Scenario derivation and consequence evaluation of dust explosion accident based on dynamic Bayesian network

In order to clarify the correlation between the evolution path of dust explosion accidents and emergency decision-making, and to accurately predict the disaster damage levels of various disaster bearing bodies. This paper extracts 56 key scenario elements from four aspects, namely state, answer, goal and environment, based on the analysis of typical dust explosion accident cases. At the same time, a general scenario evolution path of dust explosion accident is constructed. Using fuzzy number set theory and dynamic Bayes joint probability model, the accurate solution of scenario state probability was realized. With the help of accident cases and dynamic Bayes approach, the dust explosion consequence prediction index system and evaluation criteria were constructed, covering factors such as dust explosion intensity, casualties, direct economic losses, equipment damage, building damage, environmental damage and other factors. A polyethylene wax dust explosion accident in a city of China was used to verify the dust explosion accident scenario evolution model and consequences prediction model. The predicted results were in good agreement with the actual damage of various carriers of the accident, which indicated that the model could be used for dust explosion accident prediction and disaster loss prediction. The research results provided reference and technical support for the prediction of dust explosion accident evolution direction, emergency aid measures decision and deployment, disaster damage prediction and evaluation.     

水质灰色预测模型在VBA中的实现

灰色预测是环境污染预测应用相当广泛的方法。根据文献报道,基于Matlab和Excel实现灰色预测中存在着占用内存、单元格填埋计算公式等等的不足,因此提出了一种基于VBA在Excel下编写宏程序轻松实现灰色预测的方法,并将其运用到北海市地表水污染物预测。结果表明,本程序简短,操作方便,计算结果准确可靠,彻底把用户从繁琐的手工操作中解放出来。     

Predicting reproduction rate of varroa

A single equation is derived to predict population-density effects on the reproduction rate of the honey bee parasite Varroa destructor Anderson and Trueman. This equation provides a simpler alternative to the approach currently used in the literature, and additionally corrects an anomaly in that approach. The method is then extended to the case of co-existing haplotypes of Varroa. It thus derives an equation used without proof for modelling biocontrol of Varroa, and examines the error caused by an approximation necessary for a closed form solution. Additionally a varroa population model incorporating the derived equation is described.     

东亚春季边界层臭氧的数值模拟研究

利用嵌套网格空气质量模式系统(NAQPMS)对2004年4月东亚边界层(距地面.km以下)臭氧进行了数值模拟.并评估了东亚边界层光化学反应的活性.结果表明.东亚春季臭氧呈带状分布,其高值.〔φ(O3)>55×10-9.主要集中在30°N～40°N.受东亚季风气候控制.沿蒙古、中国东北以及日本一线有一强臭氧输送通道.输送通量达3×10-.mol/(m2·s).通过计算边界层O3光化学净生成率可知.光化学影响主要集中在高污染源排放地区.其与水平输送对臭氧影响的分布具有负相关性.说明光化学生成的O3可被输送至下风地区,而不仅限于局地.      

PDE accident model from a safety information perspective and its application to Zhangjiakou fire and explosion accident

Accident models can provide theoretical frameworks for determining the causes and mechanisms of accidents, and thus are theoretical bases for accident analysis and prevention. The role of safety information in accident causation is profound. Thus, safety information is an important and essential perspective for developing accident models. This study presents a new accident model developed from a safety information perspective, called the Prediction—Decision—Execution (PDE) accident model. Because the PDE accident model is an emerging accident model that was proposed in 2018, its analysis logic and viability remain to be discussed. Thus, the main contributions of this study include two aspects: (i) detailed explanation of the analysis logic of the PDE accident model, and (ii) case-study examination of the Zhangjiakou fire and explosion accident, a serious accident that occurred in China in 2018, to demonstrate the viability of the PDE accident model. Results show that this is a safety-information-driven accident model that can provide a new and effective methodology for accident analysis and prevention, and safety management.     

一种基于数据融合的林火火线动态蔓延预测方法的研究

准确预测林火蔓延对于有效防治野火危害具有重要意义。传统林火蔓延速度场经验模型需要可燃物特性、坡度、温度及湿度等众多实测参数,由于参数均具有不同的时空分布,限制了模型的实际应用。因此提出速度场实时测量及水平集法模拟林火蔓延两种技术相结合的方法,通过测定火蔓延锋面的位置获取当前时刻锋面处的速度场分布并对此速度场在未燃烧区域进行延拓,结合水平集法预测林火蔓延,并使用数值模拟及实验数据验证了该方法能够有效预测短期内林火蔓延趋势,得出基于速度场的数据融合方法可满足实际林火蔓延建模的需求。     

离心风机辐射噪声的降噪量预测

本文运用LMS Testlab软件对某风机现场工作时的噪声进行了测试分析,得出风机工作时的噪声频谱特征;运用LMS TestLab Sound Diagnosis模块的声诊断技术对采集到的风机实时信号进行了降噪量预测,为后期的噪声治理提供依据。避免了噪声治理的盲目性,更经济合理。     

基于粗糙集和BP神经网络的流域水资源脆弱性预测研究——以淮河流域为例

水资源是一种重要的自然资源和经济资源,对其未来的脆弱性进行预测可以预估研究区未来的水安全状况,对其脆弱性问题做出预警,从而及时采取治理措施。因此,合理科学的水资源脆弱性预测研究是缓解水资源脆弱性的有效手段。目前,水资源脆弱性研究主要是针对水资源现状进行评价,对其未来状况的预测较少。集成了粗糙集和BP神经网络两种方法,首先采用改进了的盲目删除法对构建的流域水资源脆弱性评价指标体系进行约简,其次通过BP神经网络拟合约简后的指标数据与脆弱度之间的映射关系,构建流域水资源脆弱性评价预测模型。基于之前研究的样本数据和脆弱性结果,探讨淮河流域未来的水资源脆弱性状况。结果表明:淮河流域2015年、2020年和2025年的水资源脆弱度分别为0.305、0.359和0.390,处于轻度脆弱和中度脆弱的状况,除2015年脆弱性状况有所好转以外,2020年和2025年的水资源脆弱性程度与近几年相比有所加剧,根据指标数据可知该现象主要是受年降水量、人均用水量、万元GDP废水排放量、垦殖指数、有效灌溉面积比和干旱面积受灾比6个指标的影响,为避免水资源脆弱性的加剧,应当有针对性的加强这几个方面的管理和控制。     

市政脱水污泥的间接干化动力学研究

以食用油为热介质,在不同油浴温度下,对市政脱水污泥进行间接干化试验,推导出间接干化动力学模型,并对收集的冷凝液进行水质分析。结果表明:冷凝液为高浓度有机废水,TOC占TC的比例为73.2010%~75.5983%;VFAs的主要组分是乙酸、丙酸、丁酸、异丁酸、异戊酸和正戊酸,其中异丁酸所占比例最高(45.597%),其次为丙酸(24.554%);通过动力学模型的参数估值,Z模型(n≠1)因高R~2值和低RSS值而能很好的预测间接干化过程。     

In the mixed world of predictive biology—Contradiction or support

Statistical prediction is a tool and aim in ecology and wildlife management and conservation. A prediction may either be supported by or contradicted by observations of an unknown set of observations. A contradiction occurs if the prediction is not included within the range of the unknown observations, i.e. the prediction misses the cloud of observations completely. Mixed-effects models, frequently used for statistical assessment of clustered data, carry information needed for calculating the probability of such contradictions. Here we present a new versatile statistic, the probability of contradiction (P (Contra)), that describes how often we would anticipate a new cluster of observations contradicting our predictions. Some benefits of P (Contra) are: (1) easy to calculate and intuitive interpretation, (2) comparability between datasets, (3) inclusion of residual correlation, (4) summary of the multitude of information from mixed models into one statistics, and (5) applicable to local mixed-effect models.