Testing Method for Intelligent Loading of Mining Emulsion Pump Based on Digital Relief Valve and BP Neural Network Control Algorithm
The mining emulsion pump is mainly used on a fully mechanized coal mining face, but it is rarely used on other occasions, so research on its loading test method is relatively limited. This paper proposes the application of a digital relief valve to the emulsion pump loading test. In addition, the sm...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Machines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-1702/10/10/896 |
| _version_ | 1797471951346728960 |
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| author | Jie Tian Wenchao Liu Hongyao Wang |
| author_facet | Jie Tian Wenchao Liu Hongyao Wang |
| author_sort | Jie Tian |
| collection | DOAJ |
| description | The mining emulsion pump is mainly used on a fully mechanized coal mining face, but it is rarely used on other occasions, so research on its loading test method is relatively limited. This paper proposes the application of a digital relief valve to the emulsion pump loading test. In addition, the small number of plungers in the emulsion pump will lead to large flow pulsation and pressure pulsation, and the nominal flow of different types of emulsion pumps varies greatly. These factors lead to the deficiency of a traditional PID control algorithm in control accuracy and efficiency. In order to improve control accuracy and efficiency, firstly, the influence of the flow rate of the tested pump and extension of the linear stepping motor shaft on the working pressure is studied. A backpropagation (BP) artificial neural network (ANN) model is used to fit a functional relationship between the three parameters. The flow rate of the tested pump and target pressure were provided as inputs to predict the extension of the linear stepping motor shaft, thereby realizing the remote intelligent control of the system pressure. Next, a BP ANN model is constructed, and its reliability is verified; the BP neural network algorithm and proportional-integral-derivative (PID) algorithm are compared through simulation. The simulation results show that the BP neural network algorithm has high control accuracy and small overshoot. Finally, two pumps with different flows are tested in a self-developed digital relief valve and test platform. The test results show that the proposed loading test method is intelligent and efficient, and it has high accuracy. |
| first_indexed | 2024-03-09T19:55:22Z |
| format | Article |
| id | doaj.art-bb5a53433cd14cc5a6541955f5dbd835 |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-03-09T19:55:22Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-bb5a53433cd14cc5a6541955f5dbd8352023-11-24T00:59:24ZengMDPI AGMachines2075-17022022-10-01101089610.3390/machines10100896Testing Method for Intelligent Loading of Mining Emulsion Pump Based on Digital Relief Valve and BP Neural Network Control AlgorithmJie Tian0Wenchao Liu1Hongyao Wang2School of Mechanical Electronic and Information Engineering, China University of Mining and Technology (Beijing), Beijing 100083, ChinaSchool of Mechanical Electronic and Information Engineering, China University of Mining and Technology (Beijing), Beijing 100083, ChinaSchool of Mechanical Electronic and Information Engineering, China University of Mining and Technology (Beijing), Beijing 100083, ChinaThe mining emulsion pump is mainly used on a fully mechanized coal mining face, but it is rarely used on other occasions, so research on its loading test method is relatively limited. This paper proposes the application of a digital relief valve to the emulsion pump loading test. In addition, the small number of plungers in the emulsion pump will lead to large flow pulsation and pressure pulsation, and the nominal flow of different types of emulsion pumps varies greatly. These factors lead to the deficiency of a traditional PID control algorithm in control accuracy and efficiency. In order to improve control accuracy and efficiency, firstly, the influence of the flow rate of the tested pump and extension of the linear stepping motor shaft on the working pressure is studied. A backpropagation (BP) artificial neural network (ANN) model is used to fit a functional relationship between the three parameters. The flow rate of the tested pump and target pressure were provided as inputs to predict the extension of the linear stepping motor shaft, thereby realizing the remote intelligent control of the system pressure. Next, a BP ANN model is constructed, and its reliability is verified; the BP neural network algorithm and proportional-integral-derivative (PID) algorithm are compared through simulation. The simulation results show that the BP neural network algorithm has high control accuracy and small overshoot. Finally, two pumps with different flows are tested in a self-developed digital relief valve and test platform. The test results show that the proposed loading test method is intelligent and efficient, and it has high accuracy.https://www.mdpi.com/2075-1702/10/10/896emulsion pumpdigital relief valveloading testBP neural networkfunction fitting |
| spellingShingle | Jie Tian Wenchao Liu Hongyao Wang Testing Method for Intelligent Loading of Mining Emulsion Pump Based on Digital Relief Valve and BP Neural Network Control Algorithm Machines emulsion pump digital relief valve loading test BP neural network function fitting |
| title | Testing Method for Intelligent Loading of Mining Emulsion Pump Based on Digital Relief Valve and BP Neural Network Control Algorithm |
| title_full | Testing Method for Intelligent Loading of Mining Emulsion Pump Based on Digital Relief Valve and BP Neural Network Control Algorithm |
| title_fullStr | Testing Method for Intelligent Loading of Mining Emulsion Pump Based on Digital Relief Valve and BP Neural Network Control Algorithm |
| title_full_unstemmed | Testing Method for Intelligent Loading of Mining Emulsion Pump Based on Digital Relief Valve and BP Neural Network Control Algorithm |
| title_short | Testing Method for Intelligent Loading of Mining Emulsion Pump Based on Digital Relief Valve and BP Neural Network Control Algorithm |
| title_sort | testing method for intelligent loading of mining emulsion pump based on digital relief valve and bp neural network control algorithm |
| topic | emulsion pump digital relief valve loading test BP neural network function fitting |
| url | https://www.mdpi.com/2075-1702/10/10/896 |
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