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

Ni, Lei; Jiang, Juncheng; Pan, Yong

doi:10.1016/j.jlp.2013.04.004

Cited by 60 publications

(21 citation statements)

References 17 publications

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“…Assuming that in a D dimensional search space, there is a population S = 12 ( , , , ) ( , , , ) g g gD P P P [14]. During each iteration, the particles update their velocity and position by individual extremum and swarm extremum, which are…”

Section: Pso Algorithm Study On Svm Temperature Compensation Of Liquimentioning

confidence: 99%

“…In order to better balance the global search and local search ability, the linear decreasing inertia weight is adopted here [13][14][15]. Four common linear inertia weight methods are as follows.…”

Section: Variable Weight Psomentioning

confidence: 99%

“…And 0 () k As can be seen from the table 1 and figure 3, when the inertia weight is a constant, the PSO algorithm has the fast convergence rate, but it is easy to get into local optimum and the accuracy is low; and the other variable weight PSO algorithms converge slightly slowly in the initial stage of the algorithm, but they will be strong in the latter search. In this way, it is advantageous to get the global optimal solution by jumping out of the local optimal solution, so as to improve the accuracy of the algorithm [14,15]. …”

Section: Variable Weight Psomentioning

confidence: 99%

See 2 more Smart Citations

Study on SVM Temperature Compensation of Liquid Ammonia Volumetric Flowmeter Based on Variable Weight Pso

Lin¹,

Ping²,

Gao³

et al. 2015

IJHT

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When the quality of liquid ammonia is measured by volumetric flowmeter, the traditional quadratic expression method can't meet the accuracy of temperature compensation in modern coal chemical industry. So the temperature compensation method by support vector machine (SVM) regression is presented, and kernel function parameter σ of SVM is optimized by variable weight particle swarm optimization (PSO). After the performance analysis and comparison in PSO, the suitable linear inertia weight method is selected. Experimental results show that the temperature compensation accuracy of the SVM method based on the variable weight PSO is significantly higher than that of the traditional quadratic expression method.

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Section: Pso Algorithm Study On Svm Temperature Compensation Of Liquimentioning

confidence: 99%

Section: Variable Weight Psomentioning

confidence: 99%

Section: Variable Weight Psomentioning

confidence: 99%

See 1 more Smart Citation

Study on SVM Temperature Compensation of Liquid Ammonia Volumetric Flowmeter Based on Variable Weight Pso

Lin¹,

Ping²,

Gao³

et al. 2015

IJHT

View full text Add to dashboard Cite

show abstract

“…Among these techniques, fiber optic sensors emerged as the most appropriate for long distance pipelines with no potential safety hazard compared with traditional electrical gauges. From the aspect of algorithms, the fluid transient state due to leakage occurrence is always analyzed, then the leak accidents can be identified, thus locating the leak point, in combination with signal processing techniques and mathematic analysis, including artificial neural network [7,8], support vector machine (SVM) [9,10], harmonic wavelet analysis [11], 2 of 13 etc. Until now, no integrated solution for leakage detection combining both advantages of the fiber sensor and advanced algorithm has been proposed in the literature.…”

Section: Introductionmentioning

confidence: 99%

“…Different leakage scenarios can be identified by some machine learning algorithms, like k nearest neighbor (kNN) [16], support vector machine [10], multi-layer perceptron neural network (MLPNN) [9], etc. The fundamental idea of these methods is to extract features from flow, pressure and temperature measurements at the inlet and outlet of the pipeline and then to train the classifier.…”

Section: Introductionmentioning

confidence: 99%

Pipeline Leak Localization Based on FBG Hoop Strain Sensors Combined with BP Neural Network

Jia

Ren

et al. 2018

Applied Sciences

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Featured Application: A pipeline leakage localization method using self-developed FBG hoop strain sensors combined with BP neural network is proposed and validated. Abstract: Pipelines function as blood vessels serving to bring life-necessities, so their safe usage is one of the foremost concerns. In our previous work, a fiber Bragg grating (FBG) hoop strain sensor with enhanced sensitivity was developed to measure the pressure drop induced by pipeline leakage. Some hoop strain information during the leakage transient process can be extracted from the amount of FBG hoop strain sensors set along the pipeline. In this paper, an integrated approach of a back-propagation (BP) neural network and hoop strain measurement is first proposed to locate the leak points of the pipeline. Five hoop strain variations are employed as input neurons to achieve pattern recognition so as to predict the leakage point. The RMS error can be as low as 1.01% when choosing appropriate hidden layer neurons. Furthermore, the influence of noise on the network's performance is investigated through superimposing Gaussian noise with a different level. The results demonstrate the feasibility and robustness of the neural network for pipeline leakage localization.

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A Review of Methodologies for Safety and Hazard Management in Chemical Industries

Chikhalikar

Jog

2018

ChemBioEng Reviews

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The growth in chemical industry has also led to the use of more hazardous chemicals, most often at extreme pressures and temperatures. This escalates the risk factors of a particular operation. Due to this, safety, risk assessment, and hazard management have gained utmost importance. Though a complete, full-proof mechanism is not achievable, efforts are made to minimize the hazard level with a comprehensive and exhaustive survey of the available methods and develop a superior safety mechanism. Various measures like risk assessment, hazard analysis, thermal safety analysis, and safety indices are in place and to be incorporated at the design stage to have inherently safer processes. The present paper reviews these safety indices, surveys several modern methods and techniques used in the modern chemical industries for ensuring safety, preventing hazards, and for risk assessment. Various methodologies and techniques are compared, categorized into qualitative, quantitative, and hybrid techniques. The current and future trends of developments in this field have also been reviewed. As a rigorous breakdown analysis of a disaster can help in designing new reliable safety measures, an exemplary case study is presented.

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

Cited by 60 publications

References 17 publications

Study on SVM Temperature Compensation of Liquid Ammonia Volumetric Flowmeter Based on Variable Weight Pso

Study on SVM Temperature Compensation of Liquid Ammonia Volumetric Flowmeter Based on Variable Weight Pso

Pipeline Leak Localization Based on FBG Hoop Strain Sensors Combined with BP Neural Network

A Review of Methodologies for Safety and Hazard Management in Chemical Industries

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