Measurement-Based Modelling of Material Moisture and Particle Classification for Control of Copper Ore Dry Grinding Process
Moisture of bulk material has a significant impact on energetic efficiency of dry grinding, resultant particle size distribution and particle shape, and conditions of powder transport. As a consequence, moisture needs to be measured or estimated (modelled) in many points. This research investigates...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/2/667 |
| _version_ | 1797409834128113664 |
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| author | Oliwia Krauze Dariusz Buchczik Sebastian Budzan |
| author_facet | Oliwia Krauze Dariusz Buchczik Sebastian Budzan |
| author_sort | Oliwia Krauze |
| collection | DOAJ |
| description | Moisture of bulk material has a significant impact on energetic efficiency of dry grinding, resultant particle size distribution and particle shape, and conditions of powder transport. As a consequence, moisture needs to be measured or estimated (modelled) in many points. This research investigates mutual relations between material moisture and particle classification process in a grinding installation. The experimental setup involves an inertial-impingement classifier and cyclone being part of dry grinding circuit with electromagnetic mill and recycle of coarse particles. The tested granular material is copper ore of particle size 0–1.25 mm and relative moisture content 0.5–5%, fed to the installation at various rates. Higher moisture of input material is found to change the operation of the classifier. Computed correlation coefficients show increased content of fine particles in lower product of classification. Additionally, drying of lower and upper classification products with respect to moisture of input material is modelled. Straight line models with and without saturation are estimated with recursive least squares method accounting for measurement errors in both predictor and response variables. These simple models are intended for use in automatic control system of the grinding installation. |
| first_indexed | 2024-03-09T04:20:20Z |
| format | Article |
| id | doaj.art-c0466e7c18454ac99c6b35c75b90fe15 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T04:20:20Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c0466e7c18454ac99c6b35c75b90fe152023-12-03T13:48:24ZengMDPI AGSensors1424-82202021-01-0121266710.3390/s21020667Measurement-Based Modelling of Material Moisture and Particle Classification for Control of Copper Ore Dry Grinding ProcessOliwia Krauze0Dariusz Buchczik1Sebastian Budzan2Department of Measurements and Control, Silesian University of Technology, Akademicka Street 16, 44-100 Gliwice, PolandDepartment of Measurements and Control, Silesian University of Technology, Akademicka Street 16, 44-100 Gliwice, PolandDepartment of Measurements and Control, Silesian University of Technology, Akademicka Street 16, 44-100 Gliwice, PolandMoisture of bulk material has a significant impact on energetic efficiency of dry grinding, resultant particle size distribution and particle shape, and conditions of powder transport. As a consequence, moisture needs to be measured or estimated (modelled) in many points. This research investigates mutual relations between material moisture and particle classification process in a grinding installation. The experimental setup involves an inertial-impingement classifier and cyclone being part of dry grinding circuit with electromagnetic mill and recycle of coarse particles. The tested granular material is copper ore of particle size 0–1.25 mm and relative moisture content 0.5–5%, fed to the installation at various rates. Higher moisture of input material is found to change the operation of the classifier. Computed correlation coefficients show increased content of fine particles in lower product of classification. Additionally, drying of lower and upper classification products with respect to moisture of input material is modelled. Straight line models with and without saturation are estimated with recursive least squares method accounting for measurement errors in both predictor and response variables. These simple models are intended for use in automatic control system of the grinding installation.https://www.mdpi.com/1424-8220/21/2/667moisturemoisture modellingpneumatic classification of particlesparticle sizegrindingelectromagnetic mill |
| spellingShingle | Oliwia Krauze Dariusz Buchczik Sebastian Budzan Measurement-Based Modelling of Material Moisture and Particle Classification for Control of Copper Ore Dry Grinding Process Sensors moisture moisture modelling pneumatic classification of particles particle size grinding electromagnetic mill |
| title | Measurement-Based Modelling of Material Moisture and Particle Classification for Control of Copper Ore Dry Grinding Process |
| title_full | Measurement-Based Modelling of Material Moisture and Particle Classification for Control of Copper Ore Dry Grinding Process |
| title_fullStr | Measurement-Based Modelling of Material Moisture and Particle Classification for Control of Copper Ore Dry Grinding Process |
| title_full_unstemmed | Measurement-Based Modelling of Material Moisture and Particle Classification for Control of Copper Ore Dry Grinding Process |
| title_short | Measurement-Based Modelling of Material Moisture and Particle Classification for Control of Copper Ore Dry Grinding Process |
| title_sort | measurement based modelling of material moisture and particle classification for control of copper ore dry grinding process |
| topic | moisture moisture modelling pneumatic classification of particles particle size grinding electromagnetic mill |
| url | https://www.mdpi.com/1424-8220/21/2/667 |
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