A comprehensive study on pyrolysis behaviors and entire process prediction of representative charring materials under widely varied heating rates using TG-FTIR-MS analysis and artificial neural network

A comprehensive study on pyrolysis behaviors and entire process prediction of representative charring materials under widely varied heating rates using TG-FTIR-MS analysis and artificial neural network | 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 A comprehensive study on pyrolysis behaviors and entire process prediction of representative charring materials under widely varied heating rates using TG-FTIR-MS analysis and artificial neural network Xiwen Zhao, Ruiyu Chen, Quanwei Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8335072/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 Wood is a common class of solid combustible material that is ubiquitous in both natural environments and everyday applications. When exposed to heat, these materials undergo thermal decomposition, releasing flammable gases that can ignite upon contact with an open flame or high-temperature surfaces, thereby posing a significant fire hazard. This study investigates the influence of different heating rate on the pyrolysis behavior of padauk , a commonly used wood. Experimental techniques, including thermogravimetric analysis (TGA) and Fourier transform infrared spectroscopy (FTIR), were employed, complemented by model-free and model-fitting approaches. A new artificial neural network ( ANN ) framework was developed to predict the entire pyrolysis process. The results demonstrate that an increase in the heating rate reduces the maximum mass loss rate and shifts the thermogravimetric curve toward a higher temperature region. The pyrolysis process can be categorized into three distinct phases. The average activation energies for the entire pyrolysis process under slow (5, 10, 20, and 40 K/min), intermediate (65, 75, 80, and 85 K/min), and fast (150, 250, 300, and 400 K/min) heating conditions were determined to be 176.9 kJ/mol, 151.2 kJ/mol, and 111.4 kJ/mol, respectively. These three phases primarily correspond to the decomposition of hemicellulose, cellulose, and lignin. Each phase was accurately described by a specific reaction model. The primary volatile gaseous products, comprising CH₄, H₂O, CO, CO₂, methanol, acetone, benzene, and phenol, were generated abundantly within the temperature range of 500 K to 700 K. The thermogravimetric data were preprocessed using linear interpolation and normalization. The processed dataset was then used to train an ANN model in conjunction with various optimization algorithms. The trained ANN model accurately predicts the entire pyrolysis process under different heating rates. Wood materials Pyrolysis kinetics Volatile products Entire process prediction Artificial neural network 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-8335072","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":589955708,"identity":"e74e7256-1d9f-43b3-a71d-8f5b5155e9a7","order_by":0,"name":"Xiwen Zhao","email":"","orcid":"","institution":"Nanjing University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Xiwen","middleName":"","lastName":"Zhao","suffix":""},{"id":589955709,"identity":"fc01fb94-27c6-41b4-b27e-e0c3961d99b5","order_by":1,"name":"Ruiyu Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIie3SMYvCMBTA8VcC6hDM+kLA+wo5Cj0O+2GeCJkcbnLsFQ46+QEqfgmnm3s4uJTOkbtBF2fBpW5X7Z52PLj8IZBAfuENAfD5/mrYLAFA7anoSVCmd0J9yV3px80+RGzK6PiaJUn4nZ7lrYbJ2FJwfXO9Xi1etMx2GP0URnGCUFpiKncQzXmEMiswsmRUM9hsa2nAeDdJMMzJyJrgvS9hqJEMNoOR7iJYDpaI1U7mluZTbvB5XZ4+lIuIFftUuEyEyBezQx3HT+P9/OvqIgAjzfCxaaZqP0KQOgHA8Bhc2k3RcdPn8/n+a790N0LcOfH/ogAAAABJRU5ErkJggg==","orcid":"","institution":"Nanjing University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Ruiyu","middleName":"","lastName":"Chen","suffix":""},{"id":589955710,"identity":"d6eabf8d-83d6-4985-b14a-201e54c2d354","order_by":2,"name":"Quanwei Li","email":"","orcid":"","institution":"Nanjing University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Quanwei","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-12-11 09:53:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8335072/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8335072/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102749148,"identity":"82b07926-e6ba-45e0-8444-c57eb590f843","added_by":"auto","created_at":"2026-02-16 09:12:05","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1949441,"visible":true,"origin":"","legend":"","description":"","filename":"revisedmanuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8335072/v1_covered_622a23cc-453f-4ace-bf19-d5efa15dda75.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A comprehensive study on pyrolysis behaviors and entire process prediction of representative charring materials under widely varied heating rates using TG-FTIR-MS analysis and artificial neural network","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":"Wood materials, Pyrolysis kinetics, Volatile products, Entire process prediction, Artificial neural network","lastPublishedDoi":"10.21203/rs.3.rs-8335072/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8335072/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWood is a common class of solid combustible material that is ubiquitous in both natural environments and everyday applications. When exposed to heat, these materials undergo thermal decomposition, releasing flammable gases that can ignite upon contact with an open flame or high-temperature surfaces, thereby posing a significant fire hazard. This study investigates the influence of different heating rate on the pyrolysis behavior of \u003cem\u003epadauk\u003c/em\u003e, a commonly used wood. Experimental techniques, including thermogravimetric analysis (TGA) and Fourier transform infrared spectroscopy (FTIR), were employed, complemented by model-free and model-fitting approaches. A new artificial neural network (\u003cem\u003eANN\u003c/em\u003e) framework was developed to predict the entire pyrolysis process. The results demonstrate that an increase in the heating rate reduces the maximum mass loss rate and shifts the thermogravimetric curve toward a higher temperature region. The pyrolysis process can be categorized into three distinct phases. The average activation energies for the entire pyrolysis process under slow (5, 10, 20, and 40 K/min), intermediate (65, 75, 80, and 85 K/min), and fast (150, 250, 300, and 400 K/min) heating conditions were determined to be 176.9 kJ/mol, 151.2 kJ/mol, and 111.4 kJ/mol, respectively. These three phases primarily correspond to the decomposition of hemicellulose, cellulose, and lignin. Each phase was accurately described by a specific reaction model. The primary volatile gaseous products, comprising CH₄, H₂O, CO, CO₂, methanol, acetone, benzene, and phenol, were generated abundantly within the temperature range of 500 K to 700 K. The thermogravimetric data were preprocessed using linear interpolation and normalization. The processed dataset was then used to train an \u003cem\u003eANN\u003c/em\u003e model in conjunction with various optimization algorithms. 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