Conference article

Recursive Dynamic Modelling in Changing Operating Conditions

Esko K. Juuso
Control Engineering Group, Faculty of Technology, University of Oulu, Finland

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Published in: Proceedings of the 56th Conference on Simulation and Modelling (SIMS 56), October, 7-9, 2015, Linköping University, Sweden

Linköping Electronic Conference Proceedings 119:17, s. 169-174

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Published: 2015-11-25

ISBN: 978-91-7685-900-1

ISSN: 1650-3686 (print), 1650-3740 (online)


Changing operating condition may require updates for the dynamic models. Recursive updates are needed when there are not sufficient information about the new situations. In machine diagnostics and prognostics, the analysis starts from good conditions and new phenomena, which activate with time, may change considerably the model. In biological wastewater treatment processes, the condition of the biomass changes drastically the dynamic operation of the treatment process. Direct measurements of the biomass condition are under development. Recursive modelling is clearly needed in these situations. The usual approachis to modify the model equations. However, the interactions do not necessarily change if the meanings of the variables are modified. This paper keeps the the model equations constant and modifies the nonlinear scaling of the variables by extending the data-driven scaling to recursive approach. The recursive methodology is tested in two applications: machine diagnostics and wastewater treatment.


intelligent modelling; recursive statistical analysis; adaptive modelling; prognostics; transitions


R. Babuška and H. Verbruggen. Neuro-fuzzy methods for nonlinear system identification. Annual Reviews in Control, 27 (1):73–85, 2003.

R. A. Collacott. Mechanical Fault Diagnosis and Condition Monitoring. Chapman and Hall, London, 1977.

J. L. Elman. Finding structure in time. Cognitive Science, 14(2): 179–211, 1990.

M. Heikkinen, T. Heikkinen, and Y. Hiltunen. Modelling of activated sludge treatment process in a pulp mill using neural networks. In The 6th International Conference on Computing, Communications and Control Technologies: CCCT 2008, Orlando, Florida, USA, June 29th - July 2nd 2008., page 6 pp. 2008a.

M. Heikkinen, T. Latvala, E. Juuso, and Y. Hiltunen. SOM based modelling for an activated sludge treatment process. In Tenth International Conference on Computer Modelling and Simulation, EUROSIM/UKSim, Cambridge, UK, April 13, 2008., pages 224–229. The Institute of Electrical and Electronics Engineers IEEE, 2008b. doi: 10.1109/UKSIM.2008.78.

A. Heng, A. C. C. Tan, J. Mathew, N. Montgomery, D. Banjevic., and A. K. S. Jardine. ‘intelligent condition-based prediction of machinery reliability. Mechanical Systems and Signal Processing, 23(5):1600–1614, 2009.

M. Henze, C. P. L. Grady Jr., W. Gujer, G. V. R. Marais, and T. Matsuo. Activated sludge model no. 1., IAWQ scientific and technical report no. 1. London, UK, 1987.

A. K. S. Jardine, D. Lin, and D. Banjevic. A review on machinery diagnostics and prognostics implementing conditionbased maintenance. Mechanical Systems and Signal Processing, 20(7):1483–1510, 2006.

E. Juuso and S. Lahdelma. Intelligent scaling of features in fault diagnosis. In 7th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies, CM 2010 - MFPT 2010, 22-24 June 2010, Stratford-upon-Avon, UK, volume 2, pages 1358–1372, 2010. URL

E. K. Juuso. Integration of intelligent systems in development of smart adaptive systems. International Journal of Approximate Reasoning, 35(3):307–337, 2004. doi:

E. K. Juuso. Hybrid models in dynamic simulation of a biological water treatment process. In J. Kunovský, P. Hanácek, F. Zboril, Al-Dabass, and A. Abraham, editors, Proceedings First International Conference on Computational Intelligence, Modelling and Simulation, 7- 9 September 2009, Brno, Czech Republik, pages 30–35. IEEE Computer Society, 2009a. doi: 10.1109/CSSim.2009.52.

E. K. Juuso. Tuning of large-scale linguistic equation (LE) models with genetic algorithms. In M. Kolehmainen, editor, Revised selected papers of the International Conference on Adaptive and Natural Computing Algorithms - ICANNGA 2009, Kuopio, Finland, Lecture Notes in Computer Science, volume LNCS 5495, pages 161–170. Springer-Verlag, Heidelberg, 2009b. doi: 10.1007/978-3-642-04921-7_17.

E. K. Juuso. Data-based development of dynamic models for biological wastewater treatment in pulp and paper industry. In SIMS 2010 Proceedings, The 51st Conference on Modelling and Simulation, Oulu, 14-15 October, 2010. 2010. 9 pp.

E. K. Juuso. Recursive tuning of intelligent controllers of solar collector fields in changing operating conditions. In S. Bittani, A. Cenedese, and S. Zampieri, editors, Proceedings of the 18th World Congress The International Federation of Automatic Control, Milano (Italy) August 28 - September 2, 2011, pages 12282–12288. IFAC, 2011. doi: 10.3182/20110828-6-IT-1002.03621.

E. K. Juuso, J.C. Bennavail, and M.G. Singh. Hybrid knowledge-based system for managerial decision making in uncertainty environment. In N. Piera Carreté and M. G. Singh, editors, Qualitative Reasoning and Decision Technologies, Proceedings of the IMACS International Workshop on Qualitative Reasoning and Decision Technologies-QUARDET’93, Barcelona, June 16 - 18, 1993, pages 234–243, Barcelona, 1993. CIMNE.

A. B. Kaufman. ‘measure machine vibration – it can help you anticipate and prevent failures. INSTRUMENTS & CONTROL SYSTEMS, 48:50–62, 1975.

S. Lahdelma and E. Juuso. Trend analysis in condition monitoring of process equipments. In Proceedings of the 8th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies, CM 2011 - MFPT 2011, 20-22 June 2011, Cardiff, UK, volume 2, pages 904–913. Curran Associates, NY, USA, 2011. ISBN 978-1-61839-014-1.

E. Lindblom. Dynamic modelling of nutrient deficient wastewater treatment process. M.Sc. Thesis. Lund University, Lund, Sweden, 2003. TEIE-5175.

L. Ljung. System Identification - Theory for the User. Prentice Hall, Upper Saddle River, N.J., 2nd edition, 1999.

S.-P. Mujunen, P. Minkkinen, P. Teppola, and R.-S. Wirkkala. Modeling of activated sludge plants treatment efficiency with PLSR: a process analytical case study. Chemometrics and Intelligent Laboratory Systems, 41(1):83–94, 1998.

K. P. Oliveira-Esquerre, M. Mori, and R. E. Bruns. Simulation of an industrial wastewater treatment plant using artificial neural networks and principal component analysis. Brazilian Journal of Chemical Engineering, 19(4):365–370, 2002.

T. S. Sankar and G. D. Xistris. Measure machine vibrations - It can help you anticipate and prevent failures. Journal of Engineering for Industry, 94:133–137, 1972.

P. Teppola, S.-P. Mujunen, and P. Minkkinen. Partial least squares modeling of an activated sludge plant: A case study. Chemometrics and Intelligent Laboratory Systems, 38(2): 197–208, 1997.

J. Tomperi, E. Koivuranta, A. Kuokkanen, E. K. Juuso, and K. Leiviskä. Real-time optical monitoring of the wastewater treatment process. Environmental Technology, pages 1–8, 2015. doi: 10.1080/09593330.2015.1069898.

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