Data Scientist, Vale, Australia
Advanced Data Analytics: A Practical Method to Increase Rail Energy Efficiency in Mining Industry
The mining industry annually consumes trillions of British Thermal Units of energy, a large part of which is saveable. Diesel fuel is a significant source of energy in mining operations, and locomotives are the primary users of this energy source. This paper aims to investigate the energy efficiency opportunities in railways and locomotives operations in the mining industry. The result of this project can have a couple of benefits for mining industry such as reduce production losses, reduce greenhouse gas emissions and associated costs, develop annual cost-effective fuel purchasing and stocking strategies, reduce fuel consumption and costs and improve energy efficiency. In this research, a comprehensive model has been developed based on artificial intelligence methods for reducing fuel consumption by locomotives. Wagon payload, locomotive speed and the rail total resistance are key parameters that affect fuel consumption. The relationship between the key parameters and the locomotive fuel consumption is determined using an artificial neural network model. This model is trained and tested using real data collected from some large surface mines in Brazil and Australia. In these mines, a fitness function for the locomotive fuel consumption is successfully generated by the artificial neural network model. This function is utilised to generate a computerised learning algorithm based on a genetic algorithm and estimate the optimum values of effective haulage parameters to reduce the diesel fuel consumption by locomotives.
Dr. Ali Soofastaei is a Research Developer at the Artificial Intelligence Centre of Excellence at Vale and an Adjunct Senior Fellow at the University of Queensland (UQ), Australia. Vale is a multinational corporation engaged in metals and mining. It is one of the world’s foremost producers of iron ore and the largest producer of nickel. UQ has been ranked in the world’s top 50 universities and is one of Australia’s leading research and teaching institutions.
Dr. Soofastaei uses innovative models based on artificial intelligence (AI) methods to improve safety, productivity, and energy efficiency, and to reduce maintenance costs. He holds a Bachelor of Engineering in Mechanical Engineering and has an in-depth understanding of energy management (EM) and equipment maintenance solutions (EMS). The extensive research he conducted on AI and value engineering (VE) methods while completing his Master of Engineering also provided him with expertise in the application of advanced analytics in EM and EMS.
In the past 15 years, Dr. Soofastaei has conducted a variety of research studies in academic and industrial environments. He has acquired in-depth knowledge of energy efficiency opportunities (EEO), VE and advanced analytics. He is an expert in the use of DL and AI methods in data analysis to develop predictive and optimization models of complex systems.