Methodology and Model to Predict HPGR Throughput Based on Piston Press Testing
Sizing High-Pressure Grinding Rolls (HPGR) requires a large quantity of material, making it not attractive and costly to be considered for new mining projects regardless of their energy consumption reduction benefits. Ongoing efforts are being made at the University of British Columbia to predict th...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-10-01
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| Series: | Minerals |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-163X/12/11/1377 |
| _version_ | 1797467191625383936 |
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| author | Giovanni Pamparana Bern Klein Mauricio Guimaraes Bergerman |
| author_facet | Giovanni Pamparana Bern Klein Mauricio Guimaraes Bergerman |
| author_sort | Giovanni Pamparana |
| collection | DOAJ |
| description | Sizing High-Pressure Grinding Rolls (HPGR) requires a large quantity of material, making it not attractive and costly to be considered for new mining projects regardless of their energy consumption reduction benefits. Ongoing efforts are being made at the University of British Columbia to predict the behaviour of the HPGR using a low quantity of material on a piston-and-die press apparatus. Although the energy requirements and size reduction predictive models are already developed, there is still a need to predict the HPGR throughput on a small-scale test. This paper presents a new model to predict the HPGR throughput based on the previously developed model to predict the operational gap by using less than 2 kg of sample. The throughput model was developed using machine learning techniques and calibrated using pilot-scale HPGR tests and piston press tests. The resulting model has an R<sup>2</sup> of 0.91 with an average prediction error of ±4.2%. The developed methodology has the potential to fill the gap of the missing throughput model. Further pilot-scale HPGR testing is required to continue validating the model. |
| first_indexed | 2024-03-09T18:49:08Z |
| format | Article |
| id | doaj.art-9d053add848e432395a078393066abdb |
| institution | Directory Open Access Journal |
| issn | 2075-163X |
| language | English |
| last_indexed | 2024-03-09T18:49:08Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Minerals |
| spelling | doaj.art-9d053add848e432395a078393066abdb2023-11-24T05:58:25ZengMDPI AGMinerals2075-163X2022-10-011211137710.3390/min12111377Methodology and Model to Predict HPGR Throughput Based on Piston Press TestingGiovanni Pamparana0Bern Klein1Mauricio Guimaraes Bergerman2Norman B. Keevil Institute of Mining Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, CanadaNorman B. Keevil Institute of Mining Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, CanadaNorman B. Keevil Institute of Mining Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, CanadaSizing High-Pressure Grinding Rolls (HPGR) requires a large quantity of material, making it not attractive and costly to be considered for new mining projects regardless of their energy consumption reduction benefits. Ongoing efforts are being made at the University of British Columbia to predict the behaviour of the HPGR using a low quantity of material on a piston-and-die press apparatus. Although the energy requirements and size reduction predictive models are already developed, there is still a need to predict the HPGR throughput on a small-scale test. This paper presents a new model to predict the HPGR throughput based on the previously developed model to predict the operational gap by using less than 2 kg of sample. The throughput model was developed using machine learning techniques and calibrated using pilot-scale HPGR tests and piston press tests. The resulting model has an R<sup>2</sup> of 0.91 with an average prediction error of ±4.2%. The developed methodology has the potential to fill the gap of the missing throughput model. Further pilot-scale HPGR testing is required to continue validating the model.https://www.mdpi.com/2075-163X/12/11/1377HPGRcomminutionpiston press testthroughputmachine learning |
| spellingShingle | Giovanni Pamparana Bern Klein Mauricio Guimaraes Bergerman Methodology and Model to Predict HPGR Throughput Based on Piston Press Testing Minerals HPGR comminution piston press test throughput machine learning |
| title | Methodology and Model to Predict HPGR Throughput Based on Piston Press Testing |
| title_full | Methodology and Model to Predict HPGR Throughput Based on Piston Press Testing |
| title_fullStr | Methodology and Model to Predict HPGR Throughput Based on Piston Press Testing |
| title_full_unstemmed | Methodology and Model to Predict HPGR Throughput Based on Piston Press Testing |
| title_short | Methodology and Model to Predict HPGR Throughput Based on Piston Press Testing |
| title_sort | methodology and model to predict hpgr throughput based on piston press testing |
| topic | HPGR comminution piston press test throughput machine learning |
| url | https://www.mdpi.com/2075-163X/12/11/1377 |
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