Methods for evaluating effect of operators on drag line energy efficiency

Draglines are dominant machines and the most significant electricity consumers in surface coal mines. With the growing price of energy, environmental concerns, and the high sensitivity of mine profitability to dragline productivity, any improvement in efficiency of dragline will be beneficial for mines. Research has shown that operator practices have a significant impact on energy efficiency of mining loading tools. However, not enough work has been done to provide guidance on how to quantitatively assess the effect of operator practices on dragline energy efficiency. The objectives of this work were to: (i) test the hypothesis that dragline operator\u27s practices and skills significantly affect dragline energy efficiency; and (ii) develop a methodology to identify the critical parameters that explain the differences in operator energy efficiency. Statistical tests are suggested to study the effect of operator practice and skills on dragline energy efficiency to achieve the first research objective. The second objective was achieved with a novel methodology based on sound statistical principles. Both approaches were illustrated with a real-life dragline operation. The suggested methodology was used on the data collected from an 85yd³ BE-1570w dragline to compare the energy efficiency of five operators during a one month period. Valid methods have been formulated for testing operator effects on dragline energy efficiency and for identifying critical parameters that explain such differences. Using the developed approaches, the case study shows that operator practices can affect dragline energy efficiency. The tests show that there is a high probability that differences in energy efficiency are due to dumping height, vertical and horizontal drag distances, and spotting and dumping time among the surveyed operators --Abstract, page iii

A Method for Data-Driven Evaluation of Operator Impact on Energy Efficiency of Digging Machines

Material handling (including digging) is one of the most energy-intensive processes in mining. Operators\u27 skills and practices are known to be some of the major factors that affect energy efficiency of digging operations. Improving operators\u27 skills through training is an inexpensive and effective method to improve energy efficiency. The method proposed in this work uses data collected by monitoring systems on digging equipment to detect the monitored parameters that lead to differences in energy efficiency of operators (responsible parameters). After data extraction, removing the outliers, and identifying the operators with sufficient working hours, correlation analysis can be used to find parameters that are correlated with energy efficiency. Regression analysis on pairs of operators is then used to detect responsible parameters. Random sampling is used to overcome missing data issues in the analysis. This statistics-based method is simple and adequately accounts for the high variability in data collected from these monitoring systems. The proposed method was illustrated using data collected on five operators working on a 64-m3 (85 yd3) Bucyrus-Erie 1570w dragline. The case study results show that dump height and engagement/disengagement position of the bucket are the most likely parameters to cause differences between energy efficiency of these operators. On the other hand, cycle time, payload, and swing in time are least likely to influence differences in operator energy efficiency

Role of the Operator in Dragline Energy Efficiency

Dragline operators, as controllers of one the most energy-intensive equipments in surface coal mines, play a significant role in dragline energy efficiency and thus mine profitability. The literature lacks work that explores monitoring system data and applies data-driven methods to gain a better understanding of dragline operation and develop more effective training approaches. This chapter provides a framework for assessing dragline energy efficiency performance using monitoring data and using such work to improve operator training. The first step in improving dragline performance is the assessment using data from dragline monitoring systems to estimate an overall performance indicator. Next, the analyst should apply a comprehensive algorithm to quantify the relationship between different operating parameters and the overall performance indicator. Finally, operators\u27 performance can be improved by using the results to optimize operator training

Energy-efficient loading and hauling operations

Approximately, 40% of the total energy used in surface mines is related to diesel consumption. Truck haulage is responsible for a majority of this. This chapter introduces the principal equipment used to load and haul materials in mines, namely trucks, electric rope shovels, hydraulic excavators and crushing and conveying systems. The chapter discusses factors that contribute to the energy-efficient operation of such equipment. Based on gross weight hauled per unit weight of payload, belt conveyors appear to be the most energy-efficient means of transporting material in surface mines. However, a number of factors, including large upfront capital expenditure and limited ability to relocate and scale up belt capacities, currently restrict their widespread applicability