Construction of COD Simulation Model for Activated Sludge Process by Recursive Fuzzy Neural Network
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概要
- 論文の詳細を見る
Using a fuzzy neural network (FNN), we constructed a simulation model which estimates the effluent chemical oxygen demand (COD) value from daily routine measurements. Since the water quality of wastewater is changing day by day, an FNN model with a recursively renewing method of learning data (R-FNN) is proposed. With this R-FNN, learning data used to construct an FNN model are renewed with elapsed time so as to estimate the effluent COD value with good accuracy. The estimation results for 9 weeks data using R-FNN were compared with those using a conventional FNN. The average error using the R-FNN model was 0.36 mg/l, while that using the conventional FNN was 1.50 mg/l. Moreover, estimation of the effluent COD throughout one year was carried out, and the average error was only 0.40 mg/l. This result can show the usefulness of the R-FNN for the simulation model of the activated sludge process.
- 社団法人 化学工学会の論文
著者
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Honda Hiroyuki
Department of Biotechnology, School of Engineering, Nagoya University
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Tomida Shuta
Department Of Biotechnology School Of Engineering Nagoya University
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Tomida Shuta
名古屋大学 工
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Tomida Shuta
Department Of Biotechnology Graduate School Of Engineering Nagoya University
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花井 泰三
Department Of Biotechnology School Of Engineering Nagoya University
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KOBAYASHI Takeshi
Department of Electrical Engineering, Faculty of Engineering Science, Osaka University
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HANAI Taizo
Department of Biochemistry and Engineering, Tohoku University
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Honda Hiroyuki
Department Of Bioengineering Tokyo Institute Of Technology
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Kobayashi T
Graduate School Of Marine Science And Technology Tokyo University Of Marine Science And Technology
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Hanai Taizo
Department Of Biochemistry And Engineering Tohoku University
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Kobayashi Takeshi
Department Of Biological Chemistry College Of Bioscience And Biotechnology Chubu University
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Honda Hiroyuki
Department Of Anesthesia Niigata City General Hospital
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