Safety evaluation of leak in a storage tank using fault tree analysis and risk matrix analysis

The work presented in this paper used a quantitative analysis of relevant risks through the development of fault tree analysis and risk analysis methods to aid real time risk prediction and safety evaluation of leak in a storage tank. Criticality of risk elements and their attributes can be used with real time data to predict potential failures likely to occur. As an example, a risk matrix was used to rank risk of events that could lead to a leak in a storage tank and to make decisions on risks to be allowed based on past statistical data. An intelligent system that recognizes increasing level(s) and draws awareness to the possibility of additional increase before unsafe levels are attained was used to analyse and make critical decisions. After a visual depiction of relationships between hazards and controls had been actualized, dynamic risk modelling was used to quantify the effect controls can potentially have on hazards by applying historical and real-time data into a probabilistic model. The output of a dynamic risk model is near real-time quantitative predictions of risk likelihood. Results from the risk matrix analysis method mixed with RTD and FTA were analyzed, evaluated, and compared.     

Effective method of predicting confined pressure behavior under isotropic turbulence

Explosion pressure prediction is indispensable to ensure process safety against accidental gas explosions. This work is aimed at establishing a theoretical method for predicting confined methane-air explosion pressure under isotropic turbulence. The results indicated that the pressure rise rate becomes significantly increased by the existence of isotropic turbulence, which effect on peak value of explosion pressure is negligible. Among various models of turbulent burning velocity, the calculated pressure rise rate using Chiu model, Williams model and Liu model is relatively closer to experimental value. With the increase of turbulent integral length and RMS turbulent fluctuation velocity, the pressure rise rate becomes increased continuously. The influence of adiabatic compression and isothermal compression on pressure rise rate could be ignored. To predict explosion pressure in a more accurate way, the dynamic variation of turbulent integral length and RMS turbulent fluctuation velocity should be considered in the future.     

基于等维新息灰色马尔科夫的地铁客流量预测*

为准确预测地铁客流量的发展趋势,采用等维新息灰色GM（1,1）预测模型与马尔科夫模型相结合的方式建立等维新息灰色马尔科夫模型,探讨等维新息灰色马尔科夫模型在地铁客流量预测领域的应用;运用平均相对误差、后验差比值和小误差概率3种指标对模型精度进行检验。结果表明:等维新息灰色马尔科夫模型与原始数列的拟合程度较高,预测精度等级为Ⅰ级（优）,优于传统灰色GM(1,1)模型和等维新息灰色GM(1,1)模型的预测精度,更加符合地铁客流的实际情况。     

库岸涉水滑坡危险性现状分析与预测评价

为合理评价库岸涉水滑坡危险性,基于层次分析法与模糊理论,构建滑坡危险性现状评价模型,并利用优化支持向量机构建滑坡变形预测模型,通过对比分析实现滑坡危险性综合判断。结果表明:大柿树滑坡危险性现状为69.78分,风险等级为Ⅲ级,属高度危险;通过危险性预测评价,滑坡变形呈持续增加趋势,将趋于不利方向发展;综合滑坡危险性现状分析与预测评价结果可知,滑坡危险性相对较大。研究结果可为滑坡灾害防治提供一定理论依据。     

CFB锅炉多点排放大气环境污染风险预测

循环流化床锅炉（CFB）脱硫除尘系统复杂,操作不当容易导致大气污染事故的发生。通过对CFB锅炉多点排放明大气污染因子及其在大气中的扩散分析,建立了多点排放时SO2和总悬浮物颗粒（TSP）的大气环境污染的风险预测模型。模型结果较好地反映了CFB锅炉风险排污的影响因素及其环境容量的限值要求,这对于合理控制风险排污条件,加强现有CFB的运行管理是必要的。     

介绍水体污染的三种鉴别方法

介绍了水体污染的三种鉴别方法,应用调查法、监测法和预测法,可以鉴别水体是否受到污染和将会受到污染,从而建立了一套水体污染鉴别方法。     

Topical scientific and practical issues of wildland fire problem

Problem of wildfires has not been resolved anywhere in the world. Mere increase of technical power does not lead to desirable results. Forests of developed countries burn as actively as those in Africa or in Russia. The main reasons of wildfire problem are as follows: (1) Constant wandering of dry seasons over the planet causing outbreaks of wildfires. (2) Unpredicted self-development of ordinary wildfires into awful fire disasters. (3) Difficulties in delivery and use of heavy machines on hardly accessible territories. (4) Absence of a perfect technique for economic evaluation of how effectively the wildfire control system works. (5) Absence of the system of payments encouraging wildfire fighters. To solve the problem of wildfires in Russia it is necessary to: (1) Create the Russian wildfire behaviour and fire effects prediction system on the basis of the developed classification of vegetation fuels and methods of their mapping as well as maximum utilization of forest inventory information and Geographic Information System (GIS). (2) Elaborate a technique of proper wildfire monitoring including estimation of vegetation damage. (3) Improve daily rating of regional fire danger. (4) Improve fire-preventive arrangement of the territory covered by vegetation, the main goal being creation of favourable conditions for active fire management. (5) Choose the main direction in elaboration of fire-fighting means and methods taking into account their universality, simplicity, reliability, etc. (6) Elaborate an improved technique for estimation of economic effectiveness of the wildfire control system. (7) Develop international cooperation of scientists and professionals in fire management.     

A cluster-based decision support system for estimating earthquake damage and casualties

This paper describes a Decision Support System for Disaster Management (DSS-DM) to aid operational and strategic planning and policy-making for disaster mitigation and preparedness in a less-developed infrastructural context. Such contexts require a more flexible and robust system for fast prediction of damage and losses. The proposed system is specifically designed for earthquake scenarios, estimating the extent of human losses and injuries, as well as the need for temporary shelters. The DSS-DM uses a scenario approach to calculate the aforementioned parameters at the district and sub-district level at different earthquake intensities. The following system modules have been created: clusters (buildings) with respect to use; buildings with respect to construction typology; and estimations of damage to clusters, human losses and injuries, and the need for shelters. The paper not only examines the components of the DSS-DM, but also looks at its application in Besiktas municipality in the city of Istanbul, Turkey.     

Identifying crash propensity using specific traffic speed conditions

INTRODUCTION: In spite of recent advances in traffic surveillance technology and ever-growing concern over traffic safety, there have been very few research efforts establishing links between real-time traffic flow parameters and crash occurrence. This study aims at identifying patterns in the freeway loop detector data that potentially precede traffic crashes. METHOD: The proposed solution essentially involves classification of traffic speed patterns emerging from the loop detector data. Historical crash and loop detector data from the Interstate-4 corridor in the Orlando metropolitan area were used for this study. Traffic speed data from sensors embedded in the pavement (i.e., loop detector stations) to measure characteristics of the traffic flow were collected for both crash and non-crash conditions. Bayesian classifier based methodology, probabilistic neural network (PNN), was then used to classify these data as belonging to either crashes or non-crashes. PNN is a neural network implementation of well-known Bayesian-Parzen classifier. With its superb mathematical credentials, the PNN trains much faster than multilayer feed forward networks. The inputs to final classification model, selected from various candidate models, were logarithms of the coefficient of variation in speed obtained from three stations, namely, station of the crash (i.e., station nearest to the crash location) and two stations immediately preceding it in the upstream direction (measured in 5 minute time slices of 10-15 minutes prior to the crash time). RESULTS: The results showed that at least 70% of the crashes on the evaluation dataset could be identified using the classifiers developed in this paper.     

基于新型拓扑指数的咪唑类离子液体捕集CO2性能预测

大量温室气体CO_2的存在严重影响环境,而咪唑型离子液体具有独特的气体溶解性,在CO_2捕集方面的应用较为广泛。基于定量结构-性质相关性(QSPR)原理提出了一种新的描述符——拓扑指数(Topological Index,TI)描述符,研究了咪唑类离子液体捕集CO_2的性能与其拓扑指数描述符之间的内在定量关系。应用遗传算法获得与捕集量最为密切相关的一组拓扑指数描述符,将其与温度和压力一起作为输入参数,分别采用多元非线性回归算法及支持向量机算法建立了咪唑类离子液体捕集CO_2性能与其拓扑指数描述符之间的非线性模型。通过多元非线性回归算法得出训练集和测试集的决定系数分别为0.771和0.754,由支持向量机算法得出训练集和测试集的决定系数分别为0.990和0.981。对预测模型进行了评价验证及稳定性分析,结果表明,两种模型均具有良好的稳定性能和预测能力。根据拓扑指数描述符所建立的预测模型为工程应用提供了一种预测咪唑类离子液体捕集CO_2性能的有效方法。