Early Warning Study of Field Station Process Safety Based on VMD-CNN-LSTM-Self-Attention for Natural Gas Load Prediction

Early Warning Study of Field Station Process Safety Based on VMD-CNN-LSTM-Self-Attention for Natural Gas Load Prediction | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Early Warning Study of Field Station Process Safety Based on VMD-CNN-LSTM-Self-Attention for Natural Gas Load Prediction Wei Zhao, Bilin Shao, Ning Tian, Weng Zhang, Xue Zhao, Shuqiang Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4706160/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Feb, 2025 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract As a high-risk production unit, natural gas supply enterprises are increasingly recognizing the need to enhance production safety management. Traditional process warning methods, which rely on fixed alarm values, often fail to adequately account for dynamic changes in the production process. To address this issue, this study utilizes deep learning techniques to enhance the accuracy and reliability of natural gas load forecasting. By considering the benefits and feasibility of integrating multiple models, a VMD-CNN-LSTM-Self-Attention interval prediction method was innovatively proposed and developed. Empirical research was conducted using data from natural gas field station outgoing loads. The primary model constructed is a deep learning model for interval prediction of natural gas loads, which implements a graded alarm mechanism based on 85%, 90%, and 95% confidence intervals of real-time observations. This approach represents a novel strategy for enhancing enterprise safety production management. Experimental results demonstrate that the proposed method outperforms traditional warning models, reducing MAE, MAPE, MESE, and REMS by 1.13096m3/h, 1.3504%, 7.6363m3/h, 1.6743m3/h, respectively, while improving R2 by 0.04698. These findings are expected to offer valuable insights for enhancing safe production management in the natural gas industry and provide new perspectives for the industry's digital and intelligent transformation. Physical sciences/Energy science and technology Physical sciences/Engineering interval prediction multi-model combination safety warning deep learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 21 Feb, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 16 Dec, 2024 Reviewers agreed at journal 10 Dec, 2024 Reviews received at journal 10 Dec, 2024 Reviewers agreed at journal 10 Dec, 2024 Reviews received at journal 13 Nov, 2024 Reviewers agreed at journal 31 Oct, 2024 Reviewers agreed at journal 22 Oct, 2024 Reviewers invited by journal 10 Oct, 2024 Editor assigned by journal 09 Oct, 2024 Editor invited by journal 16 Jul, 2024 Submission checks completed at journal 13 Jul, 2024 First submitted to journal 08 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4706160","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":334883808,"identity":"efef2904-5160-4bc8-b51c-fc8d0be30e0a","order_by":0,"name":"Wei Zhao","email":"","orcid":"","institution":"Xi'an University of Architecture and Technology","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Zhao","suffix":""},{"id":334883809,"identity":"2d67f4fd-d69c-446a-89ae-3e3a66ba7f34","order_by":1,"name":"Bilin 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Traditional process warning methods, which rely on fixed alarm values, often fail to adequately account for dynamic changes in the production process. To address this issue, this study utilizes deep learning techniques to enhance the accuracy and reliability of natural gas load forecasting. By considering the benefits and feasibility of integrating multiple models, a VMD-CNN-LSTM-Self-Attention interval prediction method was innovatively proposed and developed. Empirical research was conducted using data from natural gas field station outgoing loads. The primary model constructed is a deep learning model for interval prediction of natural gas loads, which implements a graded alarm mechanism based on 85%, 90%, and 95% confidence intervals of real-time observations. This approach represents a novel strategy for enhancing enterprise safety production management. 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