Analysis and Depiction of the Dynamical Characteristic of Burning Zone Temperature in Rotary Kiln of Cement Material Production

Abstract:

The cement is one of the most important building materials, and the rotary kiln is a key equipment or process in the cement production industry. During the operating of the cement rotary kiln, the burning zone temperature (BZT) is an important production index and has a significant role on the quality of the clinker. However, the BZT has complex dynamic characteristic and is difficult to be described online using the conventional approaches. Although the BZT can be detected by using the expensive infrared pyrometer which located at the kiln head hood, it generally loses veracity due to the complex dynamics of the cement rotary kiln. In this paper, a novel approach is proposed to analyze and depict the dynamical characteristics of the BZT. Industrial tests have been performed to demonstrate the effectiveness and validity of the proposed method.

• Periodical: Advanced Materials Research (Volume 568)
• Pages: 208-211
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.568.208
• Online since: September 2012
• Authors: Wei Qing Ge, Bo Xiang, Ping Zhou
• Keywords: Burning Zone Temperature, Cement Building Material, Cement-Rotary Kiln, Characteristic Analysis, Characteristic Depiction

References

[1] X. Zhou, H. Yue, et al. Supervisory control for rotary kiln temperature based on reinforcement learning [A]. Lecture Notes in Control and Information Sciences, 2006, 344, 428-437.

DOI: 10.1007/978-3-540-37256-1_49

[2] F. Naryam, F. Alireza, et al. A supervisory fuzzy control of back-end temperature of rotary cement kilns [A]. Proceeding of International Conference on Control, Automation and Systems, 2007 in COEX, Seoul, Korea, pp.429-434.

DOI: 10.1109/iccas.2007.4406944

[3] Q. B. Huang, X. F. Lin, S. J. Song. Model of cement rotary kiln based on Elman neural network and design of DHP controller [J]. Journal of System Simulation, 2011, 23(3): 583-587.

[4] M. Jarvensivua, K. Saari, S. L. Jamsa-Jounela. Intelligent control system of an industrial lime kiln process [J]. Control Engineering Practice, 2001, 9(6): 589-606.

DOI: 10.1016/s0967-0661(01)00017-x

[5] M. Jarvensivua, J. Esko, A. Oilli. Intelligent control a rotary kiln fired with producer gas generated from biomass [J]. Engineering of Artificial Intelligence, 2011, 14(5).

DOI: 10.1016/s0952-1976(01)00033-1

[6] R. Zanovello, H. Budman. Model predictive control with soft-constraints with application to lime kiln control. Computers and Chemical Engineering, 1999, 23(6): 791-806.

DOI: 10.1016/s0098-1354(99)00008-3

[7] R. A. Jacobs. Increased rates of convergence through learning rate adaptation [J], Neural Networks. 1988, 1(4): 295-308.

DOI: 10.1016/0893-6080(88)90003-2

[8] H. X. Li and S. Guan. Hybrid intelligent control strategy. Supervising a DCS-controlled batch process [J] IEEE Control Systems Magazine, vol. 21, no. 3, pp.36-48, (2001).

DOI: 10.1109/37.924796

[9] F. Schwenker, H. A. Kestler, G. Palm. Three learning phases for radial-basis- function networks [J], Neural Networks. 2001, 14: 439-458.

DOI: 10.1016/s0893-6080(01)00027-2

[10] Q. M. Zhu, Y. Cai, L. Z. Liu. A global learning algorithm for a RBF network [J], Neural Networks, 1999, 12: 527-540.

DOI: 10.1016/s0893-6080(98)00146-4

[11] D. Lowe. Adaptive radial basis function nonlinearities, and the problem of generalization [C], First IEE International conference on Artificial Neural Networks. pp.171-175, London.