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Tytuł artykułu

Evaluation of blasting patterns using operational research models

Treść / Zawartość
Identyfikatory
Warianty tytułu
PL
Ocena planów prac strzałowych w oparciu o metody badań operacyjnych
Języki publikacji
EN
Abstrakty
EN
Blasting is one of the most important operations, which has a great technical and economical effect on the mining projects. Criteria such as fragmentation (operation ultimate objective) and ground vibration, flyrock, airblast, etc. (operation side effects) should be considered in the assessment of blasting operation. A suitable pattern should be able to provide both reasonable (required) fragmentation and blasting side effects. In order to evaluate blasting performance, operational research models such as multi attribute decision making technique (MADM) can be applied. Technique for order preference by similarity to an ideal solution (TOPSIS), a branch of MADM, is a strong method for pattern ranking. The other quantitative method which is applied in the evaluation of systems’ efficiency is data envelopment analysis (DEA) model. In this paper, an attempt has been made to develop a new hybrid MADM model for selecting the most appropriate blasting pattern in Chadormalu iron mine, Iran. In this regard, DEA method was utilized to select the efficient blast patterns thereafter TOPSIS was used to recognize the most suitable pattern amongst the selected patterns by DEA method. It was concluded that the patterns J, G and B are the most appropriate patterns for blasting operations in the Chadormalu iron mine.
PL
Prace strzałowe to jedne z kluczowych operacji w znacznym stopniu determinujące efektywność ekonomiczną wielu projektów górniczych. W planowaniu prac strzałowych uwzględnić należy podstawowe kryteria, takie jak rozdrobnienie skał (ostateczny cel operacji), wibracje podłoża, występowanie rozrzutu skał, i podmuchów powietrza (efekty uboczne). Odpowiedni harmonogram prac zapewnić powinien zarówno odpowiedni poziom rozdrobnienia (wymiary brył) jak i ograniczenie skutków ubocznych prac. Dla oceny skuteczności prac strzałowych zastosować można modele badań operacyjnych, np. modele oparte o wielokryterialną technikę decyzyjną MADM, a technika ustalania kolejności preferowanych rozwiązań oparta o podobieństwo do rozwiązania idealnego (TOPSIS), wywodząca się z MADM, jest skuteczną metodą ustalania rankingu wzorców. Inną metodą ilościową stosowaną do oceny efektywności systemów jest metoda analizy danych DEA. W niniejszym artykule dokonano próby opracowania hybrydowego modelu MADM do wyboru najbardziej korzystnego planu prac strzałowych w kopalni rud żelaza Chadormalu, w Iranie. W ramach badań wykorzystano metodę DEA do wyboru skutecznego planu prac strzałowych, następnie zastosowano podejście TOPSIS dla rozpoznania najbardziej odpowiedniego wzorca spośród tych wybranych przy pomocy metody DEA. Stwierdzono, że wzorce oznaczone jako J, G i B są najodpowiedniejsze do zastosowania przy pracach strzałowych prowadzonych w kopalni rud żelaza Chadormalu.
Rocznik
Strony
881--892
Opis fizyczny
Bibliogr. 37 poz., rys., tab.
Twórcy
autor
  • Tarbiat Modares University, Tehran, Iran
autor
  • Islamic Azad University, Tehran South Branch, Tehran, Iran
autor
  • Islamic Azad University, Tehran South Branch, Tehran, Iran
Bibliografia
  • Allen R., Thanassoulis E., 2004. Improving envelopment in data envelopment analysis. European Journal of Operational Research, 154:363-379.
  • Athanassopoulosa A.D., Lambroukosb N., Seifordc L., 1999. Data envelopment scenario analysis for setting targets toelectricity generating plants. European Journal of Operational Research, 115:413-428.
  • Bajpayee T.S., Rehak T.R., Mowrey G.L., Ingram D.K., 2004. Blasting injuries in surface mining with emphasis onflyrock and blast area security. Journal of Safety Research, 35:47-57.
  • Bal H., Orkcu H.H., Çelebioglu S., 2010. Improving the discrimination power and weights dispersion in the data envelopmentanalysis. Computers & Operations Research, 37:99-107.
  • Bozich B., 1998. Control of fragmentation by blasting. Geotechnical magazine (Zagreb), 10:49-57.
  • Chakraborty A.K., 2004. Parametric study to develop guidelines for blast fragmentation improvement in jointed andmassive formations. Engineering Geology, 73:105-116.
  • Chen M.F., Tzeng G.H., 2004. Combining gray relation and TOPSIS concepts for selecting an expatriate host country. Mathematical and Computer Modeling, 40:1473-1490.
  • Cooper W.W., Seiford L.M., Tone K., 2006. Introduction to Data Envelopment Analysis and Its Uses. Springer.
  • Despotis D.K., Smirlis Y.G., 2002. Continuous Optimization Data envelopment analysis with imprecise data. European Journal of Operational Research, 140:24-36.
  • Erarslan K., Uysal O., Arpaz E., Cebi M.A., 2008. Barrier holes and trench application to reduce blast induced vibrationin Seyitomer coal mine. Environmental Geology, 54:1325-1331.
  • Hakan A.K., Iphar M., Yavuz M., Konuk A., 2009. Evaluation of ground vibration effect of blasting operations in a magnesitemine. Soil Dynamics and Earthquake Engineering, 29:669-676.
  • Hakan A.K., Konuk A., 2008. The effect of discontinuity frequency on ground vibrations produced from bench blasting(A case study). Soil Dynamics and Earthquake Engineering, 28:686-698.
  • Hermans E., Brijs T., Wets G., Vanhoof K., 2009. Benchmarking road safety, Lessons to learn from a d ata envelopmentanalysis. Accident Analysis & Prevention, 41:174-182.
  • Kahriman A., 2004. Analysis of parameters of ground vibration produced from bench blasting at a limestone quarry. Soil Dynamics and Earthquake Engineering, 24:887-892.
  • Kao C., Liu S.T., 2009. Stochastic data envelopment analysis in measuring the efficiency of Taiwan commercial banks. European Journal of Operational Research, 196:312-322.
  • Khandelwal M., Kankar P.K., Harsha S.P., 2010. Evaluation and prediction of blast induced ground vibration usingsupport vector machine. Mining Science and Technology (China), 20:64-70.
  • Khandelwal M., Singh T.N., 2006. Prediction of blast induced ground vibrations and frequency in opencast mine, A neuralnetwork approach. Journal of Sound and Vibration, 289:711-725.
  • Khandelwal M., Singh T.N., 2009. Prediction of blast-induced ground vibration using artificial neural network. International Journal of Rock Mechanics and Mining Sciences, p 1214-1222.
  • Kim J.K., Choi S.H., 2001. A utility range-based interactive group support system for multi attribute decision making. Computers & Operations Research, 28:485-503.
  • Kuzu C., Fisne A., Ercelebi S.G., 2009. Operational and geological induced air blast -overpressure in quarries: Applied Acoustics, 70:404-411.
  • Latham J.P., Meulen J.V., Dupray S., 2006. Prediction of fragmentation and yield curves with reference to armourstoneproduction. Engineering Geology, 87:60-74.
  • Li D.F., Wang Y.C., Liu S., Shan F., 2009. Fractional programming methodology for multi-attribute group decision--making using IFS. Applied Soft Computing.
  • Li S., Jahanshahloo G.R., Khodabakhshi M., 2007. A super-efficiency model for ranking efficient units in data envelopmentanalysis. Applied Mathematics and Computation, 184:638-648.
  • Lin Y-H., Lee P-C., Chang T.P., Ting H.I., 2008. Multi-attribute group decision making model under the condition ofuncertain information: Automation in Construction, 17:792-797.
  • Lopez C.J., Carcedo F.J.A., Lopez E.J., 1995. Drilling & Blasting of Rocks. A. A. Balkema, Rotterdam, Brookfield.
  • Mario A.M., Ficarazzo F., 2006. Monte Carlo simulation as a tool to predict blasting fragmentation based on the Kuz--Ram model. Computers & Geosciences, 32:352-359.
  • Monjezi M., Dehghani H., Singh T.N., Sayadi A.R., Gholinejad A., 2012. Application of TOPSIS method for selectingthe most appropriate blast design. Arabian Journal of Geosciences, 5:95-101.
  • Ozkahraman H.T., 2006. Fragmentation assessment and design of blast pattern at Goltas Limestone Quarry, Turkey. International Journal of Rock Mechanics & Mining Sciences, 43:628-633.
  • Post T., Spronk J., 1999. Theory and Methodology Performance benchmarking using interactive data envelopmentanalysis. European Journal of Operational Research, 115:472-487.
  • Ramanathan R., 2006. Data envelopment analysis for weight derivation and aggregation in the analytic hierarchy process. Computers & Operations Research.
  • Shim H.J., Ryu D.W., Chung S.K., Synn J.H., Song J.J., 2009. Optimized blasting design for large-scale quarrying basedon a 3-D spatial distribution of rock factor. International Journal of Rock Mechanics & Mining Sciences, 46:326-332.
  • Shih H.Sh., Shyur H.J., Lee E.S., 2007. An extension of TOPSIS for group decision making. Mathematical and Computer Modeling, 45:801-813.
  • Singh T.N., Singh V., 2005. An intelligent approach to prediction and control ground vibration in mines. Geotechnical and Geological Engineering, 23:249-262.
  • Sowlati T., Paradi J.C., Suld C., 2005. Information Systems Project Prioritization Using Data Envelopment Analysis. Mathematical and Computer Modeling, 41:1279-1298.
  • Triantaphyllou E., Shu B., Sanchez S.N., Ray T., 1998. Multi-Criteria Decision Making, An Operations Research Approach: Encyclopedia of Electrical and Electronics Engineering, 15:175-186.
  • Xu Z., 2008. On multi-period multi-attribute decision making. Knowledge-Based Systems, 21:164-171.
  • Yang T., Chou P., 2005. Solving a multi-response simulation-optimization problem with discrete variables using a multi--attribute decision-making method: Mathematics and Computers in Simulation, 68:9-21.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b26be53a-0aab-4d0e-89e1-a6af64b7466a
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