Energy efficiency in mining: a review with emphasis on the role of operators in loading and hauling operations

Journal of Cleaner Production xxx (2016) 1e9

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Review

Energy efficiency in mining: a review with emphasis on the role of operators in loading and hauling operations Kwame Awuah-Offei* Mining & Nuclear Engineering Department, Missouri University of Science & Technology, 226 McNutt Hall, Rolla, MO, 65409, USA

a r t i c l e i n f o

a b s t r a c t

Article history: Received 23 September 2015 Received in revised form 11 January 2016 Accepted 15 January 2016 Available online xxx

This paper presents a review of the literature on energy efficiency in mining with a specific emphasis on the role of operators in energy efficiency of loading and hauling operations. The objectives are to: (i) establish the current knowledge on energy efficiency in mining, in general; (ii) establish current knowledge on the role of the operator in energy efficiency of loading and hauling operations, specifically; and (iii) make recommendations for industrial best practice and future research directions to enhance energy efficiency in mining. The papers included in the review were selected through searches in major abstract databases using relevant keywords, with emphasis on recent peer-reviewed work. The review identified gaps in the literature and made recommendations for future research and industry best practice. © 2016 Elsevier Ltd. All rights reserved.

Keywords: Energy efficiency Mining Operator Material handling Loading Hauling

Contents 1. 2. 3. 4. 5.

6.

7.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Energy efficiency and mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Energy efficiency in loading and hauling operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Role of operators in efficient loading and hauling operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1. Loading operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2. Hauling operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1. Recommendations for further research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2. Recommendations for industrial best practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1. Introduction The sustainability impacts of energy generation and use are well understood. Hence, improving energy efficiency and more

* Tel.: þ1 573 341 6438; fax: þ1 573 341 6934. E-mail address: [email protected].

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sustainable energy generation have received attention recently in sustainability research. This trend is no different in the mining sector where research on how to improve energy efficiency and adoption of more sustainable energy sources appears to be on the rise. Mining is an energy intensive activity. In the United States (US), for example, mining is one of the few non-manufacturing industrial

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Please cite this article in press as: Awuah-Offei, K., Energy efficiency in mining: a review with emphasis on the role of operators in loading and hauling operations, Journal of Cleaner Production (2016), http://dx.doi.org/10.1016/j.jclepro.2016.01.035

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K. Awuah-Offei / Journal of Cleaner Production xxx (2016) 1e9

sectors identified by the US Department of Energy as energy intensive (Kaarsberg et al., 2007). It is also well established that the mining sector could significantly improve its energy efficiency. Again, using the US as an example, the US Department of Energy (DOE) estimated that the US mining sector consumes about 1315 PJ per annum and there is potential to reduce this annual energy consumption to 610 PJ, which is about 46% of current annual energy consumption (DOE, 2007). In South Africa, mining consumes 175 PJ of energy per annum and is the highest consumer of electricity at 110.9 PJ per annum, based on 2003 data (Oladiran and Meyer, 2007). The impact of this energy intensity on mining operating costs is evident in the correlation between increased interest in energy efficiency and energy prices (Levesque et al., 2014; Kecojevic et al., 2014). Such high energy intensive operations are not acceptable from a sustainability or cost standpoint, given recent policy initiatives by several governments to make industry pay for the costs associated with carbon emissions (carbon taxes and similar regulatory costs). Thus, all stakeholders have significant interest in improving the energy efficiency of mines. Energy efficiency improvement programs in mining are widespread and target all aspects of mining. These include capturing waste heat, managing electricity demand, mine drainage, ventilation, and generating energy from by-products (Levesque et al., 2014). However, comminution and material handling (including loading and hauling) operations have been identified to have the highest potential for energy efficiency improvements. For instance, in the US DOE study, grinding and diesel equipment used in material handling were identified as the operations presenting the greatest energy savings potential (DOE, 2007). The sheer number of studies dedicated to improving energy efficiency of mineral processing (or coal preparation) plants and material handling operations, compared to other processes, confirm this assertion. Yet, 40 years after the initial emphasis on energy efficiency in the 1970s, mines still struggle to control energy consumption per unit of production for these two operations, as evidenced by the US DOE study mentioned earlier. Of the two processes (comminution and material handling), material handling is, perhaps the most challenging to address because of difficulty in automating these operations. Most mineral processing and coal preparation plants today are highly monitored (with sensors) and automated, which facilitates energy efficiency programs. With minimal human input (often very complicated processing plants are run by only a few highly skilled individuals), it is easier to implement company-wide energy improvement programs since education programs to overcome the resistance to change can focus on the few individuals involved in plant operations. On the contrary, most loading and hauling operations involve significantly more employees (one operator per excavator or truck is still the norm) and overcoming those barriers to change are more challenging. And often, changing operator behavior (e.g. in curtailing truck idling) is the whole energy efficiency program. There is some evidence that operator practices in operating loading and hauling equipment in mines is the source of significant energy inefficiency. This evidence comes from both energy efficiency and continuous improvement studies. To the best of the author's knowledge, no one has critically reviewed all these studies to evaluate the extent to which operator practices affect energy consumption per unit of productivity. Such a review will be useful for identifying what we know already, in this regard, and what new research is necessary to facilitate more energy efficient loading and hauling operations. The objectives of this review paper are to use a comprehensive review of peer-reviewed literature to: (i) establish the current knowledge on energy efficiency in mining, in general; (ii) establish the current knowledge on the role of the operator in energy efficiency of loading and hauling operations, specifically;

and (iii) make recommendations for industrial best practice and future research directions. With respect to the second objective, the paper explores two important questions: (i) what do we already know about the effect of operator practices on energy efficiency of loading and hauling operations?; and (ii) what gaps exist in the literature that need to be addressed in order to facilitate better understanding of this effect in order improve the overall energy efficiency of mining? The paper is organized into seven sections, including this introductory section. The next section presents the methods used to select the literature and the general approach to answering the questions set forth. Following this are two sections that discuss the literature review findings on energy efficiency research in mining, in general, and loading and hauling operations, in particular. The next section attempts to address the second objective while the section after that addresses the third one. The paper concludes with a summary of the discussions. 2. Methods Peer-reviewed publications were identified through searches in major abstract databases (e.g. Web of Science, Scopus, Compendex, Google Scholar) using relevant keywords, which included “mining”, “energy efficiency”, “energy consumption”, and “energy use.” The author screened search results for relevance by reviewing titles and abstracts. In particular, where possible, searches were made to exclude items with the keyword “data mining.” The review mostly focused on peer-reviewed journal publications since the intention was to rely on rigorous research to address the stated objectives. In a few cases, relevant papers in peer-reviewed conference proceedings were included in the list of reviewed papers. Also, some technical reports from reputable government agencies were included in the list of references for review. In all, the author reviewed over 100 articles, mostly journal articles, for the general review of energy efficiency in mining. After the general review, he reviewed 75 articles (15 of which were contained in conference proceedings) in further detail to address the research objectives. Most of the discussions and analysis in this review is based on the critical review of these 75 articles. Of these, 22 deal directly with the impact of operators on energy (fuel) consumption or efficiency in loading and hauling operations. The review concentrated mainly on research in the last decade. For example, of the 75 articles reviewed in detail, only two were published before 2005 (one each in 1992 and 2000). There are two reasons for this. First, the author intended to focus on the most recent research in order to establish the state-of-the-art. Second, the pressure on the mining sector to be more efficient due to sustainability (climate change impacts in particular) concerns has resulted in increased research effort over the last decade. For a more comprehensive review of energy efficiency initiatives in mining spanning earlier periods, the reader is referred to Levesque et al. (2014). 3. Energy efficiency and mining Energy efficiency is defined as the ratio of useful work done (energy output) to energy input. For mining, the amount of product (e.g. tonnage of rock, grams of metal) is often used as a proxy for useful work done. These proxies can include payload or the product of payload and distance traveled (Odhams et al., 2010; Motlogelwa and Minnitt, 2013; Oskouei and Awuah-Offei, 2014). In some instances, proxies are used to describe the energy input as well. For instance, it is common to use the volume of diesel fuel used in truck haulage as a measure of energy input (Awuah-Offei et al., 2011; Motlogelwa and Minnitt, 2013).

Please cite this article in press as: Awuah-Offei, K., Energy efficiency in mining: a review with emphasis on the role of operators in loading and hauling operations, Journal of Cleaner Production (2016), http://dx.doi.org/10.1016/j.jclepro.2016.01.035

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Table 1 Highlights of mining energy efficiency research initiatives. Class Policy

Sample research initiatives  Evaluate impact of government policy on the mining sector

Sources

 Closed underground mines as low temperature geothermal sources  Hybrid/renewable generation for powering active mines

Support activities

 Managing electricity demand by optimizing ventilation and cooling systems  Optimizing mine drainage for energy efficiency or reduced energy impacts  Towards energy efficient comminution  Integrated systems (mine-to-mill) optimization  Assessing climate change impacts of processinga

Processing

Mining

a

    

Initiatives focused on better mining equipment (e.g. drive systems etc.) Automation and control Better understanding of the effect of operating conditions Better understanding of the role of operators Energy (fuel) consumption modeling and assessment of climate change impacts

References  Bolt et al. (2014), Henriksson et al. (2014), Hu and Kavan (2014), and Kohler (2014).  Hall et al. (2011), Verhoeven et al. (2014), Watzlaf and Ackman (2006).  Carvalho et al. (2014), Paraszczak and Fytas (2012), Paredes-S
anchez et al. (2015).  Chatterjee et al. (2015), Schoeman et al. (2014), Van Greunen et al. (2014), van Niekerk et al. (2014).
nchez et al. (2015), Nguyen et al. (2014a,b), Sahoo et al.  Paredes-Sa (2014b), Gunson et al. (2010), van Niekerk et al. (2014).  Nadolski et al. (2014), Napier-Munn (2015), Numbi et al. (2014), Palaniandy et al. (2015), Pothina et al. (2007).  Kojovic (2005), Kojovic et al. (2007), Powell and Bye (2009), Silva and Casali (2015).  Haque and Norgate (2014), Nimana et al. (2015), Norgate and Haque (2010).  Brown et al. (2000), Mazumdar (2013), Nessim et al. (2013).  Choi and Nieto (2011), Nessim et al. (2013), Parreira and Meech (2011).  Chang and Morlok (2005), Sahoo et al. (2014a).  Oskouei and Awuah-Offei (2014, 2015), Vukotic and Kecojevic (2014), Patnayak et al. (2008), Ahmed et al. (2012).  Ditsele and Awuah-Offei (2012), LaClair and Truemner (2005), Liu et al. (2015), Sahoo et al. (2014a).

Processing is used here to refer to mineral processing, extractive metallurgy and associated activities required to make saleable product out of run-of-mine material.

Energy efficiency gains can result from decreasing energy (fuel) consumption per unit of output or increasing output per unit of energy consumption. Energy efficiency initiatives in mining have been documented in the literature from the early 1970s. These early energy efficiency programs included waste heat recovery, employee training, adequate and timely maintenance, and demand management (Levesque et al., 2014). Today, energy management is a key performance indicator for many mines and routinely reported in annual sustainability reports for individual mines or entire corporations. The particular strategies a mine's management employs to achieve its energy management goals will depend on the mining method and the mine's unique circumstances. The energy efficiency of each mine also differs based on the mining method and circumstances (Ditsele and Awuah-Offei, 2012). A review of the literature shows that energy efficiency initiatives have targeted all aspects of mining operations and the mining sector, as a whole. In this work, these initiatives are classified into mining, processing, and support activities as well as energy sources and government policy analysis (Table 1). Most of the research on the effect of government policies on energy efficiency of mining focuses on electricity demand, although some are more comprehensive. The published research indicates that government policies, as a whole, often have contradictory effects on energy efficiency in mining (Henriksson et al., 2014; Hu and Kavan, 2014). For example, one government policy may expressly encourage industry to invest in energy efficiency projects while another may be depriving industry of the needed financing to carry out such investments (Hu and Kavan, 2014). In most cases, government policies aimed at achieving national energy efficiency goals are a combination of new taxes and tax incentives. Such policies need to balance the need to discourage energy intensive activities with the need for national or regional competitiveness in an increasingly global economy. Otherwise, an unintended outcome of such policies may be a weakening economy (resulting from, for example, capital flight as mines close and the product is mined elsewhere) and its attendant problems. Another key issue that informs such government policy is whether electricity (or energy) demand in the mining sector is sensitive to electricity (energy) prices. (If mining energy demand is elastic with respect to prices, then higher government taxes on electricity will reduce

consumption.) There appears to be some contradiction in the literature, with regards to this question. Whereas Henriksson et al. (2014) show that electricity demand in the Swedish mining sector is price elastic, Kohler (2014) does not find statistically significant elasticity in the South African sector. It appears this contradiction is explained by whether the model allows for substitution or not. A key policy tool that has not been used as frequently by governments for the mining sector is public funding of energy efficiency research. The literature shows that research and development increases energy efficiency (Henriksson et al., 2014). Yet research expenditures of mining companies are traditionally very low compared to companies in other sectors. The fact that mining companies cannot as easily protect energy efficiency technology developed through research expenditures discourages more private research expenditures on energy efficiency. Thus, it will appear this should be a perfect avenue for public research funding to encourage research on energy efficiency initiatives. For instance, in 2009, the US Department of Energy's (US DOE's) Industrial Technologies Program reported cumulative energy savings of 12.9 PJ from three energy saving technologies for the mining sector funded by the program (DOE, 2009). Recent research on energy sources emphasizes renewable energy sources as a means to lower emissions and climate change impacts of mining. Energy is needed in mining in the form of electricity, thermal energy (heating or cooling) and liquid fuel for mobile equipment. The literature shows that there is significant potential to improve the impacts associated with energy use in mining, if mines will use more energy from renewable sources (McLellan et al., 2012). The two broad trends in the recent literature are: (i) using flooded underground mines as low temperature geothermal resources (Hall et al., 2011; Verhoeven et al., 2014; Watzlaf and Ackman, 2006); and (ii) incorporating renewable energy, generated on-site, into the energy source mix (Carvalho et al.,
nchez et al., 2015). 2014; Paraszczak and Fytas, 2012; Paredes-Sa Carvalho et al. (2014) show that hybrid systems, which include renewable energy sources, are optimal in cases where grid power and other energy infrastructure (e.g. transportation routes for diesel) are unavailable or cost prohibitive. It is no surprise then that mines in remote locations have been the ones who have adopted hybrid energy systems (Paraszczak and Fytas, 2012). The main

Please cite this article in press as: Awuah-Offei, K., Energy efficiency in mining: a review with emphasis on the role of operators in loading and hauling operations, Journal of Cleaner Production (2016), http://dx.doi.org/10.1016/j.jclepro.2016.01.035

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disadvantage of these systems is the high initial capital requirements compared to conventional diesel generators, which are much more expensive to operate. However, some mines that are not so remote have adopted solar generation (e.g. using large reclaimed tailings facilities) when the economics have been favorable (Paraszczak and Fytas, 2012). It is obvious that economics remains a driver in the decision to include renewable energy projects into mine development. It is also important to note that biodiesel has become increasingly popular in underground mines as a means to meet stricter diesel particulate emission standards (Lutz et al., 2015). Although this is not motivated by energy efficiency or a desire to reduce climate change impacts, it could potentially lead to lower carbon footprint and more sustainable mining. Energy efficiency initiatives in the literature have been comprehensive, touching on every aspect: mining, processing and support activities. Most of these initiatives have focused on demand management, by mines, to reduce electricity consumption and costs. Managing electricity consumption for ventilation and cooling systems has received a lot of attention lately (Chatterjee et al., 2015; Van Greunen et al., 2014). Mine drainage systems have also received attention (Sahoo et al., 2014b). Energy research in processing is extensive and cannot be fully reviewed in this work, given its scope. A summary list of some important research initiatives is provided in Table 1. It has long been known that comminution is energy inefficient (DOE, 2007; Napier-Munn, 2015). Unsurprisingly, it has received a lot of research attention with many researchers offering models to understand specific energy, conducting benchmark studies or proposing more energy efficient alternatives (Nadolski et al., 2014; Palaniandy et al., 2015; Pothina et al., 2007). However, not much has changed in the industrial practice with respect to communition and it still represents, perhaps, the single biggest potential for energy savings in mining (when compared to theoretical limits) (DOE, 2007; Napier-Munn, 2015). Further research is required to develop more energy efficient technology that is reliable and cost effective. However, industry needs to more aggressively incorporate already existing research before it is forced to do so by regulatory pressure and energy costs (Napier-Munn, 2015). Achieving energy efficiency in size reduction by taking a systems level approach that includes ground fragmentation, material handling and comminution is not a new idea. However, it still continues to receive research attention as researchers seek to extend its applications and enhance our understanding for further gains (Kojovic et al., 2007; Powell and Bye, 2009; Silva and Casali, 2015). For example, by accounting for feed size distribution in models that estimate SAG (semi-autogeneous grinding) mill specific energy, mine and power engineers can better predict energy consumption, which is likely to result in more energy efficient comminution circuits (Silva and Casali, 2015). Some of the recent research has also focused on assessing the climate change impacts of processing (Haque and Norgate, 2014; Nimana et al., 2015; Norgate and Haque, 2010). For example, the energy intensive nature of oil sands processing has received attention even in society at large and is at the center of debates on the role of oil sands in energy policy. The next section provides an overview of recent research on energy efficiency in loading and hauling operations, which are considered the core energy intensive portions of mining activities. 4. Energy efficiency in loading and hauling operations Energy efficiency of loading and hauling operations depend on the efficiency of the equipment units, operating conditions (including those imposed by mine planning and design) and

operator practices, which are affected by skill and training. Many researchers have attempted to model energy consumption or efficiency of one or multiple material handling operations by accounting for some of these factors (Awuah-Offei and Frimpong, 2007; Awuah-Offei et al., 2011; Sahoo et al., 2014a; Zhang and Xia, 2011). Due to the number and complexity of variables and the fact that analytical models have to, out of necessity, use simplifying assumptions, none of these models comprehensively address every factor (Awuah-Offei and Frimpong, 2007; Zhang and Xia, 2011; Sahoo et al., 2014a; Wei and Gao, 2012; Yin et al., 2008). Other models use stochastic simulation, regression and other approaches to capture the complex relationships instead of modeling these relationships analytically (Acaroglu et al., 2008; Awuah-Offei et al., 2011). Some researchers have also established these relationships using empirical data from field studies (Bogunovic and Kecojevic, 2011; Patnayak et al., 2008; Vukotic and Kecojevic, 2014). Regardless of the approach or which factors are considered, it is obvious that there are factors beyond the efficiency of the equipment units used in loading and hauling that affect the overall energy consumed per unit of production. However, the energy efficiency of the equipment units is still very important in achieving overall energy efficiency. In order for mines to achieve energy efficiency targets, the equipment units need to efficiently transform the energy input into useful work. Research is required then to efficiently generate energy from the fuel source (for those units that generate their energy onboard, primarily from diesel fuel), transmit the energy to the working implements, or convert the energy into useful work by the working implements. Perhaps, one of the most significant developments in this direction is the development and widespread adoption of electric drive systems in both shovels and trucks (Brown et al., 2000). Theoretically, the electric drive systems eliminate some of the energy conversion, especially when combined with trolley-assist, thus leading to more energy efficient operations (Mazumdar, 2013). It is not clear, however, that the electric drive system, by itself, represents more efficient technology since the mechanical drives continue to perform well in several applications. There has been new automation and control algorithms for various loading and hauling applications, which lead to improved energy (or fuel) efficiency (Choi and Nieto, 2011; Middelberg et al., 2009; Nessim et al., 2013; Parreira and Meech, 2011; Ristic et al., 2012). These applications range from better dispatching systems, electricity demand management for conveyor transport, to thermal management, which all lead to lower energy consumption per unit of production. The ultimate example of automation is autonomous (driverless) dump trucks, which have the potential to increase energy efficiency significantly by removing the “human factor” completely. Since most of the technology is proprietary, the author is not aware of any empirical data that has been published on the fuel efficiency of autonomous trucks. However, early research on the subject suggests that control algorithms that ensure optimal gear shifting will outperform human operators and ensure optimal fuel efficiency (Parreira and Meech, 2011). More importantly, such control algorithms can operate optimally almost all the time, compared to human operators whose performance varies during an operating shift. However, it is important to note that control algorithms that lead to more energy efficiency are still useful, even if they do not lead to complete autonomy. Computer-assisted operation can still provide significant energy savings. Recent research confirms that operating conditions (whether natural or imposed by mine planning and design) affect the operational efficiency and, thus, energy efficiency of loading and hauling operations. For loading operations, factors like the resistance to digging (whether natural or after ground fragmentation), bench

Please cite this article in press as: Awuah-Offei, K., Energy efficiency in mining: a review with emphasis on the role of operators in loading and hauling operations, Journal of Cleaner Production (2016), http://dx.doi.org/10.1016/j.jclepro.2016.01.035

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profiles, and proper truck matching (both quantity and sizes) have been found to influence energy efficiency (Awuah-Offei and Frimpong, 2007; Awuah-Offei et al., 2011; Oskouei and AwuahOffei, 2015; Karpuz et al., 1992). For truck haulage, haul road profiles, haul road surface properties (affecting friction and rolling resistances), mine geometry and topography, and proper truck matching have been found to be influential (Awuah-Offei et al., 2011; LaClair and Truemner, 2005; Sahoo et al., 2014a). Some of these are relatively easy to control (e.g. good road surfaces are the product of good road construction and maintenance practices) whereas others are not. However, it is essential for research to continue to elucidate the exact relationships between these factors and energy efficiency, so management can properly prioritize its efforts to control them. This is particularly challenging in a field environment where many interacting factors are continually changing. However, laboratory equipment have their limitations due to issues with scaling. Given the emphasis, in the last decade, on climate change impacts, it is not surprising that some of the recent research on loading and hauling activities focus on assessment of climate change impacts (Ditsele and Awuah-Offei, 2012; Norgate and Haque, 2010; Kecojevic and Komljenovic, 2010; Liu et al., 2015). The literature shows that, as with all other aspects of mining, climate change impacts are correlated to energy efficiency. Hence, more efficient mining (and loading and hauling operations) will result in lower climate change impacts (Ditsele and Awuah-Offei, 2012).1 There is a significant amount of literature that points to the important effect of operators on loading and hauling energy efficiency. This is discussed in the next section.

5. Role of operators in efficient loading and hauling operations Energy efficiency of loading and hauling operations depends on the equipment, operating conditions, mine planning and design, and the operator (Fig. 1). The equipment determines how efficiently the machines convert energy into useful work under given conditions. However, unfavorable operating conditions (e.g. ground fragmentation, confinement, and road conditions) can result in significantly higher energy input per unit of productivity. The mine design and production plan (which determines how equipment is deployed) affect the operating conditions, interactions between equipment units, and efficient use of equipment. This combines to either enhance or detract from energy efficiency. However, there is ample evidence that operator skill and practices significantly affect the energy efficiency even in the best of scenarios (Awuah-Offei and Frimpong, 2007; Oskouei and Awuah-Offei, 2015; Lumley, 2005; Sahoo et al., 2014a). The goal of this section is to establish what we know about the effect of operators on energy efficiency of loading and hauling operations. It is important to note that loading with excavators and hauling with dump trucks do not represent all of material handling in mines. However, this section focuses on these activities for two reasons: (i) these activities are very widespread and significant in mining; and (ii) the author's own research has mainly focused on these activities.

1 Although, improving energy efficiency will reduce climate change impacts for the same level of production under similar conditions, there is no guarantee that improving energy efficiency alone will reduce overall greenhouse gas emissions from mining. Changing conditions (e.g. deeper mines, lower quality deposits, or increased demand) may negate the gains in energy efficiency.

5

Fig. 1. Factors that affect energy efficiency of loading and hauling.

5.1. Loading operations As discussed in Section 3, researchers often use proxies for energy input and useful work in describing energy efficiency in mining. For loading operations (including digging), energy input is estimated directly, if the loading tool is an electric machine (electric shovel or dragline), or engineers use a proxy such as volume of diesel consumed during loading. Often, the amount of material moved is used as a proxy for useful work done. This author believes the amount of material per unit time (production rate) is a better metric for useful work done since mine engineers are concerned with how quickly material is moved as well as how much is moved (Awuah-Offei and Frimpong, 2007). Hence, to discuss the theoretical basis of energy efficiency of loading, let us define energy efficiency of loading as the ratio of production per unit time to energy input (or volume of fuel, for non-electric excavators). The production per unit time is a function of the excavator specifications (e.g. bucket capacity and operating speeds), operating conditions (e.g. material diggability), and mine design and plan (e.g. bench heights and fleet of trucks matched to excavator). The energy input is determined by the weight of the material, the resistance of the material to digging (diggability), and the trajectory of the bucket throughout the cycle (Awuah-Offei and Frimpong, 2007; Wei and Gao, 2012; Yin et al., 2008). Some of these factors interact to affect both the production rate and energy input. For instance, the trajectory taken by the bucket affects cycle time, fill factor, and energy consumption. Fill factor also affects the weight of material in the bucket. Various researchers have presented kinematics and dynamics models for energy consumption of shovels, excavators, and draglines as a function of these parameters (Awuah-Offei and Frimpong, 2007; Demirel and Frimpong, 2009; Frimpong et al., 2005 & 2008; Wei and Gao, 2012). However, the operator affects key parameters that determine the production rate and energy consumption, such as bucket fill factor and cycle time. The literature shows significant energy inefficiency in loading operations due to operator practices (Table 2). Table 2 suggests that the other operators use up to 40% more energy per tonne of production when compared to the best operators. The potential savings could be even more since there is no guarantee that the best operator operates at the optimal energy efficiency. Research shows that during the digging phase, the most important factors are the trajectory of the bucket used by the operator and the speed at which the operator executes the trajectory (Awuah-Offei and Frimpong, 2007; Wei and Gao, 2012). The front-end of an excavator or the ropes and bucket of a dragline are very heavy and most of the energy is expended moving them through the loading cycle. Trajectories that increase the moments about the base of the excavator requires far more energy. Hence, operators that tend to

Please cite this article in press as: Awuah-Offei, K., Energy efficiency in mining: a review with emphasis on the role of operators in loading and hauling operations, Journal of Cleaner Production (2016), http://dx.doi.org/10.1016/j.jclepro.2016.01.035

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Table 2 Examples of energy inefficiency due to operator practices. Citation

Energy efficiency metric

Observed inefficiency

Comment

Oskouei and Awuah-Offei (2014), Oskouei and Awuah-Offei (2015) Vukotic and Kecojevic (2014). Komljenovic et al. (2010). Patnayak et al. (2008)

Overall dragline operation energy efficiency (t/kWh)

1.4e15.7%

Observed difference between best operator and others

Overall shovel operation energy efficiency Overall dragline operation energy efficiency (t/kWh) Shovel hoist energy efficiency (t/kJ) during digging phase only

5.3e15.0% 16.2e44.1% 1.6e28.6%

Observed difference between best operator and others Observed difference between best operator and others Observed difference in monthly average between best operator team and others

go through the muckpile with greater depths of cut tend to have higher energy consumptions (Awuah-Offei and Frimpong, 2007; Karpuz et al., 1992; Patnayak et al., 2008). For draglines, this effect shows in the position and distance the bucket is dragged through (Oskouei and Awuah-Offei, 2015). However, greater depths of cut tend to increase bucket fill factor and, thus, productivity and energy efficiency. Hence, the best practice is the shallowest depth of cut that results in optimal fill factor. The speed with which an operator executes a particular trajectory is directly proportional to the power draw (power is equal to the product of force and velocity). However, higher speeds decrease cycle times, which increase productivity and should increase energy efficiency. For example, Fig. 2, which is generated with data in Tables 3 and 5 of Patnayak et al. (2008), seems to suggest higher hoist power tends to lead to higher productivity. Since many of the research on energy efficiency of loading reported in the literature do not include the rate of mining, it is difficult to tell what effect digging speed has on energy efficiency. However, simulation experiments by Awuah-Offei and Frimpong (2007) suggest this is a factor and one that also requires optimization. Also, there are other sources of inefficiency that result from how the operator interacts with other equipment units that cause delays and idle time. These inefficiencies can be due to the excavator or hauler operator. The energy efficiency losses due to these operator practices have not been studied as much. However, anecdotal evidence from hauler-loader matching optimization shows that sub-optimal interaction between haulers and loader lead to suboptimal energy efficiency (Awuah-Offei et al., 2011). 5.2. Hauling operations To estimate energy efficiency of hauling operations in mining, researchers typically use proxies for both useful work and energy input. The most common proxy for useful work is payload (AwuahOffei et al., 2011; Motlogelwa and Minnitt, 2013). However, it is obvious that to account for varying distances and haul profiles other metrics such as the product of mass carried (payload) and distance or payload, distance and elevation may be more appropriate. The most common proxy for useful work is fuel consumption, which is usually represented by the volume of diesel consumed during hauling. Hence, fuel efficiency for mine hauling operations is usually measured as payload per unit fuel consumption or payload-distance per unit fuel consumption. The energy consumed by a land transport vehicle moving a payload from one point to the other depends on the same factors described in Fig. 1: the equipment, operating conditions, mine planning and design, and the operator. The relevant equipment characteristics are engine efficiency, aerodynamic resistance, weight of the empty vehicle, efficiency of regenerative braking (where applicable), maximum payload, and maximum engine power and speed. Relevant operating conditions include rolling resistance of the haul roads, haul distances, haul road profile, deployed fleet and dispatch system (this affects loading and

Fig. 2. Productivity vs. hoist power using data from Patnayak et al. (2008).

dumping times, waiting times at excavator, crusher or dump, traffic congestion on haul routes, etc.), and speed limits. Some of these operating conditions are controlled, at least in part, by mine planning and design. These include rolling resistance (which is determined by the type of road covering designed for the roads and road maintenance practices), haul road profile, allocated fleet and speed limits (Odhams et al., 2010; LaClair and Truemner, 2005; Sahoo et al., 2014a). Operators play a key role in determining the energy efficiency of hauling operations because they impact key input parameters which include payload (which depends mainly on the loader operator), travel speeds (the actual velocities as well as the acceleration and deceleration strategies), and gear shifting strategies2 (Ahmed et al., 2012; Sahoo et al., 2014a). As shown in Equation (1), the power, P, required by a truck traveling over a distance, D, depends on the velocity, V, weight of the truck (including the payload if it is loaded), W, and D (Sahoo et al., 2014a). a is a constant for the aerodynamic drag while b represents the rolling, friction and grade resistances.

  0:5WV 3 P ¼ V aV 2 þ bW þ D

(1)

Although increasing payloads will increase the volume of fuel consumed, due to higher power requirements (Equation (1)), increasing payloads will increase the productivity. The combined effect is that higher payloads will lead to improved fuel efficiency. For example, Motlogelwa and Minnitt (2013) observed that when payload increased from 18 to 21.5 t (19% increase), the fuel efficiency increased from 0.595 to 0.637 t/L (7% increase). Odhams et al. (2010) suggest that under-loaded trucks (lower than optimal payloads) can increase the energy required per unit of load

2 In modern trucks, gear shifting strategies are controlled by the truck itself with input from the driver mainly in the form of desired speeds, acceleration and deceleration.

Please cite this article in press as: Awuah-Offei, K., Energy efficiency in mining: a review with emphasis on the role of operators in loading and hauling operations, Journal of Cleaner Production (2016), http://dx.doi.org/10.1016/j.jclepro.2016.01.035

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by as much as 65%. However, with further increases in payload, travel speeds will decrease leading to lower productivities even though the fuel consumption will continue to increase. This will lead to lower fuel efficiency in addition to the mechanical damage that can be done to the equipment for exceeding load limits. Increasing travel speeds on haul roads will lead to higher fuel consumption (Equation (1)). However, the increasing speed will also result in higher productivity (more material delivered per unit time). The combined effect is that initially, the energy efficiency will increase until it reaches optimal and then drop with further increases in travel speeds (Sahoo et al., 2014a). Hence, speed limits at mines should be chosen, given the type and size of trucks, to be close to optimal for energy efficiency.3 However, operators need to operate at or near optimal speeds over the haul cycle to ensure maximum energy efficiency. For example, it has been shown that, on constant haul road slopes, operating at near constant speeds maximizes energy efficiency (Chang and Morlok, 2005). Fu and Bortolin (2014) also show that optimal gear shifting control strategies can be developed for varying haul slopes that reduce fuel consumption and cycle times. To the extent that operator skill and practices affect gear shifting strategies, operators can affect energy efficiency. Ahmed et al. (2012) show that using optimal gear shifting strategies for the same driving cycle characteristics can lead to up to 6.5% in fuel consumption savings. Finally, inefficiencies due to truck idling are a significant source of inefficiency that has plagued the industry for years. Levesque et al. (2014) catalogs various initiatives over the years that have focused on reducing fuel consumed for no work. Situations will always arise during operation (e.g. idling due to flawed operational plans, unanticipated events, or scheduled breaks) that require trucks to be idle or on stand-by. Many case studies in industry have demonstrated that it is possible to significantly save on energy consumption with proven policies to turn off engines during such periods (see examples cited by Levesque et al. (2014)). This is, perhaps, the most common energy efficiency initiative at mine sites. And these initiatives recognize the impact of operators on a mine's energy efficiency. 6. Recommendations In order to achieve the third objective of this work, the author makes recommendations for industrial best practice and future research directions, based on the literature review presented in the preceding discussions. 6.1. Recommendations for further research This author identified gaps in the literature in the course of this review. One of these is the limited public funding for mining energy efficiency research. There appears to be relatively more public funding for research to improve energy efficiency in other energy intensive sectors. Typically, manufacturing and residential consumers get the bulk of the attention. Given, the energy intensive nature of mining and the difficulties discussed earlier (see Section 3), it seems mining is one of those sectors that could benefit from public funding of energy efficiency research and development. There is a need for research that clearly examines the costs and benefits of public policy that will fund energy efficiency research in mining. All stakeholders will benefit from knowing the return on investment for such public funding.

3 The author recognizes that often safety concerns will be paramount in determining speed limits.

7

In spite of the amount of work already done, comminution and material handling still remain the areas with the most potential to reduce energy efficiency. The gap between the theoretical limits and the practical limits achieved today is still wide and will require disruptive technology on how mining is done to ensure removing and reducing material to the sizes required to liberate the valuable minerals is energy efficient. The current technologies are still inefficient, relative to theoretical limits, in reducing the size of rock or moving rock from one location to the other. These two areas remain the main areas with the most potential for energy savings. It is clear from the literature that operators can have a significant effect on the energy efficiency of material handling operations. However, researchers report a wide range of impacts and it is not yet clear why this disparity exists. Even for the same type of operation (e.g. shovel loading), there are often differences in the reported impacts (see Table 2). It is not clear what the underlying causes of this disparity are. Further research that either develops fundamental models of the machines or provides data from controlled experiments on operator effects is required to shed more light on this disparity. Such research is necessary to clarify the relationship between specific operator practices and energy efficiency to inform performance. Also, this can better quantify the potential gains in energy efficiency that can be achieved with improved operator performance. Also, research is needed to further facilitate the adoption of hybrid energy systems in mining. The inclusion of renewable and non-traditional energy sources into the energy mix at mine sites has great potential for powering mining in the coming years given the remote locations of the new deposits being discovered to replace current reserves (Carvalho et al., 2014; Paraszczak and Fytas, 2012). However, given that the initial capital costs of alternative energy sources are higher, further work is required to provide models for comparing the full life cycle costs of such hybrid systems to diesel generators, which are often the default option in remote locations. These models are not trivial since they have to account for the many risks (e.g. commodity prices, government regulations, and opposition from local communities) that can end or interrupt a mining project prior to its scheduled life. For example, real options valuation can be used to develop models that could evaluate how the decision to use particular generating sources (the many variations of hybrid systems and the traditional generator set option) in the presence of the available strategic options affects the value of the entire project (Slade, 2001). Also, renewable technology research can further reduce the capital expenditures for deploying renewable systems at remote mine sites so such systems can become more competitive, relative to diesel generators. (The capital costs for renewable technologies have already dropped significantly in recent years.) Finally, research is needed to rigorously evaluate the effectiveness of training programs in delivering the intended outcomes. The literature lacks work that evaluates whether operator training programs have an impact on energy efficiency (and other productivity measures), how long any gains in improvement last, and what individual operator attributes best explain differences in outcomes. This author was able to find only one paper that evaluated the efficacy of a training program in improving outcomes (Dorey and Knights, 2015). And even then, the number (four operators participated in the training and three operators were used as a control group) of participants is too limited to draw any broad inferences about the particular training program. However, the study shows what needs to be done to evaluate the many operator training programs in order to establish whether operator training really impacts key performance indicators like energy efficiency.

Please cite this article in press as: Awuah-Offei, K., Energy efficiency in mining: a review with emphasis on the role of operators in loading and hauling operations, Journal of Cleaner Production (2016), http://dx.doi.org/10.1016/j.jclepro.2016.01.035

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6.2. Recommendations for industrial best practice

7. Summary

Today's mining sector uses many management strategies and enabling technology to achieve its energy efficiency goals. Perhaps, more than ever, energy efficiency is on the agenda of the mining sector as shown by the energy efficiency measures in the sustainability reports of many of the sector's largest companies. Given the diverse nature of the mining sector (in mining methods, commodities, and energy infrastructure), it is difficult to recommend a particular set of best practices that are applicable in all circumstances. Again, the author refers the interested reader to Levesque et al. (2014), which presents a good review of energy efficiency strategies in mining. However, three recommendations seem apt in the light of the literature review presented in this work. First, mine engineers and managers should choose energy efficiency programs based on good estimates of the return on investment. There are many options to choose from in any attempt to improve on a mine's energy efficiency. For the mine's particular circumstances, some of these might yield better return on investment than others. In order to ensure energy efficiency does not lose its attraction as a viable business strategy, it is imperative that rigorous analysis is used to estimate the return on investment for each program before adoption. In some regards, it is easier to estimate the return on investment today than it was a few years ago. The advent of telemetry in mining equipment means there is abundant data on energy consumption that, taken together with other enterprise data, can provide very reliable estimates of the impact of most energy efficiency programs on a mining operation. Mine engineers and managers should take advantage of this abundance of data to carefully evaluate the options before selecting a particular energy efficiency option. For example, many of the papers reviewed that used data from on-board monitors (e.g. Komljenovic et al., 2010; Oskouei and Awuah-Offei, 2014 & 2015) estimated actual operator performance which can be used to estimate the disparity between the most and least efficient operators. In such a case, the exact energy savings from getting all operators to operate as efficiently as the most efficient operator can be estimated quite reliably to estimate the return on investment. Second, in recognition of the effect of operators on energy efficiency, operator training should be seen as a viable energy efficiency strategy. There is enough research to help mine managers to understand what operator practices have a significant effect on energy efficiency (Ahmed et al., 2012; Oskouei and Awuah-Offei, 2014 & 2015; Patnayak et al., 2008). This knowledge should be incorporated into operator training (both initial and periodic) to ensure operators operate efficiently. The training should be supported with good monitoring and feedback to the operator in a nurturing environment, if it is to be successful. Such training could be simulator-based or based on crew training so long as there is evidence that the training successfully inculcates into operators the habits and practices that are known to result in more energy efficient operations. Third, mine managers should consider technologies that guide operators to make energy efficient decisions in their work. As shown in the discussions above, there is research that demonstrates that optimal control algorithms can be developed for many of these operator functions (Chang and Morlok, 2005; Fu and Bortolin, 2014). Some of these control algorithms have been incorporated into guidance systems that are used to guide operators to more efficiently perform certain tasks. To the extent that some of this technology can assist operators to operate in an energy efficient manner, they may be viable strategies to achieve management's energy efficiency objectives.

This review paper aimed to establish the current knowledge on energy efficiency in mining, in general, and the role of the operator in the energy efficiency of loading and hauling operations, specifically. In addition, the paper sought to make recommendations for industrial best practice and future research directions to enhance energy efficiency in mining. With regards to energy efficiency in mining as a whole, the author classified the research into those that deal with energy efficiency initiatives in mining, processing, and support activities, and research on energy sources and government policy analysis. The literature shows work that develops approaches and technologies to improve energy efficiency for all mining processes and work that analyzes government policy that could enable energy efficient mining. With regards to the effect of operators on the energy efficiency of loading and hauling operations, the literature clearly explains the relationship between operator skill and practices and energy efficiency. Both theoretical (modeling) and experimental evidence exists that shows how an operator could have a significant effect on the energy efficiency of loading and hauling operations. However, there are some significant gaps in the literature that require further research. This includes a need for (i) disruptive technology that takes us closer to the theoretical efficiency limits in material handling; (ii) better understanding of the nature and extent of the influence of operators on the energy efficiency of loading and hauling operations; (iii) models for evaluating full life cycle costs of hybrid systems that account for the many risks and strategic options over the mine's life; and (iv) research that rigorously evaluates the effectiveness of training programs in delivering the energy efficiency goals. References Acaroglu, O., Ozdemir, L., Asbury, B., 2008. A fuzzy logic model to predict specific energy requirement for TBM performance prediction. Tunn. Undergr. Space Technol. 23 (5), 600e608. Ahmed, A., Zhao, C.L., Han, K., Zhang, F.J., Wu, F., 2012, November. Using design of experiment and genetic algorithm to obtain the optimum gear shifting strategy for a real driving cycles. Appl. Mech. Mater. 224, 497e503. Awuah-Offei, K., Frimpong, S., 2007. Cable shovel digging optimization for energy efficiency. Mech. Mach. theory 42 (8), 995e1006. Awuah-Offei, K., Osei, B., Askari-Nasab, H., 2011. Modeling truck/shovel energy efficiency under uncertainty. Trans. Soc. Min. Metall. Explor. 330, 573e584. Bogunovic, D., Kecojevic, V., 2011. Impact of bucket fill factor on dragline production rate and energy consumption. Min. Eng. 63 (8), 48e53. Bolt, G.D., Vosloo, J.C., Pelzer, R., 2014, August. Strategies for energy efficiency funding in the absence of industrial Eskom-IDM support. In: Industrial and Commercial Use of Energy (ICUE), 2014 International Conference on the. IEEE, pp. 1e7. Brown, G.M., Elbacher, B.J., Koellner, W.G., 2000. Increased productivity with AC drives for mining excavators and haul trucks. In: Industry Applications Conference, 2000. Conference Record of the 2000 IEEE, vol. 1. IEEE, pp. P28eP37. Carvalho, M., Romero, A., Shields, G., Millar, D.L., 2014. Optimal synthesis of energy supply systems for remote open pit mines. Appl. Therm. Eng. 64 (1), 315e330. Chang, D.J., Morlok, E.K., 2005. Vehicle speed profiles to minimize work and fuel consumption. J. Transp. Eng. 131 (3), 173e182. Chatterjee, A., Zhang, L., Xia, X., 2015. Optimization of mine ventilation fan speeds according to ventilation on demand and time of use tariff. Appl. Energy 146, 65e73. Choi, Y., Nieto, A., 2011. Optimal haulage routing of off-road dump trucks in construction and mining sites using Google earth and a modified least-cost path algorithm. Autom. Constr. 20 (7), 982e997. Demirel, N., Frimpong, S., 2009. Dragline dynamic modelling for efficient excavation. Int. J. Min. Reclam. Environ. 23 (1), 4e20. Ditsele, O., Awuah-Offei, K., 2012. Effect of mine characteristics on life cycle impacts of US surface coal mining. Int. J. Life Cycle Assess. 17 (3), 287e294. Dorey, F., Knights, P.F., 2015. Quantifying the benefits of simulator training for dragline operators. Min. Technol. http://dx.doi.org/10.1179/ 1743286315Y.0000000007. Frimpong, S., Hu, Y., Awuah-Offei, K., 2005. Mechanics of cable shovel-formation interactions in surface mining excavations. J. Terramech. 42 (1), 15e33.

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Please cite this article in press as: Awuah-Offei, K., Energy efficiency in mining: a review with emphasis on the role of operators in loading and hauling operations, Journal of Cleaner Production (2016), http://dx.doi.org/10.1016/j.jclepro.2016.01.035