance using Relevance Vector Machine

Diesel engines are being increasingly adopted by many car manufacturers today, yet no exact mathematical diesel engine model exists due to its highly nonlinear nature. In the current literature, black-box identification has been widely used for diesel engine modelling and many artificial neural network (ANN) based models have been developed. However, ANN has many drawbacks such as multiple local minima, user burden on selection of optimal network structure, large training data size, and over-fitting risk. To overcome these drawbacks, this article proposes to apply an emerging machine learning technique, relevance vector machine (RVM), to model and predict the diesel engine performance. The property of global optimal solution of RVM allows the model to be trained using only a few experimental data sets. In this study, the inputs of the model are engine speed, load, and cooling water temperature, while the output parameters are the brake-specific fuel consumption and the amount of exhaust emissions like nitrogen oxides and carbon dioxide. Experimental results show that the model accuracy is satisfactory even the training data is scarce. Moreover, the model accuracy is compared with that using typical ANN. Evaluation results also show that RVM is superior to typical ANN approach.     

静态应力降与动态应力降比值方法在东北地区震后趋势判定中的应用

计算静态应力降与动态应力降之比Y值,根据Y值的大小初步判定地震类型,是震后趋势判定的一种新方法,对震后趋势快速判定有一定意义。通过计算我省历年有影响地震的Y值,发现2005年以来黑龙江省及其周边地区中强地震静态应力降与动态应力降之比(Y值)在0.3~0.6之间,总体来说Y值较大,其地震类型以孤立型地震为主,吉林省前郭县地震Y值为0.37,与我省其他中强地震相比,Y值相对较低,初步判定为震群型地震。     

基于BP神经网络的矿车运行时矿井摩擦阻力的预测*

为了快速有效地确定矿车等运输设备在巷道内运行时矿井摩擦阻力的变化情况,克服模拟软件计算量和现场实测工作量大的问题,以巷道风流速度、矿车运行速度、阻塞比、矿车长度4个矿车运行时巷道摩擦阻力的影响因素作为切入点,采用动网格技术模拟得到矿车在巷道内运行时有关矿井摩擦阻力的数据,以此为样本构建基于BP神经网络的矿井摩擦阻力预测模型,运用MATLAB软件进行网络训练,并将BP神经网络预测值与FLUENT模拟值进行对比。研究结果表明:BP神经网络结构比较简单,能以较快速度收敛,预测值与模拟值最大误差在7%以内,该神经网络模型用于求解矿车等运输设备在巷道内运行时摩擦阻力的变化情况是可行的。     

Air assimilative capacity-based environment friendly siting of new industries--a case study of Kochi region, India

Air pollution has become a matter of grave concern, particularly in mega-cities and urban areas, where the situation is alarming and becoming more and more severe day-by-day and warrants, therefore, careful planning to facilitate future industrial development. Site selection, with the objective of minimizing adverse environmental impacts based on environmental criteria is a vital prerequisite, particularly for air polluting industries. In order to locate any air polluting industry, the assimilative capacity of the region needs to be assessed carefully and planned accordingly, so that the receiving environment is not adversely affected. Assimilative capacity of a region/airshed, widely represented through the ventilation coefficient by many researchers in the past, does not give a clear picture about the amount of emission load that can be assimilated in a given region. The ventilation coefficient, at best, can only present a broad picture about the air pollution dispersion potential (low, medium or high) of the region. A modified approach, which utilizes air quality modelling as a tool to estimate the maximum allowable emission load that a region can assimilate without violating the stipulated standards, has been used for estimating the assimilative capacity of the air environment. Details of this approach have been presented in this paper through a case study carried out for the Kochi region, located in the Kerala State of India. A variety of emission and meteorological scenarios have been considered and critical emission loads have been estimated. This approach shall provide necessary technical guidance to the environmental regulatory authorities as well as to the industries in planning environment friendly industrial development.     

Water quality modelling of the river Yamuna (India) using QUAL2E-UNCAS

This paper describes the utility of QUAL2E as a modelling package in the evaluation of a water quality improvement programme. In this study, QUAL2E was applied to determine the pollution loads in the river Yamuna during its course through the national capital territory of Delhi, India. The study aimed at examining the influence of different scenarios on river water quality. Four different pollution scenarios were analysed besides the 'business as usual' situation. The study revealed that it was necessary to treat the discharge from drains to the river Yamuna and diversion of a substantial load to the Agra canal for further treatment was also essential. It was also established through this study that maintaining a flow rate of more than 10 m(3)/s in the river could also help preserve the river's water quality. To clearly display the model outcomes and demarcate polluted zones in the river stretch, model results were interfaced with a Geographical Information System (GIS) to produce cartographic outputs. In addition, uncertainty analysis in the form of first-order error analysis and Monte Carlo analysis was performed, to realise the effect of each model parameter on DO and BOD predictions. The uncertainty analysis gave satisfactory results on simulated data.     

Alien plant invasions--incorporating emerging invaders in regional prioritization: a pragmatic approach for Southern Africa

Plant invasions are a serious threat to natural and semi-natural ecosystems worldwide. Most management-orientated research on invasions focuses on invaders that are already widespread and often have major impacts. This paper deals with "emerging" invaders-those alien species with the potential to become important problems without timely intervention. A climate matching procedure was developed to define areas of South Africa, Lesotho and Swaziland that could be invaded by 28 plant species that had previously been classified as emerging invaders. Information on the location of populations of these species in the study area was combined with information on their distributions (as native or alien) in parts of Australia and the United States of America. These two countries had the best available distribution data for this study. They also share many invasive alien plant species with South Africa. Climatic data obtained for weather stations near points of known occurrence in these countries were used to define the climatically suitable areas for each species in the study area. Almost 80% of the remaining natural environment in southern Africa was found to be vulnerable to invasion by at least one of these species, 50% by six or more and 24% by 16 or more species. The most vulnerable areas are the highveld grasslands and the eastern escarpment. The emerging invaders with the greatest potential range included Acacia podalyriifolia and Cortaderia selloana. The globally important invaders Ulex europaeus and Lythrum salicaria had a more limited invasion potential but could still become major invaders. There was no relationship between the extent of the climatically suitable areas for the different species and an expert ranking of their invasion potential, emphasising the uncertainties inherent in making expert assessments based on very little information. The methods used in this analysis establish a protocol for future modelling exercises to assess the invasion potential of other emerging invaders.     

Environmental risk perception, environmental concern and propensity to participate in organic farming programmes

This paper aims to identify the factors underlying farmers' propensity to participate in organic farming programmes in a Romanian rural region that confronts non-point source pollution. For this, we employ structural equation modelling with latent variables using a specific data set collected through an agri-environmental farm survey in 2001. The model includes one 'behavioural intention' latent variable ('propensity to participate in organic farming programmes') and five 'attitude' and 'socio-economic' latent variables ('socio-demographic characteristics', 'economic characteristics', 'agri-environmental information access', 'environmental risk perception' and 'general environmental concern'). The results indicate that, overall, the model has an adequate fit to the data. All loadings are statistically significant, supporting the theoretical basis for assignment of indicators for each latent variable. The significance tests for the structural model parameters show 'environmental risk perception' as the strongest determinant of farmers' propensity to participate in organic farming programmes.     

南方丘陵山地带土壤保持功能及其经济价值时空变化特征

土壤保持是生态系统提供的重要调节服务之一,在区域侵蚀控制以及生态安全的维持方面具有不可替代的作用。以全国主体生态功能区划中"两屏三带"的南方丘陵山地带为研究对象,在遥感和地理信息系统的支持下,以2000、2005和2010年3期生态系统类型数据为主要数据源,运用通用土壤流失方程(Universal Soil Loss Equation, USLE)分析了南方丘陵山地带生态系统土壤保持功能,并对其经济价值进行动态核算。结果表明:10 a间,南方丘陵山地带土壤保持总量呈上升趋势,土壤保持总量上升了76.79×10⁷ t,森林、灌丛和农田生态系统是研究区土壤保持功能的主要贡献者,总贡献率3个年份分别为82.29%、82.59%和80.58%,不同生态系统土壤保持总量排序为森林> 灌丛> 农田> 草地> 湿地> 人工表面> 稀疏地,土壤保持能力排序为湿地> 草地> 灌丛> 森林> 农田> 人工表面> 稀疏地;10 a间,研究区土壤保持功能总经济价值增加了270.34×10⁸元,总经济价值中以保持土壤肥力价值为主;土壤保持功能经济价值的变化以轻微增加为主,其呈现增加的区域面积和价值增幅大于经济价值减少的区域面积和价值减幅。在对该区域生态系统进行保护的基础上,因地制宜地合理增加植被盖度对防治土壤侵蚀、保持土壤养分可以起到良好效果。