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    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/39168

    题名: Application of artificial neutral network to control the coagulant dosing in water treatment plant
    作者: Yu, Ruey-fang;康世芳;Kang, Shyh-fang;Liaw, Shu-liang;Chen, Mei-chih
    贡献者: 淡江大學水資源及環境工程學系
    关键词: Artificial neural network;coagulant dosing;real-time control;on-line monitoring;water treatment plant
    日期: 1999
    上传时间: 2010-01-11 16:36:42 (UTC+8)
    摘要: Coagulant dosing is one of the major operation costs in water treatment plant, and conventional control of this process for most plants is generally determined by the jar test. However, this method can only provide periodic information and is difficult to apply to automatic control. This paper presents the feasibility of applying artificial neural network (ANN) to automatically control the coagulant dosing in water treatment plant. Five on-line monitoring variables including turbidity (NTUin), pH (pHin) and conductivity (Conin) in raw water, effluent turbidity (NTUout) of settling tank, and alum dosage (Dos) were used to build the coagulant dosing prediction model. Three methods including regression model, time series model and ANN models were used to predict alum dosage. According to the result of this study, the regression model performed a poor prediction on coagulant dosage. Both time-series and ANN models performed precise prediction results of dosage. The ANN model with ahead coagulant dosage performed the best prediction of alum dosage with a R2 of 0.97 (RMS=0.016), very low average predicted error of 0.75 mg/L of alum were also found in the ANN model. Consequently, the application of ANN model to control the coagulant dosing is feasible in water treatment.
    關聯: Proceedings of 7th IAWQ Asia-pacific reginoal conference, Taipei, Taiwan, pp.1453-1458
    显示于类别:[水資源及環境工程學系暨研究所] 會議論文





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