Rank Predictions of Internal Corrosion of Gathering Pipelines in a Natural Gas Field With a Multi-Kernel SVM Method

Li, Yuejiao; Zeng, Weiguo; Li, Xiufeng; Ren, Fajun; Hu, Haijun

doi:10.1115/pvp2020-21333

Cited by 3 publications

(2 citation statements)

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“…At the same time, with the increase of the service life, the anticorrosion coating is aging, cracking, and even peeling, and the soil corrosive medium is immersed, thus providing conditions for the occurrence and development of corrosion, and the possibility of corrosion of the pipeline is greatly increased, increasing the unsafe operation of the pipeline. In addition, due to the poor management of pipeline operation, the pipe body bears too much load, so that the pipeline is "tired into a disease" for a long time, which increases the danger of pipeline operation [8][9][10][11]. Terefore, with the increase in the service life of buried long-distance pipelines, the possibility of accidents such as perforation, leakage, and cracking has also increased, causing huge economic losses to the production and operation management departments and bringing very serious impacts on social life and the ecological environment.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Chemical Corrosion Rate and Remaining Life of Buried Oil and Gas Pipelines in Changqing Gas Field

Li¹,

Li²,

Ma³

et al. 2023

Journal of Chemistry

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Green synthesis and metal oxide composites have attracted much attention from researchers of industry and academia. As a typical application of green synthesis and metal oxide composites, the continuous change of industrial technology and the continuous improvement of the social and economic level, the demand for oil and gas are also increasing. However, the spatial gap between the place of origin and the place of demand for oil and gas resources is large, so the long-distance oil and gas pipeline came into being. However, under the action of time, coupled with the corrosion effect of the soil due to deep burial, some pipelines have serious aging and corrosion phenomena. Therefore, in order to give corresponding guarantees for economic development, we need to conduct in-depth research and analysis of the corrosion of oil and gas long-distance pipelines and give effective solutions. In this paper, the corrosion rate prediction of buried oil and gas pipelines is studied in Changqing gas field. By improving the inertial weights and learning factors of the traditional particle swarm algorithm, the parameters of the generalized regression neural network are optimized and selected, and the corrosion rate prediction model of buried pipelines is finally constructed. Comparative analysis with other swarm intelligence algorithms shows that the improved particle swarm algorithm has stronger convergence ability and higher prediction accuracy than the BP model and SVM model. In addition, based on the detection data collected at the site of the gathering and transportation pipeline in Changqing gas field, this paper uses the extreme value distribution theory and the local corrosion progress formula to establish a prediction model for the residual life of corrosion of buried pipelines. The model established in this paper can effectively determine the risk pipe segment of buried pipeline and provide a decision-making basis for pipeline management departments. The work provides an important application guidance to green synthesis and metal oxide composites.

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Section: Introductionmentioning

confidence: 99%

Prediction of Chemical Corrosion Rate and Remaining Life of Buried Oil and Gas Pipelines in Changqing Gas Field

Li¹,

Li²,

Ma³

et al. 2023

Journal of Chemistry

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“…The stack pointer will start to check the status of the signal to get correlated with the web interface javascript codes to fill up in the MySQL database. 40 The conversion rate is around 54.5 s which is lesser than the sampling and quantization timing frame time. So, the latency and traffic congestion rate will be reduced automatically.…”

Section: Cloud Associated Monitoring Parameter Database Management Fr...mentioning

confidence: 99%

Rank-based risk target data analysis using digital twin on oil pipeline network based on manifold learning

Priyanka

Thangavel

Prabhakaran

2022

Proceedings of the Institution of Mechanical Engineers, Part E:

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Oil and Gas Pipeline (OGP) projects face a wide scope of wellbeing and security Risk Factors (RFs) all around the world, especially in the oil and gas delivering nations having influencing climate and unsampled data. Lacking data about the reasons for pipeline risk predictor and unstructured data about the security of the OGP prevent endeavors of moderating such dangers. This paper, subsequently, means to foster a risk analyzing framework in view of a comprehensive methodology of recognizing, dissecting and positioning the related RFs, and assessing the conceivable pipeline characteristics. Hazard Mitigation Methods (HMMs), which are the initial steps of this approach. A new methodology has been created to direct disappointment investigation of pinhole erosion in pipelines utilizing the typical pipeline risk strategy and erosion climate reenactments during a full life pattern of the pipeline. Hence in the proposed work, manifold learning with rank based clustering algorithm is incorporated with the cloud server for improved data analysis. The probability risk rate is identified from the burst pressure by clustering the normal and leak category to improve the accuracy of the prediction system experimented on the lab-scale oil pipeline system. The numerical results like auto-correlation, periodogram, Laplace transformed P-P Plot are utilized to estimate the datasets restructured by the manifold learning approach. The obtained experimental results shows that the cloud server datasets are clustered with rank prioritization to make proactive decision in faster manner by distinguishing labelled and unlabeled pressure attributes.

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Corrosion mechanism of high temperature and O2 content in steamed CO2/O2/SO2 system and failure behavior of 20G steel on steam-injection pipelines

Zou

Liao

et al. 2022

Process Safety and Environmental Protection

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Rank Predictions of Internal Corrosion of Gathering Pipelines in a Natural Gas Field With a Multi-Kernel SVM Method

Cited by 3 publications

References 0 publications

Prediction of Chemical Corrosion Rate and Remaining Life of Buried Oil and Gas Pipelines in Changqing Gas Field

Prediction of Chemical Corrosion Rate and Remaining Life of Buried Oil and Gas Pipelines in Changqing Gas Field

Rank-based risk target data analysis using digital twin on oil pipeline network based on manifold learning

Corrosion mechanism of high temperature and O2 content in steamed CO2/O2/SO2 system and failure behavior of 20G steel on steam-injection pipelines

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