Optimization of micronizing zeolite grinding using artificial neural networks

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dc.citation.epage149
dc.citation.rankM33
dc.citation.spage143
dc.contributor.authorNikolić, Vladimir
dc.contributor.authorTrumić, Milan
dc.contributor.authorTanikić, Dejan
dc.date.accessioned2024-01-22T08:34:20Z
dc.date.available2024-01-22T08:34:20Z
dc.date.issued2023
dc.description.abstractThe aim of the experiment was to determine the optimal grinding conditions for obtaining 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 certain classes of zeolite size after micronization was performed through grinding kinetics. An artificial neural network was developed based on the obtained results, and then compared with the obtained experimental results.
dc.identifier.isbn978-86-6305-133-1
dc.identifier.urihttps://imprc.tfbor.bg.ac.rs/download/IMPRC_2023_Proceedings.pdf
dc.identifier.urihttps://repozitorijum.tfbor.bg.ac.rs/handle/123456789/5804
dc.language.isoen
dc.publisherUniversity of Belgrade, Technical Faculty in Bor
dc.rights.licenseCC-BY-NC-ND
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.sourceProceedings - XV International Mineral Processing and Recycling Conference, IMPRC, 17-19 May 2023, Belgrade, Serbia, 2023, 143-149
dc.subjectZeolite
dc.subjectMicronizing Grinding
dc.subjectSpecific Surface
dc.subjectArtificial Neural Networks
dc.titleOptimization of micronizing zeolite grinding using artificial neural networks
dc.typeconferenceObject
dc.type.versionpublishedVersion

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