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

Ryu-Gap Lim, Tae-Bum Kim (), Wan-Soo Kim, Seung-Yun Baek, Hyeon-Ho Jeon, Jee-Young Ham, Chul Yoo and Yong-Joo Kim ()
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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
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

Agriculture, 2024, vol. 14, issue 12, 1-17

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.

Keywords: agricultural tractor; plow tillage; prediction model; portable emissions measurement system (PEMS); exhaust emissions (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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