Multimodel Parameters Identification for Main Steam Temperature of Ultra-Supercritical Units Using an Improved Genetic Algorithm-Reference-Cited by-同舟云学术

Multimodel Parameters Identification for Main Steam Temperature of Ultra-Supercritical Units Using an Improved Genetic Algorithm

Published:2013-12 Issue:4 Volume:139 Page:290-298
ISSN:0733-9402
Container-title:Journal of Energy Engineering
language:en
Short-container-title:J. Energy Eng.

Author:

Hu Wenkai¹,Fang Yanjun²

Affiliation:

1. Master of Science, School of Power and Mechanical Engineering, Wuhan Univ., Wuhan 430072, China.

2. Professor, School of Power and Mechanical Engineering, Wuhan Univ., Wuhan 430072, China (corresponding author).

Publisher

American Society of Civil Engineers (ASCE)

Subject

Waste Management and Disposal,Energy Engineering and Power Technology,Nuclear Energy and Engineering,Renewable Energy, Sustainability and the Environment,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29EY.1943-7897.0000105

Reference36 articles.

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2. Modeling and Evaluation of Externally Fired Combined Cycle Using Aspen

3. A survey of parallel distributed genetic algorithms

4. Modeling and Experimental Verification of Solar Radiation on a Sloped Surface, Photovoltaic Cell Temperature, and Photovoltaic Efficiency

5. Blasco X. Herrero J. Martínez M. and Senent J. (2001). “Nonlinear parametric model identification with genetic algorithms: Application to a thermal process.” Connectionist Models Neurons Learning Processes Artificial Intelligence 466–473.

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