Skip to main content
SpringerLink
Log in
Book cover

Advanced Analytics in Mining Engineering pp 495–522Cite as

Advanced Analytics for Mineral Processing

  • Chapter
  • First Online:

  • 1114 Accesses

Abstract

Mineral processing involves methods and technologies with which valuable minerals can be separated from gangue or waste rock in an attempt to produce a more concentrated material. Crushing, grinding, and milling circuits are used to reduce the ore size to a specific range at which the mineral concentration, with procedures like gravity separation and flotation, can be maximized. Advanced data analytics (ADA) techniques including but not limited to machine learning (ML), artificial intelligence (AI), and computer vision-based pattern recognition algorithms, can be used to enhance, optimize, and automate all the activities and procedures involved in these operations. It can be applied toward the design, construction, maintenance, control, performance monitoring, and operation optimization of processes like crushing, grinding, milling, classification (by screens and cyclones), gravity concentration, medium-heavy separation, froth flotation, magnetic and electrostatic separation, and dewatering. This chapter includes brief details on each of these aforementioned processes, followed by practical instances of advanced intelligence-based data-driven frameworks and technologies being applied in these areas. Details on how these ore beneficiation processes can be improved in terms of efficiency, effectiveness, and safety with the application of these innovative data modeling and analytic techniques are also included.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Haldar, S.K. 2017. Mineral Exploration—Principles and Applications. Elsevier.

    Google Scholar 

  2. Kelly, E.G. 2003. Mineral processing. In Encyclopedia of Physical Science and Technology, 29–57.

    Google Scholar 

  3. Baek, J., and Y. Choi. 2019. Deep neural network for ore production and crusher utilization prediction of truck haulage system in underground mine. Applied Sciences 9: 4180.

    Article  Google Scholar 

  4. Avalos, S., W. Kracht, and J.M. Ortiz. 2020. Machine learning and deep learning methods in mining operations: A data-driven SAG mill energy consumption prediction application. Mining, Metallurgy & Exploration 37: 1197–1212.

    Article  Google Scholar 

  5. Conger, R., H. Robinson, and R. Sellschop. 2018. Inside a Mining Company’s AI Transformation. McKinsey & Company. https://www.mckinsey.com/industries/metals-and-mining/how-we-help-clients/inside-a-mining-companys-ai-transformation. Accessed 9 Apr 2020.

  6. Wills, B.A. 2011. Wills’ Mineral Processing Technology: An Introduction to the Practical Aspects of Ore Treatment and Mineral Recovery.

    Google Scholar 

  7. Hallen, M. 2018. Comminution Control Using Reinforcement Leaning. Sweden: Umeå University.

    Google Scholar 

  8. MotionMetrics. 2021. Artificial Intelligence and Computer Vision Based Technologies. https://www.motionmetrics.com/technologies/. Accessed 15 Jan 2021.

  9. Uahengo, F.D.L. 2014. Estimating Particle Size of Hydrocyclone Underflow Discharge Using Image Analysis. Stellenbosch University.

    Google Scholar 

  10. Burt, R.O. Gravity concentration methods. In Mineral Processing Design. Dordrecht: Springer.

    Google Scholar 

  11. Gill, C. B. 1991. Gravity concentration. In Materials Beneficiation. Materials Research and Engineering. New York, NY: Springer.

    Google Scholar 

  12. Chaurasia, R.C., and S. Nikkam. 2017. Application of artificial neural network to study the performance of multi-gravity separator (MGS) treating iron ore fines. Particulate Science and Technology 35: 93–102.

    Article  Google Scholar 

  13. Qi, C., H.B. Ly, Q. Chen et al. 2020. Flocculation-dewatering prediction of fine mineral tailings using a hybrid machine learning approach. Chemosphere 244: 125450.

    Google Scholar 

  14. Hayat, M.B. 2018. Performance, Mitigation of Environmental Hazards of Sulfide Mineral Flotation with an Insight into Froth Stability and Flotation.

    Google Scholar 

  15. Han, C. 1983. Coal Cleaning by Froth Flotation. Iowa State University.

    Google Scholar 

  16. Erol, M., C. Colduroglu, and Z. Aktas. 2003. The effect of reagents and reagent mixtures on froth flotation of coal fines. International Journal of Mineral Processing 71: 131–145. https://doi.org/10.1016/S0301-7516(03)00034-6.

    Article  Google Scholar 

  17. Keys, R. 1986. Promoters for froth flotation of coal. US Pat 4,589,980.

    Google Scholar 

  18. Demirbaş, A. 2002. Demineralization and desulfurization of coals via column froth flotation and different methods. Energy Conversion and Management 43: 885–895. https://doi.org/10.1016/S0196-8904(01)00088-7.

    Article  Google Scholar 

  19. Honaker, R.Q., and M.K. Mohanty. 1996. Enhanced column flotation performance for fine coal cleaning. Minerals Engineering 9: 931–945. https://doi.org/10.1016/0892-6875(96)00085-4.

    Article  Google Scholar 

  20. Ali, D., M.B. Hayat, L. Alagha, and O. Molatlhegi. 2018. An evaluation of machine learning and artificial intelligence models for predicting the flotation behavior of fine high-ash coal. Advanced Powder Technology 29: 3493–3506.

    Article  Google Scholar 

  21. Hayat, M.B., L. Alagha, and S.M. Sannan. 2017. Flotation behavior of complex sulfide ores in the presence of biodegradable polymeric depressants. International Journal of Polymer Science 2017: 1–9. https://doi.org/10.1155/2017/4835842.

    Article  Google Scholar 

  22. Boulton, A., D. Fornasiero, and J. Ralston. 2001. Selective depression of pyrite with polyacrylamide polymers. International Journal of Mineral Processing 61: 13–22. https://doi.org/10.1016/S0301-7516(00)00024-7.

    Article  Google Scholar 

  23. Guévellou, Y., C. Noïk, J. Lecourtier, and D. Defives. 1995. Polyacrylamide adsorption onto dissolving minerals at basic pH. Colloids Surfaces A Physicochemical Engineering and Aspects 100: 173–185. https://doi.org/10.1016/0927-7757(95)03156-8.

    Article  Google Scholar 

  24. Zhang, J., Y. Hu, D. Wang, and J. Xu. 2004. Depressing effect of hydroxamic polyacrylamide on pyrite. Journal of Central South University of Technology 11: 380–384. https://doi.org/10.1007/s11771-004-0079-1.

    Article  Google Scholar 

  25. Huang, P., L. Wang, and Q. Liu. 2014. Depressant function of high molecular weight polyacrylamide in the xanthate flotation of chalcopyrite and galena. International Journal of Mineral Processing 128: 6–15. https://doi.org/10.1016/j.minpro.2014.02.004.

    Article  Google Scholar 

  26. Huang, P., M. Cao, and Q. Liu. 2013. Selective depression of pyrite with chitosan in Pb-Fe sulfide flotation. Minerals Engineering 46–47: 45–51. https://doi.org/10.1016/j.mineng.2013.03.027.

    Article  Google Scholar 

  27. Jorjani, E., S. Chehreh Chelgani, and S. Mesroghli. 2007. Prediction of microbial desulfurization of coal using artificial neural networks. Minerals Engineering 20: 1285–1292. https://doi.org/10.1016/j.mineng.2007.07.003.

    Article  Google Scholar 

  28. Al-Thyabat, S. 2008. On the optimization of froth flotation by the use of an artificial neural network. Journal of China University of Mining and Technology 18: 418–426. https://doi.org/10.1016/S1006-1266(08)60087-5.

    Article  Google Scholar 

  29. Mohanty, S. 2009. Artificial neural network based system identification and model predictive control of a flotation column. Journal of Process Control 19: 991–999. https://doi.org/10.1016/j.jprocont.2009.01.001.

    Article  Google Scholar 

  30. Jorjani, E., H. Asadollahi Poorali, A. Sam, et al. 2009. Prediction of coal response to froth flotation based on coal analysis using regression and artificial neural network. Minerals Engineering 22: 970–976. https://doi.org/10.1016/j.mineng.2009.03.003.

    Article  Google Scholar 

  31. Cheng, J., Y. Li, J. Zhou, et al. 2010. Maximum solid concentrations of coal water slurries predicted by neural network models. Fuel Processing Technology 91: 1832–1838. https://doi.org/10.1016/j.fuproc.2010.08.007.

    Article  Google Scholar 

  32. Ze-lin, Z., Y. Jian-guo, W. Yu-ling, et al. 2011. A study on fast predicting the washability curve of coal. Procedia Environmental Sciences 11: 1580–1584. https://doi.org/10.1016/j.proenv.2011.12.238.

    Article  Google Scholar 

  33. Bekat, T., M. Erdogan, F. Inal, and A. Genc. 2012. Prediction of the bottom ash formed in a coal-fired power plant using artificial neural networks. Energy 45: 882–887. https://doi.org/10.1016/j.energy.2012.06.075.

    Article  Google Scholar 

  34. Feng, Q., J. Zhang, X. Zhang, and S. Wen. 2015. Proximate analysis based prediction of gross calorific value of coals: A comparison of support vector machine, alternating conditional expectation and artificial neural network. Fuel Processing Technology 129: 120–129. https://doi.org/10.1016/j.fuproc.2014.09.001.

    Article  Google Scholar 

  35. Pusat, S., M.T. Akkoyunlu, E. Pekel, et al. 2016. Estimation of coal moisture content in convective drying process using ANFIS. Fuel Processing Technology 147: 12–17. https://doi.org/10.1016/j.fuproc.2015.12.010.

    Article  Google Scholar 

  36. Khodakarami, M., and O. Molatlhegi, and L. Alagha. 2017. Evaluation of ash and coal response to hybrid polymeric nanoparticles in flotation process: Data analysis using self-learning neural network. International Journal of Coal Preparation and Utilization: 1–20.https://doi.org/10.1080/19392699.2017.1308927.

  37. Marais C (2010) Estimation of Concentrate Grade in Platinum Flotation Based on Froth Image Analysis. University of Stellenbosch.

    Google Scholar 

  38. Nakhaei, F., M.R. Mosavi, A. Sam, and Y. Vaghei. 2012. Recovery and grade accurate prediction of pilot plant flotation column concentrate: Neural network and statistical techniques. International Journal of Mineral Processing 110–111: 140–154. https://doi.org/10.1016/j.minpro.2012.03.003.

    Article  Google Scholar 

  39. Nakhaeie, F., A. Sam, and M.R. Mosavi. 2013. Concentrate grade prediction in an industrial flotation column using artificial neural network. Arabian Journal for Science and Engineering 38: 1011–1023. https://doi.org/10.1007/s13369-012-0350-y.

    Article  Google Scholar 

  40. Saravani, A.J., N. Mehrshad, and M. Massinaei. 2014. Fuzzy-based modelling and control of an industrial flotation column. Chemical Engineering Communications 201: 896–908. https://doi.org/10.1080/00986445.2013.790815.

    Article  Google Scholar 

  41. Hosseini, S.H., and M. Samanipour. 2015. Prediction of final concentrate grade using artificial neural networks from Gol-E-Gohar iron ore plant. American Journal of Mining Metallurgy 3: 58–62. https://doi.org/10.12691/AJMM-3-3-1.

  42. Ahmadi, A., and M.R. Hosseini. 2015. A fuzzy logic model to predict the bioleaching efficiency of copper concentrates in stirred tank reactors. International Journal of Nonferrous Metallurgy 4: 1–8. https://doi.org/10.4236/ijnm.2015.41001.

    Article  Google Scholar 

  43. Allahkarami, E., O.S. Nuri, A. Abdollahzadeh, et al. 2016. Estimation of copper and molybdenum grades and recoveries in the industrial flotation plant using the artificial neural network prediction of grade and recovery, artificial neural network, copper flotation, copper concentrator plant. International Journal of Nonferrous Metallurgy 5: 23–32. https://doi.org/10.4236/ijnm.2016.53004.

    Article  Google Scholar 

  44. Jahedsaravani, A., M.H. Marhaban, and M. Massinaei. 2016. Application of statistical and intelligent techniques for modeling of metallurgical performance of a batch flotation process. Chemical Engineering Communications 203: 151–160. https://doi.org/10.1080/00986445.2014.973944.

    Article  Google Scholar 

  45. Haldar, S. 2013. Chapter 12—Mineral processing. In Mineral Exploration—Principles and Applications, 223–250. Elsevier.

    Google Scholar 

  46. Higashiyama, Y.A. 1998. Recent progress in electrostatic separation technology. Particulate Science and Technology 16: 77–90.

    Article  Google Scholar 

  47. Haldar, S. 2018. Chapter 13—Mineral processing. In Mineral Exploration—Principles and Applications, 259–290. Elsevier.

    Google Scholar 

  48. Zhang, J.E. 2012. A review of rare earth mineral processing technology. In 44th Annual Meeting of The Canadian Mineral Processors, 79–102. Ottawa: CIM.

    Google Scholar 

  49. Lishchuk, V., C. Lund, and Y. Ghorbani. 2019. Evaluation and comparison of different machine-learning methods to integrate sparse process data into a spatial model in geometallurgy. Minerals Engineering 134: 156–165.

    Article  Google Scholar 

  50. Lai, K.C., S.K. Lim, C.P. Teh, and K.H. Yeap. 2016. Modeling electrostatic separation process using artificial neural network (ANN). Procedia Computer Science 91: 372–381.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Mining Engineering and Management, South Dakota School of Mines and Technology, Rapid City, SD, USA

    Danish Ali

Authors
  1. Danish Ali
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Danish Ali .

Editor information

Editors and Affiliations

  1. Artificial Intelligence Center, Vale, WA, Australia

    Ali Soofastaei

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Ali, D. (2022). Advanced Analytics for Mineral Processing. In: Soofastaei, A. (eds) Advanced Analytics in Mining Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-91589-6_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-91589-6_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-91588-9

  • Online ISBN: 978-3-030-91589-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation