ニューラルネットワークを用いた粉末汚泥溶融炉におけるコークス消費量の推算

• 王 家兵

  (株) 流体コンサルタント

• 常深 武志

  大阪ガスエンジニアリング (株)

• 藤井 岳

  大阪ガス (株) 開発研究所

• 市川 宗春

  大阪ガスエンジニアリング (株)

書誌事項

タイトル別名

• Prediction of Coke Consumption in a Melting Furnace for the Disposal of Dried Sewage Sludge by Use of a Neural Network Model

抄録

A Neural Network (NN) based on back-propagation algorithm is proposed to predict the coke consumption in a melting furnace, and to control the coke charge automatically. This new-type melting furnace with pulverized dried sludge injection has been developed to enhance the performance of the conventional coke bed melting furnace. Numerous operating conditions and measured data of bench-scale experiments are used as learning data to determine the network architecture and parameters. It should be noted that there is a significant drop in the computing time to implement the network model and perform simulation using the network model compared with the mathematical model. The results predicted by the network model are in reasonable agreement with the actual measurement data and the results by the mathematical model, suggesting the effectiveness and applicability of this model to predict the performance of melting furnace and to control the coke charge automatically. In addition, the network model is utilized to investigate the influence of such operating conditions as the sludge charge rate on performance of the bench-scale melting furnace.

収録刊行物

• 廃棄物学会論文誌

  廃棄物学会論文誌 7 (3), 133-141, 1996

  一般社団法人 廃棄物資源循環学会

キーワード

• ニューラルネットワーク
• 粉末汚泥
• 燃焼と溶融
• コークス充填層
• コークス比

詳細情報

• CRID: 1390282680273606272
• NII論文ID: 10002472387
• NII書誌ID: AN10414685
• DOI: 10.3985/jswme.7.133
• ISSN: 1883163X
• ISSN: 18831648
• 本文言語コード: ja
• データソース種別:
  • JaLC
  • Crossref
  • CiNii Articles
• 抄録ライセンスフラグ: 使用不可