Optimization of micronizing zeolite grinding using artificial neural networks
The 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...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
University of Belgrade, Technical Faculty Bor
2024-01-01
|
| Series: | Journal of Mining and Metallurgy. Section A: Mining |
| Subjects: | |
| Online Access: | https://scindeks-clanci.ceon.rs/data/pdf/1450-5959/2024/1450-59592401023N.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841554257315102720 |
|---|---|
| author | Nikolić V. Trumić M. Tanikić D. Trumić M.S. |
| author_facet | Nikolić V. Trumić M. Tanikić D. Trumić M.S. |
| author_sort | Nikolić V. |
| collection | DOAJ |
| description | The 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) mm 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. |
| format | Article |
| id | doaj-art-6e02a79812b94d2ead29bbf8a17707ce |
| institution | Kabale University |
| issn | 1450-5959 2560-3159 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | University of Belgrade, Technical Faculty Bor |
| record_format | Article |
| series | Journal of Mining and Metallurgy. Section A: Mining |
| spelling | doaj-art-6e02a79812b94d2ead29bbf8a17707ce2025-01-08T16:23:41ZengUniversity of Belgrade, Technical Faculty BorJournal of Mining and Metallurgy. Section A: Mining1450-59592560-31592024-01-01601233210.5937/JMMA2401023N1450-59592401023NOptimization of micronizing zeolite grinding using artificial neural networksNikolić V.0https://orcid.org/0000-0003-1885-1156Trumić M.1https://orcid.org/0000-0002-4321-5218Tanikić D.2https://orcid.org/0000-0003-0702-5721Trumić M.S.3https://orcid.org/0000-0001-9361-4412University of Belgrade, Technical Faculty, Bor, SerbiaUniversity of Belgrade, Technical Faculty, Bor, SerbiaUniversity of Belgrade, Technical Faculty, Bor, SerbiaUniversity of Belgrade, Technical Faculty, Bor, SerbiaThe 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) mm 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.https://scindeks-clanci.ceon.rs/data/pdf/1450-5959/2024/1450-59592401023N.pdfzeolitemicronizing grindingspecific surfaceartificial neural networks |
| spellingShingle | Nikolić V. Trumić M. Tanikić D. Trumić M.S. Optimization of micronizing zeolite grinding using artificial neural networks Journal of Mining and Metallurgy. Section A: Mining zeolite micronizing grinding specific surface artificial neural networks |
| title | Optimization of micronizing zeolite grinding using artificial neural networks |
| title_full | Optimization of micronizing zeolite grinding using artificial neural networks |
| title_fullStr | Optimization of micronizing zeolite grinding using artificial neural networks |
| title_full_unstemmed | Optimization of micronizing zeolite grinding using artificial neural networks |
| title_short | Optimization of micronizing zeolite grinding using artificial neural networks |
| title_sort | optimization of micronizing zeolite grinding using artificial neural networks |
| topic | zeolite micronizing grinding specific surface artificial neural networks |
| url | https://scindeks-clanci.ceon.rs/data/pdf/1450-5959/2024/1450-59592401023N.pdf |
| work_keys_str_mv | AT nikolicv optimizationofmicronizingzeolitegrindingusingartificialneuralnetworks AT trumicm optimizationofmicronizingzeolitegrindingusingartificialneuralnetworks AT tanikicd optimizationofmicronizingzeolitegrindingusingartificialneuralnetworks AT trumicms optimizationofmicronizingzeolitegrindingusingartificialneuralnetworks |