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dc.creatorTerzić, Anja
dc.creatorPezo, Lato
dc.creatorAndrić, Ljubiša
dc.creatorPavlović, Vladimir
dc.creatorMitić, Vojislav
dc.date.accessioned2022-04-18T15:16:27Z
dc.date.available2022-04-18T15:16:27Z
dc.date.issued2017
dc.identifier.issn0272-8842
dc.identifier.urihttp://rims.institutims.rs/handle/123456789/307
dc.description.abstractThe properties of seven montmorillonite-rich bentonites of different geological origin were investigated prior and subsequent to mechano-chemical processing in an ultra-centrifugal mill. The objective of the experiment was altering the bentonite types and activation parameters in order to determine the optimal milling conditions that produce material which is physico-mechanically and microstructurally applicable as a binder replacement and sorbent in the construction composites. The efficiency of bentonite activation was assessed by chemometrics and Artificial neural networks mathematical modeling. Principal component analysis and analysis of variance were used in the observation of the influence of input variables (bentonite chemical composition) and process parameters (milling duration, rotor velocity) on the product characteristics: density, specific surface area, grain size and distribution, cation exchange capacity, melting point, compressive strength, shrinkage and porosity. When the ANN models for the observed responses, related to predicted bentonite characteristics and quality, were compared to experimental results, they correctly predicted the responses. The processed data also adequately fitted to the regression second order polynomial models. The SOP models, which showed r(2) values from 0.357 to 0.948, and were able to predict the observed responses in a wide range of processing parameters, while ANN models performed high prediction accuracy (0.776-0.901) and can be considered as precise for response variables prediction. The combination of the conducted mathematical analyses showed that that increase/decrease in output values was stabilized after 30 min of activation. Mathematically attained interpretations were correlated with the results of the instrumental analyses (XRD, DTA/TG, SEM) to confirm the adoption of B6 bentonite as a preferable type and 30 min as an optimal milling time for acquiring quality of clay powder that will be used in structural and thermal applications.en
dc.publisherElsevier Sci Ltd, Oxford
dc.relationinfo:eu-repo/grantAgreement/MESTD/Integrated and Interdisciplinary Research (IIR or III)/45008/RS//
dc.relationinfo:eu-repo/grantAgreement/MESTD/Basic Research (BR or ON)/172057/RS//
dc.relationinfo:eu-repo/grantAgreement/MESTD/Technological Development (TD or TR)/31055/RS//
dc.relationinfo:eu-repo/grantAgreement/MESTD/Technological Development (TD or TR)/34006/RS//
dc.rightsrestrictedAccess
dc.sourceCeramics International
dc.subjectStructural and thermal applicationsen
dc.subjectMillingen
dc.subjectElectron Microscopyen
dc.subjectCompositesen
dc.subjectClaysen
dc.subjectChemical,mechanical and thermal propertiesen
dc.titleOptimization of bentonite clay mechano-chemical activation using artificial neural network modelingen
dc.typearticle
dc.rights.licenseARR
dc.citation.epage2562
dc.citation.issue2
dc.citation.other43(2): 2549-2562
dc.citation.rankaM21
dc.citation.spage2549
dc.citation.volume43
dc.identifier.doi10.1016/j.ceramint.2016.11.058
dc.identifier.scopus2-s2.0-85006341920
dc.identifier.wos000390732100129
dc.type.versionpublishedVersion


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