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Prediction of NOx Emission Based on Data of LHD On-Board Monitoring System in a Deep Underground Mine

Author

Listed:
  • Aleksandra Banasiewicz

    (Faculty of Geoengineering, Mining and Geology, Wroclaw University of Science and Technology, Na Grobli 15, 50-421 Wroclaw, Poland)

  • Paweł Śliwiński

    (KGHM Polska Miedz S.A., ul. Marii Skłodowskiej-Curie 48, 59-301 Lubin, Poland)

  • Pavlo Krot

    (Faculty of Geoengineering, Mining and Geology, Wroclaw University of Science and Technology, Na Grobli 15, 50-421 Wroclaw, Poland)

  • Jacek Wodecki

    (Faculty of Geoengineering, Mining and Geology, Wroclaw University of Science and Technology, Na Grobli 15, 50-421 Wroclaw, Poland)

  • Radosław Zimroz

    (Faculty of Geoengineering, Mining and Geology, Wroclaw University of Science and Technology, Na Grobli 15, 50-421 Wroclaw, Poland)

Abstract

The underground mining industry is at the forefront when it comes to unsafe conditions at workplaces. As mining depths continue to increase and the mining fronts move away from the ventilation shafts, gas hazards are increasing. In this article, the authors developed a statistical polynomial model for nitrogen oxide (NOx) emission prediction of the LHD vehicle with a diesel engine. The best-achieved prediction accuracy by the 4th order polynomial model for 11 and 10 input variables is about 8% and 13%, respectively. It is comparable with the sensors’ accuracy of 10% at a stable regime of loading and 20% in the transient periods of operation. The obtained results allow planning of ventilation system capacity and power demand for the large fleet of vehicles in the deep underground mines.

Suggested Citation

  • Aleksandra Banasiewicz & Paweł Śliwiński & Pavlo Krot & Jacek Wodecki & Radosław Zimroz, 2023. "Prediction of NOx Emission Based on Data of LHD On-Board Monitoring System in a Deep Underground Mine," Energies, MDPI, vol. 16(5), pages 1-16, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2149-:d:1077548
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    References listed on IDEAS

    as
    1. d’Ambrosio, Stefano & Finesso, Roberto & Fu, Lezhong & Mittica, Antonio & Spessa, Ezio, 2014. "A control-oriented real-time semi-empirical model for the prediction of NOx emissions in diesel engines," Applied Energy, Elsevier, vol. 130(C), pages 265-279.
    2. Zheng Yuan & Xiuyong Shi & Degang Jiang & Yunfang Liang & Jia Mi & Huijun Fan, 2022. "Data-Based Engine Torque and NOx Raw Emission Prediction," Energies, MDPI, vol. 15(12), pages 1-12, June.
    3. Hung-Ta Wen & Jau-Huai Lu & Deng-Siang Jhang, 2021. "Features Importance Analysis of Diesel Vehicles’ NO x and CO 2 Emission Predictions in Real Road Driving Based on Gradient Boosting Regression Model," IJERPH, MDPI, vol. 18(24), pages 1-28, December.
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    Cited by:

    1. Sergey Zhironkin & Dawid Szurgacz, 2023. "Mining Technologies Innovative Development II: The Overview," Energies, MDPI, vol. 16(15), pages 1-5, July.
    2. Adam Wróblewski & Arkadiusz Macek & Aleksandra Banasiewicz & Sebastian Gola & Maciej Zawiślak & Anna Janicka, 2023. "CFD Analysis of the Forced Airflow and Temperature Distribution in the Air-Conditioned Operator’s Cabin of the Stationary Rock Breaker in Underground Mine under Increasing Heat Flux," Energies, MDPI, vol. 16(9), pages 1-18, April.

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