Fuzzy Control and Simulink Model of Boiler Water Level
Authors: 
Neeraj Gupta, Lalit Rai, Devendra Kumar Somwanshi, Prashant Kumar, Rashmi Gupta, Anil KumarAuthors Info & Claims
Neeraj Gupta, Lalit Rai, Devendra Kumar Somwanshi, Prashant Kumar, Rashmi Gupta, Anil KumarAuthors Info & ClaimsArticle No.: 121, Pages 1 - 5
Abstract
In this paper, simulation analysis of water level control of boiler is presented with the help of fuzzy controller. PID controller parameters proportional, integral and differential gain (Kp, Ki, Kd) are tuned with the help of Fuzzy Logic. The ranges of Kp, Ki and Kd are the fuzzy rules for the controller. Triangular geometric function-related type is used as fuzzy. The Simulink model for water level of boiler system controlled by Fuzzy Logic is developed. The Fuzzy Logic behavior in the surface form of the Simulink model of Boiler is also described. The Fuzzy Logic Control of a boiler system has been explained and the settling point has been achieved as 208s.
References
[1]
C. Marchleuba, M. Abdalla, C. E. Ford and L. Guimaraes, “A hybrid fuzzy-PI adaptive control of U-tube steam generators,” Control Theory and Advanced Technology, Vol. 8 (3), 1992, pp. 567–575.
[2]
G. Y. Park and P. H. Seong, “Application of fuzzy learning algorithm to nuclear steam-generator level control,” Annals of Nuclear Energy, 22 (4), 1995, pp. 135–146.
[3]
P. Bendotti and B. Bodenheimer, “Identification and H∞ control design for a pressurized water reactor,” 33rd IEEE Conference on Decision and Control, Vol. 2 (4), 1994, pp. 1072–1077.
[4]
Xin, Yuan Fang, “Design of Fuzzy PID Controller for Water Level of Boiler Steam Drum Based on DSP,” Applied Mechanics and Materials, Vol. 130 (3), 2011, pp. 3455-3458.
[5]
X.J. Liu, C.W. Chan, “Neuro-fuzzy generalized predictive control of boiler steam temperature,” IEEE Transactions on Energy Conversion. Vol. 21 (4), 2006, pp. 900–908.
[6]
X. Wu, J. Shen, Y.G. Li, K.Y. Lee, “Hierarchical optimization of boiler–turbine unit using fuzzy stable model predictive control,” Control Engineering Practice, Vol. 30, 2014, pp. 112–123.
[7]
Xin, Yuan Fang, “Design of Fuzzy PID Controller for Water Level of Boiler Steam Drum Based on DSP,” Applied Mechanics and Materials, Vol. 130 (3), 2011, pp. 3455-3458.
[8]
Cheng Jiwei, Z. Huang, Ruyan Li, Zhongbao Feng, Songhan Wang, Wenbo Li, and Yingai Jin, “Simulation and analysis of main steam control system based on heat transfer calculation,” AIP Conference Proceedings, 2018, pp. 030-035.
[9]
Z. Bi, Y. Pan, Q. Zhang and F. Wang, “An approach of PI gain scheduling with hysteresis switching for the control of a boiler drum level in the coal fired power generation units,” Proceedings of 2006 IEEE International Conference on Control Applications, 2006, pp. 1517-1521
[10]
R. Vilanova and O. Arrieta, "PID tuning for cascade control system design,” Canadian Conference on Electrical and Computer Engineering, 2008, pp. 001775-001778.
[11]
Hugh F. VanLandingham and Nishith D. Tripathi, “Knowledge-based adaptive fuzzy control of drum level in a boiler system,” SouthCon’96 Conference
[12]
Record, Orlando, 1996, pp. 454–459.
[13]
K.J. Astrom and R.D. Bell, “Drum-boiler dynamics” Automatica 36 (3), 2000, pp. 363-378
[14]
G. V. S. Raju and J. Zhou, “Fuzzy-logic adaptive algorithm to improve robustness in a steam generator level controller,” Control Theory and Advanced Technology, Vol. 8 (3) 1992, pp. 479–493.
[15]
X.J. Liu, X.B. Kong, “Nonlinear fuzzy model predictive iterative learning control for drum-type boiler–turbine system,” Journal of Process Control, Vol. 23 (8), 2013, pp. 1023–1040.
[16]
X. Wu, J. Shen, Y.G. Li, K.Y. Lee, “Fuzzy modeling and stable model predictive tracking control of large-scale power plants,” Journal of Process Control, Vol. 24 (10), 2014, pp. 1609–1626.
[17]
Guo Kai, “Design and Simulation of Cascade Fuzzy Control System for Boilers”, Science Discovery. Volume 7, Issue 6, December 2019, pp. 446-452.
[18]
Tianyi Zhang, Fengzhi Dai, Peng Lu,” Analysis of boiler water level system Based on the fuzzy control”, International Conference on Artificial Life and Robotics (ICAROB2021), January 21 to 24, 2021
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November 2023
1215 pages
ISBN:9798400709418
DOI:10.1145/3647444
Copyright © 2023 ACM.
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Published: 13 May 2024
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ICIMMI 2023
ICIMMI 2023: International Conference on Information Management & Machine Intelligence
November 23 - 25, 2023
Jaipur, India
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