Hybrid LSTM-DNN Model for Predicting Fuel Consumption in Open Pit Mining Trucks

Al Taee Wahhab Isam  Hatif; Dhahir Abdulhadi  Abdullah; Alaa Nazeeh  Mohmedhussen

Al Taee Wahhab Isam Hatif National University of Science and Technology MISIS
Dhahir Abdulhadi Abdullah College of Science, Diyala University
Alaa Nazeeh Mohmedhussen MIREA- Russian Technological University

Keywords: LSTM Algorithm, DNN, Dense, Predictive, Fuel Consumption

Abstract

: A dump truck's fuel efficiency is affected by a number of real-world factors, including driver conduct, road conditions, weather, and vehicle specifications. Additionally, potential engine malfunctions, regular wear and tear, and other factors can impact the vehicle'sperformance. By utilizing dynamic on-road data to predict fuel consumption per trip, the automotive industry can effectively reduce the cost and time associated with on-road testing. Furthermore, data modeling can provide valuable insights into identifying the underlying causes of fuel consumption by analyzing the input parameters. In this paper present, 1-proposes and evaluates new models for predicting fuel consumption of dump truck in open pit mining. These models combine the power of features derived from data locally collected by dump truck sensors and their analysis. 2- The structure Predicting fuel usage in open pit mining trucks using a hybrid LSTM-DNN model Double Long Short-Term Memory (LSTM) and double thick layers of Deep Neural Networks (DNN) form the foundation of the models' basic design, which consists of two separate components. 3- The proposed model performs better than existing models because to the addition of a new hybrid architecture, especially when it comes to accuracy measurement. The model's performance indicators, which include MAE, RMSE, MSE, and R2, show that it can produce highly accurate predictions.

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