海潮引起的海底承压含水层系统中地下水位的波动——海底弱透水层的弹性储水效应

研究了由潜水含水层、在海底延伸的承压含水层和介于其间的弱透水层构成的滨海含水层系统中,弱透水层的弹性储水以及含水层的海潮荷载效应对承压含水层中的地下水位的影响.研究结果表明,当海底弱透水层延伸足够长时,它的弹性储水对承压含水层中的海潮引起的水头波动有增强作用.     

改性树脂NKZ对水溶性染料的吸附特性研究

根据聚合物絮凝剂与染料相互作用机理,用硝基胍对大孔型氯球改性得到了功能化吸附树脂NKZ.从热力学和动力学两方面研究了该树脂对5种水溶性染料的静态吸附特性.结果表明,树脂NKZ对水溶性染料的吸附具有很好的效果.     

流动危险源毒气泄漏事故伤害模型研究

笔者介绍了危险货物道路运输的现状和特点，阐述了进行流动危险源事故后果分析的必要性，提出了流动危险源运动时毒气泄漏事故伤害模型，并利用该模型进行了数值模拟，证明该模型分析流动危险源运动时毒气泄漏事故的后果是可行的。     

合肥"10·28"特大爆炸事故分析

本文对合肥"10·28"特大爆炸事故进行了分析,确定了本次爆炸事故的爆炸位置和类型,分析了爆炸混合气体来源及本次爆炸的特点.提出了今后为防止类似爆炸事故的发生,在设计规范标准、设计布置、施工和日常管理中应注意的问题.     

管道泄漏检测技术

管道运输作为一种经济、有效、环保的运输方式,在液体、气体运输中具有独特的优势,管道泄漏检测是管道运输安全运行的关键和重要保障.通过总结国内外管道泄漏检测的最新方法,按运输对象分类介绍了管道运输气体和液体时的检测方法和工作原理,分析其优缺点,提出管道运输泄漏检测的对策,为提高我国管道运输的安全生产管理水平做出有益的探讨.     

基于BP神经网络的苯储罐泄漏事故风险评价模型研究

为了在事故发生之前对苯储罐进行风险评价，提出1种基于BP神经网络的泄漏事故风险评价方法，利用该方法构建了苯储罐的风险评价模型，并对模型进行了训练及验证。研究结果表明:BP神经网络成功完成了建模任务，且模型训练结果较好，可利用基于BP神经网络所构建的苯泄漏事故风险评价模型对苯储罐发生泄漏事故的风险进行评价。     

磷酸银/树脂复合物的制备及其对亚甲基蓝的光催化降解

以聚乙烯吡咯烷酮(PVP)为稳定剂,采用化学沉积法制备了磷酸银/树脂复合物,降解亚甲基蓝,研究其光催化活性,并对样品进行了XRD和SEM表征。探讨了不同的光源、催化剂投加量、PVP含量、p H值等因素对亚甲基蓝降解率的影响。结果表明,在太阳光(夏天早上9点到11点的阳光)下,偏酸介质中,催化剂用量为0.8 g/L,PVP质量分数为0.01%的磷酸银/树脂复合物,液面高度为15 cm的条件下对亚甲基蓝有最佳降解效果。     

液化四氟乙烷小孔泄漏试验研究

采用高速摄影技术,考察了液化四氟乙烷发生小孔泄漏时,其水平泄漏和垂直泄漏的初始云团演化行为、泄漏的质量流率、喷射速度和喷射角,并与理论计算公式进行了对比。结果表明:水平喷射两相云团尾部出现涡流,涡流大幅加快了云团向空气中扩散的速率;垂直喷射的两相云团在地面形成液池,液池大幅增加了液化气体向空气中蒸发的速率。水平泄漏试验的喷射角与容器内超压变化规律相似,泄放初期喷射角逐渐增大,经历一段平坦期,到泄放末期喷射角减小。水平泄漏和垂直泄漏的初始喷射速度分别为25 m/s和20 m/s,与理论值26.6 m/s基本吻合。水平泄漏的质量流率的试验值和理论值分别为0.0598 kg/s和0.0684 kg/s,垂直泄漏的分别为0.0472 kg/s和0.059 6 kg/s,结果对比基本吻合,推荐的泄漏质量流率和小孔喷射速度公式可以用于液化四氟乙烷小孔泄漏。     

微波在废树脂中的穿透厚度研究

通过对不同功率微波在不同规格钢桶中对不同含水率的树脂中的穿透厚度的研究可知,微波在树脂中的穿透厚度主要与树脂自身含水率有关,与钢桶的规格尺寸也有一定的关系,而微波功率的影响则可基本忽略不计;对于含水率50％左右的树脂,微波在12L钢桶中的穿透厚度是130 mm;在200L钢桶中的穿透厚度是70mm.该研究成果对微波干燥工艺的具体参数的选取和扩大规模台架装置的设计研究等有重要意义.     

An integrated EDIB model for probabilistic risk analysis of natural gas pipeline leakage accidents

Natural gas pipeline construction is developing rapidly worldwide to meet the needs of international and domestic energy transportation. Meanwhile, leakage accidents occur to natural gas pipelines frequently due to mechanical failure, personal operation errors, etc., and induce huge economic property loss, environmental damages, and even casualties. However, few models have been developed to describe the evolution process of natural gas pipeline leakage accidents (NGPLA) and assess their corresponding consequences and influencing factors quantitatively. Therefore, this study aims to propose a comprehensive risk analysis model, named EDIB (ET-DEMATEL-ISM-BN) model, which can be employed to analyze the accident evolution process of NGPLA and conduct probabilistic risk assessments of NGPLA with the consideration of multiple influencing factors. In the proposed integrated model, event tree analysis (ET) is employed to analyze the evolution process of NGPLA before the influencing factors of accident evolution can be identified with the help of accident reports. Then, the combination of DEMATEL (Decision-making Trial and Evaluation Laboratory) and ISM (Interpretative Structural Modeling) is used to determine the relationship among accident evolution events of NGPLA and obtain a hierarchical network, which can be employed to support the construction of a Bayesian network (BN) model. The prior conditional probabilities of the BN model were determined based on the data analysis of 773 accident reports or expert judgment with the help of the Dempster-Shafer evidence theory. Finally, the developed BN model was used to conduct accident evolution scenario analysis and influencing factor sensitivity analysis with respect to secondary accidents (fire, vapor cloud explosion, and asphyxia or poisoning). The results show that ignition is the most critical influencing factor leading to secondary accidents. The occurrence time and occurrence location of NGPLA mainly affect the efficiency of emergency response and further influence the accident consequence. Meanwhile, the weight ranking of economic loss, environmental influence, and casualties on social influence is determined with respect to NGPLAs.