基于pso-SVM的废水厌氧处理过程软测量模型

由于厌氧消化过程的复杂性和厌氧菌的敏感性,保持厌氧消化体系的稳定和高效性是比较困难的.本文在实验室采用IC反应器构建了一套厌氧废水处理系统处理人工合成废水,基于支持向量机(SVM)提出了一种预测废水厌氧处理系统出水挥发性脂肪酸(VFA)浓度和COD去除率的软测量模型.为了提高模型的精确性和鲁棒性,加入pso算法(粒子群算法)优化SVM模型,并引入了分类策略对元数据集进行有效分类.仿真结果表明,基于pso-SVM模型的软测量模型对厌氧废水处理系统出水VFA浓度和COD去除率具有较好的预测能力,模型预测系统COD去除率及出水总VFA浓度测试样本数据相关系数分别为65.86%、85.25%;加入分类策略后,元数据集分成两类,模型预测系统COD去除率测试样本数据相关系数分别为92.34%、83.41%;模型预测系统出水总VFA浓度测试样本数据相关系数分别为99.14%、99.59%,系统预测精度明显提高.引入分类策略对元数据集进行有效分类,基于pso-SVM的软测量模型可为监控、优化和理解厌氧消化过程提供指导.     

基于共享平台的舰船装备腐蚀与防护信息分类方法研究

目的研究基于共享平台的舰船装备腐蚀与防护信息分类方法。方法运用系统工程的观点,结合舰船装备腐蚀与防护的特点,从全系统、全寿命周期角度对舰船装备腐蚀与防护信息涵盖的内容进行分析。根据相关信息分类标准,以标准化工作为基础,结合舰船装备腐蚀与防护信息特征,对舰船装备腐蚀与防护信息分类体系的要求与方法进行研究。结果依据线面结合的信息分类方法,构建了基于共享平台的舰船装备腐蚀与防护信息分类体系。结论提出了舰船装备腐蚀与防护信息分类体系的原则要求及分类方法,设计了基于共享平台的舰船装备腐蚀与防护信息分类体系。     

结合水体光学分类反演太湖总悬浮物浓度

本研究利用2008年11月、2009年4月、2010年5月及2010年8月的太湖水体原位观测数据,在对光学复杂水体进行光学分类的基础上,分别建立了针对各个类别水体的总悬浮物浓度高光谱反演模型.通过对每类水体中各个模型的性能比较,分别得到各类水体的最优模型:第一类水体,比值模型为最优模型;第二类水体,半分析模型2为最优模型;第三类水体,一阶微分模型为最优模型.同时,比较分类前后模型的精度和稳定性,结果表明分类后,两者均表现出不同程度的提高,并且分析了光学分类导致半分析模型精度下降的原因.最后针对本研究的结果在遥感数据上的适用性进行了探讨,表明在高光谱遥感数据上有很大的应用潜力.本研究结论对光学复杂湖泊水体的水色遥感具有积极重要的意义.     

Biophysical‐Regulatory Classification and Profiling of Streams Across Management Units and Ecoregions1

Caruso, Brian S. and Joshua Haynes, 2011. Biophysical‐Regulatory Classification and Profiling of Streams Across Management Units and Ecoregions. Journal of the American Water Resources Association (JAWRA) 00(0):1‐22. DOI: 10.1111/j.1752‐1688.2010.00522.x Abstract: Aquatic resources management in the United States (U.S.) under Clean Water Act Section 404 has become more complex after recent Supreme Court decisions and U.S. Army Corps of Engineers and Environmental Protection Agency (USEPA) guidance. Many intermittent/ephemeral and headwater streams may not be jurisdictional if they lack a significant nexus with navigable waters. Streams in semiarid USEPA Region 8 were classified based on hydrologic permanence and stream order using National Hydrography Dataset (NHD) Plus and GIS to provide information across broad spatial scales to aid with jurisdictional determinations (JDs). Four classes were developed for profiling across management units and ecoregions. Based on medium‐resolution NHDPlus data, intermittent streams comprise >¾, and first order streams constitute >½ of the total stream length in Region 8. Mountain states and ecoregions have the largest percentage of perennial first order streams, whereas the Dakotas, plains, and desert ecoregions have the greatest percentages of intermittent first order and intermittent higher order streams. In the Upper Colorado River Basin, >50% of reaches are intermittent first order, and 9% are perennial first order. NHDPlus data can significantly underestimate the length of headwater and intermittent streams, but can still be a valuable tool to help develop stream classes and for regional JD planning and analysis. Refinement of the stream classes using high resolution NHD data and other key catchment parameters can improve their utility for JDs.     

基于层次分析法的应急物资储备方式研究

科学合理的应急物资储备是有效应对突发事件的重要保障。在综合考虑影响应急物资储备的主要因素的基础上,将应急物资分5类;界定了3类应急物资储备方式;采用层次分析对应急物资的储备方式进行了研究,结果表明,不同类型应急物资的储备方式有较大差别,食品药品类物资应以合同储备和生产能力储备为主;应急救援类物资应以实物储备为主;灾民安置类物资应以实物储备和生产能力储备为主;基础保障类物资应以实物储备和合同储备为主;大型设备类物资主要采用合同储备;从总体上来说,应急物资储备需要采用全面综合储备的方式,而不能偏颇。     

基于非共性因子和定量化的滑坡危险性分级评价

滑坡致灾因子的选取和定量化研究是滑坡危险性分级评价的关键技术。滑坡危险性分级评价因子包括共性因子和非共性因子,共性因子在评价中定量化指标具有一致性,不能反映在滑坡个体评价体系中的差异。根据万州滑坡监测预警资料,选取了地层倾角、坡度、高差和滑坡长度与宽度的比值4个非共性因子作为滑坡危险性分级评价因子,运用Lognormal分布模型,建立了评价因子的定量化评价指标。根据建立的5级评价指标体系对万州滑坡资料进行了验证,中危险性以上的滑坡占滑坡总数的95.8%,其中高危险性和极高危险性占74.1%。研究结果表明,该研究方法的评价因子可靠性好,易于测量和获取,可应用于区域滑坡群策群防灾害点的危险性分级评价和滑坡危险性区划。     

汶川地震次生灾害提取——面向对象影像分类技术的应用

遥感技术可快速准确地提取滑坡、崩塌、泥石流、堰塞湖等次生灾害信息。面向对象影像分类技术主要通过多尺度分割,并根据对象的特征进行模糊分类,可以很好的利用影像上下文信息,提高分类精度,与传统方法相比优势明显。利用汶川地震的震前、震后的福卫二号等数据进行研究,使用面向对象方法对北川附近灾情进行了变化检测分析;并结合DEM数据对唐家山堰塞湖璇坪乡区域震后初期的水体面积和蓄水增量进行了分析。在此基础上,总结了提取地震次生灾害的关键技术和工作流程。实验结果表明,应用面向对象的变化检测方法提取地震次生灾害可以取得很好的结果。     

Monitoring and estimating the flow conditions and fish presence probability under various flow conditions at reach scale using genetic algorithms and kriging methods

The combination of current velocity and water depth influences stream flow conditions, and fish activities prefer particular flow conditions. This study develops a novel optimal flow classification method for identifying types of stream flow based on the current velocity and the water depth using a genetic algorithm. It is applied to the Datuan stream in northern Taiwan. Fish were sampled and their habitat investigated at the study site during the spring, summer, fall and winter of 2008-2009. The current velocity, water depth and maps of the presence probability of fish were estimated by ordinary and indicator kriging. The optimal classification results were compared with the classification results obtained using the Froude number and empirical methods. The flow classification results demonstrate that the proposed optimal flow classification method that considers depth-velocity and optimally identified criteria for classifying flow types, yields a current velocity and water depth of 0.32 (m/s) and 0.29 (m), respectively, and classifies the flow conditions in the study area as pool, run, riffle and slack. The variography results of the current velocity and the water depth data reveal that seasonal flows are not spatially stationary among seasons in the study area. Kriging methods and a two-dimensional hydrodynamic model (River 2D) with empirical and optimal flow classification methods are more effective than the Froude number method in classifying flow conditions in the study area. The flow condition classifications and probability maps were generated by River 2D, ordinary kriging and indicator kriging, to quantify the flow conditions preferred by Sicyopterus japonicus in the study area. However, the proposed optimal classification method with kriging and River 2D is an effective alternative method for mapping flow conditions and determining the relationship between flow and the presence probability of target fish in support of stream restoration.     

Classification of the Substances on the Basis of the Acute-Toxic-Class Method (ATC)

The acute-toxic-class method (ATC) is an alternative to the classical LD₅₀ test. Four substances were tested with an ATC testing procedure. The results were compared with LD₅₀ data obtained from the literature. Great importance was attached to the observations of toxic signs following administration. The results of this study have shown that the ATC method allows allocation to toxicity classes in the same manner as on the basis of the classical LD₅₀ tests. The ATC method uses fewer animals and yields the same information on toxic signs. Introducing the ATC method into the quality system allows estimating the acute oral toxicity of chemicals according to the Organisation for Economic Cooperation and Development (OECD; OECD, 1992, 1996).     

2012年国内外石油化工行业事故统计分析

按照事故级别、所属板块、事故类型、事故时间、涉及的化学品、发生国家,对2011年和2012年国内外石油化工行业发生的死亡1人以上的事故进行了统计分析,研究了事故动态和趋势.结果表明:重特大事故呈上升态势,具有“小概率、大后果”的特点;事故涉及的化学品种类很集中;我国伤亡事故金字塔呈现出“底部小、形状陡”的特点;死亡人数/起数比值与经济发展水平有关;爆炸事故的起数和死亡人数最高,事故起数增幅较大,火灾事故起数增幅不大,但死亡人数有大幅度增加,泄漏事故和其他事故的起数、死亡人数均有所降低.根据统计分析的结果,从加强未遂事件管理、引进OSHA绩效考核机制、加强危险品运输和化工企业安全监管、加大安全投入和提升安全技术装备水平等方面提出了建议措施.对研究国内外石化行业的事故动向,从整体上防范事故发生,具有重要的指导意义.