Environmental Persistence of Chemicals

- DOI: http://dx.doi.org/10.1065/espr2006.01.008 Background The hazard criterion of persistence as it applies to chemicals in the environment is reviewed and discussed. This quantity can not be measured directly in the environment, thus it must be estimated using models that synthesise information on chemical half-lives and partitioning properties, the nature of the environment and how the chemical is released into the environment. Main Features It is suggested that the preferred criterion is the average residence time of the chemical in the environment, i.e. conceptually the sum of the life-times of all molecules (attributable only to losses by degrading reactions) divided by the number of molecules. If all chemical fate processes are first order, this persistence is independent of the quantity of chemical introduced and whether introduction is steady- or unsteady-state in nature. It is shown that in a multimedia environment persistence is affected not only by degradation kinetics, but also by mode-of-entry and partitioning. For screening level purposes a Level II equilibrium model may be adequate but a Level III model is generally preferable for estimating the average persistence. If a distribution of persistences is required a dynamic Level IV model must be used. Discussion The implications for regulating chemicals on the basis of persistence are discussed. Conclusion It is concluded that the preferred strategy is to use Level II, III, and IV models and that the use of only degradation kinetics or media-specific half-lives can be misleading and uneconomical.     

Modeling thermal analysis for predicting thermal hazards relevant to transportation safety and runaway reaction for 2,2′-azobis(isobutyronitrile)

The pure decomposition behavior of 2,2′-azobis (isobutyronitrile) (AIBN) and its physical phase transformation were examined and discussed. The thermal decomposition of this self-reactive azo compound was explored using differential scanning calorimetry (DSC) to elucidate the stages in the progress of this chemical reaction. DSC was used to predict the kinetic and process safety parameters, such as self-accelerating decomposition temperature (SADT), time to maximum reaction rate under adiabatic conditions (TMR_ad), and apparent activation energy (E_a), under isothermal and adiabatic conditions with thermal analysis models. Moreover, vent sizing package 2 (VSP2) was applied to examine the runaway reaction combined with simulation and experiments for thermal hazard assessment of AIBN. A thorough understanding of this reaction process can identify AIBN as a hazardous and vulnerable chemical during upset situations. The sublimation and melting of AIBN near its apparent onset decomposition temperature contributed to the initial steps of the reaction and explained the exothermic attributes of the peaks observed in the calorimetric investigation.     

连续起伏管道投产过程中的气相运移研究*

为应对山区液体管道在投产过程中可能出现的气阻、超压问题,从气相运移角度出发,建立液顶气模型,研究在1个U型单元内积气形成、压缩和破碎的全过程,在此基础上,提出连接各个U型单元的气相的传递函数,探讨背压累积因素下,连续起伏管道投产过程中各个U型管段的积气情况和压力的变化,进行动态的建模和计算。以国内某原油管道的现场投产数据与模型结果进行比对。结果表明:可以更加准确地预测山区液体管道投产过程中的气相传递和压力变化过程,能为未来连续起伏大落差液体管道投产的安全稳定运行提供理论指导和技术支持。     

基于改进灰色模型的管壁蜡沉积厚度预测研究

为对含蜡原油管道中的蜡沉积厚度进行准确预测,在函数cot(x2)变换的基础上,结合平移变换思想,利用cot(x2+c)变换建立新的改进GM(1,1)模型。以现场管道结蜡数据和室内环道结蜡数据为例,对比改进GM(1,1)模型、基于函数cot(x2)变换建立的GM(1,1)模型及传统GM(1,1)模型之间的预测精度,并分析平移量c对改进GM(1,1)模型预测精度的影响。结果表明:改进GM(1,1)模型的预测精度最高,其次是基于函数cot(x2)变换建立的GM(1,1)模型,而传统GM(1,1)模型的预测精度最低;随着平移量的增大,改进GM(1,1)模型的平均相对预测误差呈现出先减小后增大的趋势,因此合理的平移量有助于模型精度的提高。应用改进GM(1,1)模型来预测管道蜡沉积厚度是可行的,该方法可为含蜡原油管道蜡沉积厚度的准确预测提供参考和借鉴。     

Risk factors associated with truck-involved fatal crash severity: Analyzing their impact for different groups of truck drivers

Introduction: Fatal crashes that include at least one fatality of an occupant within 30 days of the crash cause large numbers of injured persons and property losses, especially when a truck is involved. Method: To better understand the underlying effects of truck-driver-related characteristics in fatal crashes, a five-year (from 2012 to 2016) dataset from the Fatality Analysis Reporting System (FARS) was used for analysis. Based on demographic attributes, driving violation behavior, crash histories, and conviction records of truck drivers, a latent class clustering analysis was applied to classify truck drivers into three groups, namely, ‘‘middle-aged and elderly drivers with low risk of driving violations and high historical crash records,” ‘‘drivers with high risk of driving violations and high historical crash records,” and ‘‘middle-aged drivers with no driving violations and conviction records.” Next, equivalent fatalities were used to scale fatal crash severities into three levels. Subsequently, a partial proportional odds (PPO) model for each driver group was developed to identify the risk factors associated with the crash severity. Results' Conclusions: The model estimation results showed that the risk factors, as well as their impacts on different driver groups, were different. Adverse weather conditions, rural areas, curved alignments, tractor-trailer units, heavier weights and various collision manners were significantly associated with the crash severities in all driver groups, whereas driving violation behaviors such as driving under the influence of alcohol or drugs, fatigue, or carelessness were significantly associated with the high-risk group only, and fewer risk factors and minor marginal effects were identified for the low-risk groups. Practical Applications: Corresponding countermeasures for specific truck driver groups are proposed. And drivers with high risk of driving violations and high historical crash records should be more concerned.     

钢筋混凝土旧工业厂房主体结构的改造加固风险研究

为探索钢筋混凝土旧工业厂房的改造与加固风险,进行了实地调研和基础理论研究,借助SPSS 22.0统计软件对32个二级指标进行筛选,确立6个方面29个指标的改造加固风险评价指标体系,运用结构方程模型（SEM）实证分析关键影响因素及其作用路径。研究结果表明:设计方案、结构特征和施工技术对改造加固风险具有影响;结构特征与设计方案之间的强关联形成了隐性且重要的风险路径。基于关键影响因素及因素间关联关系,对宝鸡市某U型厂房主体结构加固实例提出建议,为此类项目的改造施工提供理论依据。     

基于偏序集的采空区塌陷危险性评价研究

针对采空区塌陷危险性评价方法中的赋权争议问题,基于偏序集理论提出偏序集评价模型。首先阐明评价指标并确定其分级标准;然后应用偏序集评价模型得到Hasse图,并通过图展现的层集信息判别采空区塌陷危险程度;最后应用该模型对大宝山矿11个采空区的塌陷程度进行判别,评价结果准确合理。研究结果表明:该模型避免了以往研究中的赋权争议问题,克服了样本量不足致使模型无法应用的问题,而且能够应用更多的赋权方法识别排序的稳定程度,体现出样本间的分层信息。     

Corrosion failure probability analysis of buried gas pipelines based on subset simulation

Corrosion is the main reason for the failure of buried gas pipelines. For effective corrosion failure probability analysis, the structural reliability theory was adopted in this study to establish two calculation models for pipeline corrosion failure: the pressure failure model and von Mises stress failure model. Then, two calculation models for the corrosion failure probability were established based on a corrosion depth growth model obtained from actual survey data of soil corrosion characteristics. In an example, Monte Carlo simulation (MCS) and subset simulation (SS) were used to analyze the corrosion failure probability of pipelines, and the results were compared. SS can compensate for the shortcomings of MCS as it has higher computational efficiency and accuracy. Therefore, SS was adopted to simulate variations in the corrosion failure probability of buried pipelines with the service time for the two failure probability calculation models, which were applied to a natural gas pipeline located in a chemical industry park in Zhuhai, China. A sensitivity analysis was carried out on the relevant parameters that affect the failure probability. The results showed that multiple loads caused by the covering soil, residual stress, temperature differential, and bending stress have a non-negligible effect on the pipeline reliability. The corrosion coefficients gradually become the most important factors that affect the failure probability with increased service time. The proposed methodology considers the actual operating conditions of pipelines to provide a reliable theoretical basis for integrity management.     

Pipeline leakage detection and isolation: An integrated approach of statistical and wavelet feature extraction with multi-layer perceptron neural network (MLPNN)

Leakage diagnosis of hydrocarbon pipelines can prevent environmental and financial losses. This work proposes a novel method that not only detects the occurrence of a leakage fault, but also suggests its location and severity. The OLGA software is employed to provide the pipeline inlet pressure and outlet flow rates as the training data for the Fault Detection and Isolation (FDI) system. The FDI system is comprised of a Multi-Layer Perceptron Neural Network (MLPNN) classifier with various feature extraction methods including the statistical techniques, wavelet transform, and a fusion of both methods. Once different leakage scenarios are considered and the preprocessing methods are done, the proposed FDI system is applied to a 20-km pipeline in southern Iran (Goldkari-Binak pipeline) and a promising severity and location detectability (a correct classification rate of 92%) and a low False Alarm Rate (FAR) were achieved.     

Modelling outflow from a ruptured pipeline transporting compressed volatile liquids

The paper presents a mathematical model for predicting outflow rates from a ruptured pipeline transporting compressed volatile liquids. The main focus of the paper is the methodology used to predict thermodynamic properties of interest. The model is validated using experimental data in the open literature. As the field scale outflow data does not include typical operating conditions the model is further validated at higher pressures and longer pipelines by comparing outflow rates calculated using a commercial pipeline simulation package, PROFES. The mathematical model predictions of mass flow rate and pipeline inventory agree well with the measured data and the more sophisticated pipeline model.

The simple pipeline rupture model is a useful tool for consequence analysis as it has a fast runtime on a standard PC. A further advantage is it is more easily, without having to address all of the numerical issues that arise when using a more sophisticated pipeline model. This allows a safety engineer to focus on the potential hazard rather than driving the model.