Skip to main content
SpringerLink
Log in

Phenomenological Based Soft Sensor for Online Estimation of Slurry Rheological Properties

  • Research Article
  • Published:
International Journal of Automation and Computing Aims and scope Submit manuscript

Abstract

This work proposes a soft sensor based on a phenomenological model for online estimation of the density and viscosity of a slurry flowing through a pipe-and-fittings assembly (PFA). The model is developed considering the conservation principle applied to mass and momentum transfer and considering frictional energy losses to include the variables directly affecting slurry properties. A reported proposal for state observers with unknown inputs is used to develop the first block of the observer structure. The second block is constructed with two options for evaluating slurry viscosity, generating two possible estimator structures, which are tested using real data. A comparison between them indicates different uses and capabilities according to available process information.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. M. C. Bourne. Texture, viscosity, and food. Food Texture and Viscosity: Concept and Measurement, 2nd ed., M. C. Bourne, Ed., London, UK: Academic Press, pp. 1–32, 2002. DOI: 10.1016/B978-012119062-0/50001-2.

    Google Scholar 

  2. R. J. Oglesby, H. J. Moynihan, R. B. Santos, A. Ghosh, P. W. Hart. Does kraft hardwood and softwood pulp viscosity correlate to paper properties? Tappi Journal, vol. 15, no. 10, pp. 643–651, 2016.

    Article  Google Scholar 

  3. S. Taborda, D. A. Muñoz, H. Alvarez. Snowball effect detection and control proposal for its correction. In Proceedings of IEEE Conference on Control Applications, IEEE, Buenos Aires, Argentina, pp. 1173–1178, 2016. DOI: 10.1109/CCA.2016.7587965.

    Google Scholar 

  4. W. Whiten, P. Steffens, J. Hitchins. An examination of pulp viscosity in tubes at higher shear rates. Minerals Engineering, vol. 6, no. 4, pp. 397–404, 1993. DOI: 10.1016/0892-6875(93)90018-I.

    Article  Google Scholar 

  5. F. N. Shi, T. J. Napier-Munn. Effects of slurry rheology on industrial grinding performance. International Journal of Mineral Processing, vol. 65, no. 3–4, pp. 125–140, 2002. DOI: 10.1016/S0301-7516(01)00060-6.

    Article  Google Scholar 

  6. M. C. Bourne. Viscosity measurement. Food Texture and Viscosity, 2nd ed., M. C. Bourne, Ed., London, UK: Academic Press, pp. 235–256, 2002. DOI: 10.1016/B978-012119062-0/50006-1.

    Chapter  Google Scholar 

  7. Y. Fangary, A. S. A. Ghani, S. M. El Haggar, R. A. Williams. The effect of fine particles on slurry transport processes. Minerals Engineering, vol. 10, no. 4, pp. 427–439, 1997. DOI: 10.1016/S0892-6875(97)00019-8.

    Article  Google Scholar 

  8. M. Z. He, Y. M. Wang, E. Forssberg. Slurry rheology in wet ultrafine grinding of industrial minerals: A review. Powder Technology, vol. 147, no. 1–3, pp. 94–112, 2004. DOI: 10.1016/j.powtec.2004.09.032.

    Article  Google Scholar 

  9. M. J. Tippett, J. Bao. Distributed control of chemical process networks. International Journal of Automation and Computing, vol. 12, no. 4, pp. 368–381, 2015. DOI: 10. 1007/s11633-015-0895-9.

    Article  Google Scholar 

  10. R. P. Chhabra, J. F. Richardson. Rheometry for non-Newtonian fluids. Non-Newtonian Flow and Applied Rheology: Engineering Applications, 2nd ed., R. P. Chhabra, J. F. Richardson, Eds., Oxford, UK: Butterworth-Heinemann, pp. 56–109, 2008. DOI: 10.1016/B978-0-7506-8532-0.00002-0.

    Chapter  Google Scholar 

  11. D. Hodouin. Methods for automatic control, observation, and optimization in mineral processing plants. Journal of Process Control, vol. 21, no. 2, pp. 211–225, 2011. DOI: 10.1016/j.jprocont.2010.10.016.

    Article  Google Scholar 

  12. D. Sbárbaro, R. Del Villar. Advanced Control and Supervision of Mineral Processing Plants, London, UK: Springer, 2010. DOI: 10.1007/978-1-84996-106-6.

    Book  Google Scholar 

  13. K. M. Hangos, I. T. Cameron. Process Modelling and Model Analysis, San Diego, USA: Academic Press, 2001.

    Google Scholar 

  14. E. Hoyos, D. López, H. Alvarez. A phenomenologically based material flow model for friction stir welding. Materials & Design, vol. 111, pp. 321–330, 2016. DOI: 10.1016/j. matdes.2016.09.009.

    Article  Google Scholar 

  15. R. B. Bird. Transport Phenomena, Sterling Heights, USA: Wiley, 2007.

    Google Scholar 

  16. R. Darby. Chemical Engineering Fluid Mechanics, 2nd ed., New York, USA: Marcel Dekker, 2001.

    Google Scholar 

  17. I. Govender, G. B. Tupper, A. N. Mainza. Towards a mechanistic model for slurry transport in tumbling mills. Minerals Engineering, vol. 24, no. 3–4, pp. 230–235, 2011. DOI: 10.1016/j.mineng.2010.08.010.

    Article  Google Scholar 

  18. D. Muñoz, J. L. Diaz, S. Taborda, H. Alvarez. Hydrocyclone phenomenological-based model and feasible operation region. International Journal of Mining, Materials, and Mechanical Engineering, vol. 3, pp. 1–9, 2017.

    Google Scholar 

  19. K. J. Astrom, B. Wittenmark, Computer-controlled Systems: Theory and Design, 3rd ed., Berlin, Germany: Prentice Hall, 1997.

    Google Scholar 

  20. M. Darouach, M. Zasadzinski, S. J. Xu. Full-order observers for linear systems with unknown inputs. IEEE Transactions on Automatic Control, vol. 39, no. 3, pp. 606–609, 1994. DOI: 10.1109/9.280770.

    Article  MathSciNet  Google Scholar 

  21. M. Roco, C. A. Shook. Modeling of slurry flow: The effect of particle size. The Canadian Journal of Chemical Engineering, vol. 61, no. 4, pp. 494–503, 1983. DOI: 10.1002/cjce. 5450610402.

    Article  Google Scholar 

  22. C. F. Ihle, A. Tamburrino. Uncertainties in key transport variables in homogeneous slurry flows in pipelines. Minerals Engineering, vol. 32, pp. 54–59, 2012. DOI: 10.1016/j. mineng.2012.03.002.

    Article  Google Scholar 

  23. H. Alvarez, R. Lamanna, P. Vega, S. Revollar. Methodology for obtaining phenomenological based semiphysical models applied to a sugar cane juice tower. Iberoamericana Journal of Automation and Industrial Information, vol. 6, no. 3, pp. 117–122, 2009.

    Google Scholar 

  24. D. Castañeda, J. Lorena. The Use of Phenomenological Based Semi-physical Models as Virtual Sensors for Density and Viscosity of Mineral Slurries, Master dissertation, Facultad de Minas, Universidad Nacional de Colombia, Colombia, 2017.

    Google Scholar 

  25. W. B. Hooper. The two-K method predicts head losses in pipe fittings. Chemical Engineering, vol. 88, pp. 96–100, 1981.

    Google Scholar 

  26. B. J. Schorle, S. W. Churchill, M. Shacham. Comments on: “An explicit equation for friction factor in pipe”. Industry & Engineering Chemistry Fundamentals, vol. 19, no. 2, pp. 228, 1980. DOI: 10.1021/i160074a019.

    Article  Google Scholar 

  27. G. Besancon. Nonlinear Observers and Applications, Berlin, Heidelberg Germany: Springer, 2007. DOI: 10.1007/978-3-540-73503-8.

    Book  Google Scholar 

  28. J. Garcia-Tirado, H. Botero, F. Angulo. A new approach to state estimation for uncertain linear systems in a moving horizon estimation setting. International Journal of Automation and Computing, vol. 13, no. 6, pp. 653–664, 2016. DOI: 10.1007/s11633-016-1015-1.

    Article  Google Scholar 

  29. K. Hfaïedh, K. Dahech, T. Damak. A sliding mode observer for uncertain nonlinear systems based on multiple models approach. International Journal of Automation and Computing, vol. 14, no. 2, pp. 202–212, 2017. DOI: 10. 1007/s11633-016-0970-x.

    Article  Google Scholar 

  30. G. F. Aguilera. Mineral slurry viscosity simulation using neural networks, Master dissertation, Facultad de Minas, Universidad Nacional de Colombia, Colombia, 2005. [Online], Available: https://doi.org/intranet.minas.medellin.unal.edu.co/index.php?option=com_docman&taskdoc_download&gid986&Itemid285

    Google Scholar 

  31. K. Madlener, B. Frey, H. K. Ciezki. Generalized Reynolds number for non-Newtonian fluids. Progress in Propulsion Physics, vol. 1, pp. 237–250, 2009. DOI: 10.1051/eucass/200901237.

    Article  Google Scholar 

  32. D. A. Muñoz, C. J. L. Diaz, H. Alvarez. Unknown input observer to estimate slurry rheological properties. In Proceedings of the 3rd Colombian Conference on Automatic Control, IEEE, Cartagena, Colombia, 2017. DOI: 10.1109/CCAC.2017.8276418.

    Google Scholar 

  33. J. L. Diaz C, C. Ocampo-Martinez, H. Alvarez. Nonlinear moving horizon estimator for online estimation of the density and viscosity of a mineral slurry. Industrial & Engineering Chemistry Research, vol. 56, no. 49, pp. 14592–14603, 2017. DOI: 10.1021/acs.iecr.7b04393.

    Article  Google Scholar 

Download references

Acknowledgments

The authors thank Colciencias and SUMICOL (Suministros de Colombia S.A.) for their support and financing for this project.

Author information

Authors and Affiliations

  1. Research Group of Dynamic Processes, Universidad Nacional de Colombia-Sede Medellín, Facultad de Minas. Cra 80, Medellín, 65-223, Colombia

    Jenny L. Diaz C. & Hernan Alvarez

  2. Mathematical Optimization of Processes, Centro de Ciencia Básica, Universidad Pontificia Bolivariana, Circular 1, Medellín, 70-01, Colombia

    Diego A. Muñoz

Authors
  1. Jenny L. Diaz C.
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Diego A. Muñoz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hernan Alvarez
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jenny L. Diaz C..

Additional information

Recommended by Associate Editor Jyh-Horng Chou

Jenny L. Diaz C. received the B. Sc. degree in chemical engineering and the M. Sc. degree in engineering with an emphasis on chemical engineering from Universidad Nacional de Colombia, Colombia in 2014 and 2016, respectively. Currently, she is a Ph. D. degree candidate in automatic at Universitat Politcnica de Catalunya, Spain.

Her research interests include modeling of chemical processes, estimation and control theory and large-scale systems management.

Diego A. Muñoz received the B. Sc. degree in chemical engineer from Universidad Nacional de Colombia, Colombia in 2004, the M. Sc. degree in mathematics from Universidad Nacional de Colombia, Colombia in 2006, and the Ph. D. degree in engineering from RWTH Aachen University of Technology, Germany in 2015. Currently he holds a full professor position at Engineering School, Universidad Pontificia Bolivariana, Venezuela, performing both research activities and teaching in the undergraduate and graduate programs.

His research interests include process optimization, modelling and control.

Hernan Alvarez received the B. Sc. degree in chemical engineer from Universidad Nacional de Colombia, Colombia in 1991, the M. Sc. degree in Systems Engineering from the Universidad Nacional de Colombia, Colombia in 1995, and the Ph. D. degree in control systems engineering from Instituto de Automática of Universidad Nacional de San Juan, Argentina in 2000. Currently, he holds a full professor position at Faculty of Mines, Processes and Energy School, Universidad Nacional de Colombia, performing both research activities and teaching in the undergraduate and graduate programs.

His research interests include chemical process modelling and control.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Diaz C., J.L., Muñoz, D.A. & Alvarez, H. Phenomenological Based Soft Sensor for Online Estimation of Slurry Rheological Properties. Int. J. Autom. Comput. 16, 696–706 (2019). https://doi.org/10.1007/s11633-018-1132-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11633-018-1132-0

Keywords

Search

Navigation