AI Based Model for Prediction of Heavy Metals Using Physio-Chemical Characterization of Agricultural Waste Ashes

AI Based Model for Prediction of Heavy Metals Using Physio-Chemical Characterization of Agricultural Waste Ashes | 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 AI Based Model for Prediction of Heavy Metals Using Physio-Chemical Characterization of Agricultural Waste Ashes Wasim Abbass, Muneeb Ahmed, Ali Ahmed, Fahid Aslam, Iram Aziz, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3865940/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 escalating volume of waste materials generated as byproducts is a growing concern in the context of recycling. These waste materials encompass a variety of heavy metals (HMs) that pose significant environmental hazards to plants, animals, and ecosystems. To address that HMs, there was a need to develop an artificial intelligence-based model capable of predicting the presence and quantity of HMs based on the chemical composition of the discards as AWAs. This study delved into a comprehensive analysis of the diverse origins of AWAs, exploring their multifaceted characteristics across different sources. In this research, a total of thirty-two types of SCBA and RHA were accumulated from various sources. The properties and attributes of residual ashes were assessed utilizing various methods of analysis, including X-ray fluorescence (XRF), Fourier-Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscope (SEM), Energy dispersive X-Ray (EDX), X-ray Diffraction Analysis (XRD), Thermogravimetric Analysis / Differential Scanning calorimetry (TGA/DSC), and Atomic Absorption Spectroscopy (AAS). The results were presented in the light of existing literature and standards. The results accordingly revealed that AWAs can be categorized in three fractions based on loss on ignition. At the end some, recommendations for the utilization of SCBA and RHA based on the characterization results were also made for utilization as supplementary material in construction industry. Moreover, the machine learning model was constructed using input variables such as the physio-chemical properties of SCBA and RHA, element properties, and total HMs concentrations to predict the HM fractions. The application of machine learning tool to procured SCBA and RHA revealed that the model utilizing deep neural networks demonstrated performance robustly, possessing strong generalization capabilities (R 2 = 0.99 on the testing set), enabling the rapid and accurate prediction of HMs fractions. The element properties were found to be the primary determinant of the HMs fractions. This study adds value to the creation of sustainable approaches for managing waste and provides a framework for the characterization of waste ashes for potential utilize as a primary substance in construction materials. Physical sciences/Engineering/Civil engineering Physical sciences/Materials science/Structural materials Sugarcane Bagasse Ash Characterization Agriculture Waste Ashes Analytical Study Rice Husk Ash 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-3865940","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":268395912,"identity":"1765a9fe-adb2-4840-9ce4-cc24f6c6580f","order_by":0,"name":"Wasim Abbass","email":"","orcid":"","institution":"University of Engineering and Technology","correspondingAuthor":false,"prefix":"","firstName":"Wasim","middleName":"","lastName":"Abbass","suffix":""},{"id":268395913,"identity":"57180e3a-0ac5-4ee6-81c2-adba4186337f","order_by":1,"name":"Muneeb Ahmed","email":"","orcid":"","institution":"University of Engineering and Technology","correspondingAuthor":false,"prefix":"","firstName":"Muneeb","middleName":"","lastName":"Ahmed","suffix":""},{"id":268395914,"identity":"bd45b76e-161d-4a42-9994-4997e510323a","order_by":2,"name":"Ali Ahmed","email":"","orcid":"","institution":"University of Engineering and Technology","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"","lastName":"Ahmed","suffix":""},{"id":268395915,"identity":"456376e7-d613-40ec-959a-5209cfcedf4e","order_by":3,"name":"Fahid Aslam","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYBAC9gYg8YCBQQ7KZyashecAkEhgYDAmXUtiA/FaGJgff0io2Za+nf/wMwmGCuvEBv7DDwhoYTOTSDh2O3fnjDQzCYYz6YkNEmkGeLXYMzCYMSSw3c7dcIPBTIKx7TBQCwN+LTwM7J8/JPy7nW5w/vg3CcZ/QC38xz8Q0MJjIJHYdjvB4EAO0JYGoBaGHAK2MPOUSST23TbcOSOn2CLhWLpxm0ROAX4t7O2bP3z4dlvenP/4xhsfaqxl+/mPb8CrBR4RYMckADEbfvVIAL/7R8EoGAWjYEQDAFuHQ/+wric9AAAAAElFTkSuQmCC","orcid":"","institution":"Prince Sattam bin Abdulaziz University","correspondingAuthor":true,"prefix":"","firstName":"Fahid","middleName":"","lastName":"Aslam","suffix":""},{"id":268395916,"identity":"6425f080-777b-4bf5-a313-5d3f0de285f4","order_by":4,"name":"Iram Aziz","email":"","orcid":"","institution":"University of Engineering and Technology","correspondingAuthor":false,"prefix":"","firstName":"Iram","middleName":"","lastName":"Aziz","suffix":""},{"id":268395917,"identity":"3cb5ab6b-9a76-44f4-b519-8094dd2a9dd1","order_by":5,"name":"Abdullah Mohamed","email":"","orcid":"","institution":"Future University in Egypt","correspondingAuthor":false,"prefix":"","firstName":"Abdullah","middleName":"","lastName":"Mohamed","suffix":""}],"badges":[],"createdAt":"2024-01-15 09:00:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3865940/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3865940/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87006949,"identity":"b0baa008-89a8-44e6-86a2-f50aef827e6b","added_by":"auto","created_at":"2025-07-18 08:32:13","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5193066,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscriptfinal512024.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3865940/v1_covered_8ec8ab48-8d2e-469b-8276-7356dc6d73c4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"AI Based Model for Prediction of Heavy Metals Using Physio-Chemical Characterization of Agricultural Waste Ashes","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":"Sugarcane Bagasse Ash, Characterization, Agriculture Waste Ashes, Analytical Study, Rice Husk Ash","lastPublishedDoi":"10.21203/rs.3.rs-3865940/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3865940/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe escalating volume of waste materials generated as byproducts is a growing concern in the context of recycling. These waste materials encompass a variety of heavy metals (HMs) that pose significant environmental hazards to plants, animals, and ecosystems. To address that HMs, there was a need to develop an artificial intelligence-based model capable of predicting the presence and quantity of HMs based on the chemical composition of the discards as AWAs. This study delved into a comprehensive analysis of the diverse origins of AWAs, exploring their multifaceted characteristics across different sources. In this research, a total of thirty-two types of SCBA and RHA were accumulated from various sources. The properties and attributes of residual ashes were assessed utilizing various methods of analysis, including X-ray fluorescence (XRF), Fourier-Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscope (SEM), Energy dispersive X-Ray (EDX), X-ray Diffraction Analysis (XRD), Thermogravimetric Analysis / Differential Scanning calorimetry (TGA/DSC), and Atomic Absorption Spectroscopy (AAS). The results were presented in the light of existing literature and standards. The results accordingly revealed that AWAs can be categorized in three fractions based on loss on ignition. At the end some, recommendations for the utilization of SCBA and RHA based on the characterization results were also made for utilization as supplementary material in construction industry. Moreover, the machine learning model was constructed using input variables such as the physio-chemical properties of SCBA and RHA, element properties, and total HMs concentrations to predict the HM fractions. The application of machine learning tool to procured SCBA and RHA revealed that the model utilizing deep neural networks demonstrated performance robustly, possessing strong generalization capabilities (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.99 on the testing set), enabling the rapid and accurate prediction of HMs fractions. The element properties were found to be the primary determinant of the HMs fractions. This study adds value to the creation of sustainable approaches for managing waste and provides a framework for the characterization of waste ashes for potential utilize as a primary substance in construction materials.\u003c/p\u003e","manuscriptTitle":"AI Based Model for Prediction of Heavy Metals Using Physio-Chemical Characterization of Agricultural Waste Ashes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-22 05:46:49","doi":"10.21203/rs.3.rs-3865940/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":"b2b4ae42-303b-42c0-bbae-3ee6088f0815","owner":[],"postedDate":"January 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":28270154,"name":"Physical sciences/Engineering/Civil engineering"},{"id":28270155,"name":"Physical sciences/Materials science/Structural materials"}],"tags":[],"updatedAt":"2025-07-18T08:24:02+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-22 05:46:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3865940","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3865940","identity":"rs-3865940","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}