• Corpus ID: 59477748

Reducing fuel consumption of haul trucks in surface mines using artificial intelligence models

@inproceedings{Soofastaei2016ReducingFC,
  title={Reducing fuel consumption of haul trucks in surface mines using artificial intelligence models},
  author={Ali Soofastaei and Saiied M. Aminossadati and Mehmet Kizil and Peter F. Knights},
  year={2016}
}
Energy saving has become an important aspect of every business activity as it is important in terms of cost savings and greenhouse gas emission reduction. This study aims to develop a comprehensive artificial intelligence model for reducing energy consumption in the mining industry. Many parameters influence the fuel consumption of surface mining haul trucks. This includes, but not limited to, truck load, truck speed and total haul road resistance. In this study, a fitness function for the haul… 
Estimating Ore Production in Open-pit Mines Using Various Machine Learning Algorithms Based on a Truck-Haulage System and Support of Internet of Things
TLDR
The results revealed that the models used can be potentially used for predicting ore production in open-pit mines and demonstrated high accuracy, with the SVM model exhibiting the most superior performance and the highest accuracy.
Energy Saving Control Strategy for the High-Frequency Start-up Process for Electric Mining Haul Trucks
TLDR
Compared with the constant power control and the driver's manual control, the ESC strategy enjoys better dynamic performances and a lower energy consumption in the start-up process.
Deep Neural Network for Ore Production and Crusher Utilization Prediction of Truck Haulage System in Underground Mine
TLDR
The trained DNN model was used to predict the ore production and crusher utilization, which were similar to the actual observed values.
Estimation of Electric Mining Haul Trucks' Mass and Road Slope Using Dual Level Reinforcement Estimator
TLDR
The experimental results show that the total mass and the road slope are estimated with higher accuracy using the DLRE, which overcomes the defects of the traditional recursive-strategy-based estimators whose cumulative estimation errors often lead to poor estimation performance or even lead to estimation divergence.
Deep Neural Network for Predicting Ore Production by Truck-Haulage Systems in Open-Pit Mines
TLDR
A deep neural network (DNN)-based method for predicting ore production by truck-haulage systems in open-pit mines and it was observed that the prediction accuracy of morning ore production was highest when the number of hidden layers and number of corresponding nodes were four and 50, respectively.
A multi-objective model for fleet allocation schedule in open-pit mines considering the impact of prioritising objectives on transportation system performance
TLDR
Implementation of the mixed-integer linear goal programming model (MILGP) with a copper mine case study demonstrated that the proposed model is effective and efficient.
Sustainable Open Pit Mining and Technical Systems: Concept, Principles, and Indicators
TLDR
It was concluded that the current management decisions are aimed at ensuring the economic and technological sustainability of MTS functioning, while achieving the goals of sustainable development of this system is not ensured.
CORRELATION OF BLASTING PERFORMANCE WITH LOADING AND CRUSHING TIME TO MINIMIZE ENERGY CONSUMPTION
Blasting performance with loading and crushing time were correlated in order to minimize energy consumption in quarry operation. A measure scaled object was placed within the muck pile of blasted
...
1
2
...

References

SHOWING 1-10 OF 26 REFERENCES
Energy Performance of Dump Trucks in Opencast Mine
Dump trucks are used worldwide for handling ore and waste in most of the opencast mines. The energy consumption in dump trucks accounts for about 32 % of the total energy requirement in opencast
Prediction of building energy consumption by using artificial neural networks
Predicting Biochemical Oxygen Demand As Indicator Of River Pollution Using Artificial Neural Networks
Artificial Neural Networks (ANNs) are frequently used to predict various ecological processes and phenomenon related to water resources. Various ANN applications involve the prediction of water
Development Artificial Neural Network Model to Study the Influence of Oxidation Process and Zinc-Electroplating on Fatigue Life of Gray Cast Iron
The fatigue behavior of Gray Cast Iron is affected to its microstructure, strength, ductility, residual stress and surface roughness etc…. In general, fatigue life increase as the magnitude of
An analysis of formability of aluminium preforms using neural network
...
1
2
3
...