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Liu, S.Q.;  Lin, Z.;  Li, D.;  Li, X.;  Kozan, E.;  Masoud, M. Mining Equipment Management. Encyclopedia. Available online: https://encyclopedia.pub/entry/39062 (accessed on 16 November 2024).
Liu SQ,  Lin Z,  Li D,  Li X,  Kozan E,  Masoud M. Mining Equipment Management. Encyclopedia. Available at: https://encyclopedia.pub/entry/39062. Accessed November 16, 2024.
Liu, Shi Qiang, Zhaoyun Lin, Debiao Li, Xiangong Li, Erhan Kozan, Mahmoud Masoud. "Mining Equipment Management" Encyclopedia, https://encyclopedia.pub/entry/39062 (accessed November 16, 2024).
Liu, S.Q.,  Lin, Z.,  Li, D.,  Li, X.,  Kozan, E., & Masoud, M. (2022, December 22). Mining Equipment Management. In Encyclopedia. https://encyclopedia.pub/entry/39062
Liu, Shi Qiang, et al. "Mining Equipment Management." Encyclopedia. Web. 22 December, 2022.
Mining Equipment Management
Edit
This entry is adapted from the peer-reviewed paper 10.3390/mining2040043

Mining sector is an economic foundation and the main source of national wealth for many countries. Modern mining operations are ever more reliant on efficient usage of costly large-scale mining equipment (e.g., trucks, shovels/excavators/loaders, conveyors and crushers). Thus, mining equipment management is becoming crucial for the mining industry. To be viable and sustainable, mining enterprises need to operate different types of mining equipment units at various stages with the objective of minimizing the total cost or maximizing the whole productivity.

open-pit mining mining equipment management shovel–truck

1. Introduction

Nowadays, with the rapid development of modern mining technology, semi-automated or automated machinery and equipment have been widely applied in a variety of mine sites around the world. A contemporary mine site typically lasts from many years to several decades, continually providing metallic ores that are important raw materials for the manufacturing industry or non-metallic ores that are also vital to other industries such as construction, agriculture and chemical industries. For mineral-rich countries (e.g., Australia, Canada, Russia, Chile, Iran), the mining sector creates millions of jobs and substantial export earnings which are sources of national wealth to drive the development of other economic sectors such as education, transportation and commerce. On the other hand, mining exploration and exploitation require a large capital investment and involve huge annual cash flows. Therefore, many researchers have studied different kinds of mining optimisation problems from different perspectives to maximize the value of the whole mining process under constraints such as resource capacity, precedence, extraction, haulage, crushing, grade control, stockpiling, railing, shipment, environmental protection and economic issues. Among these studies in mining optimisation, some were devoted to modelling the ultimate mine design and long-term strategic planning problems over the life of a mine (with the time horizon of 10–30 years, typically); the majority of works focused on open-pit mine block sequencing problems at the tactical level (with the time horizons measured in months); some focus on short-term mine equipment planning and scheduling problems (with time windows measured in weeks) at the operational level.

2. Shovel–Truck (ST) System

In open-pit mining, shovels (excavators) and trucks are the most widely used equipment, because material handling (mainly excavation with haulage) is the most important mining operation. According to previous studies, material handling accounts for nearly 50% of the total operating cost in most open-pit mines. In addition, excavation and haulage operations are highly interdependent and inter-reliant. Usually, a fleet of mining trucks is compatibly matched with a large shovel; and the productivity (e.g., reducing the total idle time) of one shovel must rely on the truck fleet management (e.g., optimising the cyclic queuing times of a truck fleet). For better understanding, the main components and operation processes of the ST system are illustrated in Figure 1.
/media/item_content/202212/63a51c8bb092emining-02-00043-g001.png
Figure 1. Illustration of main components and operation process of the ST system.
Table 1 summarises the main characteristics of recent papers on the ST system in terms of the scholars, publication year, journals, country of the first author, problem types and solution techniques. As shown in Table 1, some findings are given as follows. First, most research considered the mixture of shovels and trucks, e.g., determining the best matching factor; selection with sizing of trucks and shovels; dispatching a fleet of trucks to one shovel. In comparison, investigation of individual shovel or truck management system is rare relatively. Second, most of studies on the ST system belong to a kind of the planning-type optimisation problems such as the ST allocation/dispatching/assignment/matching problem. In contrast, few studies focused on more complicated scheduling-type problem based on the application of classical machine scheduling theory. Note that planning deals with the optimisation problems of resource capacity, facility design, equipment allocation and personnel deployment without considering timing factors. Scheduling is concerned with the efficient allocation of equipment units to jobs (operations) and sequencing the operations on each equipment unit with timing factors. For example, the parallel-machine scheduling with sequence-dependent set-up times was recently applied to a real-world mine excavators timetabling case [1]. Indeed, the dynamic vehicle routing problem could be applied to the routing optimisation of open-pit truck fleets [2][3]. Third, most solution techniques for the ST problems are mainly based on the formulation of MIP models with the use of exact MIP solvers. More efficient solution approaches, such as metaheuristic algorithms, which can efficiently solve large-scale instances, are relatively occasional. Finally, for scheduling (dispatching and sequencing) a fleet of trucks associated with a shovel, most existing mathematical programming models are relatively basic. To be more applicable in practice, the ST scheduling models should be extended by considering more actual requirements, such as the best matching factor, the selection of trucks/shovels, the layout of haulage roads, the queuing (e.g., waiting/idle times) of trucks in the scheduling process, and maintenance/failure of mining equipment, etc. 
Table 1. Characteristics analysis of publications on the shovel–truck (ST) system [1][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32].
Authors Year Country Problem Types Solution Techniques
Young and Rogers 2022 USA Mine haul truck dumping process simulation A high-fidelity modelling method
Liu et al. 2022 China Mine excavators timetabling Mixed integer programming and metaheuristics
de Carvalho and Dimitrakopoulos 2021 Canada Integrated truck-dispatching and production Reinforcement learning
Upadhyay et al. 2021 Canada Production scheduling with shovel allocation Mixed integer programming
Aguayo et al. 2021 Chile Productivity and safety of shovel–truck system Interaction analysis
Elijah et al. 2021 Kenya Shovel–truck haulage optimisation Queuing theory
Wang et al. 2021 China Mine truck fuel consumption analysis Regression analysis
Bakhtavar and Mahmoudi 2020 Iran Shovel–truck allocation Scenario-based robust optimisation
Basiri et al. 2020 Iran Reliability assessment of shovel–truck system Statistical methods
Zhang et al. 2020 China Multi-objective unmanned truck scheduling Improved genetic algorithms (NSGA-II)
Kansake and Frimpong 2020 USA Estimate tire dynamic forces on haul roads An analytical model
Shah and Rehman 2020 Pakistan Shovel–truck allocation problem Mixed integer programming
Ozdemir and Kumral 2019 Canada A two-stage shove-truck dispatching system A simulation-based optimisation approach
Dabbagh and Bagherpour 2019 Iran Matching factor of shovel–truck system Ant colony optimisation
Liu and Chai 2019 China Routing optimisation of open-pit trucks Mixed integer programming
Moniri-Morad et al. 2019 Iran Capacity analysis of shovel–truck system Discrete event simulation
Sun et al. 2018 China Prediction of travel times of trucks Machine learning techniques
Baek and Choi 2017 Korea Design of a haul road for an open-pit mine Douglas–Peucker algorithm
Dindarloo and Siami-Irdemoosa 2017 USA Classification and clustering of shovels failures Data mining techniques
Patterson, Kozan and Hyland 2017 Australia Energy efficient shovel–truck scheduling Mixed integer programming and metaheuristics
Bajany et al. 2017 South Africa Shove-truck dispatching Mixed integer programming
Burt et al. 2016 Australia Mining equipment selection Mixed integer programming
Chang et al. 2015 China Open-pit truck scheduling Mixed integer programming
Dindarloo et al. 2015 USA Truck and shovel selection and sizing Stochastic simulation
Rodrigo et al. 2013 France Dynamic open-pit mine truck allocation Simulation-and-optimisation framework
Choi and Nieto 2011 Korea Haulage routing optimisation of mining trucks Least-cost path algorithm with Google Earth
Souza et al. 2010 Brazil Dynamic truck allocation in open-pit mining Hybrid metaheuristic algorithms
Topal and Ramazan 2010 Australia Mine equipment maintenance scheduling Mixed integer programming
Choi et al. 2009 Korea Haulage routing optimisation of mining trucks Multi-criteria least-cost path analysis
Ercelebi and Bascetin 2009 Türkiye Shovel–truck dispatching Linear programming and queuing theory

3. In-Pit Crushing–Conveying (IPCC) System

The in-pit crushing and conveying (IPCC) systems are attracting more and more attention from researchers and practitioners in the mining industry, due to its advantages and benefits in comparison to the conventional ST system. The IPCC system mainly consists of the crusher and conveyor located in an open pit. The crusher is used to grind large ore blocks, and then the ground ore blocks are delivered to the surface through the belt conveyor. With the deep-mining process of an open pit, the conveyor needs to be extended while the crusher needs to be relocated at a new mining phase. An overhead view of an IPCC system in an open pit is drawn in Figure 2.
/media/item_content/202212/63a51ca6b9c7bmining-02-00043-g002.png
Figure 2. An overhead view of a sample IPCC system in an open pit in which there are one conveyor and three crushers (a–c).
As in Table 1, main characteristics of recent works on the IPCC system are summarized in Table 2. According to the analysis in Table 2, some observations are made as follows. First, the number of publications on the IPCC system are much less than that of papers on the ST system, because the IPCC system is more complex than the ST system by nature. Second, most studies considered crushers and conveyors simultaneously, while studies of a single equipment type (a crusher or a conveyor) are rare. Third, as the IPCC system is a continuous system, failure (e.g., a pause) of the IPCC system will bring substantial economic losses. Moreover, the extension of belt conveyors and the relocation of crushers have a significant impact on the production safety. Therefore, most of the problem types focused on the IPCC location and performance evaluation. In comparison, the IPCC production scheduling problem is relatively sporadic. Fourth, main solution approaches for IPCC management are based on mathematical programming. The development of more efficient solution approaches such as construction heuristics and hybrid metaheuristics for optimising the IPCC scheduling problem is a promising research direction. 
Table 2. Characteristics analysis of publications on the in-pit crushing–conveying (IPCC) system [2][3][33][34][35][36][37][38][39][40][41][42][43][44].
Authors Year Country Problem Types Solution Techniques
Gu et al. 2021 China Layout optimisation of IPCC Particle swarm optimisation algorithms
Liu and Pourrahimian 2021 Canada IPCC production scheduling Mixed integer programming
Shamsi and Nehring 2021 Australia Optimal transition point between IPCC and ST Analysis of cumulative discounted costs
Wachira et al. 2021 Kenya Performance analysis of SMIPCC Mine productivity index
Paricheh and Osanloo 2020 Iran IPCC planning with OPMPS Mixed integer programming
Samavati et al. 2020 Australia IPCC production planning and scheduling Integer non-linear programming
Hay et al. 2020 Australia Ultimate pit limit determination for SMIPCC Block model and network flow algorithm
Yakovlev et al. 2020 Russia Flow diagrams of IPCC Cyclical-and-continuous method
Abbaspour et al. 2019 Germany Optimum location and relocation of SMIPCC Transportation problem and scenarios analysis
Paricheh et al. 2018 Iran IPCC location and timing problem A heuristic approach
Paricheh et al. 2017 Iran IPCC location problem Mixed integer programming
Yarmuch et al. 2017 Chile IPCC location evaluation Markov chains
Schools 2015 USA Condition monitoring of IPCC Condition monitoring technology analysis
Roumpos et al. 2014 Greece Optimal location and distribution point of IPCC Simulation modelling

4. Hybrid IPCC-ST System

Despite the rising trends in using the IPCC system, some mining companies are still hesitating to use IPCC in their mining operations due to reliability and flexibility concerns. To improve mining reliability and reduce unexpected risks, a more flexible framework is needed to make proper transition decisions between IPCC and ST systems to satisfy the location and relocation of the semi-mobile crusher. 

Table 3 concludes the main characteristics of papers on the hybrid IPCC-ST system, which contains various mining equipment types such as trucks, shovels/excavators/loaders, conveyors, and crushers. As shown in Table 3, some insightful findings are presented. First, from the perspective of problem types, evaluation factors involved on the hybrid IPCC-ST system focused on the evaluation criteria with the consideration of environmental, social, economic, reliability and safety factors. Environmental factors include greenhouse gas, harmful gas, particular substance, and waste dumps. Efficiency factors mainly concern fuel consumption of each equipment and energy efficiency of the whole mining system. Social factors contain employment rates and salary levels. Economic factors are generally related to purchasing, renting, operating and maintenance costs. Safety issues refer to the reliability, failure rates of equipment and security of personnel. As the emphasis was placed on the performance evaluation, most papers tended to evaluate the economic value, production efficiency and environmental protection of the hybrid IPCC-ST system; but occasionally consider the system robustness, safety issues, economic factors and social indicators. Second, the majority of solution techniques for system performance evaluation are based on the multi-criteria decision-making methods. Third, due to its intrinsic complexity, the planning and scheduling optimisation methodology for the hybrid IPCC-ST system is scarce in the current literature. 

Table 3. Characteristics analysis of publications on the hybrid IPCC-ST system [45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61][62][63][64][65][66][67][68][69][70].
Authors Year Country Problem Types Solution Techniques
Patyk and Bodziony 2022 Poland Equipment selection in a surface mine Multi-criteria decision-making methods
Chinnasamy et al. 2022 India Introduction of ELECTRE for MCDM fuzzy DS-ELECTRE
Shamsi et al. 2022 Canada Production scheduling optimisation of hybrid IPCC-ST Mixed integer programming
Krysa, Bodziony and Patyk 2021 Poland Raw materials transportation Discrete simulation
Kaźmierczak and Górniak-Zimr 2021 Poland Accessibility of non-metallic mineral deposits Evaluation and classification
Purhamadani et al. 2021 Iran Energy consumption of IPCC-ST Data analysis
Bernardi et al. 2020 Canada Comparison of fixed and mobile IPCCs and ST Discrete event simulation
Kawalec et al. 2020 Poland Transition and replacement between IPCC and ST Data analysis
Almeida et al. 2019 Brazil ST system versus IPCC system Environmental and economic comparison
Ghasvareh et al. 2019 Iran Haulage system selection in open-pit mining Multi-criteria decision-making methods
Nunes et al. 2019 Canada Comparison analysis of SMIPCC and ST Multi-criteria decision-making methods
Abbaspour et al. 2018 Germany Selection analysis of ST and IPCC Evaluation of safety and social indexes
Nehring et al. 2018 Australia Strategic mine planning for ST and IPCC Mine planning and evaluation
Özfirat et al. 2018 Türkiye Selection of coal transportation mode Fuzzy analytic hierarchy process
Rahimdel and Bagherpour 2018 Iran Selection analysis of ST and IPCC Multi-criteria decision-making methods
de Werk et al. 2018 Canada Cost analysis of material handling systems A Monte Carlo simulation
Braun et al. 2017 Germany Sustainable technology diffusion of ST and IPCC Data analysis
Patterson, Kozan and Hyland 2016 Australia Integrated open-pit coal mining system Mixed integer programming
Yakovlev et al. 2016 Russia Conveyor-and-truck haulage system evaluation A cyclical-and-continuous method
Liu et al. 2015 China Energy consumption and carbon emissions of IPCC-ST Power consumption calculation model
Rahmanpour et al. 2014 Iran Comparison analysis of IPCC and ST Analytic hierarchy process
Norgate and Haque 2013 Australia Greenhouse gas impact of IPCC and ore-sorting A life-cycle assessment method
Vujić et al. 2013 Serbia Equipment Selection of Excavator–Conveyors–Spreader Multi-criteria decision-making methods
Abedi et al. 2012 Iran Analysis of mineral prospectivity mapping ELECTRE III method
Bazzazi et al. 2011 Iran Equipment selection of IPCC-ST Fuzzy multiple-attribute decision making
Owusu-Mensah and Musingwini 2011 Ghana Evaluation of ore transport options Multi-criteria decision-making methods

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Subjects: Engineering, Industrial; Mining & Mineral Processing; Computer Science, Interdisciplinary Applications
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Shi Qiang Liu
,
Zhaoyun Lin
,
Debiao Li
,
Xiangong Li
,
Erhan Kozan
,
Mahmoud Masoud
View Times: 603
Revisions: 2 times (View History)
Update Date: 23 Dec 2022
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