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Reliability Assessment and Energy Modeling for Alberta's Oil Sands Surface Mining Equipment Open Access


Other title
Mining Equipment
Reliability Assessment
Energy Modeling
Type of item
Degree grantor
University of Alberta
Author or creator
Seif, Azadeh
Supervisor and department
Professor Michael G Lipsett and Professor Amit Kumar, Mechanical Engineering
Examining committee member and department
Professor Robert Hall (Mining Engineering)
Professor Amit Kumar (Mechanical Engineering)
Professor Michael G Lipsett (Mechanical Engineering)
Dr. Albert Vette (Mechanical Engineering)
Department of Mechanical Engineering
Engineering Management
Date accepted
Graduation date
Master of Science
Degree level
Alberta’s surface mining sector is one of the largest energy-consuming industries in Canada, and so oil sands mining equipment performance has a significant impact on the economy. To achieve more sustainable oil sands mining production in Alberta, one of the influential factors is the improvement of the reliability of mining equipment. Through these reliability improvements, costs, energy consumption, and greenhouse gas (GHG) emissions can be mitigated. Energy consumption and equipment reliability have considerable risk associated with some main subsystems, and this risk must be determined in order to calculate the effect on expected operating cost. When mining equipment reliability improves, not only can costs associated with maintenance be reduced, but also energy consumption. As well, emissions quality can be improved through better maintenance, which in turn mitigates GHG emissions. The objective of this research is to develop a demand tree, the reliability modeling for oil sands mining equipment, and make a link between energy consumption and reliability. To determine how much energy, cost, and GHG emissions can be reduced through improving equipment reliability, techniques of equipment risk assessment and reliability were studied. In addition, “reference scenarios” for improving the reliability in mining equipment were identified and developed. A probabilistic Bayesian belief network (BBN) method was used for the reliability analysis. The integrated energy-reliability (E-R) model developed for oil sands mining equipment provides a detailed reliability-energy analysis. This model helps to understand the relationship between energy and reliability, and clarifies the amount of energy consumption and energy saving possible through improving the reliability of equipment. The E-R model was developed for four discrete states of reliability: State 1, the mining equipment is fully operational (reliability equals 1); State 2, the equipment operates under expected reliability (as defined by manufacturer); State 3, the equipment operates under low or limited reliability (also known as partial reliability); and State 4, the equipment fails. Partial reliability was calculated for the major subsystems of the mining equipment used in surface mining of bituminous sands, and their associated energy consumption, based on the Markov degraded multi-state model under three states, which are described as: State 1, the system operates under expected reliability; State 2, the system operates under low or limited reliability; this is also known as partial reliability; and State 3, the system fails. LEAP software was used to calculate final energy consumption by each main subsystem for the study period of forty years. It was assumed that the emissions changed only due to change in energy consumption, although partially reliable equipment may have higher specific emissions as well. The E-R model outcomes suggest that energy demand for equipment at current production rates will be reduced by an average of 603.5 million GJ, 1,151.40 million GJ, 1,125.53 million GJ, and 1,732.73 million GJ by year 2050 for states 1, 2, 3, and 4, respectively. Average annual as-spent cost savings of 60 Billion Canadian Dollars, 78 Billion Canadian Dollars, 99 Billion Canadian Dollars, and 158 Billion Canadian Dollars were obtained by year 2050 for operating in states 1, 2, 3, and 4. In addition, GHG emissions will be mitigated by an average of 27 million Metric Tons CO2 equivalent, 77 million Metric Tons CO2 equivalent, 75 million Metric Tons CO2 equivalent, and 105 million Metric Tons CO2 equivalent by year 2050 for states 1, 2, 3 and 4.
This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for the purpose of private, scholarly or scientific research. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
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