Research on Grinding Characteristics and Comparison of Particle-Size-Composition Prediction of Rich and Poor Ores
The particle size composition of grinding products will significantly affect the technical and economic indexes of subsequent separation operations. The polymetallic complex ores from Tongkeng and Gaofeng are selected as the research object in this paper. Through the JK drop-weight test, the batch g...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Minerals |
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| Online Access: | https://www.mdpi.com/2075-163X/12/11/1354 |
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| author | Shaojian Ma Hengjun Li Zhichao Shuai Jinlin Yang Wenzhe Xu Xingjian Deng |
| author_facet | Shaojian Ma Hengjun Li Zhichao Shuai Jinlin Yang Wenzhe Xu Xingjian Deng |
| author_sort | Shaojian Ma |
| collection | DOAJ |
| description | The particle size composition of grinding products will significantly affect the technical and economic indexes of subsequent separation operations. The polymetallic complex ores from Tongkeng and Gaofeng are selected as the research object in this paper. Through the JK drop-weight test, the batch grinding test, and the population-balance kinetic model of grinding with the Simulink platform, the grinding characteristics of the two types of ores and the particle-size-composition prediction methods of grinding products are studied. The results show that the impact-crushing capacity of Tongkeng ore and Gaofeng ore are “medium” grade and “soft” grade, respectively. The crushing resistance of Tongkeng ore increases with the decrease in particle size, and the crushing effect is more easily affected by particle size than that of Gaofeng ore. For the same ore, the accuracy order of the three methods is: PSO–BP method > JK drop-weight method > <i>B</i><sub>III</sub> method. For the same method, only the <i>B</i><sub>III</sub> method has higher accuracy in predicting Gaofeng ore than Tongkeng ore, and other methods have better accuracy in predicting Tongkeng ore than Gaofeng ore. The prediction accuracy of the <i>B</i><sub>III</sub> method is inferior to that of the JK drop-weight method and the PSO–BP method and is easily affected by the difference in mineral properties. The PSO–BP method has a high prediction accuracy and fast model operation speed, but the accuracy and speed of the iterative results are easily affected by parameters such as algorithm program weight and threshold. The parameter-solving process of each prediction method is based on different simplifications and assumptions. Therefore, appropriate hypothetical theoretical models should be selected according to different ore properties for practical application. |
| first_indexed | 2024-03-09T18:49:04Z |
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| id | doaj.art-844edd22cd0c4222aa1e9f6416878519 |
| institution | Directory Open Access Journal |
| issn | 2075-163X |
| language | English |
| last_indexed | 2024-03-09T18:49:04Z |
| publishDate | 2022-10-01 |
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| record_format | Article |
| series | Minerals |
| spelling | doaj.art-844edd22cd0c4222aa1e9f64168785192023-11-24T05:58:03ZengMDPI AGMinerals2075-163X2022-10-011211135410.3390/min12111354Research on Grinding Characteristics and Comparison of Particle-Size-Composition Prediction of Rich and Poor OresShaojian Ma0Hengjun Li1Zhichao Shuai2Jinlin Yang3Wenzhe Xu4Xingjian Deng5College of Chemistry and Chemical Engineering, Guangxi University, Nanning 530004, ChinaCollege of Chemistry and Chemical Engineering, Guangxi University, Nanning 530004, ChinaCollege of Resources, Environment and Materials, Guangxi University, Nanning 530004, ChinaCollege of Resources, Environment and Materials, Guangxi University, Nanning 530004, ChinaCollege of Resources, Environment and Materials, Guangxi University, Nanning 530004, ChinaCollege of Resources, Environment and Materials, Guangxi University, Nanning 530004, ChinaThe particle size composition of grinding products will significantly affect the technical and economic indexes of subsequent separation operations. The polymetallic complex ores from Tongkeng and Gaofeng are selected as the research object in this paper. Through the JK drop-weight test, the batch grinding test, and the population-balance kinetic model of grinding with the Simulink platform, the grinding characteristics of the two types of ores and the particle-size-composition prediction methods of grinding products are studied. The results show that the impact-crushing capacity of Tongkeng ore and Gaofeng ore are “medium” grade and “soft” grade, respectively. The crushing resistance of Tongkeng ore increases with the decrease in particle size, and the crushing effect is more easily affected by particle size than that of Gaofeng ore. For the same ore, the accuracy order of the three methods is: PSO–BP method > JK drop-weight method > <i>B</i><sub>III</sub> method. For the same method, only the <i>B</i><sub>III</sub> method has higher accuracy in predicting Gaofeng ore than Tongkeng ore, and other methods have better accuracy in predicting Tongkeng ore than Gaofeng ore. The prediction accuracy of the <i>B</i><sub>III</sub> method is inferior to that of the JK drop-weight method and the PSO–BP method and is easily affected by the difference in mineral properties. The PSO–BP method has a high prediction accuracy and fast model operation speed, but the accuracy and speed of the iterative results are easily affected by parameters such as algorithm program weight and threshold. The parameter-solving process of each prediction method is based on different simplifications and assumptions. Therefore, appropriate hypothetical theoretical models should be selected according to different ore properties for practical application.https://www.mdpi.com/2075-163X/12/11/1354polymetallic sulfide oregrindingdrop-weight testpopulation-balance kinetic model |
| spellingShingle | Shaojian Ma Hengjun Li Zhichao Shuai Jinlin Yang Wenzhe Xu Xingjian Deng Research on Grinding Characteristics and Comparison of Particle-Size-Composition Prediction of Rich and Poor Ores Minerals polymetallic sulfide ore grinding drop-weight test population-balance kinetic model |
| title | Research on Grinding Characteristics and Comparison of Particle-Size-Composition Prediction of Rich and Poor Ores |
| title_full | Research on Grinding Characteristics and Comparison of Particle-Size-Composition Prediction of Rich and Poor Ores |
| title_fullStr | Research on Grinding Characteristics and Comparison of Particle-Size-Composition Prediction of Rich and Poor Ores |
| title_full_unstemmed | Research on Grinding Characteristics and Comparison of Particle-Size-Composition Prediction of Rich and Poor Ores |
| title_short | Research on Grinding Characteristics and Comparison of Particle-Size-Composition Prediction of Rich and Poor Ores |
| title_sort | research on grinding characteristics and comparison of particle size composition prediction of rich and poor ores |
| topic | polymetallic sulfide ore grinding drop-weight test population-balance kinetic model |
| url | https://www.mdpi.com/2075-163X/12/11/1354 |
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