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...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
University of Belgrade, Technical Faculty Bor
2024-01-01
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| 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 |
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| Summary: | 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. |
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| ISSN: | 1450-5959 2560-3159 |