Combustion system model of a wet process clinker Kiln
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Nov 20, 2014
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Abstract
This paper presents the model of the combustion process of a clinker kiln, which is obtained from an energy balance represented in the heat generated by burning coal and how this is distributed across the process. Data comes from the actual process variables, obtained from the control system using OLE Process Control, which operate with experimental data and variables that are assumed to be constant. The resulting model is fitted with two tools: least squares and filter Infinite Impulse Response of first order. It validates and verifies the model and its settings using two statistical tools: box and whisker diagram and method of eight statistical metrics related by a fuzzy function. The use of these tools evidence satisfactory performance of the proposed model.
Keywords
Kiln, energy balance, least squares, IIR filters, clinkerHorno rotatorio, balance de energía, mínimos cuadrados, filtros IIR, clinker
References
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SHAHRIARI, K. and TARASIEWICZ, P.E.S. A model-based approach for clinker rotary kiln monitoring and instrumentation. 2009 IEEE Cement Industry Technical Conference Record. 2009, pp. 1–11.
STADLER, K.S.; POLAND, J. and GALLESTEY, E. Model predictive control of a rotary cement kiln. Control Engineering Practice. 2011, vol. 19, no. 1, pp. 1–9.
WANG, C.; WANG, S.; YU, G. and LI, X. Application research of a fault diagnosis expert system for cement kiln based on .Net Platform. 2010 Second International Conference on Intelligent Human-Machine Systems and Cybernetics. 2010, no. 8, pp. 208–212.
WANG, P. and KWOK, D.P. Analysis and synthesis of an intelligent control system based on fuzzy logic and the PID principle. Intelligent Systems Engineering. 1992, vol. 1, no. 2, p. 157.
WANG, S.; DONG, F. and YUAN, D.F. The design and implementation of a cement kiln expert system. 2007 IEEE International Conference on Automation and Logistics 2007, Jian, China, pp. 2716–2719.
XUE, Z. and LI, Z. Design of fuzzy neural network based control system for cement rotary kiln. 2010 2nd International Asia Conference on Informatics in Control Automation and Robotics CAR 2010, 2010, pp. 290–293.
ZAKAI, M. On a property of Wiener filters. n. d.: 1959.
ZIATABARI, J.; FATEHI, A. and BEHESHTI, M.T.H. Cement rotary kiln control: A supervised adaptive model predictive approach. In: India Conference 2008 INDICON 2008 Annual IEEE 2008, pp. 371–376.
EVANS, J.B. An efficient FIR filter architecture [document on line]. 1993. < http://www.ittc.ku.edu/Projects/FPGA/Fast_Filters.pdf>
FENG, G.; BIN, L.; XIAOCHEN, H. and PENG, G. Research on the fuzzy predictive control for calcining temperature of the rotary cement kiln. IEEE 10th International Conference on Signal Processing Proceedings. 2010, pp. 2568–2571.
HOLMBLAD, L.P and ØSTERGAARD, J.-J. The FLS application of fuzzy logic. Fuzzy Sets and Systems. 1995, vol. 70, no. 2-3, pp. 135–146.
KALMAN, R.E. A new approach to linear filtering and prediction problems 1. Journal of Basic Engineering. 1960, vol. 82, no. Series D, pp. 35–45.
KING, R.E. Expert supervision and control of a large-scale plant. Journal of Intelligent Robotic Systems. 1992, vol. 5, no. 2, pp. 167–176.
LI, Z. Support Vector machine model based predictive PID control system for cement rotary kiln. System. 2010, pp. 3117–3121.
LIU, J. Temperature control in big-scale rotary kiln by using grey neural network. Network. 2009, pp. 723–728.
MARQUEZ MARTINEZ, M. Combustión y quemadores. Barcelona: Marcombo, 1989.
MINTUS, F.; HAMEL, S. and KRUMM, W. Wet process rotary cement kilns: modeling and simulation. Clean Technologies and Environmental Policy. 2006, vol. 8, no. 2, pp. 112–122.
ORTIZ, O.; SUAREZ, G. and NELSON, A. Dynamic simulation of a pilot rotary kiln for charcoal activation. Computers & Chemical Engineering. 2005, vol. 29, no. 8, pp. 1837–1848.
ORTIZ-VALENCIA, P.A.M RAMÍREZ-ECHAVARRÍA, J.L. and CARDONA-RENDÓN, L. Modelo matemático y control de un sistema de fluidos. Medellín: Instituto Tecnológico Metropolitano, 2011.
PARK, O. and SEOK, M. Selection of an appropriate model to predict plume dispersion in coastal areas. Atmospheric Environment. 2007, vol. 41, no. 29, pp. 6095–6101.
PATISSON, F.; LEBAS, E. and HANROT, F. Coal pyrolysis in a rotary kiln: Part II. Overall model of the furnace. … Materials Transactions B. 2000, vol. 31, no. April
SHAHRIARI, K. and TARASIEWICZ, P.E.S. A model-based approach for clinker rotary kiln monitoring and instrumentation. 2009 IEEE Cement Industry Technical Conference Record. 2009, pp. 1–11.
STADLER, K.S.; POLAND, J. and GALLESTEY, E. Model predictive control of a rotary cement kiln. Control Engineering Practice. 2011, vol. 19, no. 1, pp. 1–9.
WANG, C.; WANG, S.; YU, G. and LI, X. Application research of a fault diagnosis expert system for cement kiln based on .Net Platform. 2010 Second International Conference on Intelligent Human-Machine Systems and Cybernetics. 2010, no. 8, pp. 208–212.
WANG, P. and KWOK, D.P. Analysis and synthesis of an intelligent control system based on fuzzy logic and the PID principle. Intelligent Systems Engineering. 1992, vol. 1, no. 2, p. 157.
WANG, S.; DONG, F. and YUAN, D.F. The design and implementation of a cement kiln expert system. 2007 IEEE International Conference on Automation and Logistics 2007, Jian, China, pp. 2716–2719.
XUE, Z. and LI, Z. Design of fuzzy neural network based control system for cement rotary kiln. 2010 2nd International Asia Conference on Informatics in Control Automation and Robotics CAR 2010, 2010, pp. 290–293.
ZAKAI, M. On a property of Wiener filters. n. d.: 1959.
ZIATABARI, J.; FATEHI, A. and BEHESHTI, M.T.H. Cement rotary kiln control: A supervised adaptive model predictive approach. In: India Conference 2008 INDICON 2008 Annual IEEE 2008, pp. 371–376.
How to Cite
Hernandez, O. D., Quiroz, J. A., & Ortiz-Valencia, P. A. (2014). Combustion system model of a wet process clinker Kiln. Ingenieria Y Universidad, 18(2), 329–354. https://doi.org/10.11144/Javeriana.IYU18-2.csmw
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