潮汐河网区水污染总量控制及其分配方法

本文介绍了潮汐河网地区水污染总量控制的两种方法和水污染负荷总量分配的六种方法。对各种方法都建立了相应的数学模型,介绍了求解方法,并给出了各种方法的应用实例。      

感潮河网水域(广州河段)纵向离散系数的研究

感潮河网地区的污染物质混合输移的机制十分复杂,本文结合广州河段1987年枯水期的水文水质同步实测资料,利用理论分析,室内实验及计算机模拟识别等方法,对感潮河网地带的纵向离散系数进行了全面的研究。对天然河流的振荡剪切离散提出了一个实用的解析分析方法;通过实验研究分析了等效离散系数D*与振荡剪切离散系数D-的关系;利用实测数据分析了典型断面的D*/D-的基本特征,并通过计算机模拟,为广州河段提供了D*的预测公式。      

基于贝叶斯网络的给水管网消毒副产物生成因素分析

给水管网消毒副产物(disinfection byproducts,DBPs)的生成受管网环境因素、微生物群落特征和水厂未完全去除的有机物等多指标共同影响.各指标间相互关联形成复杂的网络结构,导致影响DBPs在管网内生成的主控因子较难确定.以广州某高校给水管网系统为研究对象,于2021年1～2月开展终端水质调查,利用吹...     

基于DBT-DBN模型的气化炉超温动态风险分析

为加强煤气化装置核心设备气化炉的安全风险管理,利用动态领结(DBT)模型和动态贝叶斯网络(DBN)相结合的风险分析方法,构建气化炉超温事故风险分析模型.首先,分析设备故障的时序性,建立超温事故的DBT模型,结合模糊评价确定设备故障的发生概率;然后,将DBT映射到DBN中,将故障维修的动态特征定义为转移概率,双向推理气化...     

建筑消防水系统灭火可靠性动态评估模型研究

水位、水压、流量、阀门开启情况等运行状态参数是决定消防水灭火系统灭火可靠性的关键因素,基于对水灭火系统运行可靠性影响因素的研究,构建了用于评估灭火可靠性的贝叶斯网络推理模型。推理模型将影响系统灭火可靠性的因素分为给水设施可靠性、灭火系统管网供水可靠性、灭火设施启动可靠性、阀门开启状态等四个方面。网络结构图代表了监测因子与子系统可靠性的关联关系。节点变量状态值的分级方法和先验概率值参考了水灭火系统相关设计标准。根据监测因子对子系统可靠性的影响分别设置了各节点的条件概率表。应用表明,通过物联网技术实时监测灭火系统运行状态参数,将实时的状态参数信息输入到评估模型,可以实现动态评估系统灭火可靠性。     

Source term estimation of natural gas leakage in utility tunnel by combining CFD and Bayesian inference method

As an effective way to construct and maintain various life pipelines in urban areas and industrial parks, the underground utility tunnel has been developed rapidly in China in recent years. However, the natural gas pipeline leakage in a utility tunnel may cause fire, explosion or other coupling disastrous accidents that could result in fatal consequences. The effective source term estimation (STE) of natural gas leakage can provide technical supports for emergency response during natural gas leakage accidents in utility tunnels. In this paper, a STE model with the combination of gas transport model, Bayesian inference and slice sampling method is proposed to estimate the source parameters of natural gas leakage in underground utility tunnels. The observed data can be integrated into the gas transport model and realize the inversion of natural gas leakage location and release rates. The parameter sensitivity analysis is presented to evaluate the robustness of the proposed model with good practicability, and the gas sensor layouts in the utility tunnel are analyzed and optimized. The spatio-temporal distribution of the leaked gas could be well predicted based on the estimation source parameters by the proposed STE model. The results show that the proposed model is an alternative and effective tool to provide technical supports for loss prevention and mitigation for natural gas leakage accidents in urban utility tunnels.     

基于Bayesian网络的建筑火灾疏散条件安全性评估方法*

为了对建筑火灾疏散条件安全性进行评估,基于Bayesian网络对疏散条件重要构成要素及评估方法逻辑推理过程进行研究探讨。结果表明:评估网络结构、根节点、中间节点及目标节点之间存在因果关联关系;研究得出根节点先验概率与量化节点条件概率表设定方法;Bayesian网络将风险评估与人工智能分析方法相结合,实现对建筑火灾疏散条件的安全性评估,并可用于识别高风险建筑。     

基于贝叶斯网络的物流无人机失效风险评估*

为降低城市物流无人机（UAV）失效坠落风险,通过考虑其运行环境和系统故障等因素的影响,以城市物流无人机运行数据为基础,从系统故障、运行环境和人为因素3方面提取失效诱因;分析物流无人机失效模式,并构建意外坠落事故的贝叶斯网络;基于所建网络和失效诱因发生概率分别计算不同工况下意外坠落事故及各中间事件概率,并基于网络拓扑结构展开反向推理,推演事故的主要失效诱因。结果表明:物流无人机正常运行时发生安全事故的概率为6.54×10-3;其中,电池电量不足、桨叶失效和电池故障是坠落事故的主要诱因,计算结果可为无人机运行安全风险防控提供依据。     

Statistical modeling of cargo tank truck crashes: Rollover and release of hazardous materials

Introduction: Cargo Tank Trucks (CTTs) are a primary surface transportation carrier of hazardous materials (hazmat) in the United States and CTT rollover crashes are the leading cause of injuries and fatalities from hazmat transportation incidents. CTTs are susceptible to rollover crashes because of their size, distribution of weight, a higher center of gravity, and the surging and sloshing of liquid cargo during transportation. This study identified and quantified the effects of various factors on the probability of rollover and release of hazmat in traffic crashes where a CTT was involved. Method: Bayesian Model Averaging (BMA)-based logistic regression models were estimated with rollover and hazmat release as the binary response variables, and crash, truck, roadway, environment, and driver characteristics as the explanatory variables. 2010–2016 police-reported CTT-involved crash data from Nebraska and Kansas was utilized. Receiver Operating Characteristic (ROC) curves confirmed appropriateness of the modeling approach for inference and prediction on the crash dataset. Results: CTTs are more likely to rollover in crashes while turning and changing lanes relative to going straight; side impacts (side collisions) and severe crosswinds increased the likelihood of rollovers; tractor and semi-trailer body style decreased the probability of rollover, while truck tractors are more prone to rollovers; collisions with fixed objects and higher posted speeds increased the rollover probability; rollovers and intersection crash locations increased the likelihood of hazmat release. Conclusions: The findings can assist stakeholders (policy-makers, private shippers, and CTT drivers) in restricting CTTs’ operations for safety; scheduling, routing, and fleet planning; and low-level decision-making (e.g., emergency stopping or local routing). Practical Applications: This study identified and quantified the effects of different factors on the conditional probability of rollover and release of hazmat in CTT-involved crashes. The findings may assist stakeholders in decision-making towards safe operations of CTTs for transportation of hazmat.     

A systems-based approach for modeling of microbiologically influenced corrosion implemented using static and dynamic Bayesian networks

Microbiologically influenced corrosion (MIC) is a microbial community assisted degradation of materials affecting chemical processing and oil and gas industries. MIC has been implicated in incidents involving loss of containment of hazardous hydrocarbons which have led to fires and explosions, economic and environmental impact. The interplay between abiotic environmental factors and dynamic biotic factors in MIC are poorly understood. There is a lack of mechanistic understanding of MIC and very few models are available to predict or assess MIC threat. Here we report on the development of a model to assess the susceptibility to MIC. The high-resolution model utilizes 60 independent nodes, including operational and historical failure analysis data, and is built by combining empirical relationships between the abiotic and biotic variables impacting MIC. Both static and dynamic Bayesian-network (BN) approaches were used to combine heuristic and quantitative states of variables to ultimately yield a susceptibility measure for MIC. A confidence-in-information metric was generated to reflect the amount of data used in the estimation. A susceptibility to MIC of 45%–60% was estimated by the model for ten different scenarios simulated using case-studies from literature. The susceptibility to MIC estimated by these scenarios was further interpreted in the context of these cases. This systems-based MIC model can be utilized as an independent estimator of susceptibility or can be incorporated as a sub-model within comprehensive safety threat assessment models currently utilized in industry.