A correlation of the lower flammability limit for hybrid mixtures

Hybrid mixtures are widely encountered in industries such as coal mines, paint factories, pharmaceutical industries, or grain elevators. Hybrid mixtures explosions involving dust and gas can cause great loss of lives and properties. The lower flammability limit (LFL) is a critical parameter when conducting a hazard assessment or developing mitigation methods for processes involving hybrid mixtures. Unlike unitary dust or gas explosions, which have been widely studied in past decades, only minimal research focuses on hybrid mixtures, and data concerning hybrid mixtures can rarely be found. Although methods to predict the LFL have been developed by using either Le Chatelier's Law, which was initially proposed for homogeneous gas mixtures, or the Bartknecht curve, which was adopted for only certain hybrid mixtures, significant deviations still remain. A more accurate correlation to predict an LFL for a hybrid mixtures explosion is necessary for risk assessment. This work focuses on the study of hybrid mixtures explosions in a 36 L dust explosion apparatus including mixtures of methane/niacin, methane/cornstarch, ethane/niacin and ethylene/niacin in air. By utilizing basic characteristics of unitary dust or gas explosions, a new formula is proposed to improve the prediction of the LFL of the mixture. The new formula is consistent with Le Chatelier's Law.     

Breakthrough Curves Characterization and Identification of an Unknown Pollution Source in Groundwater System Using an Artificial Neural Network (ANN)

Contamination of groundwater constrains its uses and poses a serious threat to the environment. Once groundwater is contaminated, the cleanup may be difficult and expensive. Identification of unknown pollution sources is the first step toward adopting any remediation strategy. The proposed methodology exploits the capability of a universal function approximation by a feed-forward multilayer artificial neural network (ANN) to identify the sources in terms of its location, magnitudes, and duration of activity. The back-propagation algorithm is utilized for training the ANN to identify the source characteristics based on simulated concentration data at specified observation locations in the aquifer. Uniform random generation and the Latin hypercube sampling method of random generation are used to generate temporal varying source fluxes. These source fluxes are used in groundwater flow and the transport simulation model to generate necessary data for the ANN model-building processes. Breakthrough curves obtained for the specified pollution scenario are characterized by different methods. The characterized breakthrough curves parameters serve as inputs to ANN model. Unknown pollution source characteristics are outputs for ANN model. Experimentation is also performed with different number of training and testing patterns. In addition, the effects of measurement errors in concentration measurements values are used to show the robustness of ANN based methodology for source identification in case of erroneous data.     

Societal risk acceptance criteria for gas distribution pipelines based on incident data from the United States

A significant number of pipeline operators use pipeline integrity management (PIM) to improve pipeline safety and reliability. Risk assessment is a critical step in PIM, because it determines the necessity of conducting the following steps in PIM for certain pipelines. Risk acceptance criteria are required in the process of risk assessment. Individual risk and societal risk are most frequently adopted as the two indicators of the risk acceptance criteria. To the best of the authors’ knowledge, quantitative societal risk acceptance criteria, especially for gas distribution pipelines, do not exit. The aim of this paper is to establish the societal risk acceptance criteria for gas distribution pipelines. Hence, FN curves were established using historical incident data from 2002 to 2017 provided by the U.S. Department of Transportation (DOT). Linear regression and the ALARP principle are used in evaluating the limits of the negligible line and intolerable line to obtain a graphical societal risk acceptance criterion for gas distribution pipelines. A line having a slope of −1.224, and an anchor point of (1, 8.413 × 10⁻⁷) is proposed as the negligible line. Further, the intolerable line has a slope of −1.224, and an anchor point of (1, 2.524 × 10⁻⁶). Both the negligible risk and the intolerable risk for the gas distribution pipeline are lower than the current societal risk acceptance criteria for hazardous installations. The reasons for these relatively lower risk acceptance criteria are discussed.     

天山云杉的种群动态

选择我国新疆境内天山山脉从西到东处于不同经度位置5个地区(昭苏、巩留、乌苏、乌鲁木齐和哈密)的天山云杉林进行垂直样带调查,采用静态生命表法,分析5个地区天山云杉种群的存活曲线和死亡率曲线,为天山云杉种群数量统计和生态保护提供依据.结果表明:其存活曲线为DeeveyⅡ型,表明天山云杉种群处于动态稳定状态;5个地区天山云杉种群均有两个死亡率曲线高峰,死亡高峰主要出现在幼年时期、近成熟前期、成年阶段和老年期.种群的死亡强度和死亡高峰在时间格局上各地区有明显的差异.图2表6参31     

An evaluation of environmental factors affecting species distributions

Many different models can be built to explain the distributions of species. Often there is no single model that is clearly better than the alternatives, and this leads to uncertainty over which environmental factors are limiting species’ distributions. We investigated the support for different environmental factors by determining the drop in model performance when selected predictors were excluded from the model building process. We used a paired t-test over 37 plant species so that an environmental factor was only deemed significant if it consistently improved the results for multiple species. Geology and winter minimum temperatures were found to be the environmental factors with the most support, with a significant drop in model performance when either of these factors was excluded. However, there was less support for summer maximum temperature, as other environmental factors could combine to produce similar model performance. Our method of evaluating environmental factors using multiple species will not be capable of detecting predictors that are only important for one or two species, but it is difficult to distinguish these from spurious correlations. The strength of the method is that it increases inference for factors that consistently affect the distributions of many species. We discourage the assessment of models against predefined benchmarks, such as an area under the curve (AUC) of more than 0.7, as many alternative models for the same species produce similar results. Therefore, the benchmarks do not provide any indication of how the performance of the selected model compares to alternative models, and they provide weak inference to accept any selected model.     

Fragility assessment of bottom plates in above ground storage tanks during flood events

This study aims to improve the fundamental understanding on the performance of bottom plates in above ground storage tanks (ASTs) during flood events. To this end, fragility models that estimate the probability of material yielding and rupture in the bottom plates were derived. A significant number of ASTs are located in coastal areas and are susceptible to hurricane hazards. Consequently, ASTs have suffered severe damage during past hurricanes resulting in spills with catastrophic environmental and social impacts. Therefore, several failure modes such as flotation, buckling, and sliding have been studied in past research. However, the literature lacks studies that consider the failure of bottom plate due to uplift pressure generated during floods and there are no design guidelines to address this issue. To address this gap, fragility functions that provide the probability of failure as a function of tank geometry, material properties, design parameters, and hazard conditions were developed herein. For this purpose, Latin Hypercube Sampling was performed to span the space of these parameters uniformly. For each parameter combination, maximum stresses in bottom plates were determined using analytical formulations for simply supported and clamped boundary conditions and were compared against two different failure thresholds. The results were used to develop a closed form fragility model using step wise logistic regression. Fragility functions were applied to four case study tanks. Sensitivity analysis were performed to understand the impacts of different probability density functions for various variables on the bottom plates’ fragility. The results provided several insights such as ASTs with larger diameter were vulnerable to bottom plate failure. Comparison with other failure modes revealed that the probability of bottom plate failure was higher than flotation failure for anchored ASTs with clamped boundary condition.     

室内织物火灾烟气危害性分析

室内织物因其容易点燃、燃烧速度快、蔓延迅速等燃烧特性,对室内火灾的发生、发展蔓延及火灾危害都有重要影响.在低强度热源加热条件下,研究了几种常见室内织物如牛仔裤、化纤运动裤、棉布床单、混合织物等温度变化、火灾烟气危害性及喷水对热解烟气的影响.结果表明,棉类织物在低强度火源引燃后烟气毒害成分主要为CO、NOx、SO2,3种毒性气体在7～ 10 min超过人体允许极限值,喷水降低了烟气毒性成分的生成速度与生成量,如牛仔裤热解烟气中CO、SO2的超标时间分别增加了约4min、7 min,NOx则未超标;化纤类织物热解气体超标主要毒性成分为CO、SO2,喷水有利于化纤等聚合物熔融物的流动,在一定程度上反而加快了热量传递,热分解速度加快,有毒有害气体的生成量和产生速度增加.如加水后化纤运动裤热解烟气中,CO超标时间提前了10 min,SO2、NOx烟气也在20 min左右出现了超标情况.     

降雨产流计算中径流曲线法(SCS模型)局限性的探讨

SCS模型(Soil Conservation Service,水土保持局,简称SCS)是根据美国强烈的地带性气候特征和明显的农业区划所研发的小流域设计洪水模型。由降雨量估算降雨产流的SCS模型被广泛用于应用水文学和环境保护领域中。本文通过阐述SCS模型的起源和发展历史,讨论了该模型使用的基本条件、应用目标以及局限。总结并分析国内外相关研究现状,深入探讨了模型主要参数初损(Ia)和CN值的重要意义及确定方法。     

基于Matlab曲线拟合的山东省各地市人口密度预测及Cartogram地图表现

通过Matlab软件的曲线拟合功能,以1993-2012年《山东省统计年鉴》中的人口统计数据为基础数据,对山东省17地市分别选择最佳拟合模型进行人口规模预测,依据行政区划面积计算人口密度,对结果以Cartogram的技术方法进行地图表现,得出直观的结果.该研究采用了新颖的专题地图表示方法,丰富了人口预测方法,对了解未来社会的供求关系和人口发展趋势、拟定正确合理的人口规划方案具有重大的理论和实践意义.     

Can Desalination and Clean Energy Combined Help to Alleviate Global Water Scarcity?

The major present hindrance in using desalination to help alleviate global water scarcity is the cost of this technology, which, in turn is due to energy cost involved. This study examines historical trends in desalination and breaks up the cost of desalination into energy based and nonenergy based. It then develops the learning curves (relationship between cumulative production and market price) for desalination. Assuming that the photovoltaic (PV) technology will be the dominant form of energy used in the desalination process, the existing PV learning curve and desalination learning curve are combined to explore the viability of large‐scale adoption of desalination in the future. The world has been divided into seven regions and it is assumed that water demand from desalinated water will be met only within the 100‐km coastal belt. It is shown that, in most of the regions, other than sub‐Saharan Africa, Central America, and South Asia (where water tariffs are low), the desalination (without considering energy) becomes viable by 2040. For PV technology, less than 1 million MW per annum growth is required till 2050 to make it affordable. Globally, desalination with renewable energy can become a viable option to replace domestic and industrial water demand in the 100‐km coastal belt by 2050.