Methods and models in process safety and risk management: Past, present and future

Pipelines are widely used for the transportation of hydrocarbon fluids over millions of miles all over the world. The structures of the pipelines are designed to withstand several environmental loading conditions to ensure safe and reliable distribution from point of production to the shore or distribution depot. However, leaks in pipeline networks are one of the major causes of innumerable losses in pipeline operators and nature. Incidents of pipeline failure can result in serious ecological disasters, human casualties and financial loss. In order to avoid such menace and maintain safe and reliable pipeline infrastructure, substantial research efforts have been devoted to implementing pipeline leak detection and localisation using different approaches. This paper discusses pipeline leakage detection technologies and summarises the state-of-the-art achievements. Different leakage detection and localisation in pipeline systems are reviewed and their strengths and weaknesses are highlighted. Comparative performance analysis is performed to provide a guide in determining which leak detection method is appropriate for particular operating settings. In addition, research gaps and open issues for development of reliable pipeline leakage detection systems are discussed.

Recent Advances in Pipeline Monitoring and Oil Leakage Detection Technologies: Principles and Approaches

Pipeline corrosion and leakage monitoring based on the distributed optical fiber sensing technology

This paper provides an overview of the support vector machine SVM methodology and its applicability to real-world engineering problems. Specifically, the aim of this study is to review the current state of the SVM technique, and to show some of its latest successful results in real-world problems present in different engineering fields. The paper starts by reviewing the main basic concepts of SVMs and kernel methods. Kernel theory, SVMs, support vector regression SVR, and SVM in signal processing and hybridization of SVMs with meta-heuristics are fully described in the first part of this paper. The adoption of SVMs in engineering is nowadays a fact. As we illustrate in this paper, SVMs can handle high-dimensional, heterogeneous and scarcely labeled datasets very efficiently, and it can be also successfully tailored to particular applications. The second part of this review is devoted to different case studies in engineering problems, where the application of the SVM methodology has led to excellent results. First, we discuss the application of SVR algorithms in two renewable energy problems: the wind speed prediction from measurements in neighbor stations and the wind speed reconstruction using synoptic-pressure data. The application of SVMs in noninvasive cardiac indices estimation is described next, and results obtained there are presented. The application of SVMs in problems of functional magnetic resonance imaging fMRI data processing is further discussed in the paper: brain decoding and mental disorder characterization. The following application deals with antenna array processing, namely SVMs for spatial nonlinear beamforming, and the SVM application in a problem of arrival angle detection. Finally, the application of SVMs to remote sensing image classification and target detection problems closes this review. WIREs Data Mining Knowl Discov 2014, 4:234-267. doi: 10.1002/widm.1125

Support vector machines in engineering: an overview

The effects of large scale perturbation-generating obstacles on the propagation of detonation filled with methane–oxygen mixture

An experimental apparatus for the methane and air mixture explosion with N2/CO2 in a closed vessel was set up. A series of experiments have been performed to analyze the effects of N2/CO2 on explosion strength, limiting oxygen concentration, flammability limits and explosion suppression. Results show that explosion strength decreases with the volume fraction of N2/CO2 in the mixture. Limiting oxygen concentration decreases linearly with the volume fraction of N2 in N2/CO2 mixture. The flammability limits change linearly with the volume fraction of N2/CO2 in the mixture. The higher the volume fraction of CO2 in N2/CO2 mixture, the better the explosion suppression effect is. Based on experimental data, fitted equations and diagrams are made to predict methane flammability limits with N2/CO2 under different volume fraction ratio of N2 and CO2. The research results can be used as a reference for the inerting treatment design of methane explosion protection in process industries.

Effects of N2/CO2 on explosion characteristics of methane and air mixture

An improved variational mode decomposition method based on particle swarm optimization for leak detection of liquid pipelines

An improved and integrated approach of support vector machine and particle swarm optimization theory (PSO-SVM) is first used to detect the leak location of pipelines and overcome the problem of multiple leaks. The calibration and predictive ability of improved PSO-SVM is investigated and compared with that of other common method, back-propagation neural network (BPNN). Two conditions are evaluated. One with a leak involves a set of 20 samples, while another with two leaks has 127 samples. Both internal and external validations are performed to validate the performance of the resulting models. The results show that, for the two conditions, the values calculated by improved PSO-SVM are in good agreement with those simulated by transient model, and the performances of improved PSO-SVM models are superior to those of BPNN. This paper provides a new and effective method to inspect the multiple leak locations, and also reveals that improved PSO-SVM can be used as a powerful tool for studying the leak of pipeline.

Leak location of pipelines based on transient model and PSO-SVM

Effect of rubber washers on leak location for assembled pressurized liquid pipeline based on negative pressure wave method

The leakage of oil/gas pipelines is one of the major factors to influence the safe operation of pipelines. So it is significant to detect and locate the exact pipeline leakage. A novel leak location method based on characteristic entropy is proposed to extract the input feature vectors. In this approach, the combination of wavelet packet and information entropy is called “wavelet packet characteristic entropy” (WP-CE). The combination of empirical mode decomposition and information entropy is called “empirical mode decomposition characteristic entropy” (EMD-CE). Both pressure signal and flow signal of low noise and high noise of pipeline leakage are decomposed to extract the characteristic entropy. The location of pipeline leak is determined by the combination of the characteristic entropy as the input vector and particle swarm optimization and support vector machine method (PSO-SVM). The results of proposed leak location method are compared with those of PSO-SVM based on physical parameters. Under the condition of high noise, the results of proposed leak location method are better than those of PSO-SVM based on physical parameters.

Lei Ni

Papers

Effects of N2/CO2 on explosion characteristics of methane and air mixture

An improved variational mode decomposition method based on particle swarm optimization for leak detection of liquid pipelines

Leak location of pipelines based on transient model and PSO-SVM

Effect of rubber washers on leak location for assembled pressurized liquid pipeline based on negative pressure wave method

Leak location of pipelines based on characteristic entropy