Deriving to an Optimum Policy for Designing the Operating Parameters of Mahshahr Gas Turbine Power Plant Using a Self Learning Pareto Strategy

Mofid Gorji-Bandpy, Ahmad Mozaffari, Tahere B. Gorji

Abstract


In the last decades, analyzing and optimizing the power plants based on thermodynamic laws and intelligent control techniques absorb an incremental interest of researchers. This is because deriving the efficient operating parameters for designing and optimizing the performance of power plants will lead to an acceptable investment and avoiding from discarding the energy. However, there are a few areas of application of mathematical optimization method. Optimizing the governing equations and designing parameters of power plants simultaneously leads to a multi-objective problem in industry. Some of these objectives are nonlinear, non-convex and multi-modal with different type of real life engineering constraints. In this paper a new method called Synchronous Parallel Shuffling Self Organized Pareto Strategy Algorithm (SPSSOPSA) is presented which synthesized evolutionary computing, swarm intelligence techniques and Time Adaptive Self Organizing Map(TASOM) simultaneously incorporating with a data shuffling behavior.  Thereafter it will be applied to verifying the optimum decision making for parameter designing of Mahshahr power plant that produced about 117MW electricity, sited in Iran, as a multi-objective and multi-modal problem. The results show the deep relation of the unit cost on the change of the operating parameters.

Key words: Economic optimizing; Exergetic optimizing; Work output maximization; Evolutionary algorithm; Self organized map; Power plant


Keywords


Economic optimizing; Exergetic optimizing; Work output maximization; Evolutionary algorithm; Self organized map; Power plant

Full Text:

PDF

References


[1] Gorji-Bandpy, M., Goodarzian, H., & Biglari, M. (2010). The Cost-Effective Analysis of a Gas Turbine Power Plant. Energy Sources, Part B: Economic and Planning and Policy, 5(4), 348-358.

[2] Bejan, A., Tsatsaronis, G., & Moran, M. (1996). Thermal Design and Optimization. New York: John Wiley and Sons.

[3] Toffolo, A., & Lazzaretto, A. (2002). Evolutionary Algorithms for Multi-Objective Energetic and Economic Optimization in Thermal System Design. Energy, 27(6), 549-567.

[4] Cammarata, G., Fichera, A., & Marletta, L. (1998). Using Genetic Algorithms and the Exergioeconomic Approach to Optimize District Heating Net works. J Energy Resource Technology, 3(4), 241-246.

[5] Gorji-Bandpy, M., & Ebrahimian, V. (2006). Exergoeconomic Analysis of Gas Turbine Power Plants. I J. of Exergy, 7(1), 57-67.

[6] Bhargava, R., Bianchi, M., & Peretto, A. (2002). Thermoeconomic Analysis of an Intercooled, Reheat and Recuperated Gas Turbine for Cogeneration Application, Part I: Base Load Operation. J Engineering for Gas Turbine and Power, 124, 147-154.

[7] Attala, L., Facchini, B., & Ferrara, G. (2001). Thermoeconomic Optimization Method Design Tool in Gas-Steam Combined Plant Realization. J Energy Convers Mgnt, 18(4), 2163-2172.

[8] Misra, R., Sahoo, K., & Gupta, A. (2003). Thermoeconomic Optimization of a Single Effect Water/LiBr Vapor Absorption Refrigeration System. I J Refrigeration, 2(5), 158-169.

[9] Misra, R., Sahoo, K., & Gupta, A. (2005). Thermoeconomic Evaluation and Optimization of a Double-Effect H2O/LiBr Vapor Absorption Refrigeration System. I J Refrigeration, 3(6), 331-343.

[10] Wang, J., Sun, Z., Dai, Y., & Ma, S. (2010). Parametric Optimization Design for Supercritical Power Cycle Using Genetic Algorithm and Artificial Neural Network. J AP ENERGY, 87, 1317-1324.

[11] Gorji-Bandpy, M., & Goodarzian, H. (2011). Exergoeconomic Optimization of Gas Turbine Power Plant Operating Parameters Using Genetic Algorithm: A Case Study. J Thermal Science, 15, 43-54.

[12] Valdes, M., Duran, M.D., & Rovira, A. (2003). Thermoeconomic Optimization of Combined Cycle Gas Turbine Power Plants Using Genetic Algorithms. J APPL THER ENG, 23, 2169-2182.

[13] Lee, K.Y., & Mohamed, P.S. (2002). A Real-Coded Genetic Algorithm Involving a Hybrid Crossover Method for Power Plant Control System Design. In: Proceedings of Congress on Evolutionary Computation (pp.1069-1074). Honolulu: IEEE Press.

[14] Moran, M.J. (1982). Availability Analysis: A Guide to Efficient Energy Use. Englewood Cliffs: Prentice-Hall.

[15] Moran, M.J. (1982). Availability Analysis: A Guide to Efficient Energy Use. New Jersey: Prentice-Hall Englewood Cliffs.

[16] Gorji-Bandpy, M., & Ebrahimian, V. (2007). Exergy Analysis of a Steam Power Plant: A Case Study in Iran. Int J Exergy, 4, 54-71.

[17] Oh, S., Pang, H., Kim, S., & Kwak, H. (1996). Exergy Analysis for a Gas Turbine Cogeneration System. J Eng Gas Turb Power, 118(4), 782-791.

[18] Ebadi, M.J., & Gorji-Bandpy, M. (2005). Exergetic Analysis of Gas Turbine Plants. Int J Exergy, 2(4), 31-39.

[19] Gorji-Bandpy, M., Mozaffari, A., & Mohammadrezaei, S. (in press). Optimizing Maximum Power Output And Minimum Entropy Generation of Atkinson Cycle Using Mutable Smart Bees Algorithm. IJCSE.

[20] Ge, Y., Chen, L., & Sun, F. (2010). Finite Time Thermodynamic Modeling and Analysis for an Irreversible Atkinson Cycle. J Therm Sci, 14(6), 887-896.

[21] Michalaski, R.S. (2003). Learnable Evolution Model: Evolutionary Processes Guided by Machine Learning. J Machine Learning, 38(7), 9-40.

[22] Amor, H.B., & Rettinger, A. (2005). Intelligent Exploration for Genetic Algorithms: Using Self-Organizing Maps in Evolutionary Computation. In: proceeding of GECOO (pp.1531-1538).

[23] Kubota, R., Yamakawa, T., & Horio, K. (2004). Reproduction Strategy Based on Self-Organizing Map, for Real-coded Genetic Algorithm, Neural Information Processing. Letters and Reviews, 5, 27-32.

[24] Shah-Hosseini, H., & Safabakhsh, R. (2000). TASOM: The Time Adaptive Self-Organizing Map. In: Proceeding of International Conference of Information Technology: Coding and Computing (pp. 422-427). Las Vegas: IEEE Press.

[25] Shah-Hosseini, H., & Safabakhsh, R. (2000). The Adaptive Self-Organizing Map with Neighborhood Function For Bilevel Thresholding. In: Proceeding of AI, Simulation, and Planning in High Autonomy Systems Conference (pp. 123-128). Tucson: AIS Press.

[26] Talaska, T., Wojtyna, R., Dlugosz, R., & Iniewski, K. Implementation of the Conscience Mechanism for Kohonen’s Neural Network. In: Mixed design of integrated circuits and systems (pp. 310-315). Gdynia: IEEE Press.




DOI: http://dx.doi.org/10.3968/j.est.1923847920120302.178

DOI (PDF): http://dx.doi.org/10.3968/pdf

Refbacks

  • There are currently no refbacks.


Copyright (c)



Share us to:   

Reminder

If you have already registered in Journal A and plan to submit article(s) to Journal B, please click the CATEGORIES, or JOURNALS A-Z on the right side of the "HOME".

We only use three mailboxes as follows to deal with issues about paper acceptance, payment and submission of electronic versions of our journals to databases: [email protected]; [email protected]; [email protected]

 Articles published in Energy Science and Technology are licensed under Creative Commons Attribution 4.0 (CC-BY).

 ENERGY SCIENCE AND TECHNOLOGY Editorial Office

Address: 1055 Rue Lucien-L'Allier, Unit #772, Montreal, QC H3G 3C4, Canada.
Telephone: 1-514-558 6138 
Website: Http://www.cscanada.net Http://www.cscanada.org 
E-mail: [email protected]; [email protected]

Copyright © 2010 Canadian Research & Development Centre of Sciences and Cultures