Numerical modelling of ore dilution in blasthole stoping

Henning, John G.; Mitri, Hani S.

doi:10.1016/j.ijrmms.2006.11.002

Cited by 61 publications

(32 citation statements)

References 9 publications

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“…Several researchers (Clark, 1998;Henning and Mitri, 2007;Mathews et al, 1981;Mubita, 2005;Potvin, 1988;Stewart, 2005;Tatman, 2001;Villaescusa, 1998;Yihong and Weijin, 1986) have indicated that the factors that are likely to cause unplanned dilution and ore loss can be divided into three core groups with one subsidiary group (i.e. blasting, geological, stope design, and human error and others).…”

Section: Factors That Influence Unplanned Dilution and Ore Lossmentioning

confidence: 99%

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Unplanned dilution and ore loss prediction in longhole stoping mines via multiple regression and artificial neural network analyses

Jang¹,

Topal²,

Kawamura³

2015

J. S. Afr. Inst. Min. Metall.

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UB affects not only the safety of the workforce and machinery, but is also severely detrimental to the viability of the operation throughout all of the mining stages. It directly downgrades the ore and causes unnecessary mucking, haulage, crushing, hoisting, and milling activities, thus decreasing productivity. Despite these effects, current UB management Unplanned dilution and ore loss prediction in longhole stoping mines via multiple regression and artificial neural network analyses by H. Jang*, E. Topal*, and Y. Kawamura* SynopsisUnplanned dilution and ore loss directly influence not only the productivity of underground stopes, but also the profitability of the entire mining process. Stope dilution is a result of complex interactions between a number of factors, and cannot be predicted prior to mining. In this study, unplanned dilution and ore loss prediction models were established using multiple linear and nonlinear regression analysis (MLRA and MNRA), as well as an artificial neural network (ANN) method based on 1067 datasets with ten causative factors from three underground longhole stoping mines in Western Australia. Models were established for individual mines, as well as a general model that includes all of the mine data-sets. The correlation coefficient (R) was used to evaluate the methods, and the values for MLRA, MNRA, and ANN compared with the general model were 0.419, 0.438, and 0.719, respectively. Considering that the current unplanned dilution and ore loss prediction for the mines investigated yielded an R of 0.088, the ANN model results are noteworthy. The proposed ANN model can be used directly as a practical tool to predict unplanned dilution and ore loss in mines, which will not only enhance productivity, but will also be beneficial for stope planning and design.

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Section: Factors That Influence Unplanned Dilution and Ore Lossmentioning

confidence: 99%

“…Pakalnis (1986) surveyed 15 open stope Canadian mines and concluded that 47% of the operations suffered from more than 20% dilution. Likewise, Henning and Mitri (2007) reported that approximately 40% of open stoping operations in Canada suffered from 10% to 20% dilution.…”

Section: Introductionmentioning

confidence: 99%

Unplanned dilution and ore loss prediction in longhole stoping mines via multiple regression and artificial neural network analyses

Jang¹,

Topal²,

Kawamura³

2015

J. S. Afr. Inst. Min. Metall.

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“…Stress relaxation (zero or tensile stress) around the surface of an excavation is one of the major factors causing excavation instability (Henning et al, 2007). When an excavation is made in a prestressed rock, the magnitude and orientation of stresses in the vicinity of the excavation will be changed.…”

Section: Setting Of Roof Fall Assessment Criterionmentioning

confidence: 99%

Estimating Volume of Roof Fall in the Face of Longwall Mining by Using Numerical Methods / Estymacja Objętości Zawału Stropu W Rejonie Przodka Ścianowego W Oparciu O Metody Numeryczne

Saeedi¹,

Shahriar²,

Rezai³

2013

Archives of Mining Sciences

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Dilution is one of many challenges confronting professionals in mining and milling, and is perhaps one of the oldest. Longwall mining is one of the mining methods that is often affected by out-of-seam dilution (OSD). In this method, roof falls play a significant role in increasing OSD in the prop-free front of the face area. Thus, estimating the volume of roof fall can be extremely helpful to assess dilution of the run of mine coal without a sampling process. This paper presents the effect of exposed area geometry on potential roof falls using the 2D numerical modelling program FLAC. In this respect, a half-prolate ellipsoid was considered as the low stress level or plasticity zone under yield tension which roof material fall. Since FLAC software does not show roof falls in prop-free front of the face, a series of two-dimensional numerical models are developed using UDEC software. The comparison of the results of two numerical models clearly indicates that volumes of roof fall obtained by means of these methods are in good agreement with each other.Keywords: dilution; numerical modeling; longwall mining; Tabas coal mine Ścienianie warstw jest jednym z najpoważniejszych wyzwań stojących przed inżynierami górnikami i specjalistami z zakresu obróbki -jest to też jeden z najstarszych problemów. Wybieranie ścianowe jest metodą urabiania, w której często mamy do czynienia ze ścienianiem warstwy złoża. W metodzie tej strop odgrywa kluczową rolę w zapewnieniu stabilności w tych rejonach przodka, gdzie nie zastosowano obudów. Dlatego też estymacja objętości zawału stropu może być pomocna przy obliczaniu ścieniania warstwy węgla bez konieczności próbkowania. W artykule tym przeanalizowano wpływ geometrii powierzchni odkrytych na potencjalny zawał stropu przy użyciu metod modelowania numerycznego z wykorzystaniem oprogramowania FLAC. Uzyskano wydłużoną elipsoidę jako model strefy niskich naprężeń lub strefę plastyczności przed zawałem stropu. Ponieważ oprogramowanie FLAC nie pokazuje zawałów stropu w strefie przodka, gdzie nie ma obudów, opracowano serię dwuwymiarowych modeli

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“…With steeply dipping seams, vertical stresses are shed around the coalbed. As the dip of the hanging-wall becomes shallower, vertical stresses are shed onto the coalbed, leading to larger displacements and roof falls (Henning and Mitri, 2007). Therefore, severity of OSD increases as seam dip angle became increasingly shallow.…”

Section: Dip Of Seammentioning

confidence: 99%

“…The third numerical approach proposed by Henning and Mitri (2007). In the method, a series of three-dimensional numerical models are developed and analyzed to examine the effect of mining depth, in situ stress as well as stope geometry and orientation on ore dilution.…”

Section: Introductionmentioning

confidence: 99%

A Method to Relate the Affecting Parameters and Estimate Dilution in Coal Mines

Saeedi¹,

Rezai²,

Shahriar³

et al. 2009

J. of Applied Sciences

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This study provides an overview of the various issues influencing Out-of-Seam Dilution (OSD) in longwall mining method. The collected data has been statistically analyzed to examine the effect of the some factors causing OSD in front of the longwall mining face. Multiple parameter regression analysis was conducted on affecting parameters and the OSD. The SPSS (Statistics Package for Social Sciences) for Windows software package was used for the statistics analysis. Finally, a relationship between affecting parameters and the OSD is established by using the multiple parameter regression results. Results of this study have revealed that depth of seam, dip of seam, roof quality and variation in seam thickness are the most important influence factors for OSD. The proposed method may be utilized for the estimation of OSD for similar mines since, it was based on actual collected data from the coal mines.

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Numerical modelling of ore dilution in blasthole stoping

Cited by 61 publications

References 9 publications

Unplanned dilution and ore loss prediction in longhole stoping mines via multiple regression and artificial neural network analyses

Unplanned dilution and ore loss prediction in longhole stoping mines via multiple regression and artificial neural network analyses

Estimating Volume of Roof Fall in the Face of Longwall Mining by Using Numerical Methods / Estymacja Objętości Zawału Stropu W Rejonie Przodka Ścianowego W Oparciu O Metody Numeryczne

A Method to Relate the Affecting Parameters and Estimate Dilution in Coal Mines

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