Artificial intelligence applied to evaluate emissions and energy consumption in commuter railways: Comparison of liquefied natural gas as an alternative fuel to diesel

Abstract

At present, there is a common effort to reduce the environmental effect of energy consumption. With this objective, the transportation sector seeks to improve emissions in all its modes. In particular, the rail transport industry is analysing various alternatives for non-electrified lines. These services are mainly carried out with diesel units. As an alternative to diesel fuel, in the present study the use of liquefied natural gas (LNG) in railway traction was analysed. A predictive model was developed and implemented in order to estimate the emissions impact of this fuel on different rail routes or networks. The model was fitted with real data obtained from pilot tests. In these tests, a train with two engines, one diesel and the other LNG, was used. The methodology was applied to evaluate the impact on consumption and emissions of the two fuels on a narrow-gauge commuter line. An improvement was observed in some indicators, while in others there was no clear progress. The conclusions that can be drawn are that CO2 (greenhouse gas) operating emissions are lower in the LNG engine than in the diesel line; CO emissions are lower in the diesel engine and emissions of other pollutants (nitrogen oxide and particles) are higher in the diesel engine by several orders of magnitude.