Optimization of micronizing zeolite grinding using artificial neural networks

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dc.citation.epage32
dc.citation.issue1
dc.citation.rankM24
dc.citation.spage23
dc.citation.volume60
dc.contributor.authorNikolić, Vladimir
dc.contributor.authorTrumić, Milan
dc.contributor.authorTanikić, Dejan
dc.contributor.authorTrumić, Maja
dc.date.accessioned2024-12-20T08:38:19Z
dc.date.available2024-12-20T08:38:19Z
dc.date.issued2024
dc.description.abstractThe micronizing grinding of natural zeolite, of the clinoptilolite type, was investigated in a ring mill. The aim of the experiment was to determine the optimal grinding conditions to obtain a powder with appropriate physico-chemical and microstructural characteristics that would find its potential application as a binder and ion exchanger in structural composites. The analysis of specific size classes of zeolite e after micronization was performed by grinding kinetics. The research was carried out on previously prepared zeolite samples, on wider and narrower size classes (-3.35 + 0 mm;-3.35 + 2.36 mm; -2.36 + 1.18 mm; -1.18 + 0 mm) and different starting masses (50 g, 100 g, 200 g). Fine grinding was carried out at different time intervals (20 s, 45 s, 75 s, 120 s, 300 s, 900 s). A sieve analysis was performed on the grinding products, the content of the size class (-5 + 0) μm and the specific surface area of these products were determined. XRD analysis was performed on individual grinding products to take into account possible changes in the zeolite material itself. Based on the results obtained, an artificial neural network was developed and then compared with the experimental results. The artificial neural network models have achieved a satisfactory prediction accuracy (0.989 - 0.997) and can be considered accurate and very useful for the prediction of variable responses.
dc.identifier.doi10.5937/JMMA2401023N
dc.identifier.issn1450-5959
dc.identifier.issn2560-3159
dc.identifier.urihttps://repozitorijum.tfbor.bg.ac.rs/handle/123456789/5923
dc.language.isoen
dc.publisherUniversity of Belgrade, Technical faculty in Bor
dc.rights.licenseCC-BY-SA
dc.rights.urihttp://creativecommons.org/licenses/by-sa/4.0/
dc.sourceJournal of Mining and Metallurgy, Section A
dc.subjectzeolite
dc.subjectmicronizing grinding
dc.subjectspecific surface
dc.subjectartificial neural networks.
dc.titleOptimization of micronizing zeolite grinding using artificial neural networks
dc.title.alternativeOptimizacija mikronizacije mlevenja zeolita korišćenjem veštačkih neuronskih mreža
dc.typearticle
dc.type.versionpublishedVersion

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