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      |本期目錄/Table of Contents|

      [1]劉丹,周熙宏,楊冬,等.燃煤電站鍋爐爐膛結渣特性計算分析[J].西安交通大學學報,2019,53(09):150-158.[doi:10.7652/xjtuxb201909020]
       LIU Dan,ZHOU Xihong,YANG Dong,et al.Calculation and Analysis on the Slagging Performance of Coal-Fired Boilers[J].Journal of Xi'an Jiaotong University,2019,53(09):150-158.[doi:10.7652/xjtuxb201909020]
      點擊復制

      燃煤電站鍋爐爐膛結渣特性計算分析(PDF)

      《西安交通大學學報》[ISSN:0253-987X/CN:61-1069/T]

      卷:
      53
      期數:
      2019年第09期
      頁碼:
      150-158
      欄目:
      出版日期:
      2019-09-10

      文章信息/Info

      Title:
      Calculation and Analysis on the Slagging Performance of Coal-Fired Boilers
      作者:
      劉丹1 周熙宏1 楊冬1 劉朝暉1 裘立春2 滕敏華2
      1.西安交通大學動力工程多相流國家重點實驗室, 710049, 西安; 2.浙江浙能技術研究院有限公司, 311121, 杭州
      Author(s):
      LIU Dan1 ZHOU Xihong1 YANG Dong1 LIU Zhaohui1 QIU Lichun2 TENG Minhua2
      1.State Key Laboratory of Multiphase Flow in Power Engineering, Xi'an Jiaotong University, Xi'an 710049, China; 2.Zhejiang Energy Group R&D Institute Co., Ltd., Hangzhou 311121, China
      關鍵詞:
      爐膛結渣 模糊神經網絡 污染系數
      Keywords:
      furnace slagging fuzzy neural network pollution coefficient
      分類號:
      TK229
      DOI:
      10.7652/xjtuxb201909020
      摘要:
      為對爐膛結渣情況進行有效預測,通過基于燃煤特性的單一指標與多指標綜合預測模型和模糊神經網絡分別對一臺300 MW級亞臨界、一臺600 MW級亞臨界以及兩臺1 000 MW級超超臨界鍋爐機組爐膛結渣情況進行了計算分析; 針對300 MW級亞臨界鍋爐機組建立了膜式水冷壁實際熱流密度的計算模型,并利用基于污染系數的神經網絡對該電站鍋爐爐膛結渣情況進行了預測。3種預測模型的結果表明:單一指標和多指標綜合預測模型一定程度上可對爐膛結渣情況進行預測,但其分辨率較低,且模型中各指標對于不同煤種和爐型的分辨率存在差異; 模糊神經網絡相對于上述模型和傳統神經網絡分辨率較高,所構建的4種模糊神經網絡分辨率可分別達到92%、92%、92%以及100%,且統計結果的分辨率也可達到100%,對不同爐型和煤種的適用性更強。另外,基于污染系數的神經網絡可根據電站運行數據對爐膛局部結渣情況進行實時預測,誤差在3%以內,均方誤差為0.013 4,預測結果可為吹灰提供指導。
      Abstract:
      To effectively predict the slagging performance in furnace, a 300 MW sub-critical boiler, a 600 MW sub-critical boiler and two 1 000 MW ultra-supercritical boilers were taken as the test subjects to conduct slagging predication by a single-index prediction modal and a multi-index comprehensive prediction model based on coal-burning behavior and a fuzzy neural network.The calculation model of the actual heat flux density of the membrane water wall was established for the 300 MW subcritical boiler.The pollution coefficient based neural network was used to predict the slagging of the furnace.The results of three prediction models show that the single index and multi-index comprehensive prediction model can predict the slagging of the furnace to some extent but with a low resolution, and the models' resolutions are different for different coal types and furnace types; the fuzzy neural network has higher resolution than the furnace slagging prediction model based on coal-burning behavior and the traditional neural network.The resolutions of the four kinds of fuzzy neural networks constructed in this study can reach 92%, 92%, 92% and 100%, respectively.In addition, the accuracy of the statistical results can reach 100%, and the fuzzy neural network is more applicable to different furnace types and coal types.The above two methods can only predict the overall slagging situation of the furnace.The neural network based on pollution coefficient can predict local slagging situation of furnace according to the real-time operation data of the boilers, where the error is within 3% and the mean square error is 0.013 4, and it can be used to guide the soot blowing.

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      備注/Memo

      備注/Memo:
      收稿日期: 2019-03-21。作者簡介: 劉丹(1994—),女,碩士生; 楊冬(通信作者),男,教授,博士生導師;痦椖: 國家重點研發計劃資助項目(2018YFB0604400)。
      更新日期/Last Update: 2019-09-04
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