Forecasting and Analysing Real-Time Exergy of a Cement Plant Using a Machine Learning Approach

Forecasting and Analysing Real-Time Exergy of a Cement Plant Using a Machine Learning 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 Forecasting and Analysing Real-Time Exergy of a Cement Plant Using a Machine Learning Approach Fazeel Ahmad, Shuhaimi Mahadzir, Khairuddin Sanaullah, David Lokhat, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8041903/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract The cement industry is ranked among the most energy-intensive sectors worldwide, which accounts for extensive consumption of non-renewable fuels and the release of substantial untapped heat from the waste of the cement industry. Traditional energy balance methods are inadequate for identifying system inefficiencies, whereas exergy analysis provides a more rigorous evaluation by assessing energy quality losses. Conventional exergy models relay on enthalpy- based formulations derived from thermodynamics principles, correlating exergy with enthalpy and temperature through either Carnot factor. However, enthalpy cannot be measured directly with standard plant instruments, limiting the practicality of such methods for real-time monitoring. This study proposes a novel methodology for real-time exergy analysis of cement rotary kiln based exclusively on plant-measured variables, including temperature, pressure, and mass flowrates, integrated with machine learning tools. Real-time operational plant data of rotary kiln is analyzed using Python-based artificial intelligence algorithms to develop model. Results indicated that 454.38 MJ of exergy entered the kiln, primarily through raw materials and fuel combustion, while 3.9 MJ exited, leading to a calculated loss of 450.48 MJ. The integration of exergy analysis with machine learning, particularly Python-based ANN and AI models, enables real-time evaluation of process inefficiencies and system performance. The ANN predictions for both chemical and physical exergy show strong agreement with measured data, validating model accuracy. These models effectively capture non-linear thermodynamics relationships, enhancing predictive reliability. The proposed framework offers a pathway to optimize operations, reduce exergy losses, and promote sustainable industrial efficiency. Exergy Modelling Cement Plant Rotary Kiln Clinker Machine Learning Tools Full Text Supplementary Files DeclarationofInterest.docx FundingDeclaration.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 01 Dec, 2025 Reviewers invited by journal 01 Dec, 2025 Editor assigned by journal 07 Nov, 2025 First submitted to journal 06 Nov, 2025 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-8041903","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":553388263,"identity":"7b13f828-2967-4898-8c05-e33b10d5b058","order_by":0,"name":"Fazeel 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Technology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Exergy, Modelling, Cement Plant, Rotary Kiln, Clinker, Machine Learning Tools","lastPublishedDoi":"10.21203/rs.3.rs-8041903/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8041903/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe cement industry is ranked among the most energy-intensive sectors worldwide, which accounts for extensive consumption of non-renewable fuels and the release of substantial untapped heat from the waste of the cement industry. Traditional energy balance methods are inadequate for identifying system inefficiencies, whereas exergy analysis provides a more rigorous evaluation by assessing energy quality losses. Conventional exergy models relay on enthalpy- based formulations derived from thermodynamics principles, correlating exergy with enthalpy and temperature through either Carnot factor. However, enthalpy cannot be measured directly with standard plant instruments, limiting the practicality of such methods for real-time monitoring. This study proposes a novel methodology for real-time exergy analysis of cement rotary kiln based exclusively on plant-measured variables, including temperature, pressure, and mass flowrates, integrated with machine learning tools. Real-time operational plant data of rotary kiln is analyzed using Python-based artificial intelligence algorithms to develop model. Results indicated that 454.38 MJ of exergy entered the kiln, primarily through raw materials and fuel combustion, while 3.9 MJ exited, leading to a calculated loss of 450.48 MJ. The integration of exergy analysis with machine learning, particularly Python-based ANN and AI models, enables real-time evaluation of process inefficiencies and system performance. The ANN predictions for both chemical and physical exergy show strong agreement with measured data, validating model accuracy. These models effectively capture non-linear thermodynamics relationships, enhancing predictive reliability. The proposed framework offers a pathway to optimize operations, reduce exergy losses, and promote sustainable industrial efficiency.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e","manuscriptTitle":"Forecasting and Analysing Real-Time Exergy of a Cement Plant Using a Machine Learning Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-04 17:20:38","doi":"10.21203/rs.3.rs-8041903/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-12-01T13:41:24+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-01T11:58:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-07T07:10:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"The International Journal of Advanced Manufacturing Technology","date":"2025-11-06T08:56:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-international-journal-of-advanced-manufacturing-technology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jamt","sideBox":"Learn more about [The International Journal of Advanced Manufacturing Technology](https://www.springer.com/journal/170)","snPcode":"170","submissionUrl":"https://submission.nature.com/new-submission/170/3","title":"The International Journal of Advanced Manufacturing Technology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"53324c4b-ddb5-4e6b-a061-4f61055f36c8","owner":[],"postedDate":"December 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-25T21:08:44+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-04 17:20:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8041903","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8041903","identity":"rs-8041903","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}