Journals / Madencilik Dergisi / 2006 / Cilt: 45 - Sayı: 2

Use of fuzzy modelig approach in grade estimation

Bulanık modelleme yaklaşımının tenör kestiriminde kullanılması

Pages
39–47
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Estimated values obtained by using a flexible and transparent model structure are compared with actual grade values and the success of the estimation is tested.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Grade estimation has a great importance in developing mining investment and projects.
📊
Key Findings & Data:In this study, fuzzy logic approach to grade estimation is introduced and the approach is applied to the data obtained from the Karsantı (Adana) chromium deposit.
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Conclusion & Impact:Estimated values obtained by using a flexible and transparent model structure are compared with actual grade values and the success of the estimation is tested.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

Grade estimation has a great importance in developing mining investment and projects. In this study, fuzzy logic approach to grade estimation is introduced and the approach is applied to the data obtained from the Karsantı (Adana) chromium deposit. In the application, Takagi-Sugeno fuzzy modelling algorithm is used. The fuzzy model follows the steps of clustering, construction of rule based system and parameter optimization. Estimated values obtained by using a flexible and transparent model structure are compared with actual grade values and the success of the estimation is tested.

Özet

Tenör kestirimi madencilik yatırım ve projelerinin geliştirilmesinde büyük bir önem taşır. Bu çalışmada tenör kestirimine bulanık mantık yaklaşımı tanıtılmış ve yaklaşım Karsantı (Adana) krom yatağından elde edilen verilere uygulanmıştır. Uygulamada Takagi-Sugeno tipi bulanık modelleme yordamı kullanılmıştır. Bulanık model, sırasıyla verilerin kümelenmesi, kural sisteminin oluşturulması ve parametre optimizasyonu aşamalarını izlemektedir. Esnek ve şeffaf bir model yapısı kullanılarak elde edilen kestirim değerleri ölçülen tenör değerleri ile karşılaştırılmış ve kestirimlerin başarısı test edilmiştir.

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Cite This Article

TÜTMEZ, B., TERCAN, A. E. (2006). Use of fuzzy modelig approach in grade estimation. Madencilik Dergisi, Cilt: 45 - Sayı: 2, 39–47. https://ajindex.com/articles/282223

References (14)

  1. Babuska R., 1998; “Fuzzy Modeling for Control”, Kluwer Academic Publication, Boston. Baglio, S., Fortuna, L., Graziani, S., Muscato, G., 1994; “Membership Function Shape and the Dynamic Behaviour of Systems”, International Journal of Adaptive Control and Signal Processing, 8, 369-377.
  2. Bardossy, Gy., Fodor, J., 2001; “Traditional and New Ways to Handle Uncertainty in Geology”, Natural Resources Research, 10,(3), 169-187.
  3. Bardossy, G., Fodor J., 2004; “Evaluation of Uncertainties and Risks in Geology”, Springer, 221. Bezdek, J.C., Ehrlich, R., Full, W., 1984; “FCM: The Fuzzy c-means Clustering Algorithm”, Computers and Geosciences, 10,(2-3), 191-203.
  4. Diehl, P. 1997; “Quantification of the Term -Geological Assurance- in Coal Classification Using Geostatistical Methods”, Schriftenreihe der GDMB, H.79, 187-203.
  5. Gokceoglu, C., 2002; “A Fuzzy Triangular Chart to Predict the Uniaxial Compressive Strength of Ankara Agglomerates from Their Petrographic Composition”, Engineering Geology, 66, 39-51.
  6. Gokceoglu, C., Zorlu, K., 2004; “A Fuzzy Model to Predict the Uniaxial Compressive Strength and the Modulus of Elasticity of a Problematic Rock”, Engineering Applications of Artificial Intelligence, 61-72.
  7. Goovaerts, P., 1997; “Geostatistics for Natural Resources Evaluation”, Oxford University Press, New York, 483. Grima M.A., Babuska, R., 1999; “Fuzzy Model for the Prediction of Unconfined Compressive Strength of Rock Samples”, Int. J. Rock Mechanics and Mining Sciences, 36, 339-349.
  8. Jang, J.S.R., Sun, C.T., Mizutani, E., 1997; “Neuro-Fuzzy and Soft Computing”, Prentice Hall, 614. Karakus, M., Tutmez, B., 2006; “Fuzzy and Multiple Regression Modeling for Evaluation of Intact Rock Strength Based on Point Load, Schmidt Hammer and Sonic Velocity”, Rock Mech Rock Eng, 39, (1), 45-57.
  9. Kaymak, U., Babuska, R., 1995; “Compatible Cluster Merging for Fuzzy Modeling”, In Proc. FUZZ-IEEE/IFES’95, Yokohama, Japan, 897-904.
  10. Kesimal, A., Bascetin, A., 2002; “Application of Fuzzy Multiple Attribute Decision Making in Mining Applications”, Mineral Resources Engineering, 11, 1, 59-72
  11. Köse, A., 2004; “Bootstrap’a Dayalı Rezerv Kestirim Yöntemlerinin Geliştirilmesi”, Doktora Tezi, Hacettepe Üniversitesi,
  12. Ankara.
  13. Pham, T.D., 1997; “Grade Estimation Using Fuzzy-Set Algorithms”, Mathematical Geology, 29, 291-304.
  14. Piegat, A., 2001; “Fuzzy Modeling and Control”, Physica-Verlag, 728. Ross, T.J. 2004; “Fuzzy Logic with Engineering Applications”, McGraw-Hill. Setnes, M., Babuska, R., Verbruggen, H