一种简单实用的绘制电气原理图的方法

介绍了一种在PowerPoint软件中绘制电气原理图的方法,该方法简单、易学、实用,能很方便快捷的绘制出规范美观的电气原理图.     

核电站重要敏感性设备分析

核电站重要敏感性设备管理是国内首次采用归纳法和演绎法对核电站重要敏感系统和设备进行识别和设备分级的一种新方法。目标是为了找到导致反应堆紧急停堆、强迫停机和非计划跳机跳堆风险的主要部件 ,并采用 80 - 2 0的原理进行有效的管理 ,建立有效的风险管理 (预防、探测、改正行动 PDC ,Prevention/Detec tion/Correction)大纲和PDC管理数据库 ,并对大部分的重要敏感设备的子部件进行的FMEA(FailureModeandEffectAnalysis)分析 ,识别其失效机理 ,从而确定重要敏感设备清单和预防跳机跳堆的改正行动 ,减少未来由于设备失效导致的跳堆次数 ,达到提高核电站的设备可靠性、安全性和经济性的目的。     

徐州市工业固体废物产生种类主成分分析研究

文章在介绍主成成分分析方法的原理基础上,利用SPSS统计软件对徐州市1996年至2000年工业固体废物产生种类及其量8个指标原始数据进行主成分分析,提取了占总方差81.126%能够反映原始数据的绝大部分信息的二个主成分:第一主成分和冶炼废渣、粉煤灰、炉渣密切相关,第二主成分和尾矿、煤矸石密切相关。结果表明徐州市电力、钢铁和煤矿开采行业工业固体废物产生种类及产生量变化趋势与徐州市经济发展方式相吻合,为徐州市工业固体废物污染源头控制提供决策指导。     

生态脆弱区耕地集约利用水平分析

以米脂县高西沟村为例,通过构建耕地集约利用评价指标体系,利用主成分分析方法进行分析,探讨高西沟在实施有效的水土治理措施后,耕地集约利用情况,得出结论：1997—2006年米脂县高西沟村耕地集约利用情况呈波动上升趋势,1999～2001年出现下降趋势,这与当年的自然灾害有关;高西沟村非农指数、单位化肥投入、复种指数和人均纯收入,极大地影响到耕地的集约利用水平,说明农户的经济情况影响高西沟耕地集约利用程度。     

数据缺失条件下基于MLP神经网络的水华风险预警方法研究

针对水华风险预警过程中相关监测指标数据缺失的问题,借鉴多元统计和随机分析构建了一种缺失数据插补方法,用于弥补现场调查数据的不足.基于主成分分析,对水华相关影响指标进行降维,确定水体水华风险预警模型的输入层变量.同时,采用多层感知器(MLP)人工神经网络模型对水华表征指标叶绿素a的浓度进行预测,并引入风险概率的概念,提出了水华风险概率计算公式,完善了水华预警的风险表达.最后以三峡库区典型支流大宁河为案例的研究证明了上述方法的可操作性.研究结果显示,插补数据条件下和未插补数据条件下的大宁河水华风险预警模型决定系数分别为0.9711和0.7769,前者的模型准确性更高,叶绿素a浓度预测效果更好;预测时段内大宁河11 d为水华蓝色预警(无警)级别,水华发生的风险概率为1.99%~18.61%;1 d达到水华橙色预警(中警)级别,水华发生概率为90.48%.     

基于PCA-LSSVM的厌氧废水处理系统出水VFA在线预测模型

采用IC厌氧废水处理系统处理人工合成废水,并利用PCA-LSSVM模型对系统出水挥发性脂肪酸(VFA)进行预测.首先利用主成分分析法(PCA)分析影响厌氧废水出水VFA浓度的多个变量的相关性并降低输入变量维数,然后用网格搜索结合10倍交叉验证优化LSSVM模型参数sig2和gam,最后利用建立的模型对实验数据进行仿真预测.仿真结果表明,稳态LSSVM模型对稳态条件下厌氧废水处理系统出水VFA具有很好的仿真预测能力,相对误差在4.72%以内,平均相对百分比误差(MAPE)为1.61%,均方根误差(RMSE)为1.08,相关系数达0.9996;稳态干扰LSSVM模型对厌氧废水处理系统出水VFA的仿真预测精度有所降低但仍然具有较好的预测能力,平均相对百分比误差(MAPE)为15.83%,均方根误差(RMSE)为15.45,相关系数为0.9984,该方法可为厌氧出水VFA在线预测和厌氧废水处理系统的优化控制提供指导.     

Comparison of three-dimensional fluorescence analysis methods for predicting formation of trihalomethanes and haloacetic acids

This work investigated the application of several fluorescence excitation-emission matrix analysis methods as natural organic matter (NOM) indicators for use in predicting the formation of trihalomethanes (THMs) and haloacetic acids (HAAs). Waters from four different sources (two rivers and two lakes) were subjected to jar testing followed by 24 hr disinfection by-product formation tests using chlorine. NOM was quantified using three common measures: dissolved organic carbon, ultraviolet absorbance at 254 nm, and specific ultraviolet absorbance as well as by principal component analysis, peak picking, and parallel factor analysis of fluorescence spectra. Based on multi-linear modeling of THMs and HAAs, principle component (PC) scores resulted in the lowest mean squared prediction error of cross-folded test sets (THMs: 43.7 (μg/L)², HAAs: 233.3 (μg/L)²). Inclusion of principle components representative of protein-like material significantly decreased prediction error for both THMs and HAAs. Parallel factor analysis did not identify a protein-like component and resulted in prediction errors similar to traditional NOM surrogates as well as fluorescence peak picking. These results support the value of fluorescence excitation-emission matrix-principal component analysis as a suitable NOM indicator in predicting the formation of THMs and HAAs for the water sources studied.     

Application of multivariate statistical approach to identify element sources in parsley (Petroselinum crispum)

The aim of this study was to determine the content of elements in the parsley roots (Petroselinum crispum) of different geographic origin and estimate their possible sources applying chemometric analysis. The concentrations of 13 elements in parsley collected at 12 locations in five districts were examined. Cluster analysis (CA) separated elements into three statistical significant clusters: metalloids, heavy, and essential metals. Principal component analysis (PCA) permitted the reduction of 13 variables to three principal components explaining 82.3% of the total variance. The first component with 48.2% of variance comprises Al, Cu, Fe, Mn, Mo, Zn, and Co. Some of these metals are essential in low concentrations and their presence in plants is of lithogenic origin. Multivariate statistical analysis approach, such as PCA and CA can be used to assess the level of elements in vegetables. These methods can be used to identify sources of elements in plants too.     

聚乙烯醇精馏残渣组分分析及其环境风险评价

为评估聚乙烯醇生产中精馏残渣组分及其环境污染风险,为此类危险废物的环境管理提供技术支持,采集华东某聚乙烯醇生产企业的精馏残渣,分析其重金属以及有机污染物含量,并评估其环境风险。研究结果表明聚乙烯醇精馏残渣主要来自生产环节中的醋酸乙烯精制工段,残渣中的有机物组分主要为醋酸及其他有机酸类,其中醋酸的相对含量达到63.47%;残渣中的重金属主要包括Zn、Ni和Cr,其含量分别为404 537 mg·kg-1,8 654 mg·kg-1和5 084 mg·kg-1。环境风险评价的结果表明残渣中有机物污染物引起的环境风险在可接受范围内,而重金属污染严重,同时有很高潜在生态风险。精馏残渣中的主要环境污染风险因子为醋酸、Zn和Ni。     

Coal-based transformation of cities in Shandong Province,China

This article primarily discusses the issue of realizing sustainable development in Shandong Province, China through transformation. It compares the global energy structure to China’s, as well as among regions and provinces within China. The article takes six cities in Shandong Province as typical objectives, and then using principal component analysis it calculates their flexibility in regard to city shift and restructuring. The main purpose was to quantify the impact of economic activities triggered by the coal industry, a.k.a the energy economic nexus, and to assess the flexibility and sustainability in transformation among the observed coal-based cities.