基于HFACS-BN模型的危险化学品事故人因路径分析*

为提高危险化学品安全管理水平,收集我国134起以人为主因的危险化学品事故,构建危险化学品事故的HFACS-BN模型,并基于贝叶斯网络对危险化学品事故中的人因路径及各因素灵敏度进行分析。研究结果表明:行为违规是导致事故的最主要不安全行为;在复杂路径中,环境因素和操作者状态具有较高的灵敏度,与其相关的风险认知与处理不当是阻碍事故预防的关键因素;在非复杂路径中,操作者状态与运行计划不当具有较高的灵敏度,其所涉及的监督违规、组织过程和组织氛围,是导致危险化学品事故的根本起点,并可依此分析事故直接原因和间接原因;操作者状态在2种路径中均表现出高灵敏度,因此化工企业在人员管理上要加强关注职工状态,减少不安全行为的出现。     

城市燃气管网泄漏事故分析知识图谱构建及应用研究*

为深入认识燃气管网泄漏事故的发生发展机理,提高事故分析预测的自动化、智能化、数字化水平,利用知识图谱对燃气管网泄漏事故进行研究。在事故案例分析的基础上,从人-物-环-管的角度对燃气泄漏过程以及火灾爆炸次生事故的相关实体进行归纳梳理,对实体间的逻辑关系和非逻辑关系进行辨识,并对实体的属性进行分类,进而构建出较为全面的燃气管网泄漏事故知识图谱。在此基础上,搭建BP神经网络模型,基于已知实体或属性状态,预测相关联其他实体或属性的状态。研究结果表明:燃气管网知识图谱能够有效展示燃气管网泄漏事故发展的动态过程及相关要素,结合BP神经网络能够有效预测事故的发展路径及相关状态,从而提高燃气管网泄漏事故的分析预测水平与效率。     

基于多源数据融合的煤矿工作面瓦斯浓度预测*

为了解决瓦斯浓度预测使用的单一数据在预测中影响还不够深入的问题,提出基于LSTM神经网络的多源数据融合瓦斯浓度预测模型。模型将上隅角瓦斯浓度、采煤机速度、工作面吨煤瓦斯涌出量等不同数据融合作为输入层参数,使用Adam优化算法更新LSTM网络层参数,利用Attention机制突出关键影响瓦斯浓度的因素,开展多源数据融合的瓦斯浓度预测,结合某矿1008工作面的实际数据,分析不同数据在瓦斯浓度预测中的作用。研究结果表明:单变量下的Attention-aLSTM预测效果相比LSTM提升14.2%;多源数据融合下的Attention-aLSTM相比自身提升了5%。     

演马矿煤矸石堆周围环境中重金属分布特征

以焦作市演马矿煤矸石堆为对象,检测分析其周围土壤、地下水和植物中的重金属含量,并结合当地的地形地貌、土壤性质和地下水动力条件,研究煤矸石堆放与环境污染之间的联系. 根据地形地貌、地下水流向和风向,在演马矿煤矸石堆周围布置土壤、地下水和植物采样点,检测分析pH和重金属(如Pb,Mn,Zn,Cu,Cr和Cd)含量及分布规律. 结果表明:煤矸石堆放是造成周围环境污染的重要因素,煤矸石释放的重金属大部分被土壤所吸附,所检测的6种重金属在土壤中均被检出,其中部分点位的Zn和Cd含量已经超过土壤环境质量标准(GB15618-1995)中的二级标准;土壤中重金属可以进一步向地下水和植物传递,地下水中Mn含量与煤矸石堆的距离呈负相关,植物叶中Pb的含量最高,叶和茎中重金属含量表现出与煤矸石堆的相关性;矿坑排水、锅炉房飘尘也是造成周围环境重金属污染的重要来源.      

基于植被指数和神经网络的热带人工林地上蓄积量遥感估测

热带森林作为陆地生态系统的组成成分之一,研究其蓄积量估测对我们了解其在全球碳循环中的地位和作用有很重要的意义.但遥感估测森林生态参数的精度如何,还是个不确定的问题.利用LANDSAT-TM数据,基于森林清查数据和遥感技术,以尾叶桉和加勒比松为例,对中国南方地区人工林蓄积量估测进行了尝试研究.首先,通过测量样方胸径、树高,建立森林蓄积量估算模型.其次,通过对比分析不同植被指数与森林蓄积量之间的关系,选择合适植被指数组合,建立多元回归和神经网络模型.结果表明:单波段TM数据和大多数植被指数与蓄积量相关性并不好.神经网络比回归分析模拟效果好.而多元回归和神经网络模型大大提高预测精度.本研究方法对大面积的森林蓄积量估测具有一定的参考价值.     

煤矸石风化物不同粒级中重金属镉含量及其形态变化

对抚顺西露天矿煤矸石山表层煤矸石风化母岩进行多点取样,风化物筛分成>2 mm、2～0.25 mm和<0.25 mm等3个粒级,分析测定了矸石山表层母岩及其风化物不同粒级中Cd含量及其形态变化特征.结果表明:煤矸石母岩及其风化物中cd含量超过土壤环境质量2级标准,页岩、泥岩及其风化物pH值7.2～8.6,但燃后泥岩pH值4.9.碳质页岩2～0.25 mm粒级风化物中Cd损失最多,为73.6%;<0.25 mm细粒级Cd损失较少,为17.0%,这与细颗粒的表面吸附作用有关;泥岩及其各粒级风化物Cd元素不易损失,其Cd含量变化也相对较小;但燃后泥岩风化物中的Cd较容易损失.煤矸石风化物各样品类型中的Cd均以残渣态为主,可还原态和弱酸可提取态所占比例次之,可氧化态所占比例最小.弱酸可提取态Cd在细颗粒风化物中所占比例较高.     

The Reynolds transport theorem: Application to ecological compartment modeling and case study of ecosystem energetics

The Reynolds transport theorem (RTT) from mathematics and engineering has a rich history of success in mass transport dynamics and traditional thermodynamics. This paper introduces RTT as a complementary approach to traditional compartmental methods used in ecological modeling and network analysis. A universal system equation for a generic flow quantity is developed into a generic open-system differential expression for conservation of energy. Nonadiabatic systems are defined and incorporated into control volume (CV) and control surface (CS) perspectives of RTT where reductive assumptions in empirical data are then formally introduced, reviewed, and appropriately implemented. Compartment models are abstract, time-dependent systems of simultaneous differential equations describing storage and flow of conservative quantities between interconnected entities (the compartments). As such, they represent a set of flexible and somewhat informal, assumptions, definitions, algebraic manipulations, and graphical depictions subject to influence and selectively parsed expression by the modeler. In comparison, RTT compartment models are more rigorous and formal integro-differential equations and graphics initiated by the RTT universal system equation, forcing an ordered identification of simplifying assumptions, ending with clearly identified depictions of the transfer and transport of conservative substances in physical space and time. They are less abstract in the rigor of their equation development leaving less ambiguity to modeler discretion. They achieve greater consistency with other RTT compartment style models while possibly generating greater conformity with physical reality. Characteristics of the RTT approach are compared with those of a traditional compartment model of energy flow in an intertidal oyster-reef community.     

Invasive species impacts on ecosystem structure and function: A comparison of Oneida Lake, New York, USA, before and after zebra mussel invasion

Exotic species invasion is widely considered to affect ecosystem structure and function. Yet, few contemporary approaches can assess the effects of exotic species invasion at such an inclusive level. Our research presents one of the first attempts to examine the effects of an exotic species at the ecosystem level in a quantifiable manner. We used ecological network analysis (ENA) and a social network analysis (SNA) method called cohesion analysis to examine the effect of zebra mussel (Dreissena polymorpha) invasion on the Oneida Lake, New York, USA, food web. We used ENA to quantify ecosystem function through an analysis of food web carbon transfer that explicitly incorporated flow over all food web paths (direct and indirect). The cohesion analysis assessed ecosystem structure through an organization of food web members into subgroups of strongly interacting predators and prey. Our analysis detected effects of zebra mussel invasion throughout the entire Oneida Lake food web, including changes in trophic flow efficiency (i.e., carbon flow among trophic levels) and alterations of food web organization (i.e., paths of carbon flow) and ecosystem activity (i.e., total carbon flow). ENA indicated that zebra mussels altered food web function by shunting carbon from pelagic to benthic pathways, increasing dissipative flow loss, and decreasing ecosystem activity. SNA revealed the strength of zebra mussel perturbation as evidenced by a reorganization of food web subgroup structure, with a decrease in importance of pelagic pathways, a concomitant rise of benthic pathways, and a reorganization of interactions between top predator fish. Together, these analyses allowed for a holistic understanding of the effects of zebra mussel invasion on the Oneida Lake food web.     

Multiple-pattern parameter identification and uncertainty analysis approach for water quality modeling

This paper presents a multiple-pattern parameter identification and uncertainty analysis approach for robust water quality modeling using a neural network (NN) embedded genetic algorithm (GA). The modeling approach uses an adaptive NN–GA framework to inversely solve the governing equations in a water quality model for multiple parameter patterns, along with an alternating fitness method to maintain solution diversity. The procedure was demonstrated through a coupled 2D hydrodynamic and eutrophication model for Loch Raven Reservoir in Maryland. The inverse problem was formulated as a nonlinear optimization problem minimizing the degree of misfit (DOM) between model results and observed data. A set of NN models was developed to approximate the input-output response relationship of the Loch Raven Reservoir model and was incorporated into a GA framework in an adaptive fashion to search for near-optimal solutions minimizing the DOM. The numerical example showed that the adaptive NN–GA approach is capable of identifying multiple parameter patterns that reproduce the observed data equally well. The resulting parameter patterns were incorporated into the numerical model, and a multiple-pattern robust water quality modeling analysis, along with a compound margin of safety (CMOS) method, was proposed and applied to analyze the parameter pattern uncertainty.     

Effectiveness of Vegetation Surrogates for Parasitoid Wasps in Reserve Selection

Abstract:  Selecting suitable nature reserves is a continuing challenge in conservation, particularly for target groups that are time-consuming to survey, species rich, and extinction prone. One such group is the parasitoid Hymenoptera, which have been excluded from conservation planning. If basic characteristics of habitats or vegetation could be used as reliable surrogates of specific target taxa, this would greatly facilitate appropriate reserve selection. We identified a range of potential habitat indicators of the species richness of pimpline parasitoid communities (Hymenoptera: Ichneumonidae: Pimplinae, Diacritinae, Poemeniinae) and tested their efficiency at capturing the observed diversity in a group of small woodlands in the agricultural landscape of the Vale of York (United Kingdom). Eight of the 18 vegetation-based reserve-selection strategies were significantly better at parasitoid species inclusion than random selection of areas. The best strategy maximized richness of tree species over the entire reserve network through complementarity. This strategy omitted only 2–3 species more (out of 38 captured in the landscape as a whole) than selections derived from the parasitoid survey data. In general, strategies worked equally well at capturing species richness and rarity. Our results suggest that vegetation data as a surrogate for species richness could prove an informative tool in parasitoid conservation, but further work is needed to test how broadly applicable these indicators may be.