Chinese Science Citation Database Core Library(CSCD)Source Journals
Chinese Core Journals
Chinese Core Science and Technology Journals
RCCSE China Authoritative Academic Journal(A+)
Dutch Digest and Citation Database (Scopus)

Slagging characteristics of coal fired boiler furnace based on fuzzy neural network

2022 No. 04
308
130
OnlineView
Download
Authors:
ZHU Chao
YU Xiang
LI Feng
ZHOU Xihong
BI Lingfeng
YANG Dong
Unit:
Electric Power Research Institute of State Grid Shaanxi Electric Power Company;State Key Laboatory of Multiphase Flow in Power Engineering,Xi′an Jiaotong University
Abstract:

At present,coal is still the main energy consumption in the power industry. In addition,the sulfur content and ash content in the commonly used coal for power station boilers is high,which is easy to cause ash and slagging on the heated surface. The serious slagging in the furnace will limit the output of the boiler and threaten the economy and safety of the unit operation,therefore,the development of a comprehensive and comprehensive slagging prediction model will be the focus of further research,which is very important to effectively monitor the degree of slagging in boiler furnace and its development trend. Combining fuzzy mathematics theory with BP neural network,a fuzzy neural network suitable for judging characteristics of slagging in the furnace of coal-fired power plant was constructed. When selecting the input evaluation index,not only its slagging characteristics from the coal ash itself were considered,but also the dimensionless furnace maximum temperature,which reflects the slagging judgment index of unit operation,was incorporated into the model. Taking the operating conditions of the boiler into account,the judgment basis is more comprehensive. A total of 6 factors with higher resolution and the most representative were selected as the input discriminant indicators of this model. Four different types of membership functions were used to fuzz the discriminative index as the input of the fuzzy neural network model,and the neural network without fuzzification was used as the comparison. According to the principles of statistics,the result with the highest occurrence probability was selected as the final evaluation index to increase the accuracy of the prediction result. The results show that when the unit burns Huating coal,the furnace slagging discrimination indexes softening temperature,silicon ratio,silicon aluminum ratio,alkali acid ratio,comprehensive index and the dimensionless maximum temperature of the furnace are 1 220 ℃,58.71,1.63,0.48,2.55 and 0.982 respectively,which are severe slagging. When Huangling No. 1 coal is properly mixed,it is 1 255 ℃,71.02,2.04,0.31,2.15 and 0.958 respectively,which is medium slagging. Therefore,proper blending of high-quality coal can be used to improve the slagging condition of the furnace. The prediction result of this model is accurate,which can provides a new way to comprehensively evaluate the slagging characteristics of the boiler furnace.

Keywords:
coal-fired boiler
furnace slagging
fuzzy theory
neural network
membership function
pattern recogntion
Citation format:
朱超(1988—),男,河南安阳人,高级工程师,博士。E-mail:zhuchao_xjtu@163.com
通讯作者:郁翔(1967—),男,陕西凤翔人,高级工程师。E-mail:yuxiang3853@163.com
Chart:
Articles:
--
Citation format:
--

About Journal

  • Executive director

    China Coal Science and Industry Group Co., Ltd

  • Sponsored by

    Coal Science Research Institute Co., Ltd
    Coal Industry Clean Coal Engineering
    Technology Research Center

  • Editor in Chief

    XIE Qiang

  • Vice Editor-in-Chief

    YU Chang
    SHI Yixiang
    ZHAO Yongchun
    DUAN Linbo
    CAO Jingpei
    ZENG Jie

  • Publication Frequencies

    Monthly

  • ISSN

    1006-6772

  • CN

    11-3676/TD

Covered by

  • CSTPCD
  • RCCSE(A+)
  • AJ
  • EBSCO host
  • Ulrichsweb
  • JST
  • Scopus

Contact us

New Media

  • Meichuanmei
    Meichuanmei
  • Clean Coal Technology
    Clean Coal Technology
  • Online Journals
    Online Journals
Website Copyright © {year} Clean Coal Technology
京ICP备05086979号-19
地址:Coal Tower, Hepingli, Chaoyang District, Beijing, China.
邮编:100013
Tel:86-10-87986452 / 010-87986451
E-mail:jjmjs@263.net