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ESTIMATING THE POWER DRAW OF GRIZZLY FEEDERS USED IN CRUSHING–SCREENING PLANTS THROUGH SOFT COMPUTING ALGORITHMS

Year 2024, Volume: 12 Issue: 1, 100 - 108, 01.03.2024
https://doi.org/10.36306/konjes.1375871

Abstract

In this study, the power draw (P) of several grizzly feeders used in the Turkish Mining Industry (TMI) is investigated by considering the classification and regression tree (CART), random forest (RF) and adaptive neuro-fuzzy inference system (ANFIS) algorithms. For this purpose, a comprehensive field survey is performed to collect quantitative data, including power draw (P) of some grizzly feeders and their working conditions such as feeder width (W), feeder length (L), feeder capacity (Q), and characteristic feed size (F80). Before applying the soft computing methodologies, correlation analyses are performed between the input parameters and the output (P). According to these analyses, it is found that W and L are highly associated with P. On the other hand, Q is moderately correlated with P. Consequently, numerous soft computing models were run to estimate the P of the grizzly feeders. Soft computing analysis results demonstrate no superiority between the performances of RF and CART models. The RF analysis results indicate that the W is necessary for evaluating P for grizzly feeders. On the other hand, the ANFIS-based predictive model is found to be the best tool to estimate varying P values, and it satisfies promising results with a correlation of determination value (R2) of 0.97. It is believed that the findings obtained from the present study can guide relevant engineers in selecting the proper motors propelling grizzly feeders.

References

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  • S.K. Singh, S.G. Nair, “Stochastic modeling and analysis of stone crushing system used in Iron ore mines.” Journal of Ravishankar University, vol 8(1), 101-114, 1995
  • B.P. Numbi, J. Zhang, X. Xia, “Optimal energy management for a jaw crushing process in deep mines.” Energy, 68, 337-348, 2014
  • F.A. Munandar, S. Sriyanti, Y. Yuliadi, “Evaluasi Kinerja Unit Crushing Plant Batu Andesit pada PT Silva Andia Utama di Desa Giri Asih, Kecamatan Batujajar, Kabupaten Bandung, Provinsi Jawa Barat.” Prosiding Teknik Pertambangan, 486-494, 2018
  • M.R.E. Trisna, S. Widayati, P. Pramusanto, “Kajian Teknis Unit Crushing Plant Batu Andesit di PT Panghegar Mitra Abadi, Desa Lagadar, Kecamatan Marga Asih, Kabupaten Bandung, Provinsi Jawa Barat.” Prosiding Teknik Pertambangan, 41-48, 2018
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  • A. Fakhry, L. Pulungan, S. Widayati, S. “Studi Perancangan Stone Crushing Plant di PT Cahaya Baru Madani, Desa Giriasih Kecamatan Batujajar, Kabupaten Bandung Barat Provinsi Jawa Barat.” Prosiding Teknik Pertambangan, 287-296, 2020
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  • Metso, Crushing and Screening Handbook, sixth edition (eds. Keijo Viilo), Metso Corporation, 2011
  • V.I. Lyashenko, V.Z. Dyatchin, V.P. Franchuk, “The Improvement in the Efficiency and Reliability of the Operation of the GPK Type Vibrating Grizzly Feeder for the Mining Industry.” Ferrous Metallurgy. Bulletin of Scientific, Technical and Economic Information, (3), 28-33, 2015
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  • M. Hasanipanah, R.S. Faradonbeh, H.B. Amnieh, D.J. Armaghani, M. Monjezi, Forecasting blast-induced ground vibration developing a CART model. Engineering with Computers, 33, 307-316, 2017
  • A. Salimi, R.S. Faradonbeh, M. Monjezi, C. Moormann, “TBM performance estimation using a classification and regression tree (CART) technique.” Bulletin of Engineering Geology and the Environment, 77, 429-440, 2018
  • J.P. Bharti, P. Mishra, U. Moorthy, V.E. Sathishkumar, Y. Cho, P. Samui, “Slope stability analysis using Rf, gbm, cart, bt and xgboost.” Geotechnical and Geological Engineering, 39, 3741-3752, 2021
  • L. Breiman, “Random forests.” Machine learning, 45, 5-32, 2001
  • L. Collins, G. McCarthy, A. Mellor, G. Newell, L. Smith, L. “Training data requirements for fire severity mapping using Landsat imagery and random forest”. Remote Sensing of Environment, 245, 111839, 2020
  • S.S. Matin, L. Farahzadi, S. Makaremi, S.C. Chelgani, G.H. Sattari, “Variable selection and prediction of uniaxial compressive strength and modulus of elasticity by random forest”. Applied Soft Computing, 70, 980-987, 2018
  • D. Zhao, Q. Wu, “An approach to predict the height of fractured water-conducting zone of coal roof strata using random forest regression.” Scientific Reports, 8(1), 10986, 2018
  • H. Gu, M. Yang, C.S. Gu, X.F. Huang, “A factor mining model with optimized random forest for concrete dam deformation monitoring.” Water Science and Engineering, 14(4), 330-336, 2021
  • N. Yesiloglu-Gultekin, C. Gokceoglu, E.A. Sezer, “Prediction of uniaxial compressive strength of granitic rocks by various nonlinear tools and comparison of their performances.” International Journal of Rock Mechanics and Mining Sciences, 62, 113-122, 2013
  • L.K. Sharma, V. Vishal, T.N. Singh, “Developing novel models using neural networks and fuzzy systems for the prediction of strength of rocks from key geomechanical properties.” Measurement, 102, 158-169, 2017
  • D.G. Roy, T.N. Singh, “Predicting deformational properties of Indian coal: Soft computing and regression analysis approach.” Measurement, 149, 106975, 2020
  • E. Köken, T. Kadakçı Koca, “Evaluation of soft computing methods for estimating tangential young modulus of intact rock based on statistical performance indices.” Geotechnical and Geological Engineering, 40(7), 3619-3631, 2022.
  • J.S Jang, “Neuro-fuzzy modeling: architecture, analyses and applications”, Dissertation (unpublished), Department of Electrical Engineering and Computer Science, University of California, Berkeley, USA, 1992.
Year 2024, Volume: 12 Issue: 1, 100 - 108, 01.03.2024
https://doi.org/10.36306/konjes.1375871

Abstract

References

  • R.L. Attridge, “New mine developments-The Navachab Gold Mine.” Journal of the Southern African Institute of Mining and Metallurgy, vol 91(3), 104-107, 1991
  • S.K. Singh, S.G. Nair, “Stochastic modeling and analysis of stone crushing system used in Iron ore mines.” Journal of Ravishankar University, vol 8(1), 101-114, 1995
  • B.P. Numbi, J. Zhang, X. Xia, “Optimal energy management for a jaw crushing process in deep mines.” Energy, 68, 337-348, 2014
  • F.A. Munandar, S. Sriyanti, Y. Yuliadi, “Evaluasi Kinerja Unit Crushing Plant Batu Andesit pada PT Silva Andia Utama di Desa Giri Asih, Kecamatan Batujajar, Kabupaten Bandung, Provinsi Jawa Barat.” Prosiding Teknik Pertambangan, 486-494, 2018
  • M.R.E. Trisna, S. Widayati, P. Pramusanto, “Kajian Teknis Unit Crushing Plant Batu Andesit di PT Panghegar Mitra Abadi, Desa Lagadar, Kecamatan Marga Asih, Kabupaten Bandung, Provinsi Jawa Barat.” Prosiding Teknik Pertambangan, 41-48, 2018
  • G. Antarfallah, S. Widayati, S. Rancangan Crushing Plant Tambang Sirtu di CV Barokah Laksana Jaya, Desa Margaluyu, Kecamatan Leles, Kabupaten Garut, Provinsi Jawa Barat. In Bandung Conference Series: Mining Engineering vol 3(1), 203-209, 2023
  • J.W. Carson, “Step-by-step process in selecting a feeder.” Chem. Process., Powder Solids Annu, 38-41, 2000
  • A. Fakhry, L. Pulungan, S. Widayati, S. “Studi Perancangan Stone Crushing Plant di PT Cahaya Baru Madani, Desa Giriasih Kecamatan Batujajar, Kabupaten Bandung Barat Provinsi Jawa Barat.” Prosiding Teknik Pertambangan, 287-296, 2020
  • E.O. Elgendi, K. Shawki, “Automated process control system of Jaw crusher production.” In Journal of Physics: Conference Series (Vol. 2128, No. 1, p. 012034). IOP Publishing, 2021
  • G. Harbort, G. Cordingley, M. Phillips, “The Integration of Geometallurgy with Plant Design”, In metallurgical plant design and operating strategies, Perth, Australia, 2011
  • Metso, Crushing and Screening Handbook, sixth edition (eds. Keijo Viilo), Metso Corporation, 2011
  • V.I. Lyashenko, V.Z. Dyatchin, V.P. Franchuk, “The Improvement in the Efficiency and Reliability of the Operation of the GPK Type Vibrating Grizzly Feeder for the Mining Industry.” Ferrous Metallurgy. Bulletin of Scientific, Technical and Economic Information, (3), 28-33, 2015
  • R. Timofeev, Classification and regression trees (CART) theory and applications, Master Thesis (unpublished), Humboldt University, Berlin, 2004
  • W.Y. Loh, “Classification and regression trees” Wiley interdisciplinary reviews: data mining and knowledge discovery, 1(1), 14-23, 2011
  • M. Hasanipanah, R.S. Faradonbeh, H.B. Amnieh, D.J. Armaghani, M. Monjezi, Forecasting blast-induced ground vibration developing a CART model. Engineering with Computers, 33, 307-316, 2017
  • A. Salimi, R.S. Faradonbeh, M. Monjezi, C. Moormann, “TBM performance estimation using a classification and regression tree (CART) technique.” Bulletin of Engineering Geology and the Environment, 77, 429-440, 2018
  • J.P. Bharti, P. Mishra, U. Moorthy, V.E. Sathishkumar, Y. Cho, P. Samui, “Slope stability analysis using Rf, gbm, cart, bt and xgboost.” Geotechnical and Geological Engineering, 39, 3741-3752, 2021
  • L. Breiman, “Random forests.” Machine learning, 45, 5-32, 2001
  • L. Collins, G. McCarthy, A. Mellor, G. Newell, L. Smith, L. “Training data requirements for fire severity mapping using Landsat imagery and random forest”. Remote Sensing of Environment, 245, 111839, 2020
  • S.S. Matin, L. Farahzadi, S. Makaremi, S.C. Chelgani, G.H. Sattari, “Variable selection and prediction of uniaxial compressive strength and modulus of elasticity by random forest”. Applied Soft Computing, 70, 980-987, 2018
  • D. Zhao, Q. Wu, “An approach to predict the height of fractured water-conducting zone of coal roof strata using random forest regression.” Scientific Reports, 8(1), 10986, 2018
  • H. Gu, M. Yang, C.S. Gu, X.F. Huang, “A factor mining model with optimized random forest for concrete dam deformation monitoring.” Water Science and Engineering, 14(4), 330-336, 2021
  • N. Yesiloglu-Gultekin, C. Gokceoglu, E.A. Sezer, “Prediction of uniaxial compressive strength of granitic rocks by various nonlinear tools and comparison of their performances.” International Journal of Rock Mechanics and Mining Sciences, 62, 113-122, 2013
  • L.K. Sharma, V. Vishal, T.N. Singh, “Developing novel models using neural networks and fuzzy systems for the prediction of strength of rocks from key geomechanical properties.” Measurement, 102, 158-169, 2017
  • D.G. Roy, T.N. Singh, “Predicting deformational properties of Indian coal: Soft computing and regression analysis approach.” Measurement, 149, 106975, 2020
  • E. Köken, T. Kadakçı Koca, “Evaluation of soft computing methods for estimating tangential young modulus of intact rock based on statistical performance indices.” Geotechnical and Geological Engineering, 40(7), 3619-3631, 2022.
  • J.S Jang, “Neuro-fuzzy modeling: architecture, analyses and applications”, Dissertation (unpublished), Department of Electrical Engineering and Computer Science, University of California, Berkeley, USA, 1992.
There are 27 citations in total.

Details

Primary Language English
Subjects Mine Design, Management and Economy, Mining Engineering (Other)
Journal Section Research Article
Authors

Ekin Köken 0000-0003-0178-329X

Publication Date March 1, 2024
Submission Date October 14, 2023
Acceptance Date January 2, 2024
Published in Issue Year 2024 Volume: 12 Issue: 1

Cite

IEEE E. Köken, “ESTIMATING THE POWER DRAW OF GRIZZLY FEEDERS USED IN CRUSHING–SCREENING PLANTS THROUGH SOFT COMPUTING ALGORITHMS”, KONJES, vol. 12, no. 1, pp. 100–108, 2024, doi: 10.36306/konjes.1375871.