Evaluation of the Effect of Using Different Types of Clinker Grinding Aids on Grinding Performance by Numerical Analysis

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dc.authorid0000-0003-0326-5015
dc.authorid0000-0001-9944-3466
dc.authorid0000-0001-8052-6587
dc.contributor.authorKaya, Yahya
dc.contributor.authorKobya, Veysel
dc.contributor.authorEser, Murat
dc.contributor.authorMardani, Naz
dc.contributor.authorBilgin, Metin
dc.contributor.authorMardani, Ali
dc.date.accessioned2026-02-03T11:59:52Z
dc.date.available2026-02-03T11:59:52Z
dc.date.issued2025
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractTo develop more environmentally friendly and sustainable cementitious systems, the use of grinding aids (GAs) during the clinker grinding process has increasingly gained attention. Although the mechanisms of the action of grinding aids (GAs) are known, the selection of an effective grinding aid (GA) can be difficult due to the complexity of appropriate selection criteria. For this reason, it is important to model the effect of GA properties on grinding performance. In this study, seven different types of GAs were used in four different dosages, and time-dependent grinding was performed. The Blaine fineness values of cements were compared after each grinding process. In addition, the modeling of these parameters using machine learning and ensemble learning methods was discussed. The Synthetic Minority Over-sampling Technique (Smote) was used to generate artificial data and increase the number of data for the grinding efficiency experiment. The data were modeled using methods such as Artificial Neural Networks (ANNs), Attentive Interpretable Tabular Learning (TabNet), Random Forests (RFs), and the XGBoost Regressor (XGBoost), and the ranking of the parameters affecting the Blaine properties was determined using the XGBoost method. The XGBoost method achieved the best results in the MAE, RMSE, and LogCosh metrics with values of 21.0384, 33.7379, and 15.4846, respectively, in the experimental modeling studies with augmented data. This study contributes to a better understanding of the relationship between GA selection and milling process performance.
dc.description.sponsorshipBursa Uludag University BAP
dc.description.sponsorship[FPDD-2025-2210]
dc.description.sponsorshipThis research was funded by Bursa Uludag University BAP, grant number FPDD-2025-2210.
dc.identifier.doi10.3390/ma18122712
dc.identifier.issn1996-1944
dc.identifier.issue12
dc.identifier.pmid40572843
dc.identifier.scopus2-s2.0-105009023264
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/ma18122712
dc.identifier.urihttps://hdl.handle.net/20.500.12428/34446
dc.identifier.volume18
dc.identifier.wosWOS:001517240100001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofMaterials
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260130
dc.subjectgrinding efficiency
dc.subjectgrinding aids
dc.subjectBlaine fineness
dc.subjectartificial neural networks
dc.subjectTabNet
dc.subjectXGBoost
dc.subjectRandom Forest
dc.subjectmachine learning
dc.subjectregression analysis
dc.titleEvaluation of the Effect of Using Different Types of Clinker Grinding Aids on Grinding Performance by Numerical Analysis
dc.typeArticle

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