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Air-Fuel-Ratio Optimal Control of a Gas Heating Furnace Based on Fuzzy Neural Networks

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Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3973))

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Abstract

Based on Neural Network BP algorithm and self-optimizing control, taking gas heating furnace air-fuel-ratio optimized control as goal, a new heating furnace intelligent control algorithm is raised and applied in the practice. Comparing fuzzy neural network hybrid algorithm and PID control algorithm, with gas heating furnace energy-saving control reconstruct, new algorithm can achieve function of automatic tracking calorific value variable and adjusting air-fuel-ratio. The characteristics of this algorithm are high precision and reliability, and suitable for project application.

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Author information

Authors and Affiliations

  1. School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai, China

    Heng Cao, Ding Du & Yuhai Yin

  2. Shanghai Baosteel Chemical Co. Ltd., Shanghai, China

    Yunhua Peng

Authors
  1. Heng Cao
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  2. Ding Du
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  3. Yunhua Peng
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  4. Yuhai Yin
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd, 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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© 2006 Springer-Verlag Berlin Heidelberg

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Cao, H., Du, D., Peng, Y., Yin, Y. (2006). Air-Fuel-Ratio Optimal Control of a Gas Heating Furnace Based on Fuzzy Neural Networks. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_128

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  • DOI: https://doi.org/10.1007/11760191_128

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34482-7

  • Online ISBN: 978-3-540-34483-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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