Forecasting of NOx Emissions of Diesel LHD Vehicles in Underground Mines—An ANN-Based Regression Approach
An approach based on an artificial neural network (ANN) for the prediction of NOx emissions from underground load–haul–dumping (LHD) vehicles powered by diesel engines is proposed. A Feed-Forward Neural Network, the Multi-Layer Perceptron (MLP), is used to establish a nonlinear relationship between...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/17/9965 |
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| author | Aleksandra Banasiewicz Forougholsadat Moosavi Michalina Kotyla Paweł Śliwiński Pavlo Krot Jacek Wodecki Radosław Zimroz |
| author_facet | Aleksandra Banasiewicz Forougholsadat Moosavi Michalina Kotyla Paweł Śliwiński Pavlo Krot Jacek Wodecki Radosław Zimroz |
| author_sort | Aleksandra Banasiewicz |
| collection | DOAJ |
| description | An approach based on an artificial neural network (ANN) for the prediction of NOx emissions from underground load–haul–dumping (LHD) vehicles powered by diesel engines is proposed. A Feed-Forward Neural Network, the Multi-Layer Perceptron (MLP), is used to establish a nonlinear relationship between input and output layers. The predicted values of NOx emissions have less than 15% error compared to the real values measured by the LHD onboard monitoring system by the standard sensor. This is considered quite good efficiency for dynamic behaviour prediction of extremely complex systems. The achieved accuracy of NOx prediction allows the application of the ANN-based “soft sensor” in environmental impact estimation and ventilation system demand planning, which depends on the number of working LHDs in the underground mine. The proposed solution to model NOx concentrations from mining machines will help to provide a better understanding of the atmosphere of the working environment and will also contribute to improving the safety of underground crews. |
| first_indexed | 2024-03-10T23:26:20Z |
| format | Article |
| id | doaj.art-032b149f496e44dea4f77b1b4d5f64bb |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T23:26:20Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-032b149f496e44dea4f77b1b4d5f64bb2023-11-19T07:54:07ZengMDPI AGApplied Sciences2076-34172023-09-011317996510.3390/app13179965Forecasting of NOx Emissions of Diesel LHD Vehicles in Underground Mines—An ANN-Based Regression ApproachAleksandra Banasiewicz0Forougholsadat Moosavi1Michalina Kotyla2Paweł Śliwiński3Pavlo Krot4Jacek Wodecki5Radosław Zimroz6Faculty of Geoengineering, Mining and Geology, Wroclaw University of Science and Technology, Na Grobli 15, 50-421 Wroclaw, PolandFaculty of Geoengineering, Mining and Geology, Wroclaw University of Science and Technology, Na Grobli 15, 50-421 Wroclaw, PolandFaculty of Geoengineering, Mining and Geology, Wroclaw University of Science and Technology, Na Grobli 15, 50-421 Wroclaw, PolandKGHM Polska Miedz S.A, ul. Marii Skłodowskiej-Curie 48, 59-301 Lubin, PolandFaculty of Geoengineering, Mining and Geology, Wroclaw University of Science and Technology, Na Grobli 15, 50-421 Wroclaw, PolandFaculty of Geoengineering, Mining and Geology, Wroclaw University of Science and Technology, Na Grobli 15, 50-421 Wroclaw, PolandFaculty of Geoengineering, Mining and Geology, Wroclaw University of Science and Technology, Na Grobli 15, 50-421 Wroclaw, PolandAn approach based on an artificial neural network (ANN) for the prediction of NOx emissions from underground load–haul–dumping (LHD) vehicles powered by diesel engines is proposed. A Feed-Forward Neural Network, the Multi-Layer Perceptron (MLP), is used to establish a nonlinear relationship between input and output layers. The predicted values of NOx emissions have less than 15% error compared to the real values measured by the LHD onboard monitoring system by the standard sensor. This is considered quite good efficiency for dynamic behaviour prediction of extremely complex systems. The achieved accuracy of NOx prediction allows the application of the ANN-based “soft sensor” in environmental impact estimation and ventilation system demand planning, which depends on the number of working LHDs in the underground mine. The proposed solution to model NOx concentrations from mining machines will help to provide a better understanding of the atmosphere of the working environment and will also contribute to improving the safety of underground crews.https://www.mdpi.com/2076-3417/13/17/9965NOx emission predictionartificial neural networkLHD vehiclesunderground minesafetyventilation |
| spellingShingle | Aleksandra Banasiewicz Forougholsadat Moosavi Michalina Kotyla Paweł Śliwiński Pavlo Krot Jacek Wodecki Radosław Zimroz Forecasting of NOx Emissions of Diesel LHD Vehicles in Underground Mines—An ANN-Based Regression Approach Applied Sciences NOx emission prediction artificial neural network LHD vehicles underground mine safety ventilation |
| title | Forecasting of NOx Emissions of Diesel LHD Vehicles in Underground Mines—An ANN-Based Regression Approach |
| title_full | Forecasting of NOx Emissions of Diesel LHD Vehicles in Underground Mines—An ANN-Based Regression Approach |
| title_fullStr | Forecasting of NOx Emissions of Diesel LHD Vehicles in Underground Mines—An ANN-Based Regression Approach |
| title_full_unstemmed | Forecasting of NOx Emissions of Diesel LHD Vehicles in Underground Mines—An ANN-Based Regression Approach |
| title_short | Forecasting of NOx Emissions of Diesel LHD Vehicles in Underground Mines—An ANN-Based Regression Approach |
| title_sort | forecasting of nox emissions of diesel lhd vehicles in underground mines an ann based regression approach |
| topic | NOx emission prediction artificial neural network LHD vehicles underground mine safety ventilation |
| url | https://www.mdpi.com/2076-3417/13/17/9965 |
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