Publicerad: 2007-12-21
ISBN:
ISSN: 1650-3686 (tryckt), 1650-3740 (online)
The aim of dynamic modelling and simulation is to improve the control of the fluidised bed granulator. Modelling and simulation was done on the basis of data collected from several test campaigns. Several modelling methodologies have been compared in Matlab-Simulink environment. A solution based on dynamic linguistic equation models was chosen. The main input variables are humidity difference between incoming and outgoing air; temperature difference between inflowing air and granule and the rate of inflowing air. The final output is the estimated granule size but the overall models contains also dynamic models for temperature and humidity. The simulator combines several models which are specific to the operating conditions. According to the results; the spraying and drying processes included short-duration periods. Extension to fuzzy LE models provides useful information about uncertainties of the forecasted granulation results. The complexity of the models is increased only slightly with the new system based on the extension principle and fuzzy interval analysis.
Fluidised bed granulator; linguistic equations; dynamic modelling; fuzzy set systems
[1] J. Rantanen; S. Lehtola; P. Rämet; J.-P. Mannermaa;.- and J. Yliruusi (1998). ”On-line monitoring of moisture content in an instrumented fluidized bed granulator with a multi-channel NIR moisture sensor”; Powder Technology; Vol. 99; 163-170.
[2] M. E. Aulton (1992); “Pharmaceutics: The science of dosage form design”; Churchill Livingstone; New York.
[3] R. Bergman; M.E. Johansson; T. Lundstedt; E. Seifert and J. Åberg (1988). “Optimization of a granulation and tabletting process by sequential design and multivariate analysis”; Cheometrics and Intelligent Laboratory Systems; Vol 44; 271-286.
[4] E. A. Colburn and R. C. Rowe (1996). “Modelling andoptimization of a tablet formulation using neural networks and genetic algorithms”; Pharmaceutical Technology Europe; Vol 8; 46-55.
[5] H. G. Kristensen (1995). “Advances in pharmaceutical sciences”. Academic Press; 221-271.
[6] T. Lipsanen; O. Antikainen; H. Räikkönen; S. Airaksinen and J. Yliruusi (2007). ”Novel description of a design space for fluidised bed granulation”; Int. J. Pharmaceut.( 2007); doi:10.1016/j.ijpharm.2007.05.051
[7] F. Thielmann; M. Naderi; M. A. Ansari; F. Stepanek (2007).” The effect of primary particle surface energy on agglomeration rate in fluidised bed wet granulation”; Powder Technology (2007); doi:10.1016/j.powtec.2006.12.015
[8] S. Heinrich and L. Mörl (1999). “Fluidized bed spray granulation—A new model for the description of particle wetting and of temperature and concentration distribution”; Chemical Engineering and Processing 38 (1999) 635–663.
[9] E. K. Juuso and K. Leiviskä. Adaptive expert systemsformetallurgical processes. In S.-L. Jämsä-Jounela and A. J. Niemi; editors; Expert Systems in Mineral and Metal Processing; Proceedings of the IFAC Workshop; Espoo; Finland; August 26-28; 1991; IFACWorkshop Series; 1992; Number 2; pages 119–124; Oxford; UK; 1992. Pergamon.
[10] E. K. Juuso (1999). “Fuzzy Control in Process Industry: The Linguistic Equation Approach”. In: Verbruggen; H. B.; H.-J. Zimmermann and R. Babuska; editors; Fuzzy Algorithms for Control; International Series in Intelligent Technologies; pp. 243-300. 1999; Kluwer; Boston.
[11] E. K. Juuso (2004). “Integration of intelligent systems in development of smart adaptive systems. International Journal of Approximate Reasoning; 35:307–337.
[12] E. K. Juuso (2004). “Modelling and simulation with intelligent methods”. In White paper of theVirtual Institute for Simulation (Sim-Serv): www.simserv.com. Sim- Serv; 2004. 17 pp.
[13] T. Mäki; and E. K. Juuso (2000). “Multiple ModelDynamic Simulation of a Fluidised Bed Granulator with Linguistic Equations”. Proceedings of TOOLMET 2000 Workshop; Oulu; April 13-14; 2000; pp. 78-89; Oulu. Oulun yliopistopaino.
[14] T. Mäki; E. Juuso and K. Leiviskä (2004). ”Fuzzy Modelling and Dynamic Simulation of a Fluidized Bed Granulator”. In Proceedings of AFNC 2004- The 2nd IFAC Workshop on Advanced Fuzzy/Neural Control. September 16-17; 2004; Oulu; Finland; pp. 133-138.
[15] J. Rantanen (2000). “Near-Infrared Reflectance Spectroscopy in the Measurement of Water as a Part of Multivariate Process Monitoring of Fluidised Bed Granulation Process”; Dissertationes Biocentri Viikki Universitatis Helsingiensis 21/2000; pp. 46.
[16] L. Ljung (1999). “System Identification - Theory for the User”. Prentice Hall; Upper Saddle River; N.J.; 2nd edition.
[17] L. A. Zadeh (1965). “Fuzzy sets”; Information and Control; 8:338-353.
[18] J. J. Buckley and Y. Hayashi (1999). “Can neural nets be universal approximators for fuzzy functions?”; Fuzzy Sets and Systems; 101:323–330.
[19] D. Driankov; H. Hellendoorn; and M. Reinfrank (1993). “An Introduction to Fuzzy Control”; Springer; Berlin; Germany.
[20] T. Takagi and M. Sugeno (1985). “Fuzzy identification of systems and its applications to modelling and control”; IEEE Trans. Syst.; Man; & Cybern. 15(1):116--132.
[21] W. Pedrycz (1984). “An identification algorithm in fuzzy relational systems”; Fuzzy Sets and Systems 13:153--167.
[22] R. Babuska; M. Setnes; U. Kaymak and H. B. Verbruggen (1997). ”Fuzzy Modelling: a Universal and Transparent Tool”. In: Yliniemi; L. \& Juuso; E. (eds.); Proceedings of TOOLMET’97- ToolEnvironments and Development Methods for Intelligent Systems; Oulu; April 17-18; 1997}. Oulun yliopistopaino; Oulu; pp. 1-27.
[23] R. Babuska and H. Verbruggen (2003). “Neuro-fuzzy methods for nonlinear system identification”; Annual Reviews in Control 27:73--85.
[24] J. L. Elman (1990). “Finding structure in time”; Cognitive Science 14:179--211.
[25] J. J. Buckley and T. Feuring (2000). “Universal approximators for fuzzy functions”; Fuzzy Sets and Systems; 113:411–415; 2000.
[26] R. E. Moore (1966). “Interval Analysis”. Prentice Hall; Englewood Cliffs; NJ. 108