A Novel Control Algorithm Integrated RBFNN and Physical Model for Roller Kiln

Article Preview

Abstract:

This paper proposes a novel control strategy using an on-line dynamic switching mechanism for controlling the sintering process of ceramic roller kiln. The RBFNN is applied to extract key information such as temperature parameters and gas pressure, and send the prediction information to the controller of actuator. The mathematical model which based on physical and heat conduction theory is derived and the real-time controller scheme is designed to make the output temperature in the sintering area follow the temperature-curve accurately. To achieve excellent transient performance and steady-state response, an on-line switching mechanism is adopted to regulate the forecasting step size of the predictor appropriately according to the error between reference value and real temperature of the kiln. Additionally, this kind of method can control the temperature of kiln without accurate mathematical models for the kiln effectively. When the sintering operating condition is stable, it is a kind of effective control strategy for temperature of roller kiln.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

1738-1743

Citation:

Online since:

August 2010

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2010 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Guolin Hu. Ceramic Industrial Roller Kiln. China Light Industry Press, Beijing, pp.121-214, 1998. (in chinese).

Google Scholar

[2] Nguyen Quoc Dinh *, Nitin V. Afzulpurkar. Neuro-fuzzy MIMO nonlinear control for ceramic roller kiln. Industrial Systems Engineering, Asian Institute of Technology (AIT), August (2007).

DOI: 10.1016/j.simpat.2007.08.005

Google Scholar

[3] HUANG Yi-xin, FANG Yi-bin. RESEARCH AND APPLICATION OF DISTRIBUTED INTELLIGENT CONTROL SYSTEM FOR TEMPERATURE IN ROLLING KILN. Proceedings of the CSEE, Vol. 22, No. 5, May. 2002:148-151.

Google Scholar

[4] Chen Jing, Xu Guocheng, Xiao Chun, Yuan Youxin, Xiang Kui, Lang Jianxun. Logic Control Algorithm Based on Panboolean Algebra and Its Application for Temperature Control of Ceramic Roller Kiln. 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application.

DOI: 10.1109/paciia.2008.170

Google Scholar

[5] G. Stephanopoulos, C. Ng, Perspectives on the synthesis of plant-wide control structures, Process Control , 10 (2000) : 97-111.

DOI: 10.1016/s0959-1524(99)00023-2

Google Scholar

[6] S. Haykin. Neural Networks: A comprehensive foundation, McMillan College Publishing Company, New York, NY, USA, (1994).

Google Scholar

[7] Roman Neruda, Petra Vidnerovà. Learning Errors by Radial Basis Function Neural Networks and Regularization Networks. Journal of Grid and Distributed Computing 1(2) (2009): 49-58.

Google Scholar

[8] G. -B. Huang, P. Saratchandran, N. Sundararajan, A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation, IEEE Trans. Neural Networks, 16 (1) (2005) : 57-67.

DOI: 10.1109/tnn.2004.836241

Google Scholar