Reinforced ANFIS-based Filter Replacement Prediction System with Multi Sensors for VOCs Emission Reduction in Urban Industrial Facilities

Reinforced ANFIS-based Filter Replacement Prediction System with Multi Sensors for VOCs Emission Reduction in Urban Industrial Facilities | 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 Reinforced ANFIS-based Filter Replacement Prediction System with Multi Sensors for VOCs Emission Reduction in Urban Industrial Facilities Keunyoung Kim, Donghyuk Chun, Woosung Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6047284/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 May, 2025 Read the published version in Scientific Reports → Version 1 posted 14 You are reading this latest preprint version Abstract With the increase of air pollution, Volatile Organic Compounds (VOCs) emission control requirements and regulations for small air pollutant emitting facilities in urban centers are strengthening. This study proposes a multi sensor IoT network-based intelligent filter replacement prediction system for efficient operation of VOCs emission reduction facilities installed in automobile painting facilities. In the proposed system, several multi-sensor modules consisting of low-cost sensors are installed at the rear end of the adsorption tower of the prevention facility for full-time management of the facility, and to improve the sensor measurement accuracy, the measurement system is trained and optimized with Reinforced Adaptive Neural Fuzzy Inference System (RANFIS) model proposed in this study. This enables real-time monitoring by predicting VOCs emissions. Based on the predicted emissions, Decision Tree (DT) model is applied to predict the breakthrough rate of the filter material, activated carbon, and inform the filter replacement cycle for each facility manager. To verify the proposed system, eight sensor modules consisting of three types of sensors were attached to the exhaust vent of a real automobile paint booth VOCs prevention facility. To verify the accuracy of the sensors, the existing Adaptive Neural Fuzzy Inference System (ANFIS) model was applied for comparative verification. As a result, the RMSE values predicted by the RANFIS model for the three trained sensors are 14.757, 16.117, and 8.918, respectively, which are 73.6%, 82.4%, and 29.7% higher than the existing ANFIS model training. In addition, the DT model was applied based on the RANFIS results to predict the activated carbon replacement cycle, and the prediction accuracy was more than 80% for 80%, 70%, and 60% reduction efficiencies. Therefore, the proposed approach utilizing low-cost multi-sensors can be applied to continuously monitor the prevention facility and provide information on the activated carbon replacement cycle to managers, enabling efficient activated carbon filter management and pollution emission reduction in the prevention facility. Physical sciences/Engineering/Electrical and electronic engineering Physical sciences/Energy science and technology Health sciences/Risk factors monitoring system RANFIS IoT multi-sensor VOCs emission reduction facility activated carbon replacement cycle automobile painting facility Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 20 May, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 16 Apr, 2025 Reviews received at journal 10 Apr, 2025 Reviews received at journal 31 Mar, 2025 Reviewers agreed at journal 25 Mar, 2025 Reviews received at journal 24 Mar, 2025 Reviewers agreed at journal 24 Mar, 2025 Reviewers agreed at journal 23 Mar, 2025 Reviewers agreed at journal 23 Mar, 2025 Reviewers agreed at journal 22 Mar, 2025 Reviewers agreed at journal 20 Mar, 2025 Reviewers invited by journal 20 Mar, 2025 Editor assigned by journal 20 Mar, 2025 Submission checks completed at journal 20 Mar, 2025 First submitted to journal 20 Mar, 2025 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. 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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-6047284","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":431662500,"identity":"e4865364-b186-4d9a-92d1-ae412dcf2cfd","order_by":0,"name":"Keunyoung Kim","email":"","orcid":"","institution":"Kwangwoon University","correspondingAuthor":false,"prefix":"","firstName":"Keunyoung","middleName":"","lastName":"Kim","suffix":""},{"id":431662501,"identity":"bcdb55a3-cf4c-4233-9935-3dc4f8e57c31","order_by":1,"name":"Donghyuk Chun","email":"","orcid":"","institution":"Korea Institute of Energy Research","correspondingAuthor":false,"prefix":"","firstName":"Donghyuk","middleName":"","lastName":"Chun","suffix":""},{"id":431662502,"identity":"dedab22b-06cf-4e13-8afc-cfb4cbcb482a","order_by":2,"name":"Woosung Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYDCCA2BSgoGBvY0BxiRWC88x0rSAlKYRqYXv9uGDn27usbDnl3yWJsFQY8cgOfsAfi2S59KSpXOeSTBLzk47JsFwLJlBmi8BvxaDMzwG0jkHJNgMbqe33WBgO8Agx0PAYQZn+D//Bmrhsb95HKjlH1FaeNhAtkgYSLAdu8HYdoBBmpAWyTNsZtZALQYSZ9LSfyT2JfNI9hDQwneG+fHtnAN19vztx4wNPnyzk5M4Q0ALKkgAxihJGkbBKBgFo2AUYAcAjvs7mIyRmUoAAAAASUVORK5CYII=","orcid":"","institution":"Kwangwoon University","correspondingAuthor":true,"prefix":"","firstName":"Woosung","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2025-02-17 11:08:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6047284/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6047284/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-02435-8","type":"published","date":"2025-05-20T15:57:54+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83460059,"identity":"bae0d362-be7b-49af-99c0-fb2bc18b6300","added_by":"auto","created_at":"2025-05-26 16:09:39","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2258131,"visible":true,"origin":"","legend":"","description":"","filename":"ScientificReportssubmissionrevisedManuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6047284/v1_covered_1b795687-c766-4771-9792-f2094ff0835b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Reinforced ANFIS-based Filter Replacement Prediction System with Multi Sensors for VOCs Emission Reduction in Urban Industrial Facilities","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"monitoring system, RANFIS, IoT multi-sensor, VOCs emission reduction facility, activated carbon replacement cycle, automobile painting facility","lastPublishedDoi":"10.21203/rs.3.rs-6047284/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6047284/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"With the increase of air pollution, Volatile Organic Compounds (VOCs) emission control requirements and regulations for small air pollutant emitting facilities in urban centers are strengthening. This study proposes a multi sensor IoT network-based intelligent filter replacement prediction system for efficient operation of VOCs emission reduction facilities installed in automobile painting facilities. In the proposed system, several multi-sensor modules consisting of low-cost sensors are installed at the rear end of the adsorption tower of the prevention facility for full-time management of the facility, and to improve the sensor measurement accuracy, the measurement system is trained and optimized with Reinforced Adaptive Neural Fuzzy Inference System (RANFIS) model proposed in this study. This enables real-time monitoring by predicting VOCs emissions. Based on the predicted emissions, Decision Tree (DT) model is applied to predict the breakthrough rate of the filter material, activated carbon, and inform the filter replacement cycle for each facility manager. To verify the proposed system, eight sensor modules consisting of three types of sensors were attached to the exhaust vent of a real automobile paint booth VOCs prevention facility. To verify the accuracy of the sensors, the existing Adaptive Neural Fuzzy Inference System (ANFIS) model was applied for comparative verification. As a result, the RMSE values predicted by the RANFIS model for the three trained sensors are 14.757, 16.117, and 8.918, respectively, which are 73.6%, 82.4%, and 29.7% higher than the existing ANFIS model training. In addition, the DT model was applied based on the RANFIS results to predict the activated carbon replacement cycle, and the prediction accuracy was more than 80% for 80%, 70%, and 60% reduction efficiencies. Therefore, the proposed approach utilizing low-cost multi-sensors can be applied to continuously monitor the prevention facility and provide information on the activated carbon replacement cycle to managers, enabling efficient activated carbon filter management and pollution emission reduction in the prevention facility.","manuscriptTitle":"Reinforced ANFIS-based Filter Replacement Prediction System with Multi Sensors for VOCs Emission Reduction in Urban Industrial Facilities","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-21 08:10:58","doi":"10.21203/rs.3.rs-6047284/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-16T06:58:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-10T21:13:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-01T02:54:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"157168315521440431600999785931438579458","date":"2025-03-26T02:51:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-25T01:26:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"238040018414395766044148203171861896851","date":"2025-03-25T00:59:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"215453551898623456083632085627568729179","date":"2025-03-23T05:32:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"38027585702891334258310678177538655123","date":"2025-03-23T04:06:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"286001604956351706230866310055040944446","date":"2025-03-22T21:33:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"47381105687114308997843550068491424735","date":"2025-03-20T15:01:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-20T14:49:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-20T14:09:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-20T13:19:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-20T13:17:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9179d7aa-2494-4f9f-81c8-3010b908a92c","owner":[],"postedDate":"March 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":45977958,"name":"Physical sciences/Engineering/Electrical and electronic engineering"},{"id":45977959,"name":"Physical sciences/Energy science and technology"},{"id":45977960,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-05-26T16:02:24+00:00","versionOfRecord":{"articleIdentity":"rs-6047284","link":"https://doi.org/10.1038/s41598-025-02435-8","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-05-20 15:57:54","publishedOnDateReadable":"May 20th, 2025"},"versionCreatedAt":"2025-03-21 08:10:58","video":"","vorDoi":"10.1038/s41598-025-02435-8","vorDoiUrl":"https://doi.org/10.1038/s41598-025-02435-8","workflowStages":[]},"version":"v1","identity":"rs-6047284","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6047284","identity":"rs-6047284","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}