Fire and risk analysis during loading and unloading operation in liquefied petroleum gas (LPG) bottling plant

The article focuses on analyzing risks associated with the gas transfer operation in a liquified petroleum gas (LPG) bottling plant in India. The transfer operations involve transferring liquified gas from the transport tanker to the underground storage tank. Due to the rapid expansion of the cities, many LPG bottling plants in India got surrounded by residential areas and business centers. Moreover, to maintain the supply chain, the frequency of the transfer operations at the bottling plant also increased. In this scenario, an accidental release of LPG during the transfer operation may lead to various consequences such as a pool fire, a fireball, and even a catastrophic rupture of the tank with a successive explosion of its contents. In the study, the operations involved in bottling plants are classified into different hazard zones and analyzed. The probability of occurrence of events leading to an accident is modeled using modeling tools such as ALOHA and PHAST. The consequences of an accident following various events, such as jet fire, fireball, etc., are modeled, and the simulation results are compared. The thermal radiation has been estimated as 4–40 kW/m², which could adversely affect the nearby population and could result in damaging plant machinery and equipment.     

Using data mining technology to explore causes of inaccurate reliability data and suggestions for maintenance management

Reliability data reflects equipment safety and provides a reference for setting inspection period, thereby serving as crucial information for the implementation of equipment integrity management policies. The calculation foundation of reliability data is maintenance records of adequate data quality. However, maintenance records of doubtful quality are common. Despite excluding poor quality recodes and using only the remaining maintenance recodes to calculate the reliability data, the calculated results generally lack a sufficient degree of confidence. This study applied data mining technology, including quality metrics, the association rule, and clustering, to explore the cause of low-quality maintenance data. The results revealed that the low data quality of maintenance records was due to ineffective maintenance policies, the low integrity of key system columns, nonadherence to the policy, and misunderstanding of column definitions. The proposed method successfully identified the causes of low-quality maintenance records. By incorporating the method into the function module of a CMMS, operators can equip the system with self-diagnosis, self-supervision, and continuous optimization functions.     

Heavy metal analysis in fish‐kill cases in rivers in Guipuzcoa (Spain)∗

Some fish‐kills in Basque rivers were studied by gill tissue analysis: Samples were wet digested and the solution was analyzed by atomic absorption spectroscopy. In three cases, the cause was linked to the effluents of an aluminium anodizing factory, cyanide caused one kill and copper wastes were related with another. Three cases were attributed to natural reasons and one of the kills was of unknown origin.     

Network-based exploration and visualisation of ecological data

Networks – structured graphs consisting of sets of nodes connected by edges – provide a rich framework for data visualisation and exploratory analyses. Although rarely used for the visualisation of ecological data, networks are well suited to this purpose, including data that one might not normally think of as a network. We present a simple method for transforming a data matrix into network format, and show how this can be used as the basis for interactive exploratory analyses of ecological data.The method is demonstrated using a database of marine zooplankton samples acquired in the Southern Ocean. The network analyses revealed zooplankton community structures that are in good agreement with previously published results. Variations in community structure were observed to be related to the temporal and spatial pattern of sampling, as well as to physical environmental factors such as sea ice cover. The analyses also revealed a number of errors in the data, including taxon identification errors and instrument failures.The method allows the analyst to generate networks from different combinations of variables in the data set, and to examine the effects of varying parameters such as the scales of spatial, temporal, and taxonomic aggregation. This flexibility allows the analyst to rapidly gain a number of perspectives on the data and provides a powerful mechanism for exploration.     

Determination of the optimum MM5 configuration for long term CMAQ simulations of aerosol bound pollutants in Europe

Realistic meteorological fields are a prerequisite for the determination of pollutant concentrations and depositions by means of a chemistry transport model. Different configurations of the 5th generation NCAR/Penn State University mesoscale meteorological model MM5 were tested to determine the optimum set up for long term hindcasts that cover several months up to years. Four dimensional data assimilation (FDDA) significantly enhances the spatio temporal representation of temperature, humidity and wind. Best agreement with radiosonde observations could be achieved when temperature, humidity and wind were grid nudged every 6 h. The quality of the resulting meteorological fields showed no significant systematic temporal or spatial variation over Europe in a model run of the year 2000. It was found that the hydrological cycle was not correctly reproduced by the model when no nudging was applied. The relevant model run showed too high relative humidity and too high rainfall when compared to observations. This led to considerably lower aerosol concentrations close to ground and a shift in the deposition patterns of particle bound pollutants like the carcinogenic benzo(a)pyrene (B(a)P). Guest Editor: Dr. S. T. Rao.     

中国木材产业发展的阶段识别及时空分异特征

论文基于产业生命周期理论及探索性空间数据分析方法,运用1954—2014年各省木材产量数据对中国木材产业的发展阶段以及时空格局变化规律展开了分析。研究表明：1） Gompertz模型显示木材产业处于产业生命周期的成长阶段;2）探索性空间数据分析显示木材产业具有明显的地域转移趋势和显著的区域聚集特征,尤其在2000年以后,南方集体林区逐渐形成了木材生产“热点”;3）根据重心模型显示的移动轨迹,可以将木材产业发展分为三个时期,即经济驱动时期、经济驱动与生态驱动相持时期、生态驱动时期,木材产业的时空格局演变是这两种驱动力量相对均衡的结果。根据研究结论,提出了重视森林资源培育、适时放开采伐限额、由行政监督向购买社会服务转变等促进木材产业发展的建议。     

Predation, scramble competition, and the vigilance group size effect in dark-eyed juncos (Junco hyemalis)

In socially feeding birds and mammals, as group size increases, individuals devote less time to scanning their environment and more time to feeding. This vigilance “group size effect” has long been attributed to the anti-predatory benefits of group living, but many investigators have suggested that this effect may be driven by scramble competition for limited food. We addressed this issue of causation by focusing on the way in which the scan durations of free-living dark-eyed juncos (Junco hyemalis) decrease with group size. We were particularly interested in vigilance scanning concomitant with the handling of food items, since a decrease in food handling times (i.e. scan durations) with increasing group size could theoretically be driven by scramble competition for limited food resources. However, we showed that food-handling scan durations decrease with group size in an environment with an effectively unlimited food supply. Furthermore, this food-handling effect was qualitatively similar to that observed in the duration of standard vigilance scans (scanning exclusive of food ingestion), and both responded to changes in the risk of predation (proximity of a refuge) as one might expect based upon anti-predator considerations. The group size effects in both food-handling and standard scan durations may reflect a lesser need for personal information about risk as group size increases. Scramble competition may influence vigilance in some circumstances, but demonstrating an effect of competition beyond that of predation may prove challenging. Received: 22 September 1998 / Received in revised form: 1 February 1999 / Accepted: 14 February 1999     

河流水环境有机污染物的自组织预测模型及应用

自组织预测模型是前苏联学者伊万年科提出的一种非线性建模预测方法,它能有效地解决复杂非线性系统的数据处理和建模问题。该文应用它进行水环境有机污染物预测,以实际监测数据为基础,建立了一个河流有机物浓度预测的自组织模型,其检验样本的预测误差在５％以内      

基于生态环境监测机构省级监督抽查工作的分析与思考

基于2019年度11个省级监督管理部门对704家生态环境监测机构开展的监督抽查工作情况,总结了此次监督抽查的概况、发现的问题、处罚情况,重点分析了存在的主要问题.提出了应持续强化监测质量控制和管理、积极借鉴和创新监管方式、注重监督抽查结果运用、加强社会监督和行业自律的工作建议.为生态环境监测机构提高监测质量和管理水平提...     

基于数据分析的地铁智能温控系统方案设计

针对目前地铁站及地铁车辆内部的环境温度控制,提出一种通过数据分析随环境变化情况进行自动调节的智能温度控制系统方案.智能温度控制系统采用闭环控制,核心温控采用PID控制.阐述系统的设计原理、架构、关键技术、实际应用中的工作模式,说明环境因素采集预算子系统、温度实时监测子系统、温度预警和温度调整子系统的系统功能.