Experimental and Artificial Intelligence Approaches for the Analysis of Parameters Affecting Zinc Ion Adsorption in Environmental Remediation

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Abstract Pollution caused by heavy metals in industrial wastewater has become a serious environmental concern, posing significant risk to both human health and ecosystems. Industrial effluents often contain pollutants form natural and anthropogenic sources such as volcanic activity, industrial processes, and vehicle emissions. Various treatment methods have been explored, but many are energy-intensive and costly. Among them adsorption stands out for its high selectivity, ease of operation, and reusability of adsorbents. In this study, a bio-based adsorbent derived from wheat straw was used to remove Zn (II) ion from wastewater. The removal efficiency was examined under varying concentrations, temperatures, and pH levels. Statistical analysis and artificial intelligence (AI) techniques were employed to evaluate the results, with Ker Person’s correlation used to validate the data. The Langmuir isotherm model showed a strong linear relationship (correlation coefficient 0.98 ), confirming effective adsorption. A Random Forest regression model further enhanced prediction accuracy, yielding an R 2 value of 0.995 and a mean square error (MSE) of 8.384. The parity and 3D surface plots demonstrated excellent agreement between predicted and experimental outcomes. Overall, wheat straw proved to be a cost-effective and sustainable adsorbent for Zn(II) removal, offering promising potential for wastewater treatment.
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Experimental and Artificial Intelligence Approaches for the Analysis of Parameters Affecting Zinc Ion Adsorption in Environmental Remediation | 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 Experimental and Artificial Intelligence Approaches for the Analysis of Parameters Affecting Zinc Ion Adsorption in Environmental Remediation Fazeel Ahmad, Khurram Shahzad Baig, Shuhaimi Bin Mahdzir, Khairuddin Sanaullah, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8629685/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 Pollution caused by heavy metals in industrial wastewater has become a serious environmental concern, posing significant risk to both human health and ecosystems. Industrial effluents often contain pollutants form natural and anthropogenic sources such as volcanic activity, industrial processes, and vehicle emissions. Various treatment methods have been explored, but many are energy-intensive and costly. Among them adsorption stands out for its high selectivity, ease of operation, and reusability of adsorbents. In this study, a bio-based adsorbent derived from wheat straw was used to remove Zn (II) ion from wastewater. The removal efficiency was examined under varying concentrations, temperatures, and pH levels. Statistical analysis and artificial intelligence (AI) techniques were employed to evaluate the results, with Ker Person’s correlation used to validate the data. The Langmuir isotherm model showed a strong linear relationship (correlation coefficient 0.98 ), confirming effective adsorption. A Random Forest regression model further enhanced prediction accuracy, yielding an R 2 value of 0.995 and a mean square error (MSE) of 8.384. The parity and 3D surface plots demonstrated excellent agreement between predicted and experimental outcomes. Overall, wheat straw proved to be a cost-effective and sustainable adsorbent for Zn(II) removal, offering promising potential for wastewater treatment. Heavy metals water purification Zn ions removal Statistical analysis Artificial Intelligence (AI) Full Text 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. 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13:21:42","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":899937,"visible":true,"origin":"","legend":"","description":"","filename":"ResearchPaper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8629685/v1_covered_e48f24ff-1354-481f-9307-7ca13dc3872c.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eExperimental and Artificial Intelligence Approaches for the Analysis of Parameters Affecting Zinc Ion Adsorption in Environmental Remediation\u003c/p\u003e","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":"Heavy metals, water purification, Zn ions removal, Statistical analysis, Artificial Intelligence (AI)","lastPublishedDoi":"10.21203/rs.3.rs-8629685/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8629685/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePollution caused by heavy metals in industrial wastewater has become a serious environmental concern, posing significant risk to both human health and ecosystems. Industrial effluents often contain pollutants form natural and anthropogenic sources such as volcanic activity, industrial processes, and vehicle emissions. Various treatment methods have been explored, but many are energy-intensive and costly. Among them adsorption stands out for its high selectivity, ease of operation, and reusability of adsorbents. \u0026nbsp;In this study, a bio-based adsorbent derived from wheat straw was used to remove Zn (II) ion from wastewater. The removal efficiency was examined under varying concentrations, temperatures, and pH levels. \u0026nbsp;Statistical analysis and artificial intelligence (AI) techniques were employed to evaluate the results, with Ker Person’s correlation used to validate the data. The Langmuir isotherm model showed a strong linear relationship (correlation coefficient 0.98 ), confirming effective adsorption. A Random Forest regression model further enhanced prediction accuracy, yielding an R\u003csup\u003e2\u003c/sup\u003e value of 0.995 and a mean square error (MSE) of 8.384. The parity and 3D surface plots demonstrated excellent agreement between predicted and experimental outcomes. Overall, wheat straw proved to be a cost-effective and sustainable adsorbent for Zn(II) removal, offering promising potential for wastewater treatment.\u003c/p\u003e","manuscriptTitle":"Experimental and Artificial Intelligence Approaches for the Analysis of Parameters Affecting Zinc Ion Adsorption in Environmental Remediation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-23 11:58:29","doi":"10.21203/rs.3.rs-8629685/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":"53324c4b-ddb5-4e6b-a061-4f61055f36c8","owner":[],"postedDate":"January 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-23T08:14:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-23 11:58:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8629685","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8629685","identity":"rs-8629685","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}

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