Genetic algorithms and neural networks for solving water quality model of the Egyptian research reactor

• Autorzy: El-Sayed Wahed M. , Ibrahim W. Z. , Effat A. M.
• Języki publikacji: EN

Abstrakty

EN

The second Egyptian research reactor ETRR-2 became critical on 27th November, 1997. The National Center of Nuclear Safety and Radiation Control (NCNSRC) has the responsibility for the evaluation and assessment of safety of this reactor. Modern managements of water distribution system (WDS) need water quality models that are able to accurately predict the dynamics of water quality variations within the distribution system environment. Before water quality models can be applied to solve system problems, they should be calibrated. The purpose of this paper is to present an approach which combines both macro and detailed models to optimize the water quality parameters. For an efficient search through the solution space, we use a multi-objective genetic algorithm which allows us to identify a set of Pareto optimal solutions providing the decision maker with a complete spectrum of optimal solutions with respect to the various targets. This new combinative algorithm uses the radial basis function (RBF) metamodeling as a surrogate to be optimized for the purpose of decreasing the times of time-consuming water quality simulation and can realize rapidly the calibration of pipe wall reaction coefficients of chlorine model of large-scaled WDS.

Słowa kluczowe

EN

genetic algorithm; neural networks; model calibration; water distribution system; water quality model

• Wydawca: Institute of Nuclear Chemistry and Technology
• Czasopismo: Nukleonika
• Rocznik: 2009
• Tom: Vol. 54, No. 4
• Strony: 239--245
• Opis fizyczny: Bibliogr. 17 poz., rys.

Twórcy

autor

El-Sayed Wahed M.

autor

Ibrahim W. Z.

autor

Effat A. M.

• Faculty of Science, Mathematics Department, Zagazic University, Egypt,

Bibliografia

• 1. El-Sayed Wahed M, Ibrahim WZ, Effat AM (2009) Application of genetic algorithm for optimization the safety system of the nuclear reactor. Nukleonika 1:51–56
• 2. El-Sayed Wahed M, Ibrahim WZ, Effat AM (2009) Multi objective optimization of the plate element of Egyptian research reactor using genetic algorithm. Nucl Sci Eng 162;3:275–281
• 3. Fang H, Rais-Rohani M, Liu Z, Horstemeyer MF (2005) A comparative study of metamodeling methods for multiobjective crashworthiness optimization. Comput Struct 83:2121–2136
• 4. Fonseca DJ, Navaresse DO, Moynihan GP (2003) Simulation metamodeling through artificial neural networks. Eng Appl Artif Intell 16:177–183
• 5. Gutmann HM (2001) A radial basis function method for global optimization. J Global Optimiz 19;3:201–227
• 6.Haykin S (1999) Neural networks: a comprehensive foundation, 2nd ed. Prentice Hall, Inc, United Kingdom
• 7.Hussain MF, Barton RR, Joshi SB (2002) Metamodeling: radial basis functions, vs. polynomials. Eur J Operat Res 138:142–154
• 8. Jin R, Chen W, Simpson TW (2001) Comparative studies of metamodelling techniques under multiple modeling criteria. Struct Multidiscip Optim 23:1–13
• 9. Kilmer RA, Smith AE, Shuman LJ (1999) Computing confidence intervals for stochastic simulation using neural network metamodels. Comput Industr Eng 36:391–407
• 10. Kleijnen JPC, Sargent RG (2000) A methodology for fitting and validating metamodels in simulation. Eur J Operat Res 120:14–29
• 11. Maier SH, Powell RS, Woodwar CA (2000) Calibration and comparison of chlorine decay models for a test water distribution system. Water Res 34;8:2301–2309
• 12. Rossman LA, Boulos PF, Altman T (1993) Discrete volume-element method for network water-quality models. Water Resources Planning Managm 119;5:505–517
• 13. Sarimveis H, Alexandridis A, Mazarakis S, Bafas G (2004) A new algorithm for developing dynamic radial basis function neural network models based on genetic algorithms. Comput Chem Eng 28:209–217
• 14. Shahsavand A, Ahmadpour A (2005) Application of optimal RBF neural networks for optimization and characterization of porous materials. Comput Chem Eng 29:2134–2143
• 15. Tunali S, Batmaz I (2003) A metamodeling methodology involving both qualitative and quantitative input factors. Eur J Operat Res 150:437–450
• 16. Van BG, Waanders B (2004) Application of optimization methods to calibration of water distribution systems. In: Proc of the 2004 World Water and Environmental Resources Congress, 5–10 July 2004, Salt Lake City, UT, USA, ASCE
• 17. Wu ZY (2005) Water quality model calibration by means of fast messy genetic algorithm. In: Proc of the 2005 World Water and Environmental Resources Congress, 1–4 June 2005, Anchorage, Alaska, USA, ASCE