Energy Efficiency in Cable Shovel Operations

Chapter
Part of the Green Energy and Technology book series (GREEN)

Abstract

This chapter seeks to establish the current knowledge on energy efficiency of cable shovel operations. Additionally, the chapter uses a review of the literature to make recommendations for industrial best practices and for future research to address identified gaps in the literature. The chapter first presents the fundamentals of cable shovel operations and the factors that affect the energy efficiency of shovel operations. Subsequently, the chapter presents an overview of the latest research on cable shovel energy efficiency, which is used as the basis for the recommendations. The chapter recommends that industry practitioners should use the right drive systems for their cable shovels, use data analytics to understand shovel energy efficiency, and carefully evaluate the costs and benefits of energy efficiency initiatives. The chapter also recommends that future research on shovel energy efficiency should: (i) establish theoretical benchmarks for cable shovel operations; (ii) account for human factors in the design of operator guidance systems to assist operators during shovel operations; and (iii) evaluate how effective operator training programs are in improving shovel energy efficiency.

Keywords

Cable shovel Energy efficiency Data analytics Human factors 

8.1 Introduction

Mine managers and engineers use cable shovels (also referred to as electric rope shovels) in large-scale surface mining operations to excavate and load material into trucks (or at-face-crushers). Engineers regard cable shovels as durable, possessing large breakout forces, and associated with low production costs, high productivity and low ownership costs resulting from long economic operating lives. Due to these advantages, cable shovels are very popular in larger scale surface mining in diverse applications including oil sands, coal, iron ore, and metal mining. However, because of the high initial capital costs and lack of selectivity, mining professionals do not deploy them in smaller mines, those with short mine lives or those that require high mining selectivity.

Cable shovels consume significant amounts of energy in a mine. Most mines operate multiple units and each shovel requires thousands of kilojoules of electricity to execute its motions. Consequently, energy consumption of mines that use cable shovels for material handling, is significantly affected by the cable shovel electricity consumption [1]. Hence, the energy efficiency of such mines can be significantly affected by the energy efficiency of cable shovel operations. As with other equipment units, energy efficiency is inexplicably linked to the productivity of the machine and the associated production costs. Loading costs, as a percentage of the total operating costs of surface mine operating costs, range from 3 to 35%, with a mean of 15% [2]. Hence, an added benefit of increasing the energy efficiency of cable shovels is the reduction in unit production costs that results from lower energy costs per unit of production.

The threefold objective of this chapter is to: (1) establish, using a literature review, the current knowledge on energy efficiency of cable shovel operations; (2) make recommendations for industry best practices based on research in the literature; and (3) make recommendations for future research to fill the gaps in our knowledge regarding energy efficiency of cable shovel operations. The literature review is based on searches in archival databases with emphasis on peer-reviewed journal publications and work published since 2005.

8.2 Energy Efficiency of Cable Shovel Operations

Energy efficiency is the ratio of useful work done (energy output) to energy input. Since cable shovels are powered by electricity from the grid, the energy input is often directly estimated from motor current and voltage signals captured by onboard equipment monitoring systems [3, 4]. In some instances, however, proxies are used to describe the energy input [5]. Since the useful work done during cable shovel loading (i.e., work done in excavating and transporting the load into a truck or hopper) is difficult to estimate in the field [6], the amount of material loaded (payload) is often used as a proxy for useful work done.

The most common measure of energy efficiency that researchers use for cable shovel operations is energy per unit payload (or specific energy), which is actually the inverse of an energy efficiency metric [7]. This practice has its basis in initial research on shovel performance which was motivated by a desire to classify geologic materials or evaluate fragmentation results [8, 9, 10, 11, 12]. Hence, researchers intended specific energy to be a measure of how difficult it is to excavate the material or muck pile rather than a measure of the efficiency of the shovel in performing its function. Nevertheless, it is now a common practice for researchers and engineers to use specific energy or payload per unit energy consumed (which is a more theoretically accurate definition of energy efficiency) as a measure of the energy efficiency of a shovel or loader. However, this practice assumes that the rate at which a shovel loads material is of no consequence when describing efficient loading.

On the contrary, mine managers and engineers are very concerned with the loading rate of shovels since it drives the production rate of the entire material handling system. Thus, this author believes the energy per unit loading rate is a better metric for energy efficiency of cable shovel operations [13]. For example, Babaei Khorzoughi and Hall [5] showed that for similar loading rates, a shovel can consume widely varying energy during digging. Hence, to discuss the theoretical basis of energy efficiency of cable shovel operations, let us define energy efficiency of loading as the ratio of loading rate to energy input.

8.2.1 Factors Affecting Cable Shovel Energy Efficiency

The literature includes many efforts to model shovel operations or energy efficiency by accounting for some or all of the factors or variables that affect shovel efficiency [13, 14, 15]. A cable shovel operator moves the dipper through the muck by moving the crowd arm and hoist ropes. Once the dipper is out of the muck pile, the operator also uses the swing action to swing the shovel about its axis while still using the crowd arm and hoist ropes to position the dipper over the truck or hopper. Once he or she dumps the load into the truck or hopper, the operator swings the shovel back into position (the crowd arm and hoist ropes are also used to reposition the dipper) for the next cycle.

Perhaps, because of the importance of the digging phase to the overall efficiency of shovel operations (the digging phase is also the most energy-intensive [15]), the literature, almost exclusively, contains models of the kinematics and dynamics of this phase [15]. Equation 8.1 shows that the digging energy of a shovel depends on the crowd (C) and hoist (H) forces, crowd \((\dot{c})\) and hoist \((\dot{h})\) speeds, and the digging time \((t_{\text{d}} )\) [13, 16, 17]. The crowd and hoist forces are used to move the crowd arm-dipper assembly through the digging trajectory (usually defined as the trajectory of the dipper tip) and overcome the resistance, offered by the muck, to digging [13].
$$E = \underbrace {{\int_{0}^{{t_{\text{d}} }} {(H)(\dot{h}){\text{d}}t} }}_{{{\text{Hoist}}\;{\text{energy}}}} + \underbrace {{\int_{0}^{{t_{\text{d}} }} {(C)(\dot{c}){\text{d}}t} }}_{{{\text{Crowd}}\;{\text{energy}}}}$$
(8.1)

Based on our working definition, shovel energy efficiency depends on the loading rate and the energy consumed during loading. Therefore, any factor that affects these two parameters will have an impact on the energy efficiency. Research has identified shovel characteristics; operating conditions, mine plan and design, and operator skill and practice as the main factors that affect shovel energy efficiency [13, 16].

The shovel’s design and specifications are important in determining whether it efficiently converts energy input into useful work or not. For energy efficient operation, a shovel’s drives should be designed such that they efficiently transmit and convert energy into useful work. Perhaps, the most significant innovation in shovel drive systems recently is the introduction of alternate current (AC) drive systems as an alternative to the direct current (DC) drive systems. The AC drives facilitate higher loading rates (due to higher speeds, torque, and power) and reliability [18]. The higher loading rate increases the energy efficiency of shovel operations. However, whether the drives are AC or DC, regenerative drives have a significant impact on the energy consumption, and thus energy efficiency, of a shovel with the potential to regenerate about a quarter of the energy [15].

Both natural and design-imposed operating conditions affect the operational efficiency and, thus, energy efficiency of cable shovels. These include the resistance to digging from the muck pile (whether in situ material or after ground fragmentation), bench profiles, and truck matching (both quantity and sizes) [13, 19, 20, 21]. For example, poor ground fragmentation that leads to difficult digging conditions will significantly increase the required crowd and hoist forces (Eq. 8.1), which in turn increase the energy required to dig [22]. This will decrease the energy efficiency of the shovel operation. Similarly, poor truck matching that leads to the shovel waiting for a truck to arrive will increase shovel energy consumed by the shovel while doing no useful work. This will also decrease energy efficiency.

The effect of the operator (skill and practices) is another important factor affecting the energy efficiency of shovel operations. The operator, within the limits of the machine, determines the cycle time and payload, both of which affect the energy efficiency. The cycle time depends on the trajectory, taken by the operator, and rate of travel, which depends on crowd and hoist speeds. Both the trajectory and the rate of travel affect the energy consumed during digging. The trajectory affects the work done to move the crowd arm-dipper assembly and payload through the digging cycle [13, 17]. Trajectories with greater depth of cut have been shown to require more energy than those with lower depths of cut [21]. The rate of travel is directly proportional to the power draw of the crowd, hoist, and swing motors (Eq. 8.1, for hoist and crowd energy). However, higher rate of travel reduces the cycle time which increases the rate of loading and can decrease the overall energy consumed per cycle [16]. Payload, on the other hand, depends on how effective the operator is at filling the bucket (fill factor). The fill factor depends on the depth of cut (trajectory) and material characteristics [23].

These relationships can be illustrated using simulation results from the cable shovel simulator built and presented in other publications by this author [13]. Figure 8.1 is based on simulations of a P&H 4100TS shovel using this simulator. As seen from Fig. 8.1a, increasing hoist speed decreases the required energy. Although this may seem contrary to Eq. 8.1, the reason for this is the decrease in digging time that results from increasing the hoist speed (Fig. 8.1c). This relationship between hoist speed and faster digging has been observed by other researchers as well [4, 16]. On the contrary, increasing crowd speed increases the digging time (deeper depth of cut trajectories result) and, thus, leads to higher energy consumption (Fig. 8.1b, d). In fact, this author’s previous work showed that these increases in energy consumption are substantially worse when the crowd arm is extended so much that it reaches the physical limit [13]. It is important to note that in all these simulations, the resulting payload was the same. Consequently, the relationship between crowd and hoist speed and energy efficiency will be the same as that observed from these results.
Fig. 8.1

Effect of hoist and crowd speeds on shovel energy consumption: a digging energy versus hoist speed at crowd speed of 0.25 m s−1; b digging energy versus crowd speed at hoist speed of 0.7 m s−1; c digging time versus hoist speed at crowd speed of 0.25 m s−1; d Digging time versus crowd speed at hoist speed of 0.7 m s−1

8.2.2 Ongoing Research Initiatives to Improve Cable Shovel Efficiency

A review of the literature since 2012 reveals four main areas that are relevant to cable shovel energy efficiency: (i) improvements in drive systems for better energy efficiency; (ii) modeling of cable shovel dynamics to better predict shovel trajectories and to control the crowd arm-dipper assembly; (iii) characterization of dipper filling to improve fill factors and payloads; and (iv) better understanding of the role of operators and how to assist operators to do better (Table 8.1).
Table 8.1

Highlights of ongoing cable shovel energy efficiency-related research initiatives

Area

Sample initiatives

References

Drive systems

Investigating the effect of trailing cable length on substation voltage quality

Developing fault tolerance strategies for DC micro-grids including cable shovels

[24, 25]

Kinematics and dynamics modeling

Developing more accurate kinematics and dynamics models of the cable shovel front end assembly

[26, 27]

Characterizing of dipper filling

Understanding the effect of digging dynamics on bucket filling

Developing superior models for near real-time payload estimation

[23, 28, 29]

Operator and operator assistance

Further exploration of the effect of operators on shovel efficiency and productivity

Developing intelligent systems to guide operators during operation

Using human factors to design better interfaces for operator guidance systems

[5, 30, 31, 32, 33]

There is ongoing research that tries to optimize and improve how energy is delivered to the shovel mechanisms to do excavation and loading of material into trucks and hoppers. This work is crucial to ensure that the drive mechanisms are efficient and energy losses are minimal. Some of the recent work continues to explore even better ways to improve these mechanisms. For example, Abdel-Baqi et al. [24] explored the effect of the long trailing cables, which are highly capacitive, on the voltage quality in mines with cable shovels. Such voltage quality issues can lead to failures that can be expensive from a capital and operational standpoint. Their work shows how engineers can make recommendations for optimal cable length to avoid voltage amplification that can lead to unnecessary downtimes.

Other researchers continue to refine and improve the kinematics and dynamics models of the dipper-crowd arm assembly [26, 27]. Cable shovel kinematics and dynamics models have been improving in the last two decades. Researchers have increasingly used more sophisticated methods to model the motions and forces while also addressing limitations of earlier models. For instance, Awuah-Offei and Frimpong [13] improved upon the model in Hendricks et al. [34] by accounting for the width of the boom point sheave. Other researchers have used more comprehensive methods such as the Newton–Euler approach to provide more detailed models of the dipper-crowd arm assembly [14]. Recent work continues to refine such models for more accurate description of the shovel digging motions [27]. Others have implemented kinematics and dynamics models as a means to evaluate alternative designs to effect more complicated trajectories and motions by the dipper-crowd arm assembly [26].

Other research has sought to understand how the shovel dipper interacts with material (in situ or fragmented) to fill the bucket. The goal of such initiatives is to understand better dipper fill factors so as to maximize payloads [23]. Related research attempts to estimate, in near real time, the shovel payload based on shovel motor current and voltage signals [28].

However, an area of research that has perhaps received the most attention is the development of intelligent algorithms to help operators operate the shovel more efficiently. At the basic level, modern shovels display, in the operator’s cabin, various signals that provide intelligence that the operator can act upon. There are various systems (either provided by original equipment manufacturers (OEMs) or after-market installations) are commercially available that provide metrics such as payload, various operator “errors,” and machine health information. This area continues to receive research attention as researchers try to better understand the effect of operators on production and efficiency [5, 20, 31]. In addition, other researchers have been exploring the human factors in the design of the interfaces to better make these operator guidance systems useful for improving efficiency [30]. These efforts facilitate the bridging step between unaided operation by a human operator and autonomous operation and constitute a necessary step in the evolution of cable shovel technology for energy and operational efficiency.

8.3 Recommendations

Based on the fundamental understanding of cable shovel energy efficiency and a review of the current literature, the author makes the following recommendations for industrial practice and future research.

8.3.1 Industrial Best Practices

One could make any number of recommendations for industrial best practices that a mine manager or engineer can use to improve the energy efficiency of shovel operations. However, it is the opinion of this author that the best thing mine managers and engineers can do is to use the right technology to deliver energy, use rigorous data analytics to understand the efficiency of shovel operations, and carefully evaluate the costs and benefits of energy efficiency initiatives.

To maximize the energy efficiency of shovel operations, mines should ensure energy is efficiently delivered to the hoist, crowd, and swing mechanisms. It is important to properly assess the mine’s electrical system and loads to ensure adding (if new) or the continued use of the shovel(s) will not adversely affect the quality of the voltage delivered to the shovel motors. The electrical substations should be designed to handle the shovel loads in a manner that deliver quality voltage and enough power to the shovels, on demand. Beyond that, the shovel’s drive system should be efficient in delivering the breakout forces required to dig the material and hoist and swing the payload to dump into the trucks or hoppers. For existing (or used) shovels, there may be some opportunities to upgrade key systems to improve the energy efficiency of the shovel. In this regard, good preventive maintenance helps to ensure the shovel works efficiently all the time. It is important to ensure that the whole drive mechanism is efficient, if the goal is to make shovel operations energy efficient.

In addition to ensuring the machine’s drive mechanism is efficient in delivering energy to the digging tool, mines should take advantage of current equipment monitoring systems that generate lots of data and big data analytics tools to understand the effect of operators and operating conditions on the energy efficiency of shovel operations. With such data and analysis, mines can objectively understand the drivers of energy efficiency and target these to improve energy efficiency. Many commercial monitoring tools provide stock analytical reports and graphs to facilitate this analysis. Some also provide the ability to generate custom reports and graphs that are helpful for achieving the goals of various energy efficiency initiatives. There is work in the literature that shows new analytics tools that can guide mine engineers in developing such custom tools [5, 6, 20].

Mines could also use such analytical tools to guide operator training efforts aimed at improving the efficiency of operators. This provides objective feedback to operators, which can be used to spur improvements as the data can clearly correlate certain behaviors to improved efficiency. In this regard, simulator training can be very useful because the training tool itself (the simulator) can be configured to generate the same data as the data used in the analytics so that the trainer can track the correlation between training outcomes and actual performance [35].

As with other aspects of the mining operation, it is important that the costs and benefits of energy efficiency initiatives to improve shovel energy efficiency are carefully evaluated before mines implement the action plan. For example, though we know that operator guidance systems can improve energy efficiency of shovel operations, the costs and benefits differ for different operations. It is thus important for mines to evaluate these costs and benefits to ascertain how such technology would actually impact operator energy efficiency, the benefits of such improvements, and the costs to the operation. It is only then that the mine’s management can make informed decisions on whether to implement such an initiative or not.

8.3.2 Recommendations for Future Research

A review of the literature identifies some gaps in the literature that require more work. First, there is a need for research that will establish theoretical benchmarks for cable shovel operations. In the past, this author has proposed kinematics and dynamics models to predict the energy consumption of shovels during digging [13]. However, this work modeled only the energy consumed during digging and did not include the swinging energy. Also, this work did not include models of the drive systems nor did it account for any losses. However, it is possible to account for the swing and other aspects of the shovel loading cycle beyond the digging phase. Future research work should address this gap in the literature so mines have a theoretical estimate, given specific operating conditions, of the target energy consumption for shovel operations. This will be very useful in guiding shovel energy efficiency initiatives.

Second, further research is necessary to account for human factors in the design of operator guidance systems that assist operators during shovel operations. Often, these in-cabin displays provide too many pieces of information that can lead to information overload. There is already some research in this area [30]. However, more questions remain outstanding. These include: (i) What are the most significant cues that affect operator behavior? (ii) How should these cues be presented to operators to be most persuasive in affecting operator behavior? (iii) How much improvement in energy and production efficiency can be gained by operator guidance systems? These questions can be addressed by further research.

Finally, further research is also required to evaluate the effectiveness of operator training programs in improving energy efficiency of shovel operations. There is only limited work in the literature that attempts to evaluate the effectiveness of operator training in improving energy efficiency and how long any gains in improvement last [35]. Though Dorey and Knights [35] addressed some of these questions for draglines, the number (four operators participated in the training and three operators were used as a control group) of participants is too few to draw any broad inferences about the particular training program and such studies are required for shovel operators too. However, the study shows what needs to be done to evaluate the many operator training programs in order to establish whether and how operator training affects energy efficiency.

8.4 Summary

The objectives of this chapter were to: (1) establish the current knowledge on energy efficiency of cable shovel operations; (2) make recommendations for industry best practices; and (3) make recommendations for future research. The chapter presented the fundamentals of cable shovel operations and the factors that affect the energy efficiency of shovel operations. It also presented an overview of the latest research on cable shovel energy efficiency. Based on those discussions, the work recommends that mine managers and engineers should use the right technology to deliver energy to the cable shovel digging tool, use rigorous data analytics to understand the efficiency of shovel operations, and carefully evaluate the costs and benefits of shovel energy efficiency initiatives. The work also recommends that future research on shovel energy efficiency should establish theoretical benchmarks for cable shovel operations, account for human factors in the design of operator guidance systems to assist operators during shovel operations, and evaluate the effectiveness of operator training programs in improving shovel energy efficiency.

Notes

Acknowledgements

The author is grateful to Dr. Nuray Demirel who reviewed the original manuscript and made valuable suggestions that improved this manuscript tremendously.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Mining & Nuclear EngineeringMissouri University of Science & TechnologyRollaUSA

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