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Anead, Hosham Salim
- Improvement the Performance of Drum Boiler in Thermal Power Plant using Hybrid Model Through the Adaptive Neuro-Fuzzy Controller
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Authors
Affiliations
1 Electromechanical Engineering Department, University of Technology, Baghdad, IR
1 Electromechanical Engineering Department, University of Technology, Baghdad, IR
Source
Indian Journal of Science and Technology, Vol 11, No 22 (2018), Pagination: 1-7Abstract
Objectives: This Study tackles how the process of Neuro-fuzzy control that allows observing and controlling the steam pressure and temperature than conventional PI at drum boiler in the power station. It is used to increase the thermal efficiency of the boiler while it has helped to maintain the Turbine not to reach wet vapor which is considered the best method, more accurate compared to the basic conventional method of PI. Methods/Statistical Analysis: The method of hybrid control was used by Neuro-fuzzy in model design simulation by the Matlab program, which led to data training and testing. It is compared with conventional method of PI (Rise Time (sec) – Settling Time (sec) – Overshoot (%) Peak – Peak Time (sec)) by tracking the temperature, steam pressure and to reach the degree of vapor in less time and accuracy. Findings: ANFIS tracks the path of parameters (Temperature – Pressure) more accurately and superior than the traditional method PID and this leads to enhanced, improved and more thermal efficient performance in a drum of the boiler. It shows that the ratio of the overshoot in the PI is 15.68 for the temperature while in the steam for pressure 13.22 bars either in Neuro-fuzzy is in both parameters zero. Thus, ANFIS seems to be more accurate in tracking and shorter time and the absence of the ratio Overshoot compared to the basic conventional method of PI. Application/Improvements: Strategy is presented in the promotion and development of control of drum steam boilers through the simulations model, which was constructed using the hybrid theory and compared to the main theory PI as shown in the results.Full Text
References
- Lucian M, Iosif O, Catalin D, DorinI. Neuro-fuzzy models of thermoelectric power station installations. International Conference on Computational Intelligence for Modeling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce; 2005. p. 899–904.
- Nurnberger A, Nauck D, Kruse R, Merz L. A Neuro-fuzzy development tool for fuzzy controllers under MATLAB/ SIMULINK. Proceedings 5th European Congress on Intelligent Techniques and Soft Computing; 1997.
- Kumar R, Kaushik S, Kumar A. Energy and exergy analysis of non-reheat thermal power plant. Proceedings of International Conference on Energy and Environment; 2009. p. 608–11.
- Horikawa S, Furuhashi T, Uchikawa Y. A new type of fuzzy neural network for linguistic fuzzy modeling. Proceedings 2nd International Conference on Fuzzy Logic and Neural Networks; 1992. p. 1053–6.
- Culliere T, Corrieu J. Neuro-fuzzy modeling of nonlinear systems for control purposes. Proceedings IEEE International Conference on Fuzzy Systems; 1995. p. 2009– 16. Crossref
- Jang SRJ. ANFIS: Adaptive network based fuzzy inference system. IEEE Trans on Systems, Man and Cybernetics. 1993; 23(3):665–85. Crossref
- Jang J. Neuro-fuzzy modeling: Architecture, Analyses and Applications. Berkeley, USA: University of California; 1992. p. 1-310.
- Jang J, Sun C. Neuro-fuzzy modelling and control. Proceedings IEEE. 1995; 83(3):378–406. Crossref
- Jang J, Sun C, Mizutani E. Neuro-Fuzzy and Soft Computing - A Computational Approach to Learning and Machine Intelligence. New Jersey: Prentice Hall; 1997. p. 1-640.
- Ying B, Dali W. Advanced of fuzzy logic technologies in industrial application. Springer; 2006.
- Rabah AC. Improved Controller for a thermal power plant [Thesis]. Institute de Genie Electrique; 2012.
- Math Works. Fuzzy Logic Toolbox User’s Guide. The Math Works, Technical Report. 2014.
- Comparative Between Neuro–Fuzzy and PI Controller Temperature of Condenser in Thermal Power Plant (160 MW)
Abstract Views :213 |
PDF Views:0
Authors
Affiliations
1 Department of Electrical and Electronic Engineering, University of Technology, Baghdad, IQ
1 Department of Electrical and Electronic Engineering, University of Technology, Baghdad, IQ
Source
Indian Journal of Science and Technology, Vol 11, No 29 (2018), Pagination: 1-9Abstract
Objectives: The temperature in the condenser is one of the serious and necessary parameters that should be maintained to harness the fullest efficiency power plant (160 MW) operations. For this reason, the regulation can be achieved by Artificial Intelligence (AI) technology, containing (Hybrid) the neuro–fuzzy system. Methods/Statistical Analysis: ANFIS theory compares with the conventional PID method by using MATLAB/ Simulink program which tracks the temperature levels. Findings: The comparison was made using various parameters (settling time, overshoot, rise time, peak time) and used to excess the thermal efficiency of the condenser by tracking the temperature and to reach the degree of 80°C in less time with accuracy, which also lead to maintain the condenser as shown in the results. Application/Improvements: Neuro– fuzzy was the more accurate, superiority and reach the steady state in the short time, while PI represented the being of an overshoot where it causes system malfunction, leading to the driving off operation.Full Text
References
- Hemalatha B, Juliet AV. ANFIS controller for water level control of a boiler drum. International Journal of Intelligent Engineering and Systems. 2016; 9(4):1–10. https://doi.org/10.22266/ijies2016.1231.01
- Solatian P, Abbasi SH, Shabaninia F. Simulation study of flow control based on PID ANFIS controller for non–linear process plants. American Journal of Intelligent Systems. 2016; 2(5):104–10. https://doi.org/10.5923/j.ajis.20120205.04
- Ali AA, Mohammed AM. Condenser and deaerator control using fuzzy–neural technique. Iraq J Electrical and Electronic Engineering. 2007; 3(1):79–96.
- Alakhras M, Oussalah M, Hussein M. ANFIS: General description for modeling dynamic objects. IEEE/ACS 12th International Conference of Computer Systems and Applications (AICCSA); 2005. p. 1–8.
- Fuzzy Logic Controllers – Tutorial. 2010. http://uni-obuda.hu/users/fuller.robert/fuzzy-logic-controllers.pdf
- Bai Y, Wang D. Advanced of fuzzy logic technologies in industrial application. Springer; 2006. p. 1–9. https://doi.org/10.1007/978-1-84628-469-4
- Karppanen E. Advance control of an industrial circulating fluidized bed boiler using fuzzy logic. [Doctoral Dissertation]. 2000.
- Negnevitsky M. Artificial Intelligence - A guide to intelligent systems. Pearson Education; 2005. p. 1–440.
- Nurnberger A, Nauck D, Kruse R, Merz L, A neuro–fuzzy development tool for fuzzy controllers under MATLAB/ SIMULINK. Congress on Intelligent Techniques and Soft Computing (EUFIT «97); Aachen, Germany. 1997.
- Sultan KF, Anead HS, Moneeryounis M. Improvement the performance of drum boiler in thermal power plant using hybrid model through the adaptive neuro–fuzzy controller. Indian Journal of Science and Technology. 2018; 11(22):1– 7. https://doi.org/10.17485/ijst/2018/v11i22/125191
- Horikawa SI, Furuhashi T, Uchikawa Y. A new type of fuzzy neural network for linguistic fuzzy modeling. Proceedings 2nd International Conference on Fuzzy Logic and Neural Networks; 1992. p. 1053–6.
- Jang JS. Neuro–fuzzy modeling: Architecture, analyses and applications. [PhD Thesis]. Berkeley, USA: University of California; 1992. p. 1–310.
- Jang JSR. ANFIS: Adaptive network based fuzzy inference system. IEEE Transactions on Systems, Man and Cybernetics. 1993; 23(3):665–85. https://doi.org/10.1109/21.256541
- Jang JSR, Sun CT. Neuro–fuzzy modelling and control. Proceedings IEEE.1995; 83(3):378–406. https://doi.org/10.1109/5.364486
- Jang JSR, Sun CT, Mizutani E. Neuro – fuzzy and soft computing. A Computational Approach to Learning and Machine Intelligence. New Jersey: Prentice Hall; 1997. p. 1–640.
- Rabah AC. Improved controller for a thermal power plant. Institute de Genie Electrique. 2012.
- Roger Jang JS. MATLAB. Fuzzy Logic Toolbox. User’s Guide Version 1. 2014. p. 1–208.
- Saad MS, Jamluddin H, Darus IZM. PID controller tuning using evolutionary algorithms. School of Manufacturing Engineering, University Malaysia Perlis. 2012; 7(4):139–49.
- Manjunath RM, Raman SJ. Fuzzy adaptive PID for flow control system based on OPC. IJCA Special Issue on «Computational Science - New Dimensions and Perspectives; 2011. p. 1–4. PMid: 22379287.