Research on Fuel Consumption Prediction for Open-Pit Mine Trucks Using GA-LSTM

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

Institutions

Publication Details

Published
2025-05-16
DOI
https://doi.org/10.1145/3757110.3757188
Primary Topic
Vehicle License Plate Recognition
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Research on Fuel Consumption Prediction for Open-Pit Mine Trucks Using GA-LSTM

Xinming Liu, Jian Yong Bian
Vehicle License Plate Recognition
article

Research on Fuel Consumption Prediction for Open-Pit Mine Trucks Using GA-LSTM

Xinming Liu, Jian Yong Bian
article en

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.

Liaoning Technical University (CN)
Openalex Percentile: Top 59%
Vehicle License Plate Recognition
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Research on Fuel Consumption Prediction for Open-Pit Mine Trucks Using GA-LSTM — Xinming Liu, Jian Yong Bian · (2025) | TGRS Research Map | TGRS