Optimizing Performance, Combustion and Emission characteristics of Mahua Biodiesel included GO and ZnO Nanoparticles: An ANN-RSM Approach

Optimizing Performance, Combustion and Emission characteristics of Mahua Biodiesel included GO and ZnO Nanoparticles: An ANN-RSM Approach | 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 Performance, Combustion and Emission characteristics of Mahua Biodiesel included GO and ZnO Nanoparticles: An ANN-RSM Approach srinivasa reddy pala, Mangu Venkata Krishna Mohan, Varaha siva Prasad Vanthala This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5442487/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 May, 2025 Read the published version in Environmental Science and Pollution Research → Version 1 posted 5 You are reading this latest preprint version Abstract The present study focuses on the use of artificial neural networks (ANNs) and response surface methodology (RSM) with GO and ZnO nanoparticles placed in Mahua oil biodiesel blend (B20) are utilized to fuel direct injection diesel engines. These approaches are used to forecast the engine's operating characteristics. At a concentration of 75 ppm, GO and ZnO nanoparticles were taken into consideration. Additionally, a dispersant (TWEEN 80) and surfactant (CTAB) were mixed respectively at a ratio of 1:1. Using a spectrophotometer, stability analysis was carried out on a variety of nanofuel samples, and a study based on experiments was done on a diesel engine. The output factors that were examined at were BSFC, BTE, NHRR, CP, UHC, CO, NOx, and smoke visibility. These metrics were based on combustion, emissions and performance. Input parameters such as gasoline samples, injection pressure, and engine load were considered. The injection pressures were 200, 225, and 250 bars, whereas the loads were considered to be 5%, 50%, 75%, and 100%, respectively. When compared to other samples, the dispersion ZnO and GO nanoparticles in B20 shown amazing performance. The B20 + GO 75 ppm + TWEEN 80 75 ppm combination has shown a 5.293% decrease in BSFC and a 5.067% improvement in BTE at 250 bars. Furthermore, 3.13% and 43.50% improvements were made to combustion parameters including CP and NHRR, respectively. Smoke opacity, CO, UHC, and NOx were all reduced by around 38.55%, 11.07%, 37.63%, and 27.77%, respectively. Finally, the correlation coefficient (R 2 ) for all parameters ranged from 0.93 to 0.99 using ANNs and RSM predictions. Biodiesel nanoparticles artificial neural networks combustion correlation coefficient emissions Full Text Cite Share Download PDF Status: Published Journal Publication published 21 May, 2025 Read the published version in Environmental Science and Pollution Research → Version 1 posted Editorial decision: Major Revision 09 Mar, 2025 Reviewers agreed at journal 15 Nov, 2024 Reviewers invited by journal 15 Nov, 2024 Editor assigned by journal 13 Nov, 2024 First submitted to journal 13 Nov, 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. We do this by developing innovative software and high quality services for the global research community. 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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-5442487","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":378596733,"identity":"e038769d-7b40-484e-858d-bcf8694deb6b","order_by":0,"name":"srinivasa reddy pala","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0001-9889-8055","institution":"Andhra University","correspondingAuthor":true,"prefix":"","firstName":"srinivasa","middleName":"reddy","lastName":"pala","suffix":""},{"id":378596734,"identity":"5f384b38-34d7-4ddf-a20d-6f74d1c3f05e","order_by":1,"name":"Mangu Venkata Krishna Mohan","email":"","orcid":"","institution":"Lendi Institute of Engineering and Technology","correspondingAuthor":false,"prefix":"","firstName":"Mangu","middleName":"Venkata Krishna","lastName":"Mohan","suffix":""},{"id":378596735,"identity":"755b9007-a63f-4441-b2b1-64937ec4a01a","order_by":2,"name":"Varaha siva Prasad Vanthala","email":"","orcid":"","institution":"Andhra University","correspondingAuthor":false,"prefix":"","firstName":"Varaha","middleName":"siva Prasad","lastName":"Vanthala","suffix":""}],"badges":[],"createdAt":"2024-11-12 23:16:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5442487/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5442487/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11356-025-36530-0","type":"published","date":"2025-05-21T15:56:51+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83459969,"identity":"c0bbfa79-70de-4ea4-8e5d-ea672e62bd0a","added_by":"auto","created_at":"2025-05-26 16:06:49","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4932763,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5442487/v1_covered_b9702b02-cb61-4ead-bef1-2f3b9157d1ba.pdf"}],"financialInterests":"","formattedTitle":"Optimizing Performance, Combustion and Emission characteristics of Mahua Biodiesel included GO and ZnO Nanoparticles: An ANN-RSM Approach","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":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Biodiesel, nanoparticles, artificial neural networks, combustion, correlation coefficient, emissions","lastPublishedDoi":"10.21203/rs.3.rs-5442487/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5442487/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe present study focuses on the use of artificial neural networks (ANNs) and response surface methodology (RSM) with GO and ZnO nanoparticles placed in Mahua oil biodiesel blend (B20) are utilized to fuel direct injection diesel engines. 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