Fault detection and diagnostics of a three-phase separator

A high demand of oil products on daily basis requires oil processing plants to work with maximum efficiency. Oil, water and gas separation in a three-phase separator is one of the first operations that are performed after crude oil is extracted from an oil well. Failure of the components of the separator introduces the potential hazard of flammable materials being released into the environment. This can escalate to a fire or explosion. Such failures can also cause downtime for the oil processing plant since the separation process is essential to oil production. Fault detection and diagnostics techniques used in the oil and gas industry are typically threshold based alarm techniques. Observing the sensor readings solely allows only a late detection of faults on the separator which is a big deficiency of such a technique, since it causes the oil and gas processing plants to shut down.A fault detection and diagnostics methodology for three-phase separators based on Bayesian Belief Networks (BBN) is presented in this paper. The BBN models the propagation of oil, water and gas through the different sections of the separator and the interactions between component failure modes and process variables, such as level or flow monitored by sensors installed on the separator. The paper will report on the results of the study, when the BBNs are used to detect single and multiple failures, using sensor readings from a simulation model. The results indicated that the fault detection and diagnostics model was able to detect inconsistencies in sensor readings and link them to corresponding failure modes when single or multiple failures were present in the separator.     

Using a Bayesian network to clarify areas requiring research in a host–pathogen system

Bayesian network analyses can be used to interactively change the strength of effect of variables in a model to explore complex relationships in new ways. In doing so, they allow one to identify influential nodes that are not well studied empirically so that future research can be prioritized. We identified relationships in host and pathogen biology to examine disease‐driven declines of amphibians associated with amphibian chytrid fungus (Batrachochytrium dendrobatidis). We constructed a Bayesian network consisting of behavioral, genetic, physiological, and environmental variables that influence disease and used them to predict host population trends. We varied the impacts of specific variables in the model to reveal factors with the most influence on host population trend. The behavior of the nodes (the way in which the variables probabilistically responded to changes in states of the parents, which are the nodes or variables that directly influenced them in the graphical model) was consistent with published results. The frog population had a 49% probability of decline when all states were set at their original values, and this probability increased when body temperatures were cold, the immune system was not suppressing infection, and the ambient environment was conducive to growth of B. dendrobatidis. These findings suggest the construction of our model reflected the complex relationships characteristic of host–pathogen interactions. Changes to climatic variables alone did not strongly influence the probability of population decline, which suggests that climate interacts with other factors such as the capacity of the frog immune system to suppress disease. Changes to the adaptive immune system and disease reservoirs had a large effect on the population trend, but there was little empirical information available for model construction. Our model inputs can be used as a base to examine other systems, and our results show that such analyses are useful tools for reviewing existing literature, identifying links poorly supported by evidence, and understanding complexities in emerging infectious‐disease systems.     

山东半岛城市群人居环境质量综合评价

随着中国城市化的高速发展,"城市病"问题日益突出,城市群作为统筹区域协调发展、建设宜居人居环境的重点地区,其人居环境发展状况备受关注。基于此,本文以山东半岛城市群为例,从人文环境、经济环境、社会环境和生态环境4个方面构建人居环境质量综合评价指标体系,利用人居环境质量计算模型和系统协调度模型,对其人居环境质量进行静态定量化评价和动态综合性评价。研究表明:(1)山东半岛城市群人居环境质量与社会环境的关联性最大,经济实力、基础设施水平时空发展不均衡、地域差异大,是制约山东半岛城市群人居环境发展的突出问题。(2)2000—2014年山东半岛城市群人居环境综合水平呈现良好的上升趋势,且增速较稳,增幅较小;各要素间的协调性均达到了中级协调等级;系统层的评价值除人文环境外,均呈现不同程度的波动式上升态势;领域层的评价值差异明显,发展不均衡。(3)城市群内部各城市人居环境综合质量水平逐步提高,且排名相对稳定;各要素间的协调度等级差异较小,但起伏变化较大;人居环境质量时空分异特征明显,呈现东西高、中间低的格局,内部级别差异显著,可分为较好、一般、较差、差4个等级。在今后的发展中,应针对山东半岛不同城市人居环境问题采取因时因地制宜的协调对策,创造整体最优人居环境。不仅要提高城市群的整体经济水平,提升城市群的短板建设,推动人居环境协调发展,还要符合新常态下城市发展与建设的要求,加快山东半岛城市群的区域创新体系构建,科学引领产业转型升级,优化产业结构,加强山东半岛城市规划研究与实施力度,探索"多规合一"模式。     

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.     

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.     

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.     

航天器总装事故原因事件重要度分析

为解决传统安全性定量分析方法不能描述航天器总装这一复杂人-机-环系统的失效数据波动性和非严格逻辑关系的问题,结合生产实际,采用问卷调查的方法找出影响总装事故的风险因素,即导致事故的事件发生可能性、输入事件对输出事件的影响程度等。在验证调查数据的有效性之后,应用基于模糊数的模糊因果图(FCD),计算某航天器与支架车连接作业的事故风险可能性。提出原因事件重要度的计算方法。通过计算发现,人员注意力和发动机保护罩作用是导致发动机损伤的关键事件。与模糊事故树(FFT)、贝叶斯网络(BN)重要度计算结果对比表明,模糊重要度计算结果能反映事故发生可能性对原因事件发生可能性值的增减的敏感度。     

基于贝叶斯网络的航空维修人因可靠性分析

根据维修人为因素分析和分类扩展系统的框架选取影响因素,在航空维修领域应用贝叶斯网络进行人因可靠性分析,建立飞机维修效能模型,直观地表示影响因素与维修效能之间的关系。同时以目视检测为例,结合专家意见确定随机影响因素,通过专家访谈、事故报告、调查问卷、操作记录等渠道获取数据,得出条件概率表,进而建立目视检测表现模型,展示贝叶斯网络的建模流程。案例研究结果表明,组织文化、视觉信息、设备、疲劳、检测距离等因素对目视检测表现的影响非常显著,欲改善目视检测表现,必须对多影响因素进行综合管理。     

改进突变理论在井下排水方案优选中的应用

为了更精确合理地对新桥硫铁矿排水方案进行优选,对利用突变理论的方法进行优化,建立突变理论和熵权法相结合的改进模型。从经济和工程量方面出发,综合考虑了影响排水方案优选的3个层次的12个指标,统计样本数据。运用熵权法分层确定同一上层变量下各指标的客观权重,导入突变理论中,建立综合评价结构模型,消除了主观因素的影响。同时运用突变理论和EM-TOPSIS进行方案优选,通过对比3种方法的优选结果,证明了改进突变理论的可靠性与合理性。     

基于序关系法和熵值法的机场安全管理能力综合评价

为了客观科学地评价机场安全管理能力,研究了机场系统构成和安全管理概况,参考民航规章和专家咨询意见,确定安全管理能力的影响因素,建立了由4个准则层和23个具体指标组成的机场安全管理能力评价指标体系,结合实地调研结果及专家意见确定了各指标的测评方法和评分标准。根据序关系分析法和熵值法的特点,参考其他文献,提出了序关系分析法和熵值法相结合的、一种基于方差最大化的主客观赋权法来确定指标权重,构建了机场安全管理能力线性评价模型。结果表明,不同安全管理能力二级指标的变化对安全管理能力的影响程度不同,其综合权重大小降序排列为组织管理能力、人员安全能力、设备运行能力、环境保障能力。以长沙黄花国际机场为例进行评价,评价结果显示该模型具有可行性,能为机场安全管理工作提供参考。