Year: 2025
Type: conference-paper
Abstract: Real-time data collection via vehicle-mounted sensors captures multi-dimensional parameters such as fuel consumption, speed, route, and driving behavior of mining trucks. A genetic algorithm-optimized LSTM model is then employed to construct a fuel consumption prediction model, enabling precise fuel consumption forecasting. This approach reduces fuel consumption by 12-18%, effectively minimizing fuel theft, leakage, and energy waste while promoting green energy conservation and sustainable mining practices aligned with China's sustainable development objectives.
Authors: Xinming Liu, Junxin Bian
Institution: Liaoning Technical University
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4414931875