The doctoral dissertations of the former Helsinki University of Technology (TKK) and Aalto University Schools of Technology (CHEM, ELEC, ENG, SCI) published in electronic format are available in the electronic publications archive of Aalto University - Aaltodoc.
Aalto

Advanced Process Monitoring and Control Methods in Mineral Processing Applications

Antti Remes

Doctoral dissertation for the degree of Doctor of Science in Technology to be presented with due permission of the School of Electrical Engineering for public examination and debate in Auditorium AS1 at the Aalto University School of Electrical Engineering (Espoo, Finland) on the 16th of March 2012 at 12 noon.

Overview in PDF format (ISBN 978-952-60-4512-2)   [2910 KB]
Dissertation is also available in print (ISBN 978-952-60-4511-5)

Abstract

In minerals processing the high material volumes yield the fact that benefits of even small improvements in the process efficiency are remarkable. On a daily basis, the efficiency of a concentrating plant relies on the performance of the process control system, and secondly, on the adequate information of the process state provided for the plant operators. This thesis addresses the problems in the monitoring and control of the selected, widely applied mineral concentration unit processes and the processing circuits. The case studies cover operations in the ore grinding stages, including size separation units, followed subsequently by the concentration and the thickening stages in the downstream process. The developed methods and applications are all verified with industrial data, industrially identified models or by the practical implementations and tests on the industrial case plants.

Advanced control systems – including a rule-based, a fuzzy and a model predictive control, with different combinations and setups – are studied with simulated grinding and flotation processes. A new model-based expert system for controlling of the ground ore particle size and the circulating load of the grinding process is proposed. The system was tested with a simulation model representing an industrial milling of apatite ore.

In a plant-wide process monitoring, a data-based modeling approach is applied to predict the concentrate quality and the impact of the grinding stage parameters on that. The plant model, used for the monitoring, is updated adaptively; this enables timely information on the process. The monitoring system was set up based on the data of a chromite ore processing plant.

Two unit operations, a hindered settling separator and a thickener, were modeled from a viewpoint of equipment monitoring and control purposes. The hindered settling separator incorporates both a mechanistic particle-settling model and a separation efficiency characterization curve in a novel manner. Separation characteristics in a pyrite concentrate case and in a ground apatite ore case were studied with the model. Operation of a thickener was modeled based on on-line mass-balance estimation. The monitoring application was implemented in an industrial apatite concentrate thickener.

This thesis demonstrates the benefits of the above described monitoring and control methods. The metallurgical performance improvements are pointed out for each case. Also this thesis outlines the practical implementation and robustness issues for the methods. Thus the thesis promotes the advantages of more extensive use of plant models in process operation purposes.

This thesis consists of an overview and of the following 8 publications:

  1. A. Remes, N. Vaara, and S.-L. Jämsä-Jounela. 2006. Analysis of industrial grinding circuits using PCA, PLS and neural networks. In: IFAC Workshop on Automation in Mining, Mineral and Metal Industry (IFACMMM 2006). Cracow, Poland. 20-22 September 2006. International Federation of Automatic Control. 6 pages.
  2. A. Remes, K. Saloheimo, and S.-L. Jämsä-Jounela. 2007. Effect of speed and accuracy of on-line elemental analysis on flotation control performance. Minerals Engineering, volume 20, number 11, pages 1055-1066.
  3. A. Remes, N. Vaara, K. Saloheimo, and H. Koivo. 2008. Prediction of concentrate grade in industrial gravity separation plant – Comparison of rPLS and neural network. In: Myung Jin Chung and Pradeep Misra (editors). Proceedings of the 17th IFAC World Congress (IFAC 2008). Seoul, Korea. 6-11 July 2008. International Federation of Automatic Control. Pages 3280-3285. ISBN 978-3-902661-00-5.
  4. A. Remes, J. Kaartinen, K. Saloheimo, N. Vaara, H. Pekkarinen, and H. Koivo. 2008. Monitoring of the grinding and gravity separation operation at Outokumpu's Kemi concentrator. In: Wang Dian Zuo, Sun Chuan Yao, Wang Fu Liang, Zhang Li Cheng, and Han Long (editors). Proceedings of the XXIV International Mineral Processing Congress (IMPC 2008). Beijing, China. 24-28 September 2008. Science Press. Volume 2, pages 2438-2447. ISBN 978-7-03-022711-9.
  5. Jari Moilanen and Antti Remes. 2008. Control of the flotation process. In: Aldo Casali, César Gómez, and Romke Kuyvenhoven (editors). Proceedings of the V International Mineral Processing Seminar (Procemin 2008). Santiago, Chile. 22-24 October 2008. Gecamin. Pages 317-325.
  6. A. Remes, J. Aaltonen, and H. Koivo. 2009. Soft-sensor estimation of an apatite thickener operation at the Siilinjärvi concentrator. In: Luis G. Bergh (editor). Pre-prints of the IFAC Workshop on Automation in Mining, Mineral and Metal Industry (IFACMMM 2009). Viña del Mar, Chile. 14-16 October 2009. International Federation of Automatic Control. 6 pages. ISBN 978-3-902661-70-8.
  7. A. Remes, J. Aaltonen, and H. Koivo. 2010. Grinding circuit modeling and simulation of particle size control at Siilinjärvi concentrator. International Journal of Mineral Processing, volume 96, pages 70-78.
  8. A. Remes, A. Tuikka, and H. Koivo. 2011. Simulation and pilot experiments on pyrite concentrate separation in a Floatex density separator. Minerals & Metallurgical Processing, volume 28, number 2, pages 62-70.

Keywords: minerals processing, mining, modeling, simulation, control, optimisation

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Last update 2012-10-31