Energy Saving Diagnosis Model of Petrochemical Plant Based on Intelligent Curvelet Support Vector Machine

Energy Saving Diagnosis Model of Petrochemical Plant Based on Intelligent Curvelet Support Vector Machine | 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 Research Article Energy Saving Diagnosis Model of Petrochemical Plant Based on Intelligent Curvelet Support Vector Machine bin zhao, dou qin, Diankui Gao, lizhi xu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-412350/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract The energy and resources saving has become a major task of petrochemical enterprises, it is necessary to construct the energy saving diagnostic system for understand the real time operation information of petrochemical plant and provide theoretical basis for taking energy saving measures. The energy saving diagnosis process of petrochemical plant based on twin Curvelet support vector machine optimized by hybrid glowworm swarm algorithm is designed. The Curvelet kernel function is constructed based on curvelet transform to establish theory model of twin curvelet support vector machine. In order to improve the prediction precision of the twin curvelet support vector machine, the hybrid glowworm swarm optimization algorithm is constructed based on simulated annealing simulation to optimize the parameters of the twin Curvelet support vector machine. Finally, a petrochemical plant is used as research object to carry out diagnosis simulation analysis, and results showed that the proposed prediction model can effectively improve diagnostic effectiveness of the energy saving effect of petrochemical plant. Mechanical Engineering energy saving diagnosis petrochemical plant twin Curvelet support vector machine Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 10 Apr, 2021 Reviews received at journal 10 Apr, 2021 Editor assigned by journal 09 Apr, 2021 First submitted to journal 19 Jan, 2021 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-412350","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":20926052,"identity":"e836adbe-3d07-45d3-948a-1eb085982bbd","order_by":0,"name":"bin 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