Construction of COD simulation model for activated sludge process by recursive fuzzy neutral network

S. Tomida, T. Hanai, H. Honda, T. Kobayashi

研究成果: Contribution to journalArticle査読

11 被引用数 (Scopus)

抄録

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.

本文言語英語
ページ(範囲)369-375
ページ数7
ジャーナルJOURNAL OF CHEMICAL ENGINEERING OF JAPAN
34
3
DOI
出版ステータス出版済み - 3 2001
外部発表はい

All Science Journal Classification (ASJC) codes

  • 化学 (全般)
  • 化学工学(全般)

フィンガープリント

「Construction of COD simulation model for activated sludge process by recursive fuzzy neutral network」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル