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| Research on the Method of Extracting the Main Feature of the Sink in the Flotation Machine Based on Data Mining and the Soft - Sensing Technology of the Sink |
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Received:April 17, 2017
Revised:May 02, 2017
Accepted:May 05, 2017
Published Online:March 29, 2018
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| DOI： |
| KeyWord:flotation machine sink; data mining; warning; soft-sensing technology |
| Author | Institution |
| FAN Lingxiao |
Beijing Engineering Research Center on Efficient and Energy Conservation Equipment of Mineral Processing,State Key Laboratory of Mineral Processing,BGRIMM Machinery and Automation Technology Co.,Ltd. |
| Yang Wenwang |
Beijing Engineering Research Center on Efficient and Energy Conservation Equipment of Mineral Processing,State Key Laboratory of Mineral Processing,BGRIMM Machinery and Automation Technology Co.,Ltd. |
| Li Qiang |
Beijing Engineering Research Center on Efficient and Energy Conservation Equipment of Mineral Processing,State Key Laboratory of Mineral Processing,BGRIMM Machinery and Automation Technology Co.,Ltd. |
| Liu Limin |
Beijing Engineering Research Center on Efficient and Energy Conservation Equipment of Mineral Processing,State Key Laboratory of Mineral Processing,BGRIMM Machinery and Automation Technology Co.,Ltd. |
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| Abstract: |
| Flotation machine sink can clog the pulp circulation path,leading to equipment downtime, it is the most important problem facing thebeneficiation process. This paper intends to search the mechanism of inducedsettlement mechanism and dynamic sink, coupling analysis between ducing factor andappearance factor, research on feature Selection of sink main based on data mining. Finally, developed a early warning system for flotation machine sinking based on BP neural network model. This research contributes to enhance the operationefficiency of the flotation, improve structure, optimization of design parameters,reduce maintenance costs and operating strength. |
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