Show simple item record

dc.creatorTerzić, Anja
dc.creatorPezo, Lato
dc.creatorAndrić, Ljubiša
dc.creatorPavlović, Vladimir B.
dc.creatorMitić, Vojislav V.
dc.date.accessioned2018-04-11T10:12:09Z
dc.date.available2018-04-11T10:12:09Z
dc.date.issued2017
dc.identifier.issn0272-8842
dc.identifier.urihttp://dais.sanu.ac.rs/123456789/2350
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 r2 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.format43 2 (2017) 2549-2562
dc.languageen
dc.publisherElsevier
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 Internationalen
dc.subjectbentonite clay
dc.subjectneural network modeling
dc.subjectmechanochemical activation
dc.subjectmilling
dc.titleOptimization of bentonite clay mechano-chemical activation using artificial neural network modelingen
dc.typearticle
dc.rights.licenseARR
dcterms.abstractМитић, Војислав В.; Терзић, Aња; Пезо, Лато; Aндрић, Љубиша; Павловић, Владимир Б.;
dc.citation.spage2549
dc.citation.epage2562
dc.citation.volume43
dc.citation.issue2
dc.identifier.wos000390732100129
dc.identifier.doi10.1016/j.ceramint.2016.11.058
dc.identifier.scopus2-s2.0-85006341920
dc.type.versionpublishedVersion


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record