Development of a new chemical process-industry accident database to assist in past accident analysis

Past accident analysis (PAA) is one of the most potent and oft-used exercises for gaining insights into the reasons why accidents occur in chemical process industry (CPI) and the damage they cause. PAA provides invaluable ‘wisdom of hindsight’ with which strategies to prevent accidents or cushion the impact of inevitable accidents can be developed.A number of databases maintain record of past accidents in CPI. The most comprehensive of the existing databases include Major Hazard Incident Data Service (MHIDAS), Major Accident Reporting System (MARS), and Failure and Accidents Technical Information Systems (FACTS). But each of these databases have some limitations. For example MHIDAS can be accessed only after paying a substantial fee. Moreover, as detailed in the paper, it is not infallible and has some inaccuracies. Other databases, besides having similar problems, are seldom confined to accidents in chemical process industries but also cover accidents from other domains such as nuclear power plants, construction industry, and natural disasters. This makes them difficult to use for PAA relating to CPI. Operational injuries not related to loss of containment, are also often included. Moreover, the detailing of events doesn’t follow a consistent pattern or classification; a good deal of relevant information is either missing or is misclassified.The present work is an attempt to develop a comprehensive open-source database to assist PAA. To this end, information on about 8000 accidents, available in different open-source clearing houses has been brought into a new database named by us PUPAD (Pondicherry University Process-industry Accident Database). Multiple and overlapping accident records have been carefully eliminated and a search engine has been developed for retrieval of the records on the basis of appropriate classification. PUPAD doesn’t aim to replace or substitute the well established databases such as MHIDAS and MARS but, rather, aims to compliment them.     

Investigation into effects of work-related quality of life and some related factors on cognitive failures among nurses

Objective. Cognitive failure is one of the factors which can be influenced by personal and professional characteristics. This research was carried out to study the effect of work-related quality of life (WRQoL) and some related factors on cognitive failures (CF) among nurses. Methods. This cross-sectional study was conducted among nurses working in intensive care units, critical care units and emergency units in 2014. Results. In total, 750 nurses participated in the study. The mean?±?SD for the total CF and WRQoL was 40.5?±?12.7 and 75.8?±?13.7 respectively. The results show that CF have a statistically significant difference among the age groups, experience groups and working units. Multiple regression tests show that age, income and WRQoL have a significant effect on CF. Based on the results, for a unit increase in WRQoL we expect a 0.26 unit decrease in CF. Analysis of variance results show that the emergency ward had changed the overall effect of WRQoL on CF, after the effect of WRQoL was controlled. Conclusions. Overall results from the present research indicated that, despite the high level of WRQoL among the studied nurses, the rate of CF was not at an appropriate level. Development of supportive and interventional strategies is highly recommended.     

环境因素对六氯苯厌氧降解活性的影响

在厌氧污泥具备了较好的六氯苯（HCB）降解能力的基础上,采用标准血清瓶实验方法考察了反应温度、初始pH、摇床转速和营养元素等环境因素对厌氧污泥降解HCB活性的影响。实验设计的反应温度为25、30、35和40℃;初始pH为5.0、6.0、7.0和8.0;摇床转速为0和150r·min-1;营养元素有葡萄糖和维生素b12。用HCB浓度随时间的减少来表征微生物对其的降解活性。结果表明,反应温度对HCB的降解速率有很大影响,适宜温度为35℃左右。初始pH对HCB降解活性没有显著影响,但显示出有随初始pH增大而HCB降解活性增加的趋势。与静置条件相比,一定的混合作用有利于提高HCB的降解活性。有葡萄糖存在条件下,维生素b12对HCB的降解活性有明显的促进作用。温度及外加碳源（葡萄糖）是影响HCB厌氧降解活性的重要因素,并推测葡萄糖在HCB降解过程中提供电子供体方面发挥了重要作用。     

Assessment of four turbulence models in simulation of large-scale pool fires in the presence of wind using computational fluid dynamics (CFD)

Pool fires are the most common of all process industry accidents. Pool fires often trigger explosions which may result in more fires, causing huge losses of life and property. Since both the risk and the frequency of occurrence of pool fires are high, it is necessary to model the risks associated with pool fires so as to correctly predict the behavior of such fires.Among the parameters which determine the overall structure of a pool fire, the most important is turbulence. It determines the extent of interaction of various parameters, including combustion, wind velocity, and entrainment of the ambient air. Of the various approaches capable of modeling the turbulence associated with pool fires, computational fluid dynamics (CFD) has emerged as the most preferred due to its ability to enable closer approximation of the underlying physical phenomena.A review of the state of the art reveals that although various turbulence models exist for the simulation of pool fire no single study has compared the performance of various turbulence models in modeling pool fires. To cover this knowledge-gap an attempt has been made to employ CFD in the assessment of pool fires and find the turbulence model which is able to simulate pool fires most faithfully. The performance of the standard k–? model, renormalization group (RNG) k–? model, realizable k–? model and standard k–ω model were studied for simulating the experiments conducted earlier by Chatris et al. (2001) and Casal (2013). The results reveal that the standard k–? model enabled the closest CFD simulation of the experimental results.     

自适应神经网络在确定落煤残存瓦斯量中的应用

落煤残存瓦斯量的确定是采掘工作面瓦斯涌出量预测的重要环节，它直接影响着采掘工作面瓦斯涌出量预测的精度，并与煤的变质程度、落煤粒度，原始瓦斯含量、暴露时间等影响因素呈非线性关系，人工神经网络具有表示任意非线性关系和学习的能力，是解决复杂非线性，不确定性和时变性问题的新思想和新方法，基于此，作提出自适应神经网络的落煤残丰瓦斯量预测模型，并结合不同矿井落煤残存瓦斯量的实际测定结果进行验证研究，结果表明，自适应调整权值的变步长BP神经网络模型预测精度高，收敛速度快，该预测模型的应用可为采掘工作面瓦斯涌出量的动态预测提供可靠的基础数据，为采掘工作面落煤残存瓦斯量的确定提出了一种全新的方法和思路。     

A criterion for developing credible accident scenarios for risk assessment

Maximum credible accident analysis is one of the most widely used concepts in risk assessment of chemical process industries. Central to this concept is the aspect of ‘credibility’ of envisaged accident scenarios. However, thus far the term credibility is mostly treated qualitatively, based on the subjective judgement of the concerned analysts. This causes wide variation in the results of the studies conducted on the same industrial unit by different analysts.

This paper presents an attempt to develop a criterion using which credible accident scenarios may be identified from among a large number of possibilities. The credible scenarios thus identified may then be processed for detailed consequence analysis. This would help in reducing the cost of the analysis and prevent undue emphasis on less credible scenarios at the expense of more credible ones.     

Statistical Estimation and Model Selection of Species-Accumulation Functions

Abstract:  Soberón and Llorente (1993) proposed pure-birth stochastic processes as theoretical models for species-accumulation curves, and these processes have frequently been used to describe the progress of biological inventories. We describe, in algorithmic form, an alternative statistical analysis based on a likelihood approach ( Díaz-Francés & Gorostiza 2002 ) that provides mathematical rigor to the ideas in Soberón and Llorente (1993) and improves the estimation of the models by incorporating the facts that the variance of the error is not constant and that the observations are correlated. Additionally, we used the likelihood ratios between candidate models as an objective procedure for model selection, allowing comparison between the goodness of fit of various models. The software for these statistical methods can now be downloaded off the Internet. We used two examples of butterfly data sets to illustrate the use of the methods and the software.     

基于MODIS植被指数的浑善达克沙地植被物候变化

基于近20a MODIS的2种植被指数数据，利用Logistic曲线曲率极值法和动态阈值法，对浑善达克沙地植被物候进行了提取，分析其时空变化，并利用研究结果数据，分析植被物候对高程和气象因子的响应.结果表明，2000~2019年间研究区的植被物候呈微弱波动趋势，浑善达克沙地植被返青期（SOS）主要集中在110~140d，枯黄期（EOS）主要集中在250~280d，整体呈微弱推迟趋向（0.28d/a），生长季长度（LOS）主要集中在120~170d，整体呈微弱延长趋向（0.23d/a）.在空间分布上，占研究区51.51%的区域植被SOS呈提前趋势；占研究区67.02%的区域植被EOS呈提前趋势，并且占研究区32.98%的区域植被EOS呈推迟趋势.占研究区62.71%的区域植被LOS呈延长趋势.在海拔900~1500m区间，随着海拔升高，SOS显著推迟，EOS不显著提前，LOS显著缩短.前一年11、12月和当年1、2、4月降水量对SOS有提前作用.6、7、8、9月份气温和降水都对EOS推迟有明显的作用.总的来说，浑善达克沙地植被物候与气候因子的相应规律比较复杂，表现出季节性的差别及地形的差异性.