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Parameter Optimization of Spiral Fertilizer Applicator Based on Artificial Neural Network

Author

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  • Mengqiang Zhang

    (College of Mechanical Electrification Engineering, Tarim University, Alar 843300, China
    Agricultural Engineering Key Laboratory, Ministry of Higher Education of Xinjiang Uygur Autonomous Region, Tarim University, Alar 843300, China)

  • Yurong Tang

    (College of Mechanical Electrification Engineering, Tarim University, Alar 843300, China
    Agricultural Engineering Key Laboratory, Ministry of Higher Education of Xinjiang Uygur Autonomous Region, Tarim University, Alar 843300, China)

  • Hong Zhang

    (College of Mechanical Electrification Engineering, Tarim University, Alar 843300, China
    Agricultural Engineering Key Laboratory, Ministry of Higher Education of Xinjiang Uygur Autonomous Region, Tarim University, Alar 843300, China)

  • Haipeng Lan

    (College of Mechanical Electrification Engineering, Tarim University, Alar 843300, China
    Agricultural Engineering Key Laboratory, Ministry of Higher Education of Xinjiang Uygur Autonomous Region, Tarim University, Alar 843300, China)

  • Hao Niu

    (College of Mechanical Electrification Engineering, Tarim University, Alar 843300, China
    Agricultural Engineering Key Laboratory, Ministry of Higher Education of Xinjiang Uygur Autonomous Region, Tarim University, Alar 843300, China)

Abstract

To determine the optimal fertilizer discharging performance, a spiral fertilizer applicator was designed according to orchard agricultural requirements. The influence of different parameter combinations of the spiral speed, blade diameter, and pitch on the coefficient of variation (CV) of the fertilizer discharge uniformity was predicted using a neural-network-based model by using the Box–Behnken design (BBD) test. According to the extracted results, the neural network model has a good prediction ability, with the determination coefficient of the model and the mean relative error reaching 0.99 and 2.29%, respectively. The impact of the fertilizer discharge parameter combinations on the discharging performances was examined from both macroscopic and microscopic perspectives. During the fertilizer discharge process, the openness formed between the spiral blades and fertilizer outlet presented periodic changes with the continuous rotation of the spiral blade, thus resulting in the uneven discharge of the fertilizer particles. In addition, there are interacting force chains among fertilizer particles, which are not broken in time during the fertilizer discharge procedure, thus resulting in uneven fertilizer discharge. With comprehensive consideration of the fertilizer discharge efficiency, the fertilizer discharge effect, and CV of the fertilizer discharge uniformity, the spiral parameter combination of the fertilizer discharge after neural network optimization are as follows: rotating speed of 47.6 rpm, blade diameter of 90 mm, pitch of 60 mm, and CV of fertilizer discharge uniformity of 19.05%. Under this optimal spiral parameter combination, the fertilizer discharge effect and discharge efficiency were considered to be relatively good. Our work provides references for the design optimization of the spiral fertilizer applicator and fertilizer discharge parameter combination.

Suggested Citation

  • Mengqiang Zhang & Yurong Tang & Hong Zhang & Haipeng Lan & Hao Niu, 2023. "Parameter Optimization of Spiral Fertilizer Applicator Based on Artificial Neural Network," Sustainability, MDPI, vol. 15(3), pages 1-13, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:1744-:d:1038108
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    References listed on IDEAS

    as
    1. Fraz Ahmad Khan & Abdul Ghafoor & Muhammad Azam Khan & Muhammad Umer Chattha & Farzaneh Khorsandi Kouhanestani, 2022. "Parameter Optimization of Newly Developed Self-Propelled Variable Height Crop Sprayer Using Response Surface Methodology (RSM) Approach," Agriculture, MDPI, vol. 12(3), pages 1-19, March.
    2. Yuting Chen & Zhun Cheng & Yu Qian, 2022. "Research on Wet Clutch Switching Quality in the Shifting Stage of an Agricultural Tractor Transmission System," Agriculture, MDPI, vol. 12(8), pages 1-16, August.
    3. Bowen Zheng & Zhenghe Song & Enrong Mao & Quan Zhou & Zhenhao Luo & Zhichao Deng & Xuedong Shao & Yuxi Liu, 2022. "An ANN-PSO-Based Method for Optimizing Agricultural Tractors in Field Operation for Emission Reduction," Agriculture, MDPI, vol. 12(9), pages 1-16, August.
    4. Alfredo Bonini Neto & Dilson Amancio Alves & Carlos Roberto Minussi, 2022. "Artificial Neural Networks: Multilayer Perceptron and Radial Basis to Obtain Post-Contingency Loading Margin in Electrical Power Systems," Energies, MDPI, vol. 15(21), pages 1-14, October.
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