Risk assessment of chemical process considering dynamic probability of near misses based on Bayesian theory and event tree analysis

With the development of modern automatic control systems, chemical accidents are of low frequency in most chemical plants, but once an accident happens, it often causes serious consequences. Near-misses are the precursor of accidents. As the process progresses, near misses caused by abnormal fluctuation of process variables may eventually lead to accidents. However, variables that may lead to serious consequences in the production process cannot update the risk in the life cycle of the process by traditional risk assessment methods, which do not pay enough attention to the near misses. Therefore, this paper proposed a new method based on Bayesian theory to dynamically update the probability of key variables associated with process failure risk and obtain the risk change of the near-misses. This article outlines the proposed approach and uses a chemical process of styrene production to demonstrate the application. In this chemical process, the key variables include flow rate, liquid level, pressure and temperature. In order to study the dynamic risk of the chemical process with consideration of near misses, according to the accumulated data of process variables, firstly the abnormal probability of the variables and the failure rate of safety systems associated with the variables were updated with time based on Bayesian theory. On the basis of the dynamic probability of key process variables, an event tree of possible consequences caused by variable anomalies was established. From the logical relationship of the event tree, the probability of different consequences can be obtained. The results show that the proposed risk assessment method based on Bayesian theory can overcome the shortcomings of traditional analysis methods. It shows the dynamic characteristics of the probability of different near misses, and achieves the dynamic risk analysis of chemical process accidents.     

基于系统分析原理对工业企业总体地震灾害的预测

以设备单体在一定地震裂度下的破坏概率为基础 ,应用相关的概率论知识和系统分析方法 ,根据系统的网络功能图 ,得到系统总体的震害预测。在此基础上 ,对系统的薄弱环节进行分析 ,从而为系统的抗震加固提供科学、合理的依据。此方法的建立使我国企业总体的震害预测和抗震加固变得科学、方便、可行。     

An optimized approach for estimating benzene in ambient air within an air quality monitoring network

Benzene is a carcinogenic air pollutant for which European legislation has set an annual limit and criteria for the number of fixed monitoring sites within air quality networks（AQMN）. However, due to the limited number of fixed sites for benzene measurement, exposure data are lacking. Considering the relationship between benzene levels and other variables monitored within an AQMN, such as NO₂, O₃, temperature, solar radiation, and accumulated precipitation, this study propo...     

Design and prediction for highly efficient SO2 capture from flue gas by imidazolium ionic liquids

An efficient method for prediction in the capture of SO2 from flue gas by imidazolium ionic liquids was reported, where the concentration of SO2 is 2000 ppm. On the basis of quantitative calculations through a combination of Langmuir simulation, theoretical calculation and quantum chemical method, SO2 absorption and desorption performance from flue gas by twelve kinds of imidazolium ionic liquids with different anions were designed and predicted. Then, among them, five kinds of imidazolium ionic liquids were chosen and prepared to investigate their behavior of SO2 absorption capacity, desorption residue, and available absorption capacity. The results indicated that the experimental values were in good agreement with the predicted values. Thus, an ideal ionic liquid [Emim][Tetz] was obtained through the predictive method for the capture of SO2 of 2000 ppm, which showed high available absorption capacity of 0.24 g SO2 per g ionic liquid and excellent reversibility.     

炼油厂污水处理场恶臭污染源调查及预测研究

主要对某炼油厂污水处理场恶臭污染源进行调查,确定出了重点污染源与重点污染物。以污水场曝气池为例,使用风洞采样器进行采样,三点比较式臭袋法测定恶臭浓度,确定面源源强。通过恶臭评估软件预测出一次浓度、日均浓度、夏季平均浓度和全年平均浓度,并对预测结果进行了验证。     

马尔柯夫预测模型在环境预测中的应用

马尔柯夫预测模型利用系统的现在状态和发展方向去预测系统将来可能状态的概率。它在选择污染治理策略，环境状态变化的可能方向及发展趋势预测等方面地产可应用，其预测结果为某事件实现的概率即可能程度。     

降雨型泥石流（水石流）预报模型研究

根据泥石流暴发具有某种周期性的特点，在获取了大量野外调查资料和历史资料的基础上，对降雨型泥石流进行了预报模型研究，并以北京市北部山区密云县为例作了泥石流预报尝试。以层次分析法和多元回归方法所得出的泥石流空间预报和时间预报模型对于泥石流的预报具有重要意义。     

Safety line method for the prediction of deep coal-seam gas pressure and its application in coal mines

Gas pressure is an important index for evaluating the outburst risk and determining the gas content in coal seams. It is recommended to predict coal-seam gas pressure of the workface at deep levels before extending mining activities to deeper levels. According to the prediction results, measurements are taken for gas outburst prevention and control and for workload estimation. At present, regression methods are always used to process the numerous gas pressure data for prediction. Because there are many factors that influence the gas pressure which could lead to a deviation from actual values, the measured data do not possess basic conditions for regression methods; this can cause unexpected dangers if the methods are adopted.Based on a statistical analysis of actual measured results of coal-seam gas pressure in a same geological section in certain coal mine, two symbol measured points are selected to make a line for prediction, i.e. safety line, and the other measured points should be below the line except the abnormal points due to the confined water. It has been successfully applied in numerous coal mines in China. Particularly, this method is analyzed in this paper for the case of the No. 8₂ coal seam in the Taoyuan coal mine in Huaibei coalfield, China. By comparatively analyzing the relationship between gas pressure and depth from surface using regression methods, it is found that the safety line method could lead to a better prediction for deep coal-seam gas pressure, and therefore promote early warning ability and mining safety.     

An ethical analysis of hydrometeorological prediction and decision making: The case of the 1997 Red River flood

Weather, climate, and flood predictions are incorporated into human decisions in a wide variety of situations, including decisions related to hazardous hydrometeorological events. This article examines ethical aspects of such predictions and decisions, focusing on the case of the 1997 Red River flood in Grand Forks, North Dakota and East Grand Forks, Minnesota (US). The analysis employs a formal ethical framework and analytical method derived from medical and business ethics. The results of the analysis highlight issues related to forecast generation, communication of forecast meaning and uncertainty, responsibility for the use of forecasts in decision making, and trade-offs between the desire for forecast certainty and the risk of missed events. Implications of the analysis for the broader arenas of weather, climate, and flood prediction and disaster management are also discussed.     

基于人工神经网络的城市火灾事故的预测方法

随着社会经济的飞速发展,城市化进程的加快和人口的迅速增长,城市火灾频繁发生,造成的损失呈上升趋势。针对城市火灾事故发生的特点,根据人工神经网络基本原理和特性,建立了城市火灾事故神经网络预测模型;为了更精确预测城市火灾事故的发生,将城市火灾事故分为了高峰期(春节)和非高峰期两个时段分别进行预测;应用神经网络预测模型和分时段相结合方法对某城市火灾事故进行了实际预测。结果表明,神经网络模型是城市火灾事故预测的有效工具,该模型与时段法的结合能准确预测火灾事故发生的趋势。