APRON BESLEYİCİ KAPASİTESİNİN YÜZEY TEPKİ YÖNTEMİ VE BAZI YAPAY ZEKA YÖNTEMLERİ İLE DEĞERLENDİRİLMESİ

Year 2024, Volume: 10 Issue: 1, 142 - 151, 30.06.2024

Ekin Köken

https://doi.org/10.22531/muglajsci.1408783

Abstract

Bu çalışmada Apron besleyicilerin kapasitesi (Q), yüzey tepki yöntemi (RSM) ve bazı yapay zekâ yöntemleriyle araştırılmıştır. Bu bağlamda, Türk Madencilik Sektöründe (TMI) kullanılan Apron besleyicilerin yaygın çalışma koşullarına ilişkin niceliksel verilerin toplanması amacıyla kapsamlı bir saha araştırması yapılmıştır. Toplanan bu verilere göre, Apron besleyicilerin Q değerini etkileyen değiştirgelerin ortaya konması için RSM analizleri gerçekleştirilmiştir. Buna göre, besleyici hazne genişliği (B), taşınan malzemenin bant üzerindeki yüksekliği (D), konveyör hızı (V) ve doluluk faktörü (φ), Q değeri için en önemli faktörler olarak belirlenmiştir. Q değerlerindeki gözlemlemek için çeşitli etkileşim ve kontur grafikleri sunulmuştur. Ayrıca, apron besleyicilerin Q değerini tahmin için, çok değişkenli uyarlamalı regresyon analizi (MARS), uyarlamalı ağ tabanlı bulanık mantık çıkarım sistemi (ANFIS) ve yapay sinir ağları (ANN) gibi bazı yapay zekâ yöntemlerine dayılı bazı tahmin modelleri tanıtılmıştır. Kurulan tahmin modellerinin performansı dağılım grafiklerine göre değerlendirilmiş ve RSM metodolojisine dayalı tahmin modelinin, yapay zekâ tabanlı tahmin modellerine göre nispeten daha iyi sonuçlar sağladığı bulunmuştur. Sunulan tahmin modelleri, yüksek kapasiteli Apron besleyicilerin Q değerini tahmin etmek için güvenilir bir şekilde kullanılabilir. Ancak kırma-eleme tesisi tasarımcıları, düşük kapasiteli Apron besleyicileri değerlendirmek için sunulan tahmin modellerini kullanırken dikkatli olmalıdır. Elde edilen bulgulara dayanarak, bu çalışma, Apron besleyicilerinin Q değerini değerlendirmek için RSM metodolojisinin ve çeşitli yapay zekâ yöntemlerinin uygulanabilirliğini göstermiştir.

Keywords

Apron besleyiciler, Kırma – eleme tesisi, Yüzey tepki yöntemi, Yapay zekâ, Madencilik endüstrisi

EVALUATION OF THE CAPACITY OF APRON FEEDERS USED IN CRUSHING–SCREENING PLANTS BY RESPONSE SURFACE METHODOLOGY AND ARTIFICIAL INTELLIGENCE METHODS

Abstract

In this study, the capacity (Q) of Apron feeders is investigated through response surface methodology (RSM) and some artificial intelligence methods. In this regard, a comprehensive field survey is performed to compile quantitative data on the common working conditions of Apron feeders used in the Turkish Mining Industry (TMI). Based on the collected data, RSM analyses are performed to reveal the factors affecting the Q of Apron feeders. Accordingly, hopper width (B), the height of the material layer conveyed (D), conveyor speed (V), and fill factor (φ) are determined to be the most critical factors for the Q. Several interaction and contour plots are presented to observe the variations in the Q values. Moreover, several predictive models are also introduced to estimate the Q of apron feeders based on artificial intelligence methods such as multivariate adaptive regression spline (MARS), adaptive neuro-fuzzy inference system (ANFIS), and artificial neural networks (ANN). The performance of the established predictive models is assessed based on scatter plots, and it is found that the predictive model based on RSM methodology provides relatively better results than the ones found on soft computing-based predictive models. The presented predictive models can be reliably used to estimate the Q of Apron feeders with high capacity. However, crushing–screening plant designers should be careful when using established predictive models for assessing low-capacity Apron feeders. Based on the findings obtained, the present study demonstrates the applicability of RSM methodology and several artificial intelligence methods for evaluating the Q of Apron feeders.

Keywords

Apron feeders, Crushing-screening plant, Response surface methodology, Artificial intelligence, Mining industry

References

• Carson, J.W. Step-by-Step Process in Selecting a Feeder, Powder and Solids Annual. 38-41, 2000.
• Zimmermann, E. and Kruse, W. Mobile crushing and conveying in quarries- a chance for better and cheaper production! In RWTH Aachen-Institut für Bergbaukunde III, 8th International Symposium Continuous Surface Mining, 481-487, 2006.
• Utley, R.W. In-pit crushing, Chapter 10.5, SME mining engineering handbook, 941-956, 2011.
• Osanloo, M. and Paricheh, M. In-pit crushing and conveying technology in open-pit mining operations: a literature review and research agenda. International Journal of Mining, Reclamation and Environment, 34(6), 430-457, 2020.
• Roberts, A.W. Design and application of feeders for the controlled loading of bulk solids onto conveyor belts. In Proc. International Powder on Bulk Solids Handling and Processing Conference, Department of Mechanical Engineering, University of Newcastle, Australia, 2008
• Maynard, E.P. Practical solutions for solving bulk solids flow problems, IEEE-IAS/PCA, Cement Industry Technical Conference, (IEEE Cat. No04CH37518), Chattanooga, USA, 139-147, 2004.
• Tannant D.D. and Cyr D. Equipment and geology related causes of oil sands lumps and jammed crushers. International Journal of Mining, Reclamation and Environment, 21(1), 14-34, 2007.
• Bedair, O. Design of mobile facilities used in surface mining projects. Practice Periodical on Structural Design and Construction, 21(1), 04015007, 2016.
• Gharahasanlou, N.A., Ataei, M., Khalokakaie, R., Barabadi, A. and Einian, V. Risk based maintenance strategy: a quantitative approach based on time-to-failure model. International Journal of System Assurance Engineering and Management, 8, 602-611, 2017.
• Shannon, G. A. Silo feeders and discharge drives with their related controls. IEEE Transactions on Industry Applications, (4), 348-356, 1979
• Crnkovic, I., Georgiev, T., Harbort, G., Phillips, M. Commissioning and optimisation of the Phu Kham Copper-Gold Concentrator. In Proceedings Tenth Mill Operators Conference 1–13, Adelaide, Australia, 2009.
• Hartford, C.E., Orlando A.D., Carson J.W. Feeder or conveyor: what’s the difference and why does it matter? Australian Bulk Handling Review, 26-29, 2013.
• Fruchtbaum J. Bulk materials handling handbook, Springer science, ISBN: 978-1-4757-4695-2, 1988.
• Huo, J., Yu, S., Yang, J., Li T. Static and dynamic characteristics of the chain drive system of a heavy duty apron feeder. The Open Mechanical Engineering Journal, 7(1), 121-128, 2013.
• Roberts, A.W. Concepts of Feeder Design and Performance in Relation to Loading Bulk Solids onto Conveyor Belts; Mechanical handling Systems, Master of Engineering Practice Module Course Notes, Centre for Bulk Solids and Particulate Technologies, Perth, Australia, 633-663, 1997.
• Bengtsson, M., Svedensten, P., & Evertsson, C. M. Improving yield and shape in a crushing plant. Minerals Engineering, 22(7-8), 618-624, 2009.
• Donovan, J. G. Fracture toughness-based models for the prediction of power consumption, product size, and capacity of jaw crushers. PhD thesis, Virginia Polytechnic Institute and State University, 2003.
• Köken, E., & Qu, J. Comparison of secondary crushing operations through cone and horizontal shaft impact crushers. International Multidisciplinary Scientific GeoConference: SGEM, 20(1.1), 789-796, 2020.
• Grujić, M. M. Technology improvements of crushing process in Majdanpek Copper Mine. International Journal of Mineral Processing, 44, 471-483, 1996.
• Vasilyeva, N., Golyshevskaia, U., & Sniatkova, A. Modeling and Improving the Efficiency of Crushing Equipment. Symmetry, 15(7), 1343, 2023.
• Metso. Crushing and Screening Handbook, sixth edition (eds. Keijo Viilo), Metso corporation, 2011.
• TS EN 1097−3. Tests for mechanical and physical properties of aggregates- Part 3: Determination of loose bulk density and voids, Turkish Standards Institution, Ankara – Turkey, 1999.
• Myers, R.H. Response surface methodology: Process and product optimization using designed experiments, 4th ed. (Raymond H. Myers, Douglas C. Montgomery, Christine M. Anderson-Cook. Eds.), Wiley, ISBN 978-1-118-91601-8, 2016.
• Kowalski S.M. and Montgomery D.C. Design and analysis of experiments, Minitab manual, 7th edition., Wiley, 2011.
• Friedman, J.H. Multivariate adaptive regression splines. The Annals of Statistics, 19(1), 1-67, 1991.
• Jang, J.S.R. Neuro-fuzzy modeling: architecture, analyses and applications, dissertation, department of electrical engineering and computer science, University of California, Berkeley, CA 94720, 1992.
• Singh, V.K., Singh, D., Singh, T.N. Prediction of strength properties of some schistose rocks from petrographic properties using artificial neural networks. International Journal of Rock Mechanics and Mining Sciences, 38(2), 269-284, 2001.
• Rabbani, E., Sharif, F., Salooki, M. K., Moradzadeh, A. Application of neural network technique for prediction of uniaxial compressive strength using reservoir formation properties. International journal of rock mechanics and mining sciences, (56), 100-111, 2012.
• Das, S.K. Artificial neural networks in geotechnical engineering: modeling and application issues, Metaheuristics in water, geotechnical and transport engineering, 231–270, 2013.