Share:


Fuzzy arithmetical modeling of a steam turbine and a boiler system

    Wan Munirah Wan Mohamad Affiliation
    ; Tahir Ahmad Affiliation
    ; Niki Anis Ab Karim Affiliation
    ; Azmirul Ashaari Affiliation

Abstract

The steam turbine and a boiler are important components of a power generation plant. Improved efficiency of a power plant leads to increase of energy production and less waste. In this paper, a model of the power plant as a multivariable system using fuzzy arithmetic which is based on the Transformation Method (TM) is presented. The analytical solution is used to evaluate the state space model. The TM is then used to quantify the inuence of each parameter and their gain factors are calculated to allow estimation of relative measures of uncertainty. The method is applied to a boiler and a steam turbine systems for simulation, analysis and indication of TM's efficiency. The efficiency of TM is presented in this paper.

Keyword : fuzzy arithmetic, steam turbines, boiler

How to Cite
Wan Mohamad, W. M., Ahmad, T., Ab Karim, N. A., & Ashaari, A. (2018). Fuzzy arithmetical modeling of a steam turbine and a boiler system. Mathematical Modelling and Analysis, 23(1), 101-116. https://doi.org/10.3846/mma.2018.007
Published in Issue
Feb 20, 2018
Abstract Views
1396
PDF Downloads
758
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

A.Chaibakhsh and A. Ghaffari. Steam turbine model. Simulation Modelling Practice and Theory, 16(9):1145-1162, 2008. https://doi.org/10.1016/j.simpat.2008.05.017

M. Hanss. The transformation method for the simulation and analysis of systems with uncertain parameters. Fuzzy Sets and Systems, 130(3):277-289, 2002. https://doi.org/10.1016/S0165-0114(02)00045-3

M. Hanss. Applied Fuzzy Arithmetic. An Introduction with Engineering. Springer, Berlin, 2005. https://doi.org/10.1007/b138914

M. Hanss and O. Nehls. Simulation of the human glucose metabolism using fuzzy arithmetic. In PeachFuzz 2000. 19th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.00TH8500), pp. 201-205, 2000. https://doi.org/10.1109/NAFIPS.2000.877420

M. Hanss and O. Nehls. Enhanced parameter identification for complex biomedical models on the basis of fuzzy arithmetic. In Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569), volume 3, pp. 1631-1636, 2001. https://doi.org/10.1109/NAFIPS.2001.943795

M. Hanss and S. Turrin. A fuzzy-based approach to comprehensive modeling and analysis of systems with epistemic uncertainties. Structural Safety, 32(6):433-441, 2010. https://doi.org/10.1016/j.strusafe.2010.06.003

N.A. Harish, R. Ismail and T. Ahmad. Graphical representation of fuzzy state space of a boiler system. In Proc. of the 11th WSEAS Int. Conf. on Neural Networks, NN'10, Proceedings of the 11th WSEAS Int. Conf. on Evolutionary Computing, EC'10, Proc. of the 11th WSEAS Int. Conf. on Fuzzy Systems, FS'10, pp. 99-103, Iasi, Romania, 2010. World Scientific and Engineering Academy and Society (WSEAS) Stevens Point, Wisconsin, USA.

I.U. Khan, T. Ahmad and N. Maan. Feedback fuzzy state space modeling and optimal production planning for steam turbine of a combined cycle power generation plant. Research Journal of Applied Science, 7(2):100-107, 2012. https://doi.org/10.3923/rjasci.2012.100.107

W.M. Wan Munirah, T. Ahmad, S. Ahmad and A. Ashaari. Simulation of furnace system with uncertain parameter. Malaysian Journal of Fundamental and Applied Sciences, 11(1):5-9, 2015. https://doi.org/10.11113/mjfas.v11n1.334

W.M. Wan Munirah, T. Ahmad, A. Ashaari and M. Adib Abdullah. Modeling fuzzy state space of reheater system for simulation and analysis. In AIP Conference Proceedings, volume 1605, pp. 488-493, Malaysia, 2014. AIP. https://doi.org/10.1063/1.4887637

W.M. Wan Munirah, A. Tahir and A. Azmirul. Identifying the uncertain model parameter of a steam turbine system. Pertanika Journal of Science and Technology, 25(2):545-560, 2017.

A. Ordys, A.W. Pike, M.A. Johnson, R.M. Katebi and M.J. Grimble. Modelling and Simulation of Power Generation Plants. Advances in Industrial Control. Springer-Verlag, London, 1994. https://doi.org/10.1007/978-1-4471-2114-5