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An ANN-PSO-Based Method for Optimizing Agricultural Tractors in Field Operation for Emission Reduction

Author

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  • Bowen Zheng

    (College of Engineering, China Agricultural University, Beijing 100083, China
    Beijing Key Laboratory of Modern Agricultural Equipment Optimization Design, China Agricultural University, Beijing 100083, China)

  • Zhenghe Song

    (College of Engineering, China Agricultural University, Beijing 100083, China
    Beijing Key Laboratory of Modern Agricultural Equipment Optimization Design, China Agricultural University, Beijing 100083, China)

  • Enrong Mao

    (College of Engineering, China Agricultural University, Beijing 100083, China
    Beijing Key Laboratory of Modern Agricultural Equipment Optimization Design, China Agricultural University, Beijing 100083, China)

  • Quan Zhou

    (College of Engineering, China Agricultural University, Beijing 100083, China
    Beijing Key Laboratory of Modern Agricultural Equipment Optimization Design, China Agricultural University, Beijing 100083, China)

  • Zhenhao Luo

    (College of Engineering, China Agricultural University, Beijing 100083, China
    Beijing Key Laboratory of Modern Agricultural Equipment Optimization Design, China Agricultural University, Beijing 100083, China)

  • Zhichao Deng

    (College of Engineering, China Agricultural University, Beijing 100083, China
    Beijing Key Laboratory of Modern Agricultural Equipment Optimization Design, China Agricultural University, Beijing 100083, China)

  • Xuedong Shao

    (College of Engineering, China Agricultural University, Beijing 100083, China
    Beijing Key Laboratory of Modern Agricultural Equipment Optimization Design, China Agricultural University, Beijing 100083, China)

  • Yuxi Liu

    (College of Engineering, China Agricultural University, Beijing 100083, China
    Beijing Key Laboratory of Modern Agricultural Equipment Optimization Design, China Agricultural University, Beijing 100083, China)

Abstract

Aiming at the serious problem of agricultural tractor emission pollution, especially the limitation of nitrogen dioxide (NOx) and soot emissions, we took an agricultural diesel engine as the research object, and a diesel engine combustion chamber model was established for both simulated calculations and experimental verification analysis. The in-cylinder pressure and heat release obtained from the combustion chamber model simulation calculations were within 6% error of the experimental data. The overall trend of change was basically consistent. The established model can simulate the working conditions of the experimental engine relatively well. An artificial neural network (ANN) was also established as an agent model based on the indentation rate, tab depth, and combustion chamber depth as the input, and NOx and soot as the output. The decision coefficients of the ANN model were R 2 = 0.95 and 0.93, with corresponding Mean Relative Error (MRE) values of 10.13 and 8.18%, respectively, which are within the generally required interval, indicating that the obtained ANN model has good adaptability and accuracy. On the basis of the general particle swarm optimization (PSO) algorithm, an improved PSO algorithm was proposed, in which the inertia factor is continuously adjusted with the help of the skip line function in the optimization process so that the inertia factor adapts to different rates and adjusts the magnitude of the corresponding values in different periods. The improved PSO algorithm was used to optimize the optimal input parameter matching of the agent model to form a new combustion chamber structure, which was imported into CONVERGE CFD software for emission simulation and comparison with the emissions of the original combustion chamber. It was found that the NOx reduction was about 1.21 g·(kW·h) −1 , and the soot reduction was about 0.06 g·(kW·h) −1 with the new combustion chamber structure. The ANN + PSO optimization method proved to be effective in reducing the NOx and soot emissions of diesel engine pollutants, and it may also provide a reference and ideas for the design and development of relevant agricultural engine combustion chamber systems.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:9:p:1332-:d:900246
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    References listed on IDEAS

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    1. Bhowmik, Subrata & Paul, Abhishek & Panua, Rajsekhar & Ghosh, Subrata Kumar & Debroy, Durbadal, 2018. "Performance-exhaust emission prediction of diesosenol fueled diesel engine: An ANN coupled MORSM based optimization," Energy, Elsevier, vol. 153(C), pages 212-222.
    2. Dhahad, Hayder Abed & Hasan, Ahmed Mudheher & Chaichan, Miqdam Tariq & Kazem, Hussein A., 2022. "Prognostic of diesel engine emissions and performance based on an intelligent technique for nanoparticle additives," Energy, Elsevier, vol. 238(PB).
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    Cited by:

    1. 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.

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