Bottleneck identification and ranking model for mine operations

Kahraman, Muhammet Mustafa; Rogers, W. Pratt; Dessureault, Sean

doi:10.1080/09537287.2019.1701231

Cited by 12 publications

(7 citation statements)

References 22 publications

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“…The TOC approach consists of four major steps: recognizing the bottlenecks, exploiting the barriers, bringing everything under these barriers or limitations, and finding the actual bottleneck. Many coal mining enterprises believe that the mining process is itself a bottleneck; however, these bottlenecks are mostly shiftable from one process to another [38]. The recognition of these bottlenecks is anticipated to spur the business management to take action to resolve these issues, which seem to create a hindrance in productivity.…”

Section: -5-bottlenecks In Achieving the Desired Competitiveness Mana...mentioning

confidence: 99%

Managing Coal Enterprise Competitiveness in the Context of Global Challenges

Hia¹,

Waruwu

Komariah

et al. 2023

Emerg Sci J

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Increased geopolitical tensions and economic sanctions imposed by the U.S., the European Union, and other countries against Russian sectors of the economy have caused a slowdown of economic growth in Russia and significantly restricted access to international capital markets, creating many problems for coal enterprises due to the rapid growth of competition. Russian and Indonesian coal companies need to adopt coping strategies and implement effective management practices to successfully counter the various global challenges facing the coal sector. The article aims to develop coal enterprise competitiveness management in the context of global challenges in 2022, as exemplified by Russia and Indonesia, considering the main role of these two countries in global coal exports. The management process was empirically assessed, and a comprehensive qualitative focus group session was conducted to achieve this goal. Fifty-five top managers of Russian and Indonesian coal companies participated in the focus group session to collect data for identifying all the factors and indicators to be accounted for in a holistic assessment of the companies’ competitiveness. Suggestions were worked out for the development of coal companies in Russia and Indonesia, regarding current changes, to increase their competitiveness. Doi: 10.28991/ESJ-2023-07-02-021 Full Text: PDF

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Section: -5-bottlenecks In Achieving the Desired Competitiveness Mana...mentioning

confidence: 99%

Managing Coal Enterprise Competitiveness in the Context of Global Challenges

Hia¹,

Waruwu

Komariah

et al. 2023

Emerg Sci J

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“…Establishing the stability of the model to carry out improved accuracy in the coal blending process leads to resolving environmental issues, boosts the quality of the coke and improves the life of the BF (Yin et al, 2019). Kahraman et al (2020) outline mine value chain with the ranking of bottlenecks through ranking method. North et al (2018) present a detailed review based on published models in the literature for coke quality prediction through analysis framework on data mining and compare the models in terms of analytical techniques and data treatment.…”

Section: Existing Coal Blending Frameworkmentioning

confidence: 99%

Dynamic coal blending under sustainability concerns: a case study from the Indian steel industry

Ahmad

Firouz²,

Srivastava³

2023

IJQRM

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PurposeIncreasing scarcity of natural resources and the adverse effects of unsustainable practices call for more and more efficient management strategies in the energy industry. The quality of the coke plays a significant role in the quality and durability of the output steel which is produced using the energy from the coal. This paper aims to investigate the dynamic coal blending problem under overall cost and coke quality constraints in the steel industry within a periodic cycle of operations.Design/methodology/approachConsidering the variability of the natural properties over a periodic cycle, this study proposes a multi-period mixed-integer non-linear programming formulation to optimize the total blending costs while taking various coke quality constraints into account. Besides, this study applies factorial design to investigate about the significant effect of coal proportions as well as improvement into the overall cost of blending.FindingsIn this case study, utilizing real data from a coal blending facility in India, through a factorial design, the authors obtain optimal desirable levels of coal proportions and their criticality levels towards the total cost of blending (TCB) or objective function. This analysis reflects the role of the coke quality constraints in the objective function value while characterizing the price of sustainability for the case study among other critical insights.Originality/valueObjective function (or TCB) includes basic coal cost, movement cost and environmental costs during the coal and coke processing at a coke-oven and blast furnace of steel industry. The price of sustainability provides managerial insights on that sacrifices the industry has to make in order to become more “sustainable”.

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“…If a modern mine can be considered as a digital system of production, stockpiles may be thought of as digital bottlenecks, since they output less information into downstream processes than was input into them [36]. According to Kahraman et al [37], identifying, tracking, and managing bottlenecks will enable significant improvement in mining operations. However, data gaps, such as stockpiles, short circuit the bottleneck tracking capabilities of sites not to mention, automation, mine-to-mill, and other operational improvement initiatives.…”

Section: Classification Of Stockpilesmentioning

confidence: 99%

Modelling Large Heaped Fill Stockpiles Using FMS Data

Young¹,

Rogers²

2021

Minerals

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The frequent best practice for managing large low-grade run-of-mine (ROM) stockpiles is to average the entire stockpile to only one grade. Modern ore control and mineral processing procedures need better precision. Low-precision models hinder the ability to create a digital mine-to-mill model and optimize the holistic mining process. Prior to processing, poorly characterized stockpiles are often drilled and sampled, despite there being no geological reason for relationships between samples to exist. Stockpile management is also influenced by reserve accounting and lacks a common operational workflow. This paper provides a review of base and precious metal run-of-mine (ROM) pre-crusher stockpiles in the mining industry, and demonstrates how to build a spatial model of a large long-term stockpile using fleet management system (FMS) data and geostatistical code in Python and R Studio. We demonstrate a framework for modelling a stockpile believed to be readily workable for most modern mines through use of established geostatistical modelling techniques applied to the type of data generated in a FMS. In the method presented, each bench of the stockpile is modeled as its own geological domain. Size of dump loads is assumed to contain the same volume of material and grade values that match those of the grade data tracked in the FMS. Despite the limitations of these inputs, existing interpolation techniques can lead to increased understanding of the grade distribution within stockpiles. Using the framework demonstrated in this paper, engineers and stockpile managers will be able to leverage operational data into valuable insight for empowered decision making and smoother operations.

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Bottleneck identification and ranking model for mine operations

Cited by 12 publications

References 22 publications

Managing Coal Enterprise Competitiveness in the Context of Global Challenges

Managing Coal Enterprise Competitiveness in the Context of Global Challenges

Dynamic coal blending under sustainability concerns: a case study from the Indian steel industry

Modelling Large Heaped Fill Stockpiles Using FMS Data

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