某储罐区火灾多米诺效应场景推演

化工园区中危险源众多,一旦发生事故很容易在整个园区内蔓延和发展.针对化工园区内储罐密集,容易引发连锁反应导致事故扩大的特点,利用FDS软件对储罐火灾场景进行数值模拟,根据储罐所受热辐射确定化工园区内储罐火灾最可能的事故发生序列,并引入基于设备失效前时间的机械设备故障概率模型对罐区内单个储罐的火灾风险进行研究,得到储罐区...     

Application of dynamic risk analysis in offshore drilling processes

Process safety is the common global language used to communicate the strategies of hazard identification, risk assessment and safety management. Process safety is identified as an integral part of process development and focuses on preventing and mitigating major process accidents such as fires, explosions, and toxic releases in process industries. Accident probability estimation is the most vital step to all quantitative risk assessment methods. Drilling process for oil is a hazardous operation and hence safety is one of the major concerns and is often measured in terms of risk. Dynamic risk assessment method is meant to reassess risk in terms of updating initial failure probabilities of events and safety barriers, as new information are made available during a specific operation. In this study, a Bayesian network model is developed to represent a well kick scenario. The concept of dynamic environment is incorporated by feeding the real-time failure probability values (observed at different time intervals) of safety barriers to the Bayesian network in order to obtain the corresponding time-dependent variations in kick consequences. This study reveals the importance of real-time monitoring of safety barrier performances and quantitatively shows the effect of deterioration of barrier performance on kick consequence probabilities. The Macondo blowout incident is used to demonstrate how early warnings in barrier probability variations could have been observed and adequately managed to prevent escalation to severe consequences.     

基于社会技术系统的复工企业疫情防控风险路径研究*

为对复工企业疫情防控中的风险致因因素进行分析,探寻复工企业疫情防控的风险路径,基于社会技术系统理论,建立宏观工效学模型,通过查阅并分析国家、地方政府、企业文件以及相关文献,从人员、技术、组织管理、内部环境及外部环境5个子系统出发,识别出16个复工企业疫情失控致因因素;运用社会网络分析（SNA）构建复工企业疫情防控关系网络,对各因素节点中心度进行计算,分析各节点在网络中的位置及影响程度;采用贝叶斯网络（BN）进行参数学习和推理学习,找出复工企业疫情失控的最大致因链。结果表明:复工企业疫情失控关系网络中,存在政府监督管理不力→防疫物资筹备不足→日常监管不到位,政府监督管理不力→ 防疫宣传、培训缺失→员工防疫知识欠缺,政府监督管理不力→防疫宣传、培训缺失→日常监管不到位3条最长风险路径。研究结果可帮助复工企业针对最长风险路径中的因素进行管理,从而有效地为复工企业防疫提供理论支持。     

A Bayesian network-based approach for the assessment and management of ageing in major hazard establishments

Currently, there is an increasing attention towards ageing of industrial equipment, as the phenomenon has been recognised as a cause of severe accidents, recorded in the last years in many process establishments. Recent studies described ageing through a number of key-factors affecting the phenomenon by accelerating or slowing it down. The Italian Competent Authority for the prevention of chemical accidents (Seveso III Directive) adopted a short-cut method, accounting for the assessment of these factors, to evaluate the adequateness of ageing management during inspections at Seveso sites. In this paper, a Bayesian Network was developed, by using the data gathered during the first application of the short-cut method, with the aim to verify the robustness of the approach for ageing assessment and the validity of the a priori assumptions used in assessing the key-factors. The structure of the Bayesian network was established by using experts’ knowledge, whereas the Counting Learning algorithm was adopted to execute the parameter learning by means of the software Netica. The results showed that this network could effectively explore the complex logical and uncertain relationships amongst factors affecting equipment ageing. Results of the present study were exploited to improve the short-cut method.     

基于CREAM和贝叶斯网络的空管人误概率预测方法

针对研究管制人因可靠性时存在的模糊性和片面性问题,采用认知可靠性与失误分析方法（CREAM）中的扩展预测法,计算10项管制通用任务的人误概率;在此基础上,以管制行为形成因子作为根节点构建贝叶斯网络,建立其与情景控制模式的不确定关系模型,对管制员在多任务中的人误概率进行预测。研究结果表明:在由相同评判者给出行为形成因子影响效应的前提下,由CREAM扩展预测法和构建贝叶斯网络的方法预测得到的多数任务的人误概率差异较大,从方法的客观性、合理性和适用性角度分析,贝叶斯网络在研究该问题时更具优势。     

Inverse modeling of Asian Rn flux using surface air Rn concentration

When used with an atmospheric transport model, the ²²²Rn flux distribution estimated in our previous study using soil transport theory caused underestimation of atmospheric ²²²Rn concentrations as compared with measurements in East Asia. In this study, we applied a Bayesian synthesis inverse method to produce revised estimates of the annual ²²²Rn flux density in Asia by using atmospheric ²²²Rn concentrations measured at seven sites in East Asia. The Bayesian synthesis inverse method requires a prior estimate of the flux distribution and its uncertainties. The atmospheric transport model MM5/HIRAT and our previous estimate of the ²²²Rn flux distribution as the prior value were used to generate new flux estimates for the eastern half of the Eurasian continent dividing into 10 regions.The ²²²Rn flux densities estimated using the Bayesian inversion technique were generally higher than the prior flux densities. The area-weighted average ²²²Rn flux density for Asia was estimated to be 33.0 mBq m⁻² s⁻¹, which is substantially higher than the prior value (16.7 mBq m⁻² s⁻¹). The estimated ²²²Rn flux densities decrease with increasing latitude as follows: Southeast Asia (36.7 mBq m⁻² s⁻¹); East Asia (28.6 mBq m⁻² s⁻¹) including China, Korean Peninsula and Japan; and Siberia (14.1 mBq m⁻² s⁻¹). Increase of the newly estimated fluxes in Southeast Asia, China, Japan, and the southern part of Eastern Siberia from the prior ones contributed most significantly to improved agreement of the model-calculated concentrations with the atmospheric measurements. The sensitivity analysis of prior flux errors and effects of locally exhaled ²²²Rn showed that the estimated fluxes in Northern and Central China, Korea, Japan, and the southern part of Eastern Siberia were robust, but that in Central Asia had a large uncertainty.     

Probability of intellectual disability is associated with soil concentrations of arsenic and lead

Background

The association between metals in water and soil and adverse child neurologic outcomes has focused on the singular effect of lead (Pb), mercury (Hg), and arsenic (As). This study describes the complex association between soil concentrations of As combined with Pb and the probability of intellectual disability (ID) in children.

Methods

We used a retrospective cohort design with 3988 mother child pairs who were insured by Medicaid and lived during pregnancy and early childhood in South Carolina between 1/1/97 and 12/31/02. The children were followed until 6/1/08, using computerized service files, to identify the diagnosis of ID in medical records and verified by either school placement or disability service records. The soil was sampled using a uniform grid and analyzed for eight metals. The metal concentrations were interpolated using Bayesian Kriging to estimate concentration at individual residences.

Results

The probability of ID increased for increasing concentrations of As and Pb in the soil. The Odds Ratio for ID, for one unit change in As was 1.130 (95% confidence interval 1.048-1.218) for Pb was 1.002 (95% confidence interval 1.000-1.004). We identified effect modification for the infants based on their birth weight for gestational age status and only infants who were normal size for their gestational age had increased probability of ID based on the As and Pb soil concentrations (OR for As at normal weight for gestational age = 1.151 (95% CI: 1.061-1.249) and OR for Pb at normal for gestational age = 1.002 (95% CI: 1.002-1.004)). For normal weight for gestational age children when As = 22 mg kg⁻¹ and Pb = 200 mg kg⁻¹ the risk for ID was 11% and when As = 22 mg kg⁻¹and Pb = 400 mg kg⁻¹ the probability of ID was 65%.

Conclusion

The probability of ID is significantly associated with the interaction between Pb and As for normal weight for gestational age infants.     

Predicting land cover using GIS, Bayesian and evolutionary algorithm methods

Modelling land cover change from existing land cover maps is a vital requirement for anyone wishing to understand how the landscape may change in the future. In order to test any land cover change model, existing data must be used. However, often it is not known which data should be applied to the problem, or whether relationships exist within and between complex datasets. Here we have developed and tested a model that applied evolutionary processes to Bayesian networks. The model was developed and tested on a dataset containing land cover information and environmental data, in order to show that decisions about which datasets should be used could be made automatically. Bayesian networks are amenable to evolutionary methods as they can be easily described using a binary string to which crossover and mutation operations can be applied. The method, developed to allow comparison with standard Bayesian network development software, was proved capable of carrying out a rapid and effective search of the space of possible networks in order to find an optimal or near-optimal solution for the selection of datasets that have causal links with one another. Comparison of land cover mapping in the North-East of Scotland was made with a commercial Bayesian software package, with the evolutionary method being shown to provide greater flexibility in its ability to adapt to incorporate/utilise available evidence/knowledge and develop effective and accurate network structures, at the cost of requiring additional computer programming skills. The dataset used to develop the models included GIS-based data taken from the Land Cover for Scotland 1988 (LCS88), Land Capability for Forestry (LCF), Land Capability for Agriculture (LCA), the soil map of Scotland and additional climatic variables.     

Reflections on the use of Bayesian belief networks for adaptive management

A broad range of tools are available for integrated water resource management (IWRM). In the EU research project NeWater, a hypothesis exists that IWRM cannot be realised unless current management regimes undergo a transition toward adaptive management (AM). This includes a structured process of learning, dealing with complexity, uncertainty etc. We assume that it is no longer enough for managers and tool researchers to understand the complexity and uncertainty of the outer natural system-the environment. It is just as important, to understand what goes on in the complex and uncertain participatory processes between the water managers, different stakeholders, authorities and researchers when a specific tool and process is used for environmental management. The paper revisits a case study carried out 2001-2004 where the tool Bayesian networks (BNs) was tested for groundwater management with full stakeholder involvement. With the participation of two researchers (the authors) and two water managers previously involved in the case study, a qualitative interview was prepared and carried out in June 2006. The aim of this ex-post evaluation was to capture and explore the water managers' experience with Bayesian belief networks when used for integrated and adaptive water management and provide a narrative approach for tool enhancement.     

Modeling the Effects of Potential Salinity Shifts on the Recovery of Striped Bass in the Savannah River Estuary,Georgia–South Carolina,United States

Increased salinity in spawning and nursery grounds in the Savannah River estuary was cited as the primary cause of a 97% decrease in adult striped bass (Morone saxatilis) and a concomitant 96% decrease in striped bass egg production. Restoration efforts focused on environmental remediation and stock enhancement have resulted in restored salinity patterns and increased egg and adult abundances. However, future water needs or harbor development may preclude further recovery by reducing freshwater inflow or increasing salinity intrusion. To assess the effect of potential changes in the salinity regime, we developed models relating discharge, tidal phase, and salinity to striped bass egg and early larval survival and re-cast these in a quantitative Bayesian belief network. The model indicated that a small upstream shift (≤1.67 km) in the salinity regime would have the least impact on striped bass early life history survival, whereas shifts >1.67 km would have progressively larger impacts, with a 8.33-km shift potentially reducing our estimated survival probability by >28%. Such an impact could have cumulative and long-term detrimental effects on the recovery of the Savannah River striped bass population. The available salinity data were collected during average and low flows, so our model represents some typical and some extreme conditions during a striped bass spawning season. Our model is a relatively simplistic, “first-order” attempt at evaluating potential effects of changes in the Savannah River estuarine salinity regime and points to areas of concern and potential future research.