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Development and Validation of Prediction Model for Exhaust Emissions During Tractor Plow Tillage

Author

Listed:
  • Ryu-Gap Lim

    (Department of Convergent Biosystems Engineering, Sunchon National University, Suncheon 57922, Republic of Korea)

  • Tae-Bum Kim

    (Fuel Injection System Korea Central Distributor, Diesel Service Korea Co. Ltd., Daejeon 34357, Republic of Korea)

  • Wan-Soo Kim

    (Department of Bio-Industrial Machinery Engineering, Kyungpook National University, Daegu 41566, Republic of Korea
    Upland Field Machinery Research Center, Kyungpook National University, Daegu 41566, Republic of Korea)

  • Seung-Yun Baek

    (Department of Biosystems Machinery Engineering, Chungnam National University, Daejeon 34134, Republic of Korea)

  • Hyeon-Ho Jeon

    (Department of Biosystems Machinery Engineering, Chungnam National University, Daejeon 34134, Republic of Korea)

  • Jee-Young Ham

    (Emission Inventory Management Team, National Air Emission Inventory and Research Center, Cheongju 28160, Republic of Korea)

  • Chul Yoo

    (Emission Inventory Management Team, National Air Emission Inventory and Research Center, Cheongju 28160, Republic of Korea)

  • Yong-Joo Kim

    (Department of Biosystems Machinery Engineering, Chungnam National University, Daejeon 34134, Republic of Korea
    Department of Smart Agriculture Systems, Chungnam National University, Daejeon 34134, Republic of Korea)

Abstract

In this study, to compensate for the constraints of high unit cost of portable emission measurement system (PEMS) and measurement environment, we developed a tractor operation-based emission prediction model. We also evaluated the developed prediction model using validation metrics. In addition to engine load data, correlation analysis was conducted on engine temperature and fuel consumption variables. The results showed a high correlation of more than 0.5 between emissions and engine temperature, and a high correlation of more than 0.5 between emissions and fuel consumption for emissions except CO and THC. The R 2 values of the CO, THC, NOx, and PM emission prediction models were 0.81, 0.82, 0.85, and 0.97, respectively, showing good overall predictive performance. The prediction models for CO, THC, NOx, and PM emissions developed using the third-order regression analysis all showed excellent performance with an average absolute percentage error of around 2%. Therefore, the developed emission regression model can be used to predict tractor emissions using various variables. Through the exhaust emissions prediction model developed in this study, eco-friendly technology according to the optimal engine design is expected to increase. In addition, it is expected that agricultural machinery prices will be stabilized and export competitiveness will be secured.

Suggested Citation

  • Ryu-Gap Lim & Tae-Bum Kim & Wan-Soo Kim & Seung-Yun Baek & Hyeon-Ho Jeon & Jee-Young Ham & Chul Yoo & Yong-Joo Kim, 2024. "Development and Validation of Prediction Model for Exhaust Emissions During Tractor Plow Tillage," Agriculture, MDPI, vol. 14(12), pages 1-17, December.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:12:p:2334-:d:1547927
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    References listed on IDEAS

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    1. Pachiannan, Tamilselvan & Zhong, Wenjun & Rajkumar, Sundararajan & He, Zhixia & Leng, Xianying & Wang, Qian, 2019. "A literature review of fuel effects on performance and emission characteristics of low-temperature combustion strategies," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
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    1. Nebojša Balać & Zoran Mileusnić & Aleksandra Dragičević & Mihailo Milanović & Andrija Rajković & Rajko Miodragović & Olivera Ećim-Đurić, 2025. "Implementation of XGBoost Models for Predicting CO 2 Emission and Specific Tractor Fuel Consumption," Agriculture, MDPI, vol. 15(11), pages 1-19, May.

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