曙光油田节能减排现状调查及对策研究

曙光采油厂随着超稠油的不断开发,生产能耗也随之增加,通过开展节能减排及节能挖潜工作,全厂能耗增长状态得到了良好的控制,能耗总量由2005年的58.21×10~4t标煤下降到2008年的47.75×10~4t标煤;原油(气)液生产单位综合能耗为52.02 kg标煤/t,同期对比减少0.38 kg标煤/t;生产系统运行效率不断提高。文章详细介绍了曙光油田从机采系统、热注系统、集输系统等方面推广应用节能新技术、新产品,达到其投资和运行费用最少,经济性较好,对油田减排挖潜进行了探讨。     

我国火电厂脱硫脱硝行业2008年发展综述

介绍了2008年我国火电厂脱硫脱硝行业的发展概况,分析了行业存在的主要问题,并提出了对策及建议。     

城市污水处理厂环境影响评价中的问题探讨

针对城市污水处理厂项目的特点,分析探讨了此类项目环境影响评价中应注意的几个问题。     

水泥厂煤粉制备系统袋除尘器的选型及应用

本文介绍了煤粉制备系统含尘废气的特性及治理技术的发展与应用,并从袋除尘器类型的确定、处理风量的确定、过滤风速的选取、过滤面积的计算和除尘器规格的选取等方面介绍了煤磨袋除尘器的选型,以及FGM（M）型气箱脉冲防爆袋除尘器、MMC型脉冲喷吹防爆袋除尘器和LPMC型低阻高效防爆袋除尘器在煤粉制备系统除尘中的应用推广情况。     

电厂对珍禽保护区的环境影响评价实例探讨

以位于自然保护区边缘的火电厂建设项目环境影响评价为实例,阐述了火电厂生态环境评价中对自然保护区珍稀鸟类的保护方法,以供类似项目的生态环境影响评价借鉴。     

战时火力发电厂重要目标分类防护探讨

基于重要目标防护准则和模糊聚类算法,构建火力发电厂目标评价准则指标体系和模糊聚类模型,实现了火力发电厂重要目标的分类防护,进而选用具有针对性的防护措施,解决了重要目标在战时防护措施选择不完善、不合理的问题,有效地提升了战时火力发电厂的生存能力;通过模糊聚类模型实现分类防护,不仅有利于提高重要目标防护效率,而且使得防护资源少、力量弱,供需突出的问题得以改善,同时,在应对自然灾害等重大突发事件发生时,有助于保护发电厂的生产能力。通过案例给出战时某火力发电厂的具体防护措施,合理地解决了发电厂重要目标和关键部位的防护问题。     

基于粗糙集-神经网络的矿山地质环境影响评价模型及应用

采用衡山白果地区石膏矿山的11个评价指标,综合运用粗糙集和神经网络理论,构建了基于粗糙集-神经网络(RS-ANN)的矿山地质环境影响评价模型,对RSES软件约简的数据和无约简的数据采用EasyNN-plus软件进行预测评价。神经网络模型的输入属性为8个,而粗糙集-神经网络模型的输入属性为6个,训练样本均为13个,预测样本均为4个,前者的平均预测精度为1.85%～24.86%,后者为1.23%～15.28%。研究发现,粗糙集在保留关键信息的前提下可有效地对数据表进行约简,约简后的神经网络预测结果与实际情况吻合,并比无约简时总体精度有较大幅度提高。     

提高乌鲁木齐市水磨沟区植被覆盖率的设想

通过对乌鲁木齐市水磨沟区植被的现状调查和对存在问题的分析,结合生态示范区建设指标体系,提出提高该区植被覆盖率及改善生态环境的设想。     

Artificial neural network based carbon monoxide persistence models for episodic urban air quality management

This paper describes the development of artificial neural network (ANN) based carbon monoxide (CO) persistence (ANNCOP) models to forecast 8-h average CO concentration using 1-h maximum predicted CO data for the critical (winter) period (November–March). The models have been developed for three 8-h groupings of 10 p.m. to 6 a.m., 6 a.m. to 2 p.m. and 2–10 p.m., at two air quality control regions (AQCRs) in Delhi city, representing an urban intersection and an arterial road consisting heterogeneous traffic flows. The result indicates that time grouping of 2–10 pm is dominantly affected by inversion conditions and peak traffic flow. The ANNCOP model corresponding to this grouping predicts the 8-h average CO concentrations within the accuracy range of 68–71%. The CO persistence values derived from ANNCOP model are comparable with the persistence values as suggested by the Environmental Protection Agency (EPA), USA. This work demonstrates that ANN based model is capable of describing winter period CO persistence phenomena.     

Using Multi-Scale Sampling and Spatial Cross-Correlation to Investigate Patterns of Plant Species Richness

Land managers need better techniques to assess exoticplant invasions. We used the cross-correlationstatistic, I _YZ, to test for the presence ofspatial cross-correlation between pair-wisecombinations of soil characteristics, topographicvariables, plant species richness, and cover ofvascular plants in a 754 ha study site in RockyMountain National Park, Colorado, U.S.A. Using 25 largeplots (1000 m²) in five vegetation types, 8 of 12variables showed significant spatial cross-correlationwith at least one other variable, while 6 of 12variables showed significant spatial auto-correlation. Elevation and slope showed significant spatialcross-correlation with all variables except percentcover of native and exotic species. Percent cover ofnative species had significant spatialcross-correlations with soil variables, but not withexotic species. This was probably because of thepatchy distributions of vegetation types in the studyarea. At a finer resolution, using data from ten1 m² subplots within each of the 1000 m² plots, allvariables showed significant spatial auto- andcross-correlation. Large-plot sampling was moreaffected by topographic factors than speciesdistribution patterns, while with finer resolutionsampling, the opposite was true. However, thestatistically and biologically significant spatialcorrelation of native and exotic species could only bedetected with finer resolution sampling. We foundexotic plant species invading areas with high nativeplant richness and cover, and in fertile soils high innitrogen, silt, and clay. Spatial auto- andcross-correlation statistics, along with theintegration of remotely sensed data and geographicinformation systems, are powerful new tools forevaluating the patterns and distribution of native andexotic plant species in relation to landscape structure.