An artificial neural network modeling of solar drying of mint: Energy, exergy, and drying kinetics

An artificial neural network modeling of solar drying of mint: Energy, exergy, and drying kinetics | 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 An artificial neural network modeling of solar drying of mint: Energy, exergy, and drying kinetics Fevzi Gülçimen, Hakan Karakaya, Aydın Durmuş This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4373121/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 The energy and exergy analysis of thin-layer drying of mint leaves was performed in a forced convective solar dryer with new design solar collector. The effects of inlet airflow rates on the energy utilization ratio (EUR), energy generated by the solar air collector, exergy losses, exergy efficiency, and kinetics of drying were determined. The EUR varied between 7.45 to 87.1% and it increased when the flow rate decreased. The average exergy loss for the air with mass flow rates of 0.012, 0.026, and 0.033 kg/s was calculated as 16.2 W, 8.2 W, and 6.88 W, respectively. Unlike other studies, exergy and EUR data obtained from experimental data were modeled with an artificial neural network (ANN). The experimental data were modeled by an artificial neural network (ANN) via a feed-forward back-propagation network. The values obtained from ANN modeling were significantly closed to the experimental values. In both experimental and ANN models, EUR and exergy loss decreased with increasing airflow rate. The importance of airflow rates was promising to modify EUR and exergy losses. Exergy analysis Energy analysis Solar drying Artificial neural network Kinetics of drying Mint Full Text Additional Declarations No competing interests reported. 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-4373121","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":299481771,"identity":"ccd4d49b-2152-4e3c-be6d-304f566e4065","order_by":0,"name":"Fevzi Gülçimen","email":"","orcid":"","institution":"Munzur University","correspondingAuthor":false,"prefix":"","firstName":"Fevzi","middleName":"","lastName":"Gülçimen","suffix":""},{"id":299481774,"identity":"b2a8abe9-3be2-435f-802b-99eb8d8f6df4","order_by":1,"name":"Hakan Karakaya","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEklEQVRIiWNgGAWjYBACAwY2EJUAxGCGTQJMhrGBSC1pCRAarIWZKC2HCWsxZz+W+LmAIU3evB3I4Kk5n8c/vxskYiO74QD/sQ9YtFj2pB2WnsGQYzjnDJDBc+x2scQx3s1AkTTjDQeYmWdgc9iB9AZpHoYKxhkMIAbb7cSGY7wbgCKHE0FasPrl/PPm30At9jP4QYx/5xLnA20BivzHreVG2jGgmTmJMySADN62A4kbjvFuA4ocwKPlWZo1j0Fa8gyJZ2mWc/uSiw2P5W4DiiQbzzzMbIzdYWnGt3kqkm1n8KcZ33jzzS5P7vDZzUARO9m+442PsYYyRCOEYuJBEcEeLaiA8QcRikbBKBgFo2DkAQBu92O/8romJAAAAABJRU5ErkJggg==","orcid":"","institution":"Batman University","correspondingAuthor":true,"prefix":"","firstName":"Hakan","middleName":"","lastName":"Karakaya","suffix":""},{"id":299481777,"identity":"136faaa7-92af-4cbc-ba54-63ad1fff874e","order_by":2,"name":"Aydın Durmuş","email":"","orcid":"","institution":"Ondokuz Mayis University","correspondingAuthor":false,"prefix":"","firstName":"Aydın","middleName":"","lastName":"Durmuş","suffix":""}],"badges":[],"createdAt":"2024-05-05 22:08:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4373121/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4373121/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63357634,"identity":"49361c41-1af8-4dbd-b444-2361a10a4702","added_by":"auto","created_at":"2024-08-27 09:36:12","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":763894,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4373121/v1_covered_ef94b841-40f4-4830-bf67-73f010166d0b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An artificial neural network modeling of solar drying of mint: Energy, exergy, and drying kinetics","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":"Exergy analysis, Energy analysis, Solar drying, Artificial neural network, Kinetics of drying, Mint","lastPublishedDoi":"10.21203/rs.3.rs-4373121/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4373121/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe energy and exergy analysis of thin-layer drying of mint leaves was performed in a forced convective solar dryer with new design solar collector. 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