A survey on gas leak detection and localization techniques

Gas leaks can cause major incidents resulting in both human injuries and financial losses. To avoid such situations, a considerable amount of effort has been devoted to the development of reliable techniques for detecting gas leakage. As knowing about the existence of a leak is not always enough to launch a corrective action, some of the leak detection techniques were designed to allow the possibility of locating the leak. The main purpose of this paper is to identify the state-of-the-art in leak detection and localization methods. Additionally we evaluate the capabilities of these techniques in order to identify the advantages and disadvantages of using each leak detection solution.     

A hybrid approach to faults detection and diagnosis in batch and semi-batch reactors by using EKF and neural network classifier

This work deals with a new hybrid approach for the detection and diagnosis of faults in different parts of fed-batch and batch reactors. In this paper, the fault detection method is based on the using of Extended Kalman Filter (EKF) and statistical test. The EKF is used to estimate on-line in added to the state of reactor the overall heat transfer coefficient (U). The diagnosis method is based on a probabilistic neural network classifier. The Inputs of the probabilistic classifier are the input–output measurements of reactor and the parameter U estimated by EKF, while the outputs of the classifier are fault types in reactor. This new approach is illustrated for simulated as well as experimental data sets using two cases of reactions: the first is the oxidation of sodium thiosulfate by hydrogen peroxide and the second is alkaline hydrolyse of ethyl benzoate in homogeneous hydro-alcoholic. Finally, the combination of the estimated parameter U using EKF and probabilistic neural network classifier provided the best results. These results show the performance of the proposed approach to monitoring the semi-batch and batch reactors.     

焦炉煤气泄漏、着火的处理对策

介绍了焦炉煤气生产工艺流程中的管网、设备发生煤气泄露、着火时的应急处理和灭火预案；根据焦炉煤气的理化性质、燃烧特性，进行了危险性分析，制定出发生煤气事故后的可行性处置对策。     

Probabilistic multiple model neural network based leak detection system: Experimental study

This paper presents an effective decision making system for leak detection based on multiple generalized linear models and clustering techniques. The training data for the proposed decision system is obtained by setting up an experimental pipeline fully operational distribution system. The system is also equipped with data logging for three variables; namely, inlet pressure, outlet pressure, and outlet flow. The experimental setup is designed such that multi-operational conditions of the distribution system, including multi pressure and multi flow can be obtained. We then statistically tested and showed that pressure and flow variables can be used as signature of leak under the designed multi-operational conditions. It is then shown that the detection of leakages based on the training and testing of the proposed multi model decision system with pre data clustering, under multi operational conditions produces better recognition rates in comparison to the training based on the single model approach. This decision system is then equipped with the estimation of confidence limits and a method is proposed for using these confidence limits for obtaining more robust leakage recognition results.     

Experimental study on leak detection and location for gas pipeline based on acoustic method

The leak of gas pipelines can be detected and located by the acoustic method. The technologies of recognizing and extracting wave characteristics are summarized in details in this paper, which is to distinguish leaking and disturbing signals from time and frequency domain. A high-pressure and long distance leak test loop is designed and established by similarity analysis with field transmission pipelines. The acoustic signals collected by sensors are de-noised by wavelet transform to eliminate the background noises, and time-frequency analysis is used to analyze the characteristics of frequency domain. The conclusion can be drawn that most acoustic signals are concentrated on the ranges of 0-100 Hz. The acoustic signal recognition and extraction methods are verified and compared with others and it proves that the disturbing signals can be efficiently removed by the analysis of time and frequency domain, while the new characteristics of the accumulative value difference, mean value difference and peak value difference of signals in adjacent intervals can detect the leak effectively and decrease the false alarm rate significantly. The formula for leak location is modified with consideration of the influences of temperature and pressure. The positioning accuracy can be significantly improved with relative error between 0.01% and 1.37%.     

Real-time leak detection in oil pipelines using an Inverse Transient Analysis model

Based on Inverse Transient Analysis (ITA) method, a real-time leak detection method is proposed to capture leak location and the associated leak rate in oil pipe conveyance systems. In the proposed approach, location and flow rate of leak (if any), the fluid properties, as well as physical parameters of the system, are calculated in consecutive periods through minimizing the discrepancy between the calculated and measured flow parameters of the system. The method of characteristics is employed to numerically calculate the transient responses of the system and the genetic algorithm is utilized as the optimization engine. The proposed approach was applied to several real pipeline systems in which the required transient flow data are either directly collected from the field or fabricated with a third-party numerical software. Extensive numerical explorations were conducted to investigate the performance of the proposed method in real-time leak detection and to determine the extent to which field data errors, stemming from Supervisory Control and Data Acquisition (SCADA) systems and measurement equipment, affect the leak flow rate and location detectability of the proposed approach. The results show that the proposed approach provides promising results under a variety of transient and steady-state flow conditions even in the case with small leak flow rate of around 2% of the line rate. The results also reveal that the noises in the measurement data and the errors originated from SCADA systems do not significantly compromise the leak detectability of the proposed approach, confirming that the proposed approach can be utilized in practice.     

如何对泄漏进行应急

有害物品的泄漏在任何时间、任何地点都有可能发生.例如,我们常见的机械设备的机油漏油,化学品在储存、运输或使用过程中发生的化学品泄漏,码头、海上发生的原油泄漏等.对泄漏如果处理不当或不及时,其结果可能会导致生产中断,产品报废,特别是对环境(包括空气、水体、土壤及地下水)造成严重污染,甚至危及到人类的健康与安全.随着人类对自身生存环境的日益重视,如何正确地对有害物的泄漏进行处理,便成了摆在人类面前的一个新的课题.     

A proactive approach to human error detection and identification in aviation and air traffic control

In recent years, there has been a realization that total elimination of human error may be difficult to achieve. A further reduction of accidents will require a better understanding of how practitioners manage their errors in ways that consequences are contained or mitigated. With this goal in mind, the present study has set out to propose a framework of cognitive strategies in error detection that would make human performance resilient to changes in work demands. The literature regarded error detection as a spontaneous process that occurs either while an action is executed (action-based detection) or after action feedback (outcome-based detection). To help practitioners maintain a state of mindfulness and introspection, this study proposes several cognitive strategies such as, rehearsing tasks for future execution, bringing into conscious attention routine tasks, seeing how trajectories change over time, and cross-checking data for reliability. Two further detection mechanisms are proposed at the situation assessment and planning stages of performance. Awareness-based detection may include revising a model of the situation, finding hidden assumptions, and testing the plausibility of assumptions. Planning-based detection addresses issues such as, identifying uncertainties in a plan, thinking out possible errors, and deciding when and how often to review task progress. Finally, several attitudinal factors and team factors are presented that affect the processes of error detection and identification. The cognitive strategies in error detection together with the attitudinal and team factors constitute a framework for designing the content of error management training.     

A wave change analysis (WCA) method for pipeline leak detection using Gaussian mixture model

This paper presents a novel pipeline leak detection scheme based on gradient and slope turns rejection (GSTR). Instead of monitoring the pipeline under constant working pressure, GSTR introduces a new testing method which obtains data during the transient periods of different working pressures. A novel pipeline leak detection method based on those transient data without failure history is proposed. Wavelet packet analysis (WPA) is applied to extract features which capture the dynamic characteristics from the non-stationary pressure data. Principal component analysis (PCA) is used to reduce the dimension of the feature space. Gaussian mixture model (GMM) is utilized to approximate the density distribution of the lower-dimensional feature space which consists of the major principal components. Bayesian information criterion (BIC) is used to determine the number of mixtures for the GMM and a density boosting method is applied to achieve better accuracy of the distribution estimation. An experimental case study for oil pipeline system is used as an example to validate the effectiveness of the proposed method.     

Model selection and fault detection approach based on Bayes decision theory: Application to changes detection problem in a distillation column

The fault detection of industrial processes is very important for increasing the safety, reliability and availability of the different components involved in the production scheme. In this paper, a fault detection (FD) method is developed for nonlinear systems. The main contribution consists in the design of this FD scheme through a combination of the Bayes theorem and a neural adaptive black-box identification for such systems. The performance of the proposed fault detection system has been tested on a real plant as a distillation column. The simplicity of the developed neural model of normal condition operation, under all regimes (i.e. steady-state and unsteady state), used in this case is realised by means of a NARX (Nonlinear Auto-Regressive with eXogenous input) model and by an experimental design. To show the effectiveness of proposed fault detection method, it was tested on a realistic fault of a distillation plant of laboratory scale.     

Fault detection approach for systems involving soft sensors

This paper develops a new approach for fault detection which involves soft sensors for process monitoring. Unlike existing approaches, which compare current measurements, or linear combinations thereof, to values of these measurements representing normal operations, the methodology presented here deals directly with the state estimates that need to be monitored. The advantage of such an approach is that the effect of abnormal process conditions on the state variables can be directly observed and that it is possible to include nonlinear relationships between measurements and states. At the same time, this type of approach has the drawback that the variances of the unmeasured states are not equal to the variances of the actual process variables due to the use of a soft sensor. However, for many popular soft sensor techniques, such as Kalman filters and related approaches, it is possible to compute variances of the predicted states that correspond to normal operating conditions. This paper presents a general framework for using soft sensors for process monitoring, i.e., soft sensor design and computation of the statistics that represent normal operating conditions, and illustrates this framework in three specific applications. It should be pointed out that the contribution of this work does not lie with the soft sensor design or the computation of the statistics itself as either part has individually already been addressed in the existing literature. However, the authors are not aware of any studies where both tasks are combined for process monitoring, which forms the contribution of this work.     

注水顶代处置液烃罐体的泄漏

液烃燃料罐泄漏酿成事故时,向罐内强制连续注水,使水占据罐内下部空间,使泄漏物低害或无害化,以得到紧急处理事故的时间,创造抢险的条件。本措施易于进行,可用于现场管道和设施的接口实施,亦可供罐区安全设计时参考。     

障碍物对房间人群疏散过程影响的宏观模拟研究

公共建筑内随机分布的障碍物可能对突发事件下密集人群疏散过程中人员速度、路径选择行为产生影响.考虑较为密集人群疏散过程与宏观流体流动过程的相似性,用连续性方程描述人群密度变化过程,结合障碍物对人员运动行为的影响,建立了考虑障碍物影响的人员疏散宏观模型.采用该模型计算重现了疏散出口附近的高密度人群拱形和椭圆形分布,定量分析了障碍物以不同概率分布于建筑内不同位置时对疏散过程中人员密度演化以及完成疏散所需时间的影响.     

民航维修人员习惯性违章行为的SD建模研究

掌握习惯性违章行为形成过程中的反馈机制和发展规律可以为消除习惯性违章行为提供理论依据。基于系统动力学(SD)理论,建立了民航机务维修人员习惯性违章行为的动态模型,利用VENSIM软件模拟仿真。结果表明,惩罚力度从第1等级(惩罚力度由小到大分别对应1~10等级)到第6等级时,习惯性违章行为活跃水平依次减小,从第6等级到第10等级时,习惯性违章行为活跃水平依次增加。此外习惯性违章行为活跃水平对工作单卡的合理程度敏感性最强,组织应优先提高工作单卡的合理程度。     

A classification tree based modeling approach for segment related crashes on multilane highways

Introduction

This study presents a classification tree based alternative to crash frequency analysis for analyzing crashes on mid-block segments of multilane arterials.

Method

The traditional approach of modeling counts of crashes that occur over a period of time works well for intersection crashes where each intersection itself provides a well-defined unit over which to aggregate the crash data. However, in the case of mid-block segments the crash frequency based approach requires segmentation of the arterial corridor into segments of arbitrary lengths. In this study we have used random samples of time, day of week, and location (i.e., milepost) combinations and compared them with the sample of crashes from the same arterial corridor. For crash and non-crash cases, geometric design/roadside and traffic characteristics were derived based on their milepost locations. The variables used in the analysis are non-event specific and therefore more relevant for roadway safety feature improvement programs. First classification tree model is a model comparing all crashes with the non-crash data and then four groups of crashes (rear-end, lane-change related, pedestrian, and single-vehicle/off-road crashes) are separately compared to the non-crash cases. The classification tree models provide a list of significant variables as well as a measure to classify crash from non-crash cases. ADT along with time of day/day of week are significantly related to all crash types with different groups of crashes being more likely to occur at different times.

Conclusions

From the classification performance of different models it was apparent that using non-event specific information may not be suitable for single vehicle/off-road crashes.

Impact on Industry

The study provides the safety analysis community an additional tool to assess safety without having to aggregate the corridor crash data over arbitrary segment lengths.     

Contribution of a systemic modeling approach applied to support risk analysis of a storage unit of chemical products in Morocco

This paper proposes a new systemic modeling approach using the unified modeling language (UML) as an operational tool to model a complex industrial system and analyzes its risks. This approach is presented as a modeling process divided into three phases corresponding to functional analysis, structural analysis and risk analysis. This study aims to formalize the interactions within an industrial system and to identify the abnormal situations which could generate risks. The application of this approach is demonstrated with an example of a storage unit of chemical products located in Morocco. This approach provides a comprehensive view that facilitates the understanding of the organization of an industrial system and leads to more effective analysis of its safety.     

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.     

Ignition characteristics, conditions of criticality and disappearance of criticality of cumene hydroperoxide reaction by modeling approach

The thermal explosion problem of cumene hydroperoxide exothermic reaction which is used in chemical industries for production of some chemical materials is investigated. The analytical solutions of the problem to determine the margin between ignition and non-ignition systems are presented. The solution offers different analytical expressions which relate between the critical parameters for both steady and unsteady-states in different planes of solutions for different cases. The numerical solutions in different planes offer different trajectories of solution as sub-critical (non-ignition) and supercritical (ignition). Also from the numerical solution the relations between the critical parameters are presented. The critical behaviors from both analytical and numerical solutions are concise and pertained the same results.