Dynamic modelling of aerobic granular sludge artificial neural networks
Aerobic Granular Sludge (AGS) technology is a promising development in the field of aerobic wastewater treatment system. Aerobic granulation usually happened in sequencing batch reactors (SBRs) system. Most available models for the system are structurally complex with the nonlinearity and uncertaint...
保存先:
| 主要な著者: | , |
|---|---|
| フォーマット: | 論文 |
| 出版事項: |
Institute of Advanced Engineering and Science
2017
|
| 主題: | |
| オンライン･アクセス: | http://eprints.utm.my/id/eprint/77068/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021099147&doi=10.11591%2fijece.v7i3.pp1568-1573&partnerID=40&md5=859fc21d0d90a552c63dc549c35be65d |
| タグ: |
タグ追加
タグなし, このレコードへの初めてのタグを付けませんか!
|
| id |
my.utm.77068 |
|---|---|
| record_format |
eprints |
| spelling |
my.utm.770682018-04-30T14:37:58Z http://eprints.utm.my/id/eprint/77068/ Dynamic modelling of aerobic granular sludge artificial neural networks Mahmod, N. Wahab, N. A. TK Electrical engineering. Electronics Nuclear engineering Aerobic Granular Sludge (AGS) technology is a promising development in the field of aerobic wastewater treatment system. Aerobic granulation usually happened in sequencing batch reactors (SBRs) system. Most available models for the system are structurally complex with the nonlinearity and uncertainty of the system makes it hard to predict. A reliable model of AGS is essential in order to provide a tool for predicting its performance. This paper proposes a dynamic neural network approach to predict the dynamic behavior of aerobic granular sludge SBRs. The developed model will be applied to predict the performance of AGS in terms of the removal of Chemical Oxygen Demand (COD). The simulation uses the experimental data obtained from the sequencing batch reactor under three different conditions of temperature (30°C, 40°C and 50°C). The overall results indicated that the dynamic of aerobic granular sludge SBR can be successfully estimated using dynamic neural network model, particularly at high temperature. Institute of Advanced Engineering and Science 2017 Article PeerReviewed Mahmod, N. and Wahab, N. A. (2017) Dynamic modelling of aerobic granular sludge artificial neural networks. International Journal of Electrical and Computer Engineering, 7 (3). pp. 1568-1573. ISSN 2088-8708 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021099147&doi=10.11591%2fijece.v7i3.pp1568-1573&partnerID=40&md5=859fc21d0d90a552c63dc549c35be65d DOI:10.11591/ijece.v7i3.pp1568-1573 |
| institution |
Universiti Teknologi Malaysia |
| building |
UTM Library |
| collection |
Institutional Repository |
| continent |
Asia |
| country |
Malaysia |
| content_provider |
Universiti Teknologi Malaysia |
| content_source |
UTM Institutional Repository |
| url_provider |
http://eprints.utm.my/ |
| topic |
TK Electrical engineering. Electronics Nuclear engineering |
| spellingShingle |
TK Electrical engineering. Electronics Nuclear engineering Mahmod, N. Wahab, N. A. Dynamic modelling of aerobic granular sludge artificial neural networks |
| description |
Aerobic Granular Sludge (AGS) technology is a promising development in the field of aerobic wastewater treatment system. Aerobic granulation usually happened in sequencing batch reactors (SBRs) system. Most available models for the system are structurally complex with the nonlinearity and uncertainty of the system makes it hard to predict. A reliable model of AGS is essential in order to provide a tool for predicting its performance. This paper proposes a dynamic neural network approach to predict the dynamic behavior of aerobic granular sludge SBRs. The developed model will be applied to predict the performance of AGS in terms of the removal of Chemical Oxygen Demand (COD). The simulation uses the experimental data obtained from the sequencing batch reactor under three different conditions of temperature (30°C, 40°C and 50°C). The overall results indicated that the dynamic of aerobic granular sludge SBR can be successfully estimated using dynamic neural network model, particularly at high temperature. |
| format |
Article |
| author |
Mahmod, N. Wahab, N. A. |
| author_facet |
Mahmod, N. Wahab, N. A. |
| author_sort |
Mahmod, N. |
| title |
Dynamic modelling of aerobic granular sludge artificial neural networks |
| title_short |
Dynamic modelling of aerobic granular sludge artificial neural networks |
| title_full |
Dynamic modelling of aerobic granular sludge artificial neural networks |
| title_fullStr |
Dynamic modelling of aerobic granular sludge artificial neural networks |
| title_full_unstemmed |
Dynamic modelling of aerobic granular sludge artificial neural networks |
| title_sort |
dynamic modelling of aerobic granular sludge artificial neural networks |
| publisher |
Institute of Advanced Engineering and Science |
| publishDate |
2017 |
| url |
http://eprints.utm.my/id/eprint/77068/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021099147&doi=10.11591%2fijece.v7i3.pp1568-1573&partnerID=40&md5=859fc21d0d90a552c63dc549c35be65d |
| _version_ |
1643657488989421568 |
| score |
13.251813 |