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Mine Production Assurance Program- Development and Application
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

ssuring production forms a crucial part of mining business profitability. Factors related to various mine operations, activities and business processes can threaten required/planned mine production.   To address problems and ensure production level in mining, it is necessary to implement a mine production assurance program (MPA). In order to propose a guideline and its component, this study started by reviewing four such techniques used in process industries. Comparing the tools, techniques   and focus with mining productivity and production factors, it was observed that applicability of these methods for mining is limited due to lack of focus on equipment focus, cost focus and other parameters. Similarity of objectives and requirements of equipment focus lead to conclusion that PAP from oil and gas industry seems to be method which can guide MPA.\parAs a basis of MPA, an index is required to create a clear relationship between different situations which can occur in mining operation and production loss. A literature review on mining productivity improvement methods shows availability, utilisation and production performance of equipment are the key factors in determining overall production. A single index applicable for chain operation in mining is needed. A Mine Production index (MPi) is thus proposed. This index involves all three parameters for equipment productivity mentioned above.  Weights associated with MPi calculation for bottleneck equipment can point out critical factors in equipment operation. Once bottleneck equipment and relevant critical factors are known, further analysis can be carried out to determine the possible causes of production loss. By using MPi for machine operations, it is possible to rank machines in terms of production effectiveness. When the study applied MPi to chain operations in a mining case study, a crusher was determined as bottleneck equipment.\parMining operation is heavily influenced by internal and external uncertainties. Operational uncertainties related to equipment includes its key factors leading to production i.e. availability, utilisation and performance. These factors are in turn dependent upon downtime, idle time, rated capacities. External parameters related to weather are based upon location of mining operation. Influence of these factors on production volume, could be used for better decision making during mining operations optimization. To effectively propose a method for correlating internal and external parameters with production volume, case studies in an open pit mine were conducted. During these case studies a multi-regression modelling methodology is used. It was found that at system level availability is important criteria for increasing production. At level of shovel and truck fleet, availability and utilisation are most important characteristics to be focused for reduction in production uncertainty. Environmental factors are although correlate to less variation in production volume compared to operational factors.  Amongst considered environmental factors snowfall is highly influencing followed by rainfall.  At system level  use of maximum capacities of equipment and availability are key point for increasing production. Based on analysis of internal operational factors, it was concluded that capacity of shovel and trucks is underutilised. For shovels availability and idle time are influential factors. For trucks utilisation is highly correlated to production volume generated.  Analysis of environmental factors concluded that, period of zero snowfall and rainfall are perfect condition for equipment production increase. Period when either snowfall or rainfall stabilisation are also equivalent to achieve higher production. Although these production levels are significantly less than period without snow and rain

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2016.
Series
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
National Category
Other Engineering and Technologies
Research subject
Mining and Rock Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-61123ISBN: 978-91-7583-787-1 (print)ISBN: 978-91-7583-788-8 (electronic)OAI: oai:DiVA.org:ltu-61123DiVA, id: diva2:1057209
Public defence
2016-01-31, F1031, Luleå, Luleå, 10:00 (English)
Supervisors
Available from: 2016-12-19 Created: 2016-12-16 Last updated: 2017-11-24Bibliographically approved
List of papers
1. Uncertainty Analysis of Production in Open Pit Mines: operational parameter regression analysis of Mining Machinery
Open this publication in new window or tab >>Uncertainty Analysis of Production in Open Pit Mines: operational parameter regression analysis of Mining Machinery
2016 (English)In: Mining science, ISSN 2353-5423, Vol. 23, p. 147-160, article id msc162312Article in journal (Refereed) Published
Abstract [en]

In mining uncertainties related to equipment and operation are major reasons for loss of production. In order to address this issue a wide literature review was done in this study. It showed that reliability of equipment, spare part availability, automation of equipment are researched areas focused. However, a methodology which relates operational issues directly to production levels have been not studied with detailed analysis. In order to overcome this issue and propose, a method to achieve production assurance is the objective of this study. A case study with 2.5 years of data from a large open pit mine is carried out. Following the statistical principles, multiple regressions modeling with details analysis, optimization of payload and interpretation of analysis are used. It showed that at system level availability, utilization and maximum capacities are important criteria for finding root cause in loss of production. Model for shovel fleet showed that availability is most important characteristics hindering it to achieve higher level of production. It was also seen that 3 to 4 number of shovels are optimal for achieving current level of production. For truck fleet model represented that capacities involved are less important factor as compared to utilization of fleet.

National Category
Other Engineering and Technologies Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-61117 (URN)10.5277/msc162312 (DOI)2-s2.0-85021130262 (Scopus ID)
Projects
CAMM
Available from: 2016-12-16 Created: 2016-12-16 Last updated: 2017-11-24Bibliographically approved
2. Uncertainty Analysis of Production in Open Pit Mines: Effect of environmental conditions
Open this publication in new window or tab >>Uncertainty Analysis of Production in Open Pit Mines: Effect of environmental conditions
2016 (English)In: Archives of Mining Sciences, ISSN 0860-7001, E-ISSN 1689-0469Article in journal (Refereed) Submitted
Abstract [en]

Production volume by mining equipment is influenced by internal and external parameters. External parameters include weather conditions, human factors etc. This study shows impact of temperature, rainfall and snowfall on production volume achieved under influence of these factors in open pit mine.  The case study is carried out which include data from weather station near an open pit mine and production tonnage. Multi regression modelling in performed using stated factors and production volume.  It was observed that Snowfall and rainfall has impact on production volume. Temperature has no effect on payload achieved as represented by model. With increasing snowfall and rainfall decreases. Higher snowfall (0.8 meter to 1 meter) although has tends to lead higher tonnage compared to low snowfall (0 to 0.8meters). Rainfall causes decrease in production of ore, with increase in rainfall from 1.2 mm, there is sharp decrease in production volume. The optimization table shows that with either no snowfall coupled with maximum rainfall (39 mm) it is possible to achieve production levels of 120 thousand per day. With high snowfall (1.06 metres) and no rainfall, it is possible to achieve maximum of 118 thousand tonnes per day.

National Category
Other Engineering and Technologies Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-61122 (URN)
Projects
CAMM
Available from: 2016-12-16 Created: 2016-12-16 Last updated: 2017-11-29
3. Production improvement techniques in process industries for adoption in mining: A comparative study
Open this publication in new window or tab >>Production improvement techniques in process industries for adoption in mining: A comparative study
2016 (English)In: International Journal of Productivity and Quality Management, ISSN 1746-6474, E-ISSN 1746-6482, Vol. 19, no 3, p. 366-386Article in journal (Refereed) Published
Abstract [en]

High profitability and customer satisfaction are of supreme importance for any business. To achieve both objectives, an organisation must design a structured approach. To achieve profitability, organisations look to principles of lean manufacturing and techniques such as EFQM, business excellence. This paper reviews such methodologies across different industries, comparing techniques and elements. Its objective is to determine which methodologies are most applicable to the Swedish mining industry and propose a method to achieve lean mining. To this end, the paper looks at the methodologies of a food manufacturing industry, an automobile component manufacturing company, the manufacturing and service sector, and the oil and gas industry. It finds that the method used in the oil and gas industry is more relevant to mining, even though it has some flaws. Further research is needed to adapt this method to the mining industry.

National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-7778 (URN)10.1504/IJPQM.2016.079781 (DOI)2-s2.0-84992208955 (Scopus ID)632e6575-ed42-46a5-9c34-398928c48da3 (Local ID)632e6575-ed42-46a5-9c34-398928c48da3 (Archive number)632e6575-ed42-46a5-9c34-398928c48da3 (OAI)
Note

Validerad; 2016; Nivå 1; 2016-11-10 (andbra)

Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2017-11-24Bibliographically approved
4. Mine production index (MPI)-extension of OEE for bottleneck detection in mining
Open this publication in new window or tab >>Mine production index (MPI)-extension of OEE for bottleneck detection in mining
2016 (English)In: International Journal of Mining Science and Technology, ISSN 2095-2686, Vol. 26, no 5, p. 753-760Article in journal (Refereed) Published
Abstract [en]

Although mining production depends on various equipments, significant amount of production loss can be attributed a specific equipment or fleet. Bottleneck is defined not only by production loss but also by our satisfaction from the equipment. The user satisfaction could be measured as machine effectiveness. Mining literature on performance improvement and optimization of equipment operations assert importance of availability, utilization and production performance as key parameters. These three parameters are useful for evaluating effectiveness of equipment. Mine production index (MPI), which can represent the effect of these factors, has been applied for continuous operation in mining. MPI uses Fuzzy Delphi Analytical Hierarchy Process to determine importance of each three parameter for individual equipment. A case study in a Swedish open pit mine was done to evaluate the field application of MPI. The results reveal that crusher is the bottleneck equipment in studied mine. As a methodical approach, an algorithm which uses MPI and detects bottleneck in continuous mining operation has been proposed.

National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-14320 (URN)10.1016/j.ijmst.2016.05.050 (DOI)000383712400002 ()2-s2.0-85006335485 (Scopus ID)dac441fb-01e7-4abb-8794-deb04c2d27fc (Local ID)dac441fb-01e7-4abb-8794-deb04c2d27fc (Archive number)dac441fb-01e7-4abb-8794-deb04c2d27fc (OAI)
Note

Validerad; 2016; Nivå 1; 20151130 (hadhos)

Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-07-10Bibliographically approved
5. Mine Production index (MPi): New method to evaluate effectiveness of mining machinery
Open this publication in new window or tab >>Mine Production index (MPi): New method to evaluate effectiveness of mining machinery
2014 (English)Conference paper, Oral presentation only (Refereed)
Abstract [en]

OEE has been used in many industries as measure of performance. However due to limitations of original OEE, it has been modified by various researchers. OEE for mining application is special version of classic equation, carries these limitation over. In this paper it has been aimed to modify the OEE for mining application by introducing the weights to the elements of it and termed as Mine Production index (MPi). As a special application of new index MPishovel has been developed by authors. This can be used for evaluating the shovel effectiveness. Based on analysis, utilization followed by performance and availability were ranked in this order. To check the applicability of this index, a case study was done on four electrical and one hydraulic shovel in a Swedish mine. The results shows that MPishovel can evaluate production effectiveness of shovels and determine effectiveness values in optimistic view compared to OEE. MPi with calculation not only give the effectiveness but also can predict which elements should be focused for improving the productivity.

National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-30690 (URN)495b4eb9-da2c-4a7f-a685-b9ff750cc268 (Local ID)495b4eb9-da2c-4a7f-a685-b9ff750cc268 (Archive number)495b4eb9-da2c-4a7f-a685-b9ff750cc268 (OAI)
Conference
International Conference on Mining and Mineral Engineering : 13/11/2014 - 14/11/2014
Projects
CAMM - Lean mining
Note
Godkänd; 2014; 20140817 (hadhos)Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2017-11-25Bibliographically approved

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