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

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.

Keywords

• NOx emission
• LHD machines
• deep underground mine
• statistical model
• ventilation
• prediction