Optimization of the Screw Conveyor Device Based on a GA-BP Neural Network
As technology advances, so does digital farming, revolutionizing the industry. Drones, sprayers equipped with GPS and other sensors, combine harvesters, and other machinery can greatly improve agricultural productivity. This paper studies the impact of the straw baler screw conveyor on the efficienc...
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MDPI AG
2025-01-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/13/1/24 |
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| author | Qiang Guo Yunpeng Zhuang Houzhuo Xu Wei Li Haitao Li Zhidong Wu |
| author_facet | Qiang Guo Yunpeng Zhuang Houzhuo Xu Wei Li Haitao Li Zhidong Wu |
| author_sort | Qiang Guo |
| collection | DOAJ |
| description | As technology advances, so does digital farming, revolutionizing the industry. Drones, sprayers equipped with GPS and other sensors, combine harvesters, and other machinery can greatly improve agricultural productivity. This paper studies the impact of the straw baler screw conveyor on the efficiency of the baler. Via theoretical analysis, GA—BP (Genetic Algorithm—Back Propagation) simulation, and comparative experiments, the structural parameters and rotational speed of the spiral shaft in the screw conveying device are optimized. In this paper, we analyze the force and velocity components acting on the straw, give the design principles for the screw’s conveying parameters under the premise of ensuring maximum conveying capacity and minimum power consumption, and determine the optimal design variables, objective functions, and constraints according to the specific optimization problem; we establish a specific mathematical model, and introduce algorithm optimization for nonlinear problems with many variables and large amounts of calculations. In MATLAB, an optimization calculation and analysis were performed. The optimization results of the traditional BP (Back Propagation) and GA—BP were compared. It was proven that GA—BP could effectively compensate for the deficiencies of the BP neural network and substantially enhance the model’s accuracy. Through an analysis of the optimization results, the conclusion of attaining the optimization objective was drawn. Specifically, when the outer diameter of the spiral for screw conveyance in the straw baler was <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mrow><mi mathvariant="normal">D</mi><mo>=</mo><mn>320</mn><mo> </mo><mi>mm</mi></mrow></mrow></semantics></math></inline-formula>, the pitch was <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mrow><mi mathvariant="normal">S</mi><mo>=</mo><mn>200</mn><mo> </mo><mi>mm</mi></mrow></mrow></semantics></math></inline-formula>, and the rotational speed of the pickup shaft was <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>n</mi><mo>=</mo><mn>138</mn><mo> </mo><mi>r</mi><mo>/</mo><mi>min</mi></mrow></semantics></math></inline-formula>, the straw baler could achieve the maximum conveying capacity and the minimum power consumption. At this moment, the power consumption was <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="normal">P</mi><mo>=</mo><mn>0.079</mn><mo> </mo><mi>kW</mi></mrow></semantics></math></inline-formula>, and the conveying capacity was <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi mathvariant="normal">Q</mi><mi mathvariant="normal">m</mi></msub><mrow><mo>=</mo><mn>23.98</mn><mo> </mo><mi mathvariant="normal">t</mi><mo>/</mo><mi mathvariant="normal">h</mi></mrow></mrow></semantics></math></inline-formula>. Subsequently, the optimization results were contrasted with those of other mainstream domestic models, and a comparative experiment was conducted. The experimental results indicated that the model’s prediction results were reliable and exhibited higher efficiency compared to other combinations. The results could provide a reference for the research on screw conveyance of balers. |
| format | Article |
| id | doaj-art-f508e20922a6438d8892ba8e8c928091 |
| institution | Kabale University |
| issn | 2075-1702 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj-art-f508e20922a6438d8892ba8e8c9280912025-01-24T13:39:10ZengMDPI AGMachines2075-17022025-01-011312410.3390/machines13010024Optimization of the Screw Conveyor Device Based on a GA-BP Neural NetworkQiang Guo0Yunpeng Zhuang1Houzhuo Xu2Wei Li3Haitao Li4Zhidong Wu5College of Mechanical and Electrical Engineering, Qiqihar University, Qiqihar 161000, ChinaCollege of Mechanical and Electrical Engineering, Qiqihar University, Qiqihar 161000, ChinaCollege of Mechanical and Electrical Engineering, Qiqihar University, Qiqihar 161000, ChinaCollege of Mechanical and Electrical Engineering, Qiqihar University, Qiqihar 161000, ChinaHeilongjiang Provincial Academy of Agricultural Machinery Engineering Sciences, Qiqihar 161000, ChinaCollege of Mechanical and Electrical Engineering, Qiqihar University, Qiqihar 161000, ChinaAs technology advances, so does digital farming, revolutionizing the industry. Drones, sprayers equipped with GPS and other sensors, combine harvesters, and other machinery can greatly improve agricultural productivity. This paper studies the impact of the straw baler screw conveyor on the efficiency of the baler. Via theoretical analysis, GA—BP (Genetic Algorithm—Back Propagation) simulation, and comparative experiments, the structural parameters and rotational speed of the spiral shaft in the screw conveying device are optimized. In this paper, we analyze the force and velocity components acting on the straw, give the design principles for the screw’s conveying parameters under the premise of ensuring maximum conveying capacity and minimum power consumption, and determine the optimal design variables, objective functions, and constraints according to the specific optimization problem; we establish a specific mathematical model, and introduce algorithm optimization for nonlinear problems with many variables and large amounts of calculations. In MATLAB, an optimization calculation and analysis were performed. The optimization results of the traditional BP (Back Propagation) and GA—BP were compared. It was proven that GA—BP could effectively compensate for the deficiencies of the BP neural network and substantially enhance the model’s accuracy. Through an analysis of the optimization results, the conclusion of attaining the optimization objective was drawn. Specifically, when the outer diameter of the spiral for screw conveyance in the straw baler was <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mrow><mi mathvariant="normal">D</mi><mo>=</mo><mn>320</mn><mo> </mo><mi>mm</mi></mrow></mrow></semantics></math></inline-formula>, the pitch was <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mrow><mi mathvariant="normal">S</mi><mo>=</mo><mn>200</mn><mo> </mo><mi>mm</mi></mrow></mrow></semantics></math></inline-formula>, and the rotational speed of the pickup shaft was <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>n</mi><mo>=</mo><mn>138</mn><mo> </mo><mi>r</mi><mo>/</mo><mi>min</mi></mrow></semantics></math></inline-formula>, the straw baler could achieve the maximum conveying capacity and the minimum power consumption. At this moment, the power consumption was <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="normal">P</mi><mo>=</mo><mn>0.079</mn><mo> </mo><mi>kW</mi></mrow></semantics></math></inline-formula>, and the conveying capacity was <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi mathvariant="normal">Q</mi><mi mathvariant="normal">m</mi></msub><mrow><mo>=</mo><mn>23.98</mn><mo> </mo><mi mathvariant="normal">t</mi><mo>/</mo><mi mathvariant="normal">h</mi></mrow></mrow></semantics></math></inline-formula>. Subsequently, the optimization results were contrasted with those of other mainstream domestic models, and a comparative experiment was conducted. The experimental results indicated that the model’s prediction results were reliable and exhibited higher efficiency compared to other combinations. The results could provide a reference for the research on screw conveyance of balers.https://www.mdpi.com/2075-1702/13/1/24digital farmingscrew conveyorBP neural networkGA-BP algorithm |
| spellingShingle | Qiang Guo Yunpeng Zhuang Houzhuo Xu Wei Li Haitao Li Zhidong Wu Optimization of the Screw Conveyor Device Based on a GA-BP Neural Network Machines digital farming screw conveyor BP neural network GA-BP algorithm |
| title | Optimization of the Screw Conveyor Device Based on a GA-BP Neural Network |
| title_full | Optimization of the Screw Conveyor Device Based on a GA-BP Neural Network |
| title_fullStr | Optimization of the Screw Conveyor Device Based on a GA-BP Neural Network |
| title_full_unstemmed | Optimization of the Screw Conveyor Device Based on a GA-BP Neural Network |
| title_short | Optimization of the Screw Conveyor Device Based on a GA-BP Neural Network |
| title_sort | optimization of the screw conveyor device based on a ga bp neural network |
| topic | digital farming screw conveyor BP neural network GA-BP algorithm |
| url | https://www.mdpi.com/2075-1702/13/1/24 |
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