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題名 | 高爐操作爐內通氣異常之可視化預警技術=A Visualization Technique to Predict Abnormal Channeling Phenomena in the Blast Furnace Operation |
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作者姓名(中文) | 蕭嘉賢; 何忠根; | 書刊名 | 臺灣鑛業 |
卷期 | 70:4 2018.12[民107.12] |
頁次 | 頁49-59 |
分類號 | 454.89 |
關鍵詞 | 高爐; 通氣異常; 爐身壓力; 可視化; 預警系統; Blast furnace; Channeling; Shaft pressure; Visualization; Prediction system; |
語文 | 中文(Chinese) |
中文摘要 | 高爐發生爐內通氣異常(channeling)現象,有可能造成爐況不穩、產能降低及燃料率上升,甚或有黑煙竄出爐頂之空污疑慮。倘若能有效預測通氣異常之發生,即可藉由原料品質改善、布角、爐操技術調整和出鐵作業等方式來降低其發生機會。本研究擷取高爐廠內資訊管理系統(PIMS)之即時爐身壓力數據,利用類神經網路演算法建立3D模型,以圖控方式觀察即時高爐爐身壓力、溫度等變化;並使用前後時間之爐身壓力變化為通氣異常之預測來源,以異常爐身壓力變化、垂直方向異常爐身壓降之均方根偏差(RMSD)所達高程、及高爐爐頂洗塵器控制閥(AGE或RSE)開度等來訂立發布通氣異常警戒訊息準則,再依此準則建立了各高爐爐內通氣異常預警訊息暨可視化預警系統。分別以異常爐身壓力變化>0.15kg/cm^2、RMSD達第6~7層為開始警戒、及洗塵器控制閥開度值大於60%準備發生通氣異常為準則。分析歷史數據結果可獲得8-16分鐘的預警訊息來適時調整爐操避免通氣異常發生。 |
英文摘要 | An uneven gas distribution through the burden layers inside a blast furnace (BF) results in abnormal gas resistance or pressure changes. Rapid variations in the abnormal gas resistance will lead to the occurrence of channeling. A visualization technology mainly made of pressure distribution was developed to predict BF channeling phenomena in this study. The real-time data of BF shaft pressure was used to create a 3D visual model by neural network algorithms and 3D real-time BF pressure changes were observed. The root mean square deviation (RMSD) of the BF shaft pressure was used as an index to set up the predicting criteria of channeling occurrence with the opening of BF Annular Gap Element (AGE), and a predicting system based on the criteria was built. The two criteria for the channeling alarm are the RMSD of the BF shaft pressure (> 0.15 kg/cm^2) at the upper two levels, and the AGE opening being greater than 60%. This system has been installed in the China Steel Corporation (CSC) BFs, and the test results showed that a prediction can be obtained 8 to 16 minutes ahead of channeling allowing sufficient time for the operator to adjust the BF operation in order to avoid the occurrence of channeling. |
本系統之摘要資訊系依該期刊論文摘要之資訊為主。