Identification of cement rotary kiln using hierarchical wavelet fuzzy inference system

Abstract Rotary kiln is the central and the most complex component of cement production process. It is used to convert calcineous raw meal into cement clinkers, which plays a key role in quality and quantity of the final produced cement. This system has complex nonlinear dynamic equations that have not been completely worked out yet. In conventional modeling procedure, a large number of the involved parameters are crossed out and an approximation model is presented instead. Therefore, the performance of the obtained model is very important and an inaccurate model may cause many problems for designing a controller. This study presents hierarchical wavelet TS-type fuzzy inference system (HWFIS) for identification of cement rotary kiln. In the proposed method, wavelet fuzzy inference system (WFIS) with two input variables is used as sub-model in a hierarchical structure and gradient descent (GD) algorithm is chosen for training parameters of antecedent and conclusion parts of sub-models. The results show that the proposed method has higher performance in comparison with the other models. The data collected from Saveh White Cement Company is used in our simulations.

[1]  Feng-Hsiag Hsiao,et al.  Fuzzy control of dithered chaotic systems via neural-network-based approach , 2010, J. Frankl. Inst..

[2]  Ajith Abraham.  EvoNF: a framework for optimization of fuzzy inference systems using neural network learning and evolutionary computation , 2002, Proceedings of the IEEE Internatinal Symposium on Intelligent Control.

[3]  P. V. Barr,et al.  A heat-transfer model for the rotary kiln: Part I. pilot kiln trials , 1989 .

[4]  Michel Pasquier,et al.  Application of a hierarchical coevolutionary fuzzy system for financial prediction and trading , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[5]  Ronald R. Yager,et al.  Fuzzy sets, neural networks, and soft computing , 1994 .

[6]  Vasilis N. Burganos,et al.  CFD predictions for cement kilns including flame modelling, heat transfer and clinker chemistry , 1999 .

[7]  Leandro S. Oliveira,et al.  Modeling and simulation of petroleum coke calcination in rotary kilns , 2001 .

[8]  Santiago Aja-Fernández,et al.  Hierarchical fuzzy systems with FITM , 2004, 2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542).

[9]  Ming-Ling Lee,et al.  Modeling of hierarchical fuzzy systems , 2003, Fuzzy Sets Syst..

[10]  Marcio Arêdes Martins,et al.  Modellierung und Simulation der Kalksteincalcination im Drehofen. Teil 1: Pilot-Drehofen , 2002 .

[11]  P. V. Barr,et al.  A thermal model for the rotary kiln including heat transfer within the bed , 1996 .

[12]  Vivek V. Ranade,et al.  Simulation of rotary cement kilns using a one-dimensional model , 2006 .

[13]  Chia-Feng Juang,et al.  Hierarchical Singleton-Type Recurrent Neural Fuzzy Networks for Noisy Speech Recognition , 2007, IEEE Transactions on Neural Networks.

[14]  Lotfi A. Zadeh,et al.  A rationale for fuzzy control , 1972 .

[15]  V. Devedzic.  Knowledge-based control of rotary kiln , 1995, Proceedings IEEE Conference on Industrial Automation and Control Emerging Technology Applications.

[16]  Kaustubh Mujumdar.  Modeling of rotary cement kilns , 2008 .

[17]  Eduardo Bayro-Corrochano,et al.  Decentralized neural identification and control for uncertain nonlinear systems: Application to planar robot , 2010, J. Frankl. Inst..

[18]  Zheng Li,et al.  Design of fuzzy neural network based control system for cement rotary kiln , 2010, 2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010).

[19]  Pei-Hwa Huang,et al.  Passive fuzzy controller design for nonlinear systems with multiplicative noises , 2010, J. Frankl. Inst..

[20]  R. Longchamp,et al.  Supervisory fuzzy control of a rotary cement kiln , 1994, Proceedings of MELECON '94. Mediterranean Electrotechnical Conference.

[21]  J. Li,et al.  Self-generating hierarchical fuzzy systems , 2004, International Conference on Intelligent Sensing and Information Processing, 2004. Proceedings of.

[22]  Tianyou Chai,et al.  Pattern-based Hybrid Intelligent Control for Rotary Kiln Process , 2007, 2007 IEEE International Conference on Control Applications.

[23]  Mohammad Ababneh,et al.  Design of digital controllers for uncertain chaotic systems using fuzzy logic , 2009, J. Frankl. Inst..

[24]  Rolf Isermann.  Digital Control Systems , 1981 .

[25]  Dervis Karaboga,et al.  Evolutionary algorithm-based fuzzy PD control of spillway gates of dams , 2007, J. Frankl. Inst..

[26]  Alireza Fatehi,et al.  Identification, Prediction and Detection of the Process Fault in a Cement Rotary Kiln by Locally Linear Neuro-Fuzzy Technique , 2009, 2009 Second International Conference on Computer and Electrical Engineering.

[27]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[28]  H. A. Spang.  A dynamic model of a cement kiln , 1972 .

[29]  Alexander G. Loukianov,et al.  Discrete-time recurrent high order neural networks for nonlinear identification , 2010, J. Frankl. Inst..

[30]  Kuo-Hsiang Cheng,et al.  Auto-structuring fuzzy neural system for intelligent control , 2009, J. Frankl. Inst..

[31]  D.P. Filev,et al.  An approach to online identification of Takagi-Sugeno fuzzy models , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[32]  Alireza Fatehi,et al.  Identification of Nonlinear Predictor and Simulator Models of a Cement Rotary Kiln by Locally Linear Neuro-Fuzzy Technique , 2009 .

[33]  Kang G. Shin,et al.  Design of a Knowledge-Based Controller for Intelligent Control Systems , 1990, 1990 American Control Conference.

[34]  Yucai Zhu,et al.  Multivariable System Identification For Process Control , 2001 .

[35]  Liu Chong Chun,et al.  A method based on Morlet wavelet for extracting vibration signal envelope , 2000, WCC 2000 - ICSP 2000. 2000 5th International Conference on Signal Processing Proceedings. 16th World Computer Congress 2000.

[36]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[37]  Nikola K. Kasabov,et al.  DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction , 2002, IEEE Trans. Fuzzy Syst..

[38]  Babak Nadjar Araabi,et al.  Lipschitz Numbers: A Medium for Delay Estimation , 2008 .

[39]  B.N. Araabi,et al.  A supervisory fuzzy control of back-end temperature of rotary cement kilns , 2007, 2007 International Conference on Control, Automation and Systems.

[40]  Babak Nadjar Araabi,et al.  Comparison of rotary cement kiln identified models , 2009, 2009 IEEE International Conference on Control and Automation.