Optimizing Electrostatic Precipitator Collection Efficiency: Taguchi Design, Grey Relational Analysis, and Machine Learning Integration​

Optimizing Electrostatic Precipitator Collection Efficiency: Taguchi Design, Grey Relational Analysis, and Machine Learning Integration​ | 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 Optimizing Electrostatic Precipitator Collection Efficiency: Taguchi Design, Grey Relational Analysis, and Machine Learning Integration​ Amogh Anil Sambare This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8925669/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study presents a modeling and optimization approach for electrostatic precipitator (ESP) collection efficiency using physics simulations, Taguchi design, gray relational analysis (GRA), and machine learning. A corona discharge and turbulent flow model simulated fluid velocity, electric field, charge density, and particle trajectories in a wire-to-plane ESP. Taguchi L27 simulation run varied electrode radius, gas velocity, and applied voltage, showing voltage as the key efficiency factor, with lower gas velocities and larger electrode radii improving performance. GRA combined multi-size particle efficiencies into a single metric and determined optimal conditions. Machine learning models, Gradient Boosting and Artificial Neural Networks (ANNs), predicted collection efficiency with different strengths such as Gradient Boosting for fine particles and ANN for micron-sized particles. Both models showed limitations for ultra-fine and coarse particles, suggesting the need for expanded datasets. This framework provides a methodology for optimizing ESP performance. Electrostatic Precipitators Taguchi L27 orthogonal array Gradient boosting Artificial Neural network Full Text Supplementary Files NomenclatureandSymbols.docx SupplementaryMaterialESP.docx Cite Share Download PDF Status: Posted Version 1 posted 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-8925669","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":600553962,"identity":"79cc258c-cc8b-42d4-ac4f-5d76c1966c89","order_by":0,"name":"Amogh Anil Sambare","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYJACZgiVYMDYcMAmASbKQ6yWNNK1HE7ApxIM+PsPP91cUHNPjoE9eZvkjDPn8/innTFg+FHDIGOOQ4vEjTSz2zOOFRsz8Dwrk9xw43axxO0cA8aeYww8lg049NxgMLvNw5aQ2CCRYyb54MPtxAagFgbeBgYegwPYdcifP/7tNs+/hHqolnOJ80G2/MWjxeBAjtlt3raEBAaQlg03DiRuAGphxmeL4Y2cstsz+xIM23ieFVvOOJNcbHg7reCwzDEJnFrkzh/fdrvgW4I8P3vyxps9x+zy5G4nb3z4psbGHpcWOGBD5gAVSxBQPwpGwSgYBaMAHwAAITZhwmA6z8cAAAAASUVORK5CYII=","orcid":"","institution":"Dr Babasaheb Ambedkar Marathwada University","correspondingAuthor":true,"prefix":"","firstName":"Amogh","middleName":"Anil","lastName":"Sambare","suffix":""}],"badges":[],"createdAt":"2026-02-20 11:59:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8925669/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8925669/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104835042,"identity":"45a3bc45-56ea-414a-8cac-9ea73ae01bcb","added_by":"auto","created_at":"2026-03-17 17:39:24","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2538065,"visible":true,"origin":"","legend":"","description":"","filename":"ESPPaper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8925669/v1_covered_7229ab38-a5da-4d32-ac95-ac073df4c35d.pdf"},{"id":104268765,"identity":"74dcb90e-0922-4fd2-be0f-5338c82554b7","added_by":"auto","created_at":"2026-03-09 21:22:56","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":15673,"visible":true,"origin":"","legend":"","description":"","filename":"NomenclatureandSymbols.docx","url":"https://assets-eu.researchsquare.com/files/rs-8925669/v1/1deb9920dd617ad2005a1ab2.docx"},{"id":104268766,"identity":"f6450f73-0cca-45ee-9a4f-49f66815aff6","added_by":"auto","created_at":"2026-03-09 21:22:56","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":10303513,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialESP.docx","url":"https://assets-eu.researchsquare.com/files/rs-8925669/v1/40f08b62aebe2ad31701f44d.docx"}],"financialInterests":"","formattedTitle":"Optimizing Electrostatic Precipitator Collection Efficiency: Taguchi Design, Grey Relational Analysis, and Machine Learning Integration​","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Electrostatic Precipitators, Taguchi L27 orthogonal array, Gradient boosting, Artificial Neural network","lastPublishedDoi":"10.21203/rs.3.rs-8925669/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8925669/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study presents a modeling and optimization approach for electrostatic precipitator (ESP) collection efficiency using physics simulations, Taguchi design, gray relational analysis (GRA), and machine learning. A corona discharge and turbulent flow model simulated fluid velocity, electric field, charge density, and particle trajectories in a wire-to-plane ESP. Taguchi L27 simulation run varied electrode radius, gas velocity, and applied voltage, showing voltage as the key efficiency factor, with lower gas velocities and larger electrode radii improving performance. GRA combined multi-size particle efficiencies into a single metric and determined optimal conditions. Machine learning models, Gradient Boosting and Artificial Neural Networks (ANNs), predicted collection efficiency with different strengths such as Gradient Boosting for fine particles and ANN for micron-sized particles. Both models showed limitations for ultra-fine and coarse particles, suggesting the need for expanded datasets. This framework provides a methodology for optimizing ESP performance.\u003c/p\u003e","manuscriptTitle":"Optimizing Electrostatic Precipitator Collection Efficiency: Taguchi Design, Grey Relational Analysis, and Machine Learning Integration​","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-09 21:22:51","doi":"10.21203/rs.3.rs-8925669/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2fe1005a-3c19-4909-a748-9ce7833462aa","owner":[],"postedDate":"March 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-16T21:39:38+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-09 21:22:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8925669","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8925669","identity":"rs-8925669","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}