Improve the Energy Efficiency of Haul Trucks in Surface Mines

Truck haulage is responsible for a majority of cost in a surface mining operation. Diesel fuel, which is costly and has a significant environmental footprint, is used as a source of energy for haul trucks in surface mines. Reducing diesel fuel consumption would lead to a reduction in haulage cost and greenhouse gas emissions. The determination of fuel consumption is complex and requires multiple parameters including the mine, fleet, truck, fuel, climate and road conditions as input. Data analytics is used to simulate the complex relationships between the input parameters affecting truck fuel consumption. This technique is also used to optimize the input parameters to minimize fuel consumption without losing productivity or further capital expenditure for a specific surface mining operation. The aim of this research work is to develop an advanced data analytics model to improve the energy efficiency of haul trucks in surface mines.

1. Introduction
2. Review of Haul Truck Energy Efficiency Opportunities and Data Analytics Models
3. A Comprehensive Investigation of Loading Variance Influence on Fuel Consumption and Gas Emissions in Mine Haulage Operation
4. A Discrete-Event Model to Simulate the Effect of Payload Variance on Truck Bunching, Cycle Time, and Hauled Mine Materials
5. The Influence of Rolling Resistance on Haul Truck Fuel Consumption in Surface Mines
6. Development of a Multi-Layer Perceptron Artificial Neural Network Model to Determine Haul Truck Energy Consumption
7. Reducing Fuel Consumption of Haul Trucks in Surface Mines Using Genetic Algorithm
8. Conclusions and Recommendations

Dr. Ali Soofastaei

Biography 

Dr. Ali Soofastaei is a global artificial intelligence (AI) projects leader, an international keynote speaker, and a professional author.

He completed his Ph.D. and Postdoctoral Research Fellow at The University of Queensland, Australia, in the field of AI applications in mining engineering, where he led a revolution in the use of deep learning and AI methods to increase energy efficiency, reduce operation and maintenance costs, and reduce greenhouse gas emissions in surface mines. As a scientific supervisor, for many years, he has provided practical guidance to undergraduate and postgraduate students in mechanical and mining engineering and information technology.

Dr. Soofastaei has more than fifteen years of academic experience as an Assistant Professor and leader of global research activities. Results from his research and development projects have been published in international journals and keynote presentations; He has presented his practical achievements at conferences in the United States, Europe, Asia, and Australia.

He has been involved in industrial research and development projects in several industries, including oil and gas (Royal Dutch Shell); steel (Danieli); and mining (BHP, Rio Tinto, Anglo American, and Vale). His extensive practical experience in the industry has equipped him to work with complex industrial problems in highly technical and multi-disciplinary teams.

Dr. Soofastaei is working actively with some prestigious global publishers same as Mc Graw-Hill Education, Intech Open, Springer, and CRC Press as an author and academic editor.

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