Using overall equipment effectiveness as a driver for improving the productivity of continuous mining systems

The gradual depletion of mineral deposits with favorable stripping ratios and ore grades, combined with increased demand for raw materials used in digital and energy technologies, makes the transition to exploiting deeper deposits with a high degree of technical difficulty unavoidable. The mines developed for the exploitation of these deposits must effectively utilize the installed equipment to minimize their production costs. Overall equipment effectiveness (OEE) can be a key driver for improved productivity and equipment performance in continuous surface mining projects, as it supports decisions relevant to equipment allocation, maintenance planning and personnel management. Background Increasing production and sustaining productivity are key challenges for mining companies seeking to remain competitive in a resource-constrained market. To address these challenges, measuring equipment performance is essential, and this is typically done through key performance indicators (KPIs). Among the most widely used KPIs are availability, performance, utilization, efficiency and overall equipment effectiveness (OEE). Nakajima (1988) first introduced OEE within the framework of total productive maintenance (TPM), a lean management practice developed in the 1970s. TPM emphasizes proactive and preventive maintenance, involving both operators and technical staff to minimize breakdowns and enhance utilization. Nakajima defined OEE as the product of three dimensions: availability, performance and quality. In the mining sector, OEE has been adapted to reflect the industry’s operational realities. As defining the quality of extracted ore is challenging, researchers proposed substituting it with utilization, producing the following mining-specific formula: OEE = (Availability) ∙ (Utilization) ∙ (Performance) where availability is the ratio of the actual time that a production unit was ready to operate to the time planned for this unit to be in operating readiness, utilization is the ratio of the operating time to the equipment readiness time (operating time is equal to readiness time minus idle time and delays), and performance is a measure of the achieved production compared to the rated capacity of the equipment.Furthermore, the mining production index (MPi) was developed to integrate weighted contributions of availability, utilization and performance: MPi = (Availability)a ∙ (Utilization)b ∙ (Performance)c Method The South Field Mine of the Western Macedonia Lignite Center, located in northern Greece, was selected as a case study to demonstrate the possibility of using OEE to improve production performance in continuous surface mines. An analysis of downtime and production time was carried out, examining five groups of equipment that were in operation at the mine from 2015 to 2020. Each equipment group consists of a bucket-wheel excavator (BWE), a series of belt conveyors that connect the BWE with the mine distribution point (conveyor group I), several series of belt conveyors that start from the distribution point and reach the stackers of lignite stockyards (conveyor group II), and waste dumps (conveyor group III). These equipment groups were selected because they utilized BWEs by the same manufacturer and similar capacity: SchRs1760/32.5 SchRs2000/33.6, SchRs2000/32.5 and SchRs2300/32.5. Thus, it is possible to make comparisons and draw conclusions about the causes of reduced productivity, regardless of the manufacturer’s reliability and the equipment’s size. The data required for determining scheduled and nonscheduled downtime and operating time distributions were collected using a software application driven by real-time events. This application has been in operation since 2011 and serves as the primary productivity monitoring tool, providing all the necessary information for decision-making and planning. The application has been installed in the control center of the mine, comprising aspect servers, OPC servers, and connects to the SCADA system available at the mine. This system interfaces with programmable logic controllers (PLCs) at each equipment unit in the field and stores data in a separate database. To collect the data, the application utilizes event-driven functionality and data polling. The control center handles between 5,000 and 10,000 records of primary events per day from redundant event and alarm OPC servers. Also, it generates statistics for all equipment groups of the continuous mining system. Figure 1 presents the classification of time losses recorded by the data management software for all equipment groups at the South Field Mine. Figure 1 presents a fault tree analysis of time losses, which was applied in the statistical analysis of availability and utilization for the five equipment groups of the South Field Mine examined in this study. The tree-based classification is a reliable method that deduces causal factors through a top-down approach. Fig. 1 Time losses classification for the excavation groupsat the South Field Mine (two additional levels of eventscausing time losses, which are presented in the full article,have been omitted for the sake of brevity). Results Figure 2 presents a general estimate of the breakdown of downtime and operating time for all excavation groups composing the continuous mining system in the South Field Mine for the year 2019. Moreover, Table 1 presents the performance indicators calculated for the five equipment groups and the entire continuous operating system at the South Field Mine for the same year. It is evident that among the indicators of availability, utilization and performance, the latter exhibited the worst value in all cases. This confirmed that the selective mining of thin lignite layers has a detrimental effect on the equipment performance. Furthermore, the equipment groups that operated in benches with excessive volumes of hard rocks, such as E2 and SE1, had low availability percentages. To address this problem, mine managers tried to keep personnel assigned to these equipment units to increase their utilization. This improved but did not drastically change the OEE and MPi of these equipment groups. Fig. 2 Distribution of calendar time in various categories of downtime and operating time, based on the data collected by the specialized software installed in the South Field Mine in 2019. Conclusions Based on the results of this study, it was concluded that the OEE and MPi indicators could be considered for short- and medium-term production planning of every mine equipped with continuous mining systems, quantifying the combined effect of availability, utilization and performance parameters on the effective use of equipment. In this way, the mine planning team could make decisions for several operating parameters, including: Optimal planning of preventive maintenance. Optimal materials and spare parts supplies. Timely recruitment of operators and other personnel. Efficient distribution of personnel to various tasks. The site-specific conditions and peculiarities of every mine determine OEE and MPi values. This was also the case of the South Field Mine, as the multilayered structure of the lignite strata, the hard-rock inclusions in the overburden and the reduction of the personnel number in the context of the lignite phase-out strategy shaped the low availability, utilization and performance values exhibited by all continuous mining systems. Nevertheless, the results obtained from this research proved that OEE can be the driver for improved production performance in surface mining projects equipped with continuous mining systems. References A list of all references is available in the full paper.

Using overall equipment effectiveness as a driver for improving the productivity of continuous mining systems

Authored by: Francis Pavloudakis, Christos Roumpos and Zach Agioutantis

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