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Design of Neuro-Fuzzy Controller for a Rotary Dryer

by F. F. G. Areed, M. S. El-Kasassy, Kh. A. Mahmoud
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 37 - Number 5
Year of Publication: 2012
Authors: F. F. G. Areed, M. S. El-Kasassy, Kh. A. Mahmoud
10.5120/4606-6584

F. F. G. Areed, M. S. El-Kasassy, Kh. A. Mahmoud . Design of Neuro-Fuzzy Controller for a Rotary Dryer. International Journal of Computer Applications. 37, 5 ( January 2012), 34-41. DOI=10.5120/4606-6584

@article{ 10.5120/4606-6584,
author = { F. F. G. Areed, M. S. El-Kasassy, Kh. A. Mahmoud },
title = { Design of Neuro-Fuzzy Controller for a Rotary Dryer },
journal = { International Journal of Computer Applications },
issue_date = { January 2012 },
volume = { 37 },
number = { 5 },
month = { January },
year = { 2012 },
issn = { 0975-8887 },
pages = { 34-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume37/number5/4606-6584/ },
doi = { 10.5120/4606-6584 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:23:32.867964+05:30
%A F. F. G. Areed
%A M. S. El-Kasassy
%A Kh. A. Mahmoud
%T Design of Neuro-Fuzzy Controller for a Rotary Dryer
%J International Journal of Computer Applications
%@ 0975-8887
%V 37
%N 5
%P 34-41
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Drying is a very complex non-linear process including the movement of solids in addition to the thermal drying. This means that both the modeling and control of a rotary dryer is difficult with conventional methods, and most of previous researches goes to directly linearize the non-linear model, estimating many parameters that affect the model or work on specific linear model so applying modern controllers even fuzzy or Neuro-Fuzzy always gives approximate results and valid only in certain operating points, in this paper we try to make a combination between two dynamic models of dryer plant to solve the drying rate equation which mainly causes the non-linearity of the model. This article will validate the proposed model then applying modern adaptive control techniques Fuzzy and Neuro-Fuzzy controllers, comparing the behavior of the plant dryer when connected to each of this controllers versus the conventional PID , the aim is to improve dryer control, this will lead to energy, cost savings and increasing efficiency of the drying, the behavior of the control systems has been tested with simulations based on the model of a plant dryer using The Matlab®, and Simulink and Fuzzy Logic Toolbox, ANFIS Toolbox.

References
  1. L. Pirrello 1, L. Yliniemi 2, K. Leiviskä2 and M. Galluzzo 1 2002 Self-Tuning fuzzy control of a rotary dryer .
  2. Leena Yliniemi, 2001 Adaptive Fuzzy Control of a Rotary Dryer, In: Industrial Applications of Soft Computing (K. Leiviska, Ed.). Studies in Fuzziness and Soft Computing, Vol. 71, Springer Verlag.
  3. Carmen Jover, Carlos F. Alastruey 2004 Multivariable control for an industrial rotary dryerDepartment of Automatic Control and Computing Systems, Public University of Navarra, 31006 Pamplona, Spain.
  4. Yliniemi, Leena, 1999 Advanced control of a rotary dryer Department of Process Engineering, University of Oulu, FIN-90570 Oulu.
  5. Luca Cammarata and Leena Yliniemi 1999 Development of a Self-Tuning Fuzzy Logic Controller (STFLC) for a Rotary Dryer.
  6. Leonardo Pirrello, Leena Yliniemi, Kauko Leiviskä 2001 Development of a Fuzzy Logic Controller for a Rotary Dryer with Self-Tuning of Scaling Factor.
  7. Davide Fratantonio, Leena Yliniemi and Kauko Leiviskä 2001 Fuzzy modelling for a rotary dryer.
  8. Harshdeep Singh Ahluwalia 2002 Modeling and Optimization of a Rotary Dryer in a Manufacturing Plant.
  9. Tsourveloudis, N.C., Kiralakis, L. , 2005 Rotary Drying of Olive Stones: Fuzzy Modeling and Control, WSEAS Transactions on Systems 4 (12), pp. 2361-2368
  10. M.G. Ortega_, F. Castan˜ o, M. Vargas, F.R. Rubio 2005 Multivariable robust control of a rotary dryer: Analysis and designDepto. Ingenier?´a de Sistemas y Automa´tica, Universidad de Sevilla, Escuela Superior de Ingenieros, Camino de los Descubrimientos sn, 41092 Sevilla, Spain Received 27 April (2005).
  11. Francisco A. Cubillos a, Pedro I. Alvarez % Jose C. Pinto b Enrique L. Lima u 1995 Hybrid-neural modeling for particulate solid drying processes
  12. Iguaz , A. Esnoz , G. Mart_?nez , A. L_opez , P. V_?rseda 2003 Mathematical modelling and simulation for the drying of vegetable wholesale by-products in a rotary dryer Journal of Food Engineering 59 (2003) 151–160
  13. J. R. Pkez-Correa,F. Cubillos, E. Zavala, C. Shene and P. I. Alvarez Dynamic simulation and control of direct rotary dryers.
Index Terms

Computer Science
Information Sciences

Keywords

Rotary Dryer Fuzzy control Neuro-Fuzzy control

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