Evaluating and optimizing the effectiveness of mining equipment; the case of Chibuluma South underground mine

Journal Pre-proof Evaluating and Optimizing the Effectiveness of Mining Equipment; the case of Chibuluma South Underground Mine

Brighton Samatemba, Long Zhang, Bunda Besa PII:

S0959-6526(19)34567-6

DOI:

https://doi.org/10.1016/j.jclepro.2019.119697

Reference:

JCLP 119697

To appear in:

Journal of Cleaner Production

Received Date:

10 November 2019

Accepted Date:

11 December 2019

Please cite this article as: Brighton Samatemba, Long Zhang, Bunda Besa, Evaluating and Optimizing the Effectiveness of Mining Equipment; the case of Chibuluma South Underground Mine, Journal of Cleaner Production (2019), https://doi.org/10.1016/j.jclepro.2019.119697

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier.

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Evaluating and Optimizing the Effectiveness of Mining Equipment; the case of Chibuluma South Underground Mine

Brighton Samatemba1 School of Economics and Management China University of Geosciences (Beijing) Haidian District, Beijing, 100083, China [email protected]

Long Zhang2, *(Corresponding Author) School of Economics and Management China University of Geosciences (Beijing) Haidian District, Beijing, 100083, China [email protected]

Bunda Besa3 School of Mines University of Zambia Marshlands, Lusaka 32379, Zambia [email protected]

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Evaluating and Optimizing the Effectiveness of Mining Equipment; the case of Chibuluma South Underground Mine

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Evaluating and Optimizing the Effectiveness of Mining Equipment; the case of Chibuluma South Underground Mine

ABSTRACT The effectiveness of mining equipment is a huge challenge in mining which affects mining companies’ competitive advantage in the global mining sector. It is therefore, very important for the mining industry to utilize its intensive equipment with proper measurements. In this paper an algorithm has been developed using Rstudio software to determine and evaluate the sensitive inputs to the life cycle of mining equipment as well as determine and analyze the Overall Equipment Effectiveness as a measure to use in evaluating and optimizing the effectiveness of mining equipment. The algorithm was validated in the fact that the trends of the sensitive inputs to the equipment life cycle were determined. The most sensitive inputs to the equipment life cycle such as process cycle times, utilization, maintainability, performance and production efficiencies of drill rigs, loaders and dump trucks were all evaluated and the overall equipment effectiveness was found to be below 50 percent for all the three types of equipment. The model developed in this paper is an algorithm with code that’s reproducible and reusable with a few changes of variables for an underground mine. The algorithm uses equipment operational records for effective planning and optimizing of mine production operations. Keywords:

Optimization, Mining and Technology, Equipment Management, Equipment

Productivity using R, OEE

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1.0 INTRODUCTION Equipment selection and optimization is one of the most challenging problems in the mining industry today, especially matching the correct number of equipment required to the amount of ore to be hauled. In mining ore production involves drilling, loading and hauling. Chibuluma South underground mine was chosen as the case study due to its modernization and use of trackless equipment. The utilization of its equipment was used to figure out a way to optimize the equipment and reduce the overall cost of materials handling. The problem of equipment optimization is often cascaded by the intermediate parameters. Using a piece of equipment effectively depends on its utilization, availability and the age of the equipment. The main objectives of a mining operation are to produce resources at the minimum possible cost and equipment selection and productivity play a major role at ensuring this is possible because materials handling is a very costly operation in the mining industry. The future of most industries today is in the industry’s effective use of technology and the mining industry is not an exception to this phenomenon. This paper focuses on using Rstudio software to develop a productive and effective algorithm that will be used as an important tool for equipment optimization and effectiveness at Chibuluma mine as well as other mining companies and reduce the overall cost of materials handling. The cost of haulage operations in mines is on the rise due to the longer haul routes and higher fuel consumption necessitated by deepening mines (Park and Choi, 2016) and Chibuluma mine is currently more than 600 meters below the surface with a haul route of more than 3km.Various techniques have been developed to date by many researchers (Ercelebi, S.G.; Bascetin, A, 2009) to optimize the haulage system which form a considerable part of mining costs. The analysis of the rate of operation in terms of loading equipment, ore yield, and rate of return based on the transporting equipment, and the proposal of a model to optimize the number and combination of the equipment deployed. (Salama, A.; Greberg, J., 2012) did a simulation on a loading-haulage system to optimize the number of trucks used in an underground mine, and proposed a system operation plan to improve the efficiency of the haulage system. According to studies that have so far been conducted we can easily tell that a productive and optimal haulage equipment forms a very important part of the mining operation and since modern equipment is capital intensive and increasingly sophisticated, it is important that it stays in revenue generating jobs and be highly efficient and effective in its available time. There are several available means of ensuring that the equipment is achieving the factors that lead to better 3

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asset management which include but are not limited to increase in utilization rate, higher reliability and effective maintainability. Since it is well known that these factors differ considerably in terms of the sustained cost to equipment effectiveness ratio, it is, therefore, very important to know the exact areas where efforts need to be focused in order to improve the equipment effectiveness as whatever gets measured, gets done (Campbell et al, 2015). 1.1 Background of Equipment Optimization at Chibuluma South Mine The selected site was Chibuluma South Mine which is located at latitude 12°53ʼS and longitude 28°05ʼE approximately 15 km west of Kitwe on the Copperbelt province of Zambia. Chibuluma mine is owned by Jinchuan Group Company Limited with 85% shares and Zambia Consolidated Copper Mines (ZCCM) with 15% shares and it is operated by Metorex group of companies (Mine Reports, 2016). The town of Kalulushi was developed in the 1950s by Roan Selection Trust (RST) to support the Chibuluma East and West mines. The mine has its main operating asset the Chibuluma south and under development is the Chifupu ore body which is approximately 1.7 km south west of Chibuluma south mine. Chibuluma south mine is an underground mine capable of treating up to 50,000 tonnes of Run off Mine ore per month and is located in Kalulushi town of Zambia. The main products of the mine are copper and cobalt which are usually exported as processed ore after metallurgical treatment. Chibuluma Mine was selected due to its modernization and use of trackless equipment. 1.2 Importance and Significance of Research The mine is a modern mine which is designed to use trackless equipment therefore, measuring the factors influencing the equipment effectiveness and thorough analysis of well-defined performance indicators will allow the mine to assess the progress of improvement initiatives and prioritize resources. In the mining industry, equipment optimization or effectiveness is mostly associated with both its utilization and availability but the impacts of the aforementioned factors are not that substantial compared to other factors which are usually ignored in the mining industry as well as at Chibuluma Mine in this case. The objective of this paper is to develop a stochastic equipment life cycle model to determine the productivity of equipment for mining companies with two main areas of focus; 1) Determination of the most sensitive inputs to the life cycle of the equipment; 4

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2) Equipment life cycle model to estimate the optimization of the equipment based on availability, utilization and production efficiency.

1.3 Stages of Equipment Life In order to develop a reliable and effective equipment life cycle model, the stages of equipment life have to be defined. Equipment life can be defined using three different variables namely physical life, economic life and profit life (Mitchell, 1998). Both the physical and economic life must be calculated when considering equipment life because they provide an effective equipment replacement analysis, on which major equipment replacement decisions are made (Douglass, 1975) and the profit life can then be calculated from the information taking note of the depreciation, downtime, investment, maintenance and repairs of which these factors are also integral to replacement analysis (Gransberg et al, 2006). All three stages of equipment life can be compared in accordance with Figure 1.0 which helps illuminate the relationship amongst the three stages where it can be clearly seen that it takes some time for a newly acquired equipment to cover the capital cost of its procurement cost. When it covers its procurement cost it then goes into a phase where it earns its maximum profit just before finishing into a phase where it’s productive time costs more than it earns due to repairs and maintenance during operational hours. Therefore, mines need to have tools which will help them determine a point in time when the equipment needs replacing either by purchase or lease based on the usage plan of a particular equipment.

--------------------------------Insert Figure 1 about here ---------------------------------

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1.3.1

Physical Life

The physical life in this research paper will be taken as the service life of the equipment. This phase of the equipment life is largely impacted by repair and maintenance (Gransberg et al, 2006) and comes to an end when the equipment is no longer operational. Preventive maintenance is a very important part of this physical life because it determines how long a piece of equipment will last. From experience a well serviced equipment usually has a longer lifespan compared to one which is not serviced at all. 1.3.2

Profit Life

The profit life is the time when an equipment earns more than the maintenance and repair costs as well as more than its cost to own and operate. According to (Douglass, 1978) this is the most desirable time of owning and operating the equipment. When major components of the equipment wear out, then the equipment moves into a phase where it is no longer profitable and a decision to replace it is made by its owner. It is therefore, important for mining companies to be able to estimate this time period so as to maximize on production and equipment efficiency. 1.3.3

Economic Life

The economic life is the period when the ownership costs are equivalent to the operating costs. Economic life is based on decreasing ownership costs and increasing operating costs (Mitchell, 1998) meaning that when the operating costs become more than the ownership costs then a piece of equipment is costing more to operate than own. Therefore, to maximize on profits a piece of equipment should be planned to be replaced before it reaches the economic life. 1.3.4

Life-cycle Cost Analysis

The equipment life-cycle cost analysis consists of equipment replacement analysis and models, life-cycle costs and equipment decision procedures. Life-cycle costs for equipment consist of ownership and operating costs. Operating costs include but not limited to operator costs, repair and maintenance, tire and tire repair costs and fuel (Gransberg, 2006) while ownership costs include but also not limited to investment costs, storage costs, taxes, depreciation costs, insurance and initial costs (Peurifoy and Schexnayder, 2002). This research utilized a stochastic model because stochastic functions improve the accuracy of life-cycle costs. 6

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2.0 METHODOLOGY The profit life in equipment calendar hours will be one of the tools that will be used in this paper to determine the effectiveness and optimization of equipment in ensuring that maximum profit is gained from the deploying of a piece of equipment. The Chibuluma South Mine equipment data is the one mainly utilized in this research to conduct the analysis to determine the optimal profit life of the equipment. The equipment life-cycle models have been proposed according to Peurifoy and Schexnayder (2002). 2.1 Overall Equipment Effectiveness One of the tools that can be used to evaluate the effectiveness of mining equipment is a tool called the Overall Equipment Effectiveness (OEE) Index which was developed by the Japan Institute of Plant Maintenance (JIPM). This was proposed by Seiichi Nakajima to measure the effectiveness of equipment in the manufacturing industry (Nakajima, 1988). For manufacturing industries OEE is defined generally as follows; eqn (1)

𝑂𝐸𝐸 = 𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑖𝑙𝑖𝑡𝑦 × 𝑝𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒 𝑟𝑎𝑡𝑒 × 𝑞𝑢𝑎𝑙𝑖𝑡𝑦 𝑟𝑎𝑡𝑒 Where; 𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑖𝑙𝑖𝑡𝑦 (𝐴) =

𝐴𝑐𝑡𝑢𝑎𝑙 𝑟𝑢𝑛𝑛𝑖𝑛𝑔 𝑡𝑖𝑚𝑒 𝑡𝑜𝑡𝑎𝑙 ℎ𝑜𝑢𝑟𝑠

𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒 𝑟𝑎𝑡𝑒 (𝑃𝑟) = 𝑄𝑢𝑎𝑙𝑖𝑡𝑦 𝑟𝑎𝑡𝑒 (𝑄) =

𝑡𝑜𝑡𝑎𝑙 𝑜𝑢𝑡𝑝𝑢𝑡 𝑝𝑜𝑡𝑒𝑛𝑡𝑖𝑎𝑙 (𝑟𝑎𝑡𝑒𝑑) 𝑜𝑢𝑡𝑝𝑢𝑡

𝑔𝑜𝑜𝑑 𝑜𝑢𝑡𝑝𝑢𝑡 𝑡𝑜𝑡𝑎𝑙 𝑜𝑢𝑡𝑝𝑢𝑡

eqn (2)

× 100 × 100

× 100

eqn (3) eqn (4)

However, for these equations to be applied in the mining industry some slight modifications need to be made for them to suit. The most difficult to define in the mining industry is the quality rate, which seems more relevant and applicable to drill rigs in terms of hole deviations as well as jams. However, this is less relevant to dump trucks and loaders although for this study it was measured as the number of loads of ore that was moved successfully without much delays associated with stoppages or equipment failure hence, referred to as production efficiency rather than quality rate. The performance rate in the mining industry is similar to the utilization rate and is necessarily referred to as such, hereby, leading to the following equations; 7

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eqn (5)

𝑂𝐸𝐸 = 𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑖𝑙𝑖𝑡𝑦 × 𝑢𝑡𝑖𝑙𝑖𝑠𝑎𝑡𝑖𝑜𝑛 𝑟𝑎𝑡𝑒 × 𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦 Where; 𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑖𝑙𝑖𝑡𝑦 (𝐴) =

𝑎𝑐𝑡𝑢𝑎𝑙 𝑟𝑢𝑛𝑛𝑖𝑛𝑔 𝑡𝑖𝑚𝑒 𝑡𝑜𝑡𝑎𝑙 ℎ𝑜𝑢𝑟𝑠

𝑈𝑡𝑖𝑙𝑖𝑠𝑎𝑡𝑖𝑜𝑛 𝑟𝑎𝑡𝑒 (𝑈) = 𝑃𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 (𝑃𝑒) =

eqn (6)

× 100

𝑡𝑜𝑡𝑎𝑙 ℎ𝑜𝑢𝑟𝑠 ― 𝑠𝑡𝑎𝑛𝑑𝑏𝑦 (𝑖𝑑𝑙𝑒 ℎ𝑜𝑢𝑟𝑠) ― 𝑑𝑜𝑤𝑛𝑡𝑖𝑚𝑒 𝑡𝑜𝑡𝑎𝑙 ℎ𝑜𝑢𝑟𝑠 ― 𝑑𝑜𝑤𝑛𝑡𝑖𝑚𝑒

𝑎𝑐𝑡𝑢𝑎𝑙 𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 𝑡𝑖𝑚𝑒 (𝑎𝑐𝑡𝑢𝑎𝑙 𝑟𝑢𝑛𝑛𝑖𝑛𝑔 𝑡𝑖𝑚𝑒 ― 𝑠𝑡𝑎𝑛𝑑𝑏𝑦(𝑖𝑑𝑙𝑒 ℎ𝑜𝑢𝑟𝑠)) × 𝑟𝑎𝑡𝑒𝑑 𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦

× 100

eqn (7)

× 100

eqn (8)

Where; Pe = Production efficiency; 𝑟𝑎𝑡𝑒𝑑 𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦 =

𝑇𝑜𝑡𝑎𝑙 ℎ𝑜𝑢𝑟𝑠 ― (𝑆ℎ𝑒𝑑𝑢𝑙𝑒𝑑 𝑀𝑎𝑖𝑛𝑡𝑒𝑛𝑎𝑛𝑐𝑒 ℎ𝑜𝑢𝑟𝑠) 𝑇𝑜𝑡𝑎𝑙𝑠 ℎ𝑜𝑢𝑟𝑠

There is need to have a clear understanding of the factors above and their impact on the equipment effectiveness in order to help know which areas require optimization efforts and focus, at Chibuluma south mine and the mining industry at large. 2.1.1

Availability of Equipment

Equipment availability is one of the most widely used factors in the evaluation of equipment effectiveness in the mining industry including Chibuluma South Mine. It is widely used because of its simplicity in terms of its applicability as well as understanding. Availability is defined as the ratio of the time during which an equipment is capable of performing its functions (actual running time) to the total number of hours in a given period (total hours). However, there is no industry agreement on the definition of the input parameters such as the number of hours, different mines have different operating shifts. Chibuluma South Mine operates three 8 hours shifts resulting in a 24 hours operating period per day for 7 days a week. The scheduled equipment operating hours are used as inputs. Availability depends on reliability and maintainability (Dhillon, 2008) which are both functions of several other factors such as Mean Time Before Failure (MTBF), equipment waiting times etc. 8

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2.1.2

Utilization of Equipment

Equipment utilization refers to the time an equipment is used to perform its functions during its available time. During the time the equipment is in the running state, it’s rarely entirely used to perform the functions it was designed to do throughout the time. Sometimes the equipment is designated as a back-up unit, it may also be idle due to the lack of an operator or due to the process cycles. The values of equipment utilization largely depend on input data which most of the times comes from the onboard instruments of the equipment. Unfortunately, this is not the case for some values at Chibuluma South Mine as the underground equipment lack these instruments therefore, the load weight and time measures are done using an external equipment installed at the open pit or portal entrance and exit point. However, the values obtained in this manner are usually subject to a bias of their own. They are mostly rough estimates which require an accurate availability follow up which is also currently deficient at the mine, resulting in a utilization record which is biased. Equipment utilization only gives a partial picture of the equipment effectiveness due to the lack of assurance of reasonable input data. Downtime is one of the factors that plays an important role in equipment utilization (Dhillon, 2008). However, downtime itself is even a more complex concept to clearly understand because it is interpreted in several different ways and depends on a lot of factors. It is usually related to equipment reliability whose measure is connected to the Mean Time Between Failures (MTBF). The Mean Time Between Failures or Mean Time Between Shutdowns (MTBS) includes all interruptions due to equipment failure or maintenance procedures regardless of their nature. Although the MTBF has some constraints of its own such as when the operation does not reflect the adequate quantity of useful work undertaken by the equipment. Then the expressed MTBF in the unit of time is inadequate but it is worth noting that it is one of the adequate measures of equipment effectiveness although it is not used by a lot of mines, it is in-fact recorded at Chibuluma South Mine. Downtime is not limited to active maintenance during which some preventive maintenance is carried out on the equipment but there is more such as waiting periods and unexpected equipment breakdowns etc. Therefore, reliable information comprising all the components of downtime duration is a key factor in identifying and eliminating the processes responsible for the most time losses which lead to poor equipment effectiveness.

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2.1.3

Production Efficiency

Production efficiency is a very important measure of a company’s performance in that it requires the minimization of costs and the maximization of profits for a given level of output (Dhillon, 2008). In the manufacturing industry efficiency makes the best of a company’s resources. An efficient company will produce a greater number of quality products with less waste, using less energy and other resources during a given period as compared to an inefficient one. A machine’s production efficiency depends on several factors such as an operator’s motivation, attitude, skill level and the training the operator receives pertinent to the equipment. However, a machine with an ideal operator in charge of the controls can still underperform due to factors pertinent to the equipment itself such as hydraulic pump issues, air pressure problems and several other factors that make the equipment perform below the Original Equipment Manufacturers’ (OEM) technical specifications. In the mining industry the operating conditions usually differ from the ones assumed by the Original Equipment Manufacturers (OEM). This is due to different mine designs of development ends as well as roadway designs. Therefore, the OEM rated capacity may not be achieved in the mining industry in comparison to the manufacturing industry. A machine’s rated capability and its actual output can be very different and a little confusing (Gillerspie, and Hyde, 2004). However, an equipment is supposed to work, with an assurance of quality and at the right pace. Some equipment may record well enough operational hours to generate acceptable revenue by generating work which is the main concern in the mining industry while still underperforming (running below rated capacity) due to poor management strategies. At Chibuluma South Mine the assessment of the revenue generating work achieved by a machine is compared to the rated capacities of the machine. To help evaluate a few other factors such as how much bonus would be allocated to operators and the safety factor for the calculation of the safety allowance and future reference as safety is very cardinal to mining. 2.2 Data Collection 2.2.1 Primary Data The data utilized in this research were collected from Chibuluma South Mine. This was done through various ways including but not limited to the use of questionnaires and interviews with the mine staff such as equipment operators, maintenance engineers, mining engineers, control 10

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room personnel, as well as the mine planning department and management. Fieldwork to record and observe equipment operation for data validation and elimination of biasness was conducted. The questionnaires were very short and precise to help in the reduction of confusion and biasness; the completed questionnaires were collected and analyzed. 2.2.2

Secondary Data and Analysis

Mine reports, equipment checklists, books related to the topic, articles from journals and the worldwide web were consulted for other sources of information on mining equipment performance evaluation related reports. The information in this paper was evaluated using Rstudio (Version 3.5.0) and the results were then compared to the set standards at Chibuluma South Mine. The graphs were designed with the help of Rstudio’s dplyr (Hadley Wickham, et al, 2019), readbulk (Kieslich and Henninger, 2016) and tidyverse packages (Hadley Wickham, 2017). An algorithm (Figure 2) was developed to assist with the data analysis.

--------------------------------Insert Figure 2 about here ---------------------------------

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3.0 DATA ANALYSIS AND RESULTS DATA ANALYSIS 3.1 Process Cycle Times Since equipment availability is one of the most important concepts of this research as the utilisation of equipment depends on it. Equipment availability is derived as shown in equation (6) above but it can also be expressed as in equation (9) below; 𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑖𝑙𝑖𝑡𝑦 =

𝑃𝑙𝑎𝑛𝑛𝑒𝑑 𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒 𝑇𝑖𝑚𝑒 ― 𝑇𝑜𝑡𝑎𝑙 𝐷𝑜𝑤𝑛𝑡𝑖𝑚𝑒 𝑇𝑜𝑡𝑎𝑙 𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒 𝑇𝑖𝑚𝑒

× 100 eqn (9)

Where; 𝑇𝑜𝑡𝑎𝑙 𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒 𝑇𝑖𝑚𝑒 = 𝑂𝑝𝑒𝑟𝑎𝑡𝑒𝑑 𝑇𝑖𝑚𝑒 + 𝐼𝑑𝑙𝑒 𝑇𝑖𝑚𝑒 ― 𝑈𝑛𝑒𝑥𝑝𝑒𝑐𝑡𝑒𝑑 𝑏𝑟𝑒𝑎𝑘𝑑𝑜𝑤𝑛𝑠 3.1.1

Drill Rigs

The cycle starts with drilling followed by loading and finally ends with hauling to the run of mine (ROM) pad for processing. Some of the parameters for drilling are discussed in this section while other parameters for drill rigs and the other equipment are discussed in the subsequent sections; a) Time taken to drill one support hole; 𝐴𝑣𝑒 𝑆𝐷𝐶𝑇 = 𝐴𝑣𝑔 𝑇𝑖𝑚𝑒 𝑇𝑎𝑘𝑒𝑛 𝑇𝑜 𝐷𝑟𝑖𝑙𝑙 𝑜𝑛𝑒 ℎ𝑜𝑙𝑒 + 𝐴𝑣𝑔 𝑆𝑒𝑡𝑡𝑙𝑖𝑛𝑔 𝑡𝑖𝑚𝑒 + Delays (10) Where; Ave SDCT= Average support hole drilling cycle time b) Time taken to drill one hole at the face; 𝐴𝑣𝑒 𝑇𝑇𝐷𝐹𝐻 = 𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑡𝑖𝑚𝑒 𝑡𝑎𝑘𝑒𝑛 𝑡𝑜 𝑑𝑟𝑖𝑙𝑙 𝑜𝑛𝑒 ℎ𝑜𝑙𝑒 + 𝑠𝑒𝑡𝑡𝑙𝑖𝑛𝑔 𝑡𝑖𝑚𝑒 + Delays Where; TTDFH = average time taken to drill one face hole. c) Average time taken to drill one stope hole; 𝑛

𝐴𝑣𝑒 𝑇𝑖𝑚𝑒 =

∑1(𝑡𝑜𝑡𝑎𝑙 𝑡𝑖𝑚𝑒 𝑡𝑎𝑘𝑒𝑛 𝑡𝑜 𝑑𝑟𝑖𝑙𝑙 ℎ𝑜𝑙𝑒𝑠 + 𝑠𝑒𝑡𝑡𝑙𝑖𝑛𝑔 𝑡𝑖𝑚𝑒𝑠 + 𝐷𝑒𝑙𝑎𝑦𝑠) 𝑡𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑑𝑟𝑖𝑙𝑙𝑒𝑑 ℎ𝑜𝑙𝑒𝑠

Where: 12

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n = number of holes drilled. 3.1.2

Loaders The cycle time for a loader is given as in equation 11 below; 𝐿𝐶𝑇 = 𝐷𝐷𝑃 + 𝐷𝐿𝑃 + 𝑇𝑇𝐿 + 𝑇𝑇𝐷

equation (11)

Where; 𝐿𝐶𝑇 = 𝑙𝑜𝑎𝑑𝑒𝑟 𝑐𝑦𝑐𝑙𝑒 𝑡𝑖𝑚𝑒, (min); 𝐷𝐿𝑃 = 𝑡𝑖𝑚𝑒 𝑡𝑎𝑘𝑒𝑛 𝑡𝑜 𝑙𝑜𝑎𝑑𝑖𝑛𝑔 𝑝𝑜𝑖𝑛𝑡, (𝑠); 𝐷𝐷𝑃 = 𝑡𝑖𝑚𝑒 𝑡𝑎𝑘𝑒𝑛 𝑡𝑜 𝑑𝑟𝑎𝑤𝑖𝑛𝑔 𝑝𝑜𝑖𝑛𝑡, (𝑠); 𝑇𝑇𝐿 = 𝑡𝑖𝑚𝑒 𝑡𝑎𝑘𝑒𝑛 𝑡𝑜 𝑙𝑜𝑎𝑑, (𝑠); 𝑇𝑇𝐷 = 𝑡𝑖𝑚𝑒 𝑡𝑎𝑘𝑒𝑛 𝑡𝑜 𝑑𝑟𝑎𝑤, (𝑠); Whereas the loading time is calculated as in equation 12; 𝐿𝑜𝑎𝑑𝑖𝑛𝑔 𝑇𝑖𝑚𝑒 = 𝐿𝑜𝑎𝑑𝑒𝑟 𝐶𝑦𝑐𝑙𝑒 𝑇𝑖𝑚𝑒 × 𝑁𝑜. 𝑜𝑓 𝐵𝑢𝑐𝑘𝑒𝑡 𝐿𝑜𝑎𝑑𝑠

3.1.3

equation (12)

Dump Trucks

For this research, the cycle time for a dump truck was calculated as in equation 13; 𝐷𝑇𝐶𝑇 = 𝑆𝑇𝐿 + 𝐿𝑇 + 𝑇𝐿 + 𝑆𝑇𝐷 + 𝐷𝑇 + 𝑇𝐸 + 𝐴𝐷;(min), Where; 𝐷𝑇𝐶𝑇 = 𝑑𝑢𝑚𝑝 𝑡𝑟𝑢𝑐𝑘 𝑐𝑦𝑐𝑙𝑒 𝑡𝑖𝑚𝑒, (𝑚𝑖𝑛); 𝑆𝑇𝐿 = 𝑠𝑝𝑜𝑡 𝑡𝑖𝑚𝑒 𝑎𝑡 𝑡ℎ𝑒 𝑙𝑜𝑎𝑑𝑒𝑟, (𝑚𝑖𝑛); 𝐿𝑇 = 𝑙𝑜𝑎𝑑 𝑡𝑖𝑚𝑒 𝑜𝑓 𝑑𝑢𝑚𝑝 𝑡𝑟𝑢𝑐𝑘, (𝑚𝑖𝑛); 𝑇𝐿 = 𝑡𝑟𝑎𝑣𝑒𝑙 𝑡𝑖𝑚𝑒 𝑤ℎ𝑒𝑛 𝑙𝑜𝑎𝑑𝑒𝑑, (𝑚𝑖𝑛); 𝑆𝑇𝐷 = 𝑠𝑝𝑜𝑡 𝑡𝑖𝑚𝑒 𝑎𝑡 𝑡ℎ𝑒 𝑑𝑢𝑚𝑝, (𝑚𝑖𝑛); 𝐷𝑇 = 𝑑𝑢𝑚𝑝 (𝑜𝑓𝑓𝑙𝑜𝑎𝑑𝑖𝑛𝑔) 𝑡𝑖𝑚𝑒, (min); 𝑇𝐸 = 𝑡𝑟𝑎𝑣𝑒𝑙 𝑡𝑖𝑚𝑒 𝑤ℎ𝑖𝑙𝑒 𝑒𝑚𝑝𝑡𝑦, (𝑚𝑖𝑛); 𝐴𝐷 = 𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑑𝑒𝑙𝑎𝑦𝑠 𝑜𝑛 ℎ𝑎𝑢𝑙 𝑐𝑦𝑐𝑙𝑒, (𝑚𝑖𝑛);

3.2 Availability and Operation Times 3.2.1 Drill Rigs 13

equation (13)

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The best way to account for the operating cost of the equipment is by considering the utilisation of the equipment, which is achieved by considering the utilised hours of the equipment. In a mining setup, the utilisation of the equipment is usually based on the equipment cost while the cost of operating the equipment is mainly based on its age. The cost of operating an equipment usually increases with increase in age, which usually reflects maintenance expenses. The aging of the equipment is usually non-uniform because equipment is rarely utilised in a linear manner. Engineering availability is the time the equipment is available for maintenance purposes. Figure 3, shows the drill rigs idle time, operated time, mining availability time and engineering availability time for a continuous evaluation time of two years. The idle time was found to follow an s-curve of steady increment and then decrement with a continuous steady dropping trend and an average idle time of about 460 hours per month as shown in Figure 3 (A). The dip (2015-01 and 2015-07) was due to the influence of new trucks which were added to the fleet. The operated time was found to have increased from about 100 hours per month to a maximum of 140 hours per month before dropping and maintaining a steady operation time of about 110 hours per month as shown in the graph in Figure 3 (B). The average mining availability time of the Drill Rigs was found to vary between 525 with a maximum of 640 hours per month before dropping to a steady rate of 570 hours as shown in Figure 3 (C). The average engineering availability time shown in Figure 3 (D) was also found to vary between 550 hours with a maximum of 670 hours but quickly dropped to an average of 610 hours per month with an average decreasing rate of 5 hours per month.

--------------------------------Insert Figure 3 about here ---------------------------------

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3.2.2

Loaders

Figure 4 shows the Loaders’ idle times, operated times, mining availability times and engineering availability times for a continuous evaluation time of two years. The idle time was also found to follow an s-curve of steady increment and then decrement with a continuous steady increment trend and average idle time of about 250 hours per month as shown in Figure 4 (A). the operated time was found to first have been operating steadily at about 320 hours per month for the first year before increasing steadily to a maximum of about 350 hours per month between October of the first year and April of the second year, before dropping at a steady rate to an operation time of about 290 hours per month as shown in the graph of Figure 4 (B). The average mining availability times of the Loaders were found to have been between 550 and 580 hours per month, having dropped only in October of 2014 to about 560 hours per month and increasing steadily to 580 hours and maintaining it as shown in Figure 4 (C). The average engineering availability times shown in Figure 4 (D) were also found to vary between 550 and 620 hours but they dropped at a steady rate to maintain an average of about 580 hours. Engineering availability times are the times the equipment is available for maintenance purposes.

--------------------------------Insert Figure 4 about here ---------------------------------

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3.2.3

Dump Trucks

In Figure 5, the Articulated Dump Trucks’ (ADTs) idle times, operated times, mining and engineering availability times for a continuous evaluation time of two years were also plotted and assessed. The idle times were also found to follow a semi s-curve of steady increment and then decrement with a continuous steady increment trend due to the influence of newer equipment added to the fleet. The curve has a starting average minimum of 180 hours with a sharp increase occurring between Sept and Oct 2014 and then maintaining a steady increase rate to an average maximum idle time of 250 hours per month as shown in Figure 5 (A). the operated times were found to first have had a sharp increase from a minimum of 320 to a maximum of 400 hours per month in the first half of the year 2014, before dropping at a steady rate to an operation time of about 350 hours per month as shown in the graph of Figure 5 (B). The average mining availability times of the ADTs were found to be between 550 and 600 hours per month, having dropped only in October of 2014 to about 560 hours per month and increasing steadily to 580 hours and maintaining a steady availability time as in Figure 5 (C). The average engineering availability times were also found to vary between 530 and reaching a maximum of 620 hours but then dropped at a steady rate to an average of about 580 hours before maintaining a steady increasing rate as shown in Figure 5 (D).

--------------------------------Insert Figure 5 about here ---------------------------------

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3.3 Utilization of Equipment 3.3.1 Drill Rigs Mining equipment is rarely utilised to full capacity while drill rigs are alsounder-utilised at Chibuluma south as shown in Figures 6 (a) and (b) below. The drill rigs are utilized more than 5 percent per month though their utilisation does not exceed 40 percent per month. As can be seen from Figure 6(a), all the drill rigs show a normal cyclic production curve except for boomer 1 and solo whose individual graphs show a continuous decrease in utilisation until their decommissioning, this might be attributed to the equipment’s old age and also the equipment having exceeded their economical life.

--------------------------------Insert Figure 7a about here ---------------------------------

3.3.2

--------------------------------Insert Figure 7b about here ---------------------------------

Loaders

The loaders used at Chibuluma south mine are the underground front-end wheel loaders. The utilizations of the loaders are as indicated in Figures 7(a) and (b) below. The utilization can be clearly seen from Figure 7, to vary between 40 and 80 percent with most of the loaders falling below 70 percent. The loaders’ utilization decreases with increase in the age of the loaders. The EJC 1 loader was decommissioned in August, 2014 after having reached its economic life while the ST1530 loader joined the fleet in November of 2015, an anomaly can be seen on the utilization graph as the loader was put to different tests and used to its full capacity that month. The capacity of the loader is defined by the bucket size in conjunction with the loaders cycle time. The cycle time is defined above as the time the loader needs to fill the bucket and drop its contents into a truck and this also assists in defining the productivity of the loader. The productivity of the loader is the number of passes the loader requires to fill each truck. According to Chibuluma mine policies, except for two loaders, the rest of them are manufactured by the same company as the manufacture of their ADTs. In this paper a rule of thumb was taken 17

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that if an additional bucket is required to fill a truck then it was rounded off to the nearest whole number of passes required to fill a truck. The passes usually fell in between 3 and 5 depending on the rock size, soil properties and other factors leading to the loaders’ cycle times shown in table 3 above.

--------------------------------Insert Figure 7a about here ---------------------------------

3.3.3

--------------------------------Insert Figure 7b about here ---------------------------------

Dump Trucks

The Dump Trucks used at Chibuluma south mine are the underground articulated dump trucks (ADTs). The utilizations of the different trucks are as shown in Figures 8 (a) and (b) below. It can be clearly seen from the Figure that the utilization varies between 50 and 80 percent with half of the ADTs performance falling within 60-75 percent but mostly below 80 percent.

--------------------------------Insert Figure 8a about here ---------------------------------

--------------------------------Insert Figure 8b about here ---------------------------------

The ADTs’ utilization also decrease with increase in the age of the equipment as can be seen from the steady drop in the utilization rate. Truck number 1 was decommissioned in January, 2015 after coming to the end of its economic life as can be seen from the graph while truck 18

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numbers 9 and 10 were added to the fleet in November, 2014 and May, 2015, and their utilization can be seen to follow the same productivity profile as the other ADTs. Truck number 2 has a totally different graph profile from the rest as can be seen from the graph and its performance and behavior can be attributed to its age. The capacity of an ADT is defined by the tray size in conjunction with the truck’s cycle time which is defined above as the time the truck needs to move from the loading point to the dumping point plus the time it takes to drop its contents at the dump site and this also assists in defining the productivity of the truck. The productivity of the Dump Truck is the number of complete loads the truck makes in an hour. The speed at which a given dump truck can travel is inversely proportional to the size of the load the truck carries but the size and cost of running trucks are directly proportional to their tray capacity.

RESULTS Figure 9 is a screenshot of some of the results obtained after data analyis using the algorithm designed in RStudio and the data obtained from Chibuluma South Mine.

--------------------------------Insert Figure 9 about here ---------------------------------

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3.4 Process Cycle Times 3.4.1 Drill Rigs The average minimum and maximum times taken to drill the whole face in readiness for production (stope) purposes were found to be about 74 and 148 minutes. The minimum and maximum times taken to drill a face for development (drill face hole + ream hole) purposes were found to be about 24 and 81 minutes, respectively and the average minimum and maximum time taken to drill and insert split sets for support and safety purposes were found to be about 16 and 41 minutes, respectively. All the different drilling processes and times were calculated and the results are as shown in Table 2 below;

--------------------------------Insert Table 1 about here ---------------------------------

3.4.2

Loaders

In this research the loaders’ cycle time (LCT) was taken to be one complete cycle that the loader makes from the drawing point to the loading point while completing both the drawing and loading actions of both ore and waste. The average loading time is the average time taken to load one articulated dump truck. The LCT was mainly found to be affected by the distance between the loading point and drawing point. The loading point was selected to be the point with the shortest distance from the drawing point and enough room to accommodate the safe maneuverability of the loader. The results are summarized in Table 3 below;

--------------------------------Insert Table 2 about here ---------------------------------

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3.4.3

Dump Trucks

In this research a dump truck’s cycle time is considered to start at the loading point, when the truck is fully loaded by a loader and then travels to the dump site. For Chibuluma south mine the dump site, the mill and stock pile are all within the vicinity. The truck dumps the load and travels back to the loading point usually empty but sometimes carrying other necessary utilities such as backfill, drill rods et cetera. The truck’s cycle time is a very important parameter because it can be used to determine other related parameters, it can also be used with other parameters such as the haul grade, rolling resistance and haul distance to determine the effectiveness of the truck. According to (Smith et al, 2000) the common method for estimating the cycle time is to estimate the truck’s speed using equipment manufacturer’s guidelines. These guidelines are simulation results which take into consideration the capacity, road gradients, engine transmission efficiency, engine power, truck weight and other road conditions (Blackwell, 1999). To determine the lag in cycle time, the results must be combined with the topographic information and the rolling resistance. The haul grade which is defined as the incline of the haul road intensifies the effects of the rimpull and rolling resistance of mining trucks. Rimpull is defined as the natural resistance of the ground to the torque of the tire and is equal to the radius of the wheel multiplied by the torque of the wheel axle. The rolling resistance is created by the softness of the road soil and affects the propelling of the truck forward. These factors are all very important in the calculation of the cycle times summarised in Table 4 and very cardinal to the accurate utilization of the trucks. At Chibuluma south mine seven trucks are operated against one loader, making spotting, loading and queuing a bit of a challenge to estimate but a direct observation was made for a period of six months and the average times were calculated and tabulated as shown below. To avoid the numbers skewing towards the highest or lowest cycles when getting the mean only complete trips without any delays were utilized for the calculation.

--------------------------------Insert Table 3 about here ---------------------------------

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3.5 Performance and Production Efficiency 3.5.1 Drill Rigs Production efficiency is in simple terms defined as the ratio of the output to the input as defined in Section 2.1.3 above. Since the equipment calendar hours are being used in this paper, the production efficiency is also defined in terms of calendar hours. The production efficiency of mining equipment consists of two major components namely the management efficiency and the equipment operation efficiency (Misra G. B, 2006). These also depend on the maintainability of the equipment, experience and ability of the operator and environment operating conditions. The operation efficiency lies between 5 and 55 percent with the frequency count varying from 1.25 to 12 as shown in Figure 10 (a). Figure 10 (b) shows similar results with a more accurate frequency density plot with the density varying between 0 to about 2.5.

--------------------------------Insert Figure 10a about here ---------------------------------

3.5.2

--------------------------------Insert Figure 10b about here ---------------------------------

Loaders

The production efficiency of loaders is shown in in Figures 11(a) and (b), from which it can be clearly seen that the loaders’ production efficiency lies between 30 and 80 percent with associated densities of 0.5 and 2.5. Both Figures show that the data is centered around 30 and 70 percent. --------------------------------Insert Figure 11a about here ---------------------------------

--------------------------------Insert Figure 11b about here ---------------------------------

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3.5.3

Dump Trucks

The dump trucks’ production efficiency is shown in Figures 12 (a) and (b), from which it can be clearly seen that the trucks’ production efficiency lies between 30 and 80 percent with the density plots’ associated densities varying between 0 and about 1.8. The two plots were found to show a similar trend. The shapes of the density plot profiles from Section 3.5.1 to 3.5.2 suggests that the data can be estimated using a Normal Distribution.

--------------------------------Insert Figure 12a about here ---------------------------------

--------------------------------Insert Figure 12b about here ---------------------------------

3.6 Overall Equipment Effectiveness 3.6.1 Mean and Standard Deviation

--------------------------------Insert Table 4 about here ---------------------------------

The overall Effectiveness Efficiency was calculated in Rstudio version 3.6.0 (2019-04-26) using the average means of the variables in Section 3.3 to 3.4 and the results are as presented in Table 5. The Table also contains all the other variables that were used to calculate the utilisation, the production efficiency of the equipment OEE. The Table shows both the mean and the standard 23

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deviations of the values, the standard deviation here shows how the measurements of the different groups really spread out from the mean. 3.6.2

Boxplots of OEE of Rigs, Loaders and Dump Trucks

The boxplots of the Mining Utilisation rate and the Overall Equipment Effectiveness were plotted (Figures 13 (a) and (b)) and a correlation was found to occur between the two variables. The OEE and the mining utilisation were compared with the help of boxplots and found to be similar in nature, a succinct conviction that the data was following the Normal Distribution. The plots below also revealed that on average dump trucks are utilised more than loaders and rigs as should be the case in accordance with mine policy and the difference in their operating conditions.

--------------------------------Insert Figure 13a about here ---------------------------------

--------------------------------Insert Figure 13b about here ---------------------------------

Finally a graph of the log of mining utilisation vs log of the overall equipment effectiveness was plotted and the results are as shown in Figure 14 (a). The ratio of the oee to utilisation was found to be linear with most of the proportion above average as can be seen from the figure below. The correlation of the variables were also plotted and the results are as shown in Figure 14 (b).

--------------------------------Insert Figure 14a about here ---------------------------------

--------------------------------Insert Figure 14b about here ---------------------------------

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4.0 DISCUSSION The different idle times and mean time before failure (MTBF) of the dump trucks were plotted against time, analyzed and the results showed that trucks number 4 and 9 had the longest periods of average idle times for much of the period. The minimum distance moved by the trucks was found to be just above a 100 km and the maximum slightly above 1000 km. While the minimum amount of hauled tonnage was just over 100t and the maximum slightly above 17000t of both ore and waste per month. A positive correlation was found between the ADTs productivity and hauled tonnage of both ore and waste as expected since productivity is defined as the ratio of the volume of outputs to volume of inputs. The production efficiency graphs of each of the types of equipment were plotted, the mean, standard deviations, theoretical and observed quantiles were calculated and the data was found to follow a Normal Distribution. When the normal probability test was conducted by plotting the percentage cumulative frequency values against upper class boundaries a straight line was the result confirming a Normal Distribution for the data. In this research, the MTBF standard deviation values were found to be higher than the associated mean values for all the three equipment showing a spread out in the mean time before failure which is predictable for each of the equipment by applying the algorithm developed in this paper. The recording of equipment failure was a challenge at the local mine at the time because the equipment didn’t have a real time tracking and monitoring system. Equipment that were fitted with communication devices had issues with the accurate reporting of the equipment breakdown due to human error and the biasness of the equipment operators. The overall equipment effectiveness of all the three types of equipment were estimated and all the values were found to be below 50%. When compared to the manufacturing industry’s set 85 percent for world class performing discrete manufacturers and 60 percent for the fairly typical discrete manufacturers, it suggests substantial room for improvement. Furthermore, the boxplots of utilization and overall equipment effectiveness were each plotted against equipment type and the graphs showed a positive correlation meaning that if the utilization of the equipment improves so does its OEE. The influences of the Availability, Utilization and Production efficiency on the OEE were found to be 0.247, 0.956 and 0.549 in terms of correlation respectively. This simply means that the OEE at Chibuluma South Mine is greatly influenced by the utilization of the equipment, fairly influenced by the production efficiency and only slightly influenced by the availability of the equipment. In this paper, an algorithm has been developed with reproducible and reusable code 25

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which with a change of parameters can be applied to any underground mine setup to determine the sensitive inputs to the life cycle of the equipment as well as the determination of the optimization estimates.

5.0 CONCLUSION In this paper an algorithm which determines the most sensitive inputs to the equipment life cycle and estimates the optimization of the equipment was developed. It utilizes the OEE and uses equipment operational records to assist in effectively planning and optimizing mine production operations. The process cycle times were determined for each of the three types of equipment, these process times help in estimating the exact times of each of the processes involved in the production stage and help improve the accuracy of equipment deployment. For drilling, the three types of drilling required for support, development and production drilling were determined respectively. For loaders the average loading and LCT times were determined and found to vary at each level of operation, this is attributed to the loading distances and to the changing rock mechanical properties. The overall equipment effectiveness is a function of a lot of complex factors which are not so easy to quantify but it is still one of the most useful tools currently available for the measure of equipment optimization and effectiveness. Although Key Performance Indicators (KPI) do a very good job in the evaluation and optimization of equipment in manufacturing industries, in practice most mines do not use these indicators expansively (Paraszczak, Vachon, and Grammond, 1997), (De Ron and Rooda, 2006). The OEE seems to be one of the functions that bring all the aforementioned factors together by distinctively highlighting the areas that need improvement for proper evaluation of production, productivity and optimization of mining equipment. However, in dealing with OEE caution must be taken as it also has its own drawbacks which might lead to abuse of equipment usage which might lead to high operational costs. It is recommended therefore, that when adopting OEE for equipment evaluation, capable software such as the algorithm developed in this paper should be used to analyze the information fully and then decide on the statistics that are suitable for the mines’ operating conditions in terms of production efficiency, maintainability, utilization and availability. Although OEE is not a perfect remedy in its use in the evaluation of equipment effectiveness due to its own limitations in that it is a ratio of what a system practically produces 26

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to what it can theoretically produce, leading to a questioning of whether three variables in its equation having an equal weighting which might not be the case practically, but an adoption of OEE as one of the measures for performance evaluation with the help of the OEM guidelines and operating conditions will greatly help improve the utilization of equipment in the mining industry. The research is being carried out further by incorporating the developed algorithm into a machine learning platform. This will be used to monitor the operation of equipment in the prediction of appropriate equipment maintenance time and help determine the actual weights to be used in the OEE equation in the case of underground mining.

6.0 REFERENCES

1. Park, S.; Choi, Y.; Park, H.S, (2016). “Optimization of truck-loader haulage systems in an underground mine using simulation methods.” Geosyst. Eng. pp; 19 222–231. https://doi.org/10.1080/12269328.2016.1176538; 2. Ercelebi, S.G.; Bascetin, A (2009). “Optimization of shovel-truck system for surface mining.” J. S. Afri. Inst. Min. Metall. pp; 109, 433–439. ISSN 2411-9717; 3. Salama, A.; Greberg, J (2012). “Optimization of truck-loader haulage system in an underground mine: A simulation approach using SimMine.” In Proceedings of the 6th International Conference & Exhibition on Mass Mining, Sudbury, ON, Canada, 10–14 June; 4. John D. Campbell, James V. Reyes-Picknell, Hyung Sik Kim (2015). “Uptime – Strategies

for

excellence

in

maintenance

management”,

3rd

Edition,

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York. ISBN9780429256738 https://doi.org/10.1201/b18778; 5. Chibuluma South Underground Mine Reports, (2016). “Chibuluma Mine official website and daily mining reports”; 6. Mitchell Z, (1998). “A Statistical Analysis of Construction Equipment Repair Costs Using Field Data and the Cumulative Cost Model.” Virginia Polytechnic Institute. Blacksburg, VA. http://hdl.handle.net/10919/30468; 7. Douglas. J. (1975). “Construction Equipment Policy,” McGraw-Hill, series in construction engineering and management New York, NY. ISBN-13: 978-0070176584; 27

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8. Gransberg, D. Popescu R. and Ryan, R. (2006). “Construction Equipment Management for Engineers, Estimators, and Owners.” 1st Ed., Taylor & Francis Group, Boca Raton, FL. ISBN13 9781280513411; 9. Douglas, J. (1978). “Equipment Costs by Current Methods.” Journal of the Construction Division, 1978, Vol. 104, Issue 2, Pg. 191-205; 10. Peurifoy, R. and Schexnayder, C. (2002). “Construction Planning, Equipment, and Methods.” Sixth edition, McGraw-Hill, New York; ISBN-13: 978-0073401126; 11. Nakajima, S. (1988). “Introduction to Total Production Management.”; 12. Dhillon B.S. (2008). “Mining Equipment Reliability, Maintainability, and Safety”, Springer-Verlag London Limited, London. ISBN 978-1-84800-287-6, DOI 10.1007/9781-84800-288-3; 13. Gillerspie, J.S. and Hyde, A. S. (2004). “The Replacement/Repair Decision for Heavy Equipment”, Report VTRC 05-R8, Virginia Transportation Research Council, Charlottesville, VA; 14. Hadley Wickham, Romain François, Lionel Henry and Kirill Müller (2019). “dplyr: A Grammar of Data Manipulation.” R package version 0.8.1. https://CRAN.Rproject.org/package=dplyr; 15. Kieslich, P. J., & Henninger, F. (2016). “Readbulk: An R package for reading and combining multiple data files.” https://doi.org/10.5281/zenodo.596649; 16. Hadley Wickham (2017). “tidyverse: Easily Install and Load the 'Tidyverse'. R package version 1.2.1. https://CRAN.R-project.org/package=tidyverse”; 17. Smith, S. D., Wood, G. S. & Gould, M, (2000) “A new earthworks estimating methodology”, Construction Management and Economics 18(2), 219-228. DOI: 10.1080/014461900370843; 18. Blackwell, G. H., (1999). “Estimation of large open pit haulage truck requirements”, CIM Bulletin, Vol. 92, No. 1028, 143-148; 19. Misra G.B., (2006). “Surface Mining”, Geominetech Publisher, Bhubaneswar, India; 20. J. Paraszczak, J. Vachon and L. Grammond, (1997): “Benefits of studies on LHD reliability and availability for mines”, Proc. 6th Int. Symp. on Mine Planning and Equipment Selection, Strakos et al. (eds.), Ostrava, Czech Republic, September, 469–475;

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21. De Ron A.J. and Rooda J.E. (2006) “OEE and equipment effectiveness: an evaluation”, International Journal of Production Research, Taylor & Francis, 44 (23), pp.4987-5003. ff10.1080/00207540600573402ff. ffhal-00512884f.

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Figure 1: Equipment Life (Douglas, 1978)

Figure 2: An Rstudio clip of the code used for data analysis in the algorithm

Figure 3: Drill Rigs Availability and Utilization graph Figure 4: Graphs of Loaders Availability and Operations Figure 5: ADTs Availability and Operated Times

Figure 6 (a): Graph of individual Utilization of Drill Rigs

Figure 6 (b): Graph of Overall Utilization of Drill Rigs

Figure 7 (a): Graph of individual utilization of Loaders

Figure 7(b): Graph of the overall utilization of all Loaders

Figure 8 (a): Graph of the individual utilisations of Dump Trucks

Figure 8 (b): Graph of the overall utilisation of Dump Trucks

Figure 9: A screenshot showing some of the results obtained from the data analysis

Table 1: Drill rigs drilling process cycle times Table 2: Loaders’ cycle times at different loading points (LCT) Table 3: Dump Trucks’ Queuing and Cycle times Figure 10 (a): Histogram of Rigs production efficiency

Figure 10 (b): Density plot of Rigs production efficiency

Figure 11 (a): Histogram of the loaders' production efficiency Figure 11 (b): Density plot of Loaders’ Production efficiency

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Figure 12(a): Histogram of ADTs Production Efficiencies

Figure 12(b): Density plot of ADTs Production Efficiencies

Table 4:Average variables results of achieved results against set targets Figure 13 (a): The boxplots of equipment utilization vs equipment type Figure 13 (b): Boxplots of equipment OEE vs equipment type

Figure 14(a): Graph of Mining utilization vs OEE paper and their associated correlation values

Figure 14(b): Graph of correlation of all variables used in this

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Table 1: Drill rigs drilling process cycle times Task Average time taken for one hole Average time taken to drill support hole Average time taken to insert split set Average time taken to drill face hole Average time taken to drill ream hole Average time taken to drill stope hole

00:02:11 00:01:58 00:02:43 00:05:28 00:14:50

Average total time taken to finish task Min Max 00:08:44 00:21:50 00:07:52 00:08:09 00:16:14 0:74:10

00:19:40 00:27:10 00:54:40 02:28:20

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Table 1: Loaders’ cycle times at different loading points (LCT) Loading 360m level 373m level 529m level Level Average 00:04:50 00:08:15 00:04:13 Loading time LCT 00:01:13 00:02:04 00:01:03

541m level

551m level

00:10:58

00:09:14

00:02:45

00:02:18

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Table 1: Dump Trucks’ Queuing and Cycle times Loading 360m level 373m level 529m level Level Average 00:07:07 00:07:58 00:09:19 queuing time Dump Truck Average Loading and hauling time

Cycle Time 1:21:46

Cycle Time 1:30:36

Cycle Time 1:21:51

541m level

551m level

00:16:06

00:19:10

Cycle Time 1:40:43

Cycle Time 1:43:10

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Table 1:Average variables results of achieved results against set targets Variable/Equipment Rigs Loaders mean sd mean sd Availability hours 593.731 107.102 574.734 91.552 MTBF (Hrs) 18.084 56.921 66.212 73.799 Operated hours 121.583 61.394 327.050 94.530 Maintenance hours 29.922 13.898 45.647 29.093 Idle Hours 472.153 103.929 247.684 87.042 Productive hours 101.703 54.769 263.482 95.296 Production cap. (%) 82.874 3.060 82.308 4.966 Availability (%) 82.046 12.186 80.773 9.023 Utilisation rate (%) 16.646 8.124 45.474 13.592 Production effic. (%) 30.646 14.408 51.468 15.379 OEE (%) 7.007 5.383 37.492 62.590

Dump Trucks Mean sd 574.807 126.235 92.754 120.767 374.305 118.418 29.702 16.175 200.472 94.455 310.763 122.554 76.682 53.539 81.399 12.334 52.821 14.206 56.414 19.135 41.01 43.332