ANFIS modelling of carbon removal in domestic wastewater treatment plant

Modelling of an ill-defined system such as the wastewater treatment plant is quite tedious and difficult. However, successful and optimal operation of the system relied upon a suitable model. Most of the available developed models were applied to industrial wastewater treatment plants. This paper pr...

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Main Authors: Gaya, Muhammad Sani, Abdul Wahab, Norhaliza, Md. Sam, Yahaya, Nor Anuar, Aznah, Samsuddin, Sharatul Izah
Format: Conference or Workshop Item
Published: 2013
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Online Access:http://eprints.utm.my/id/eprint/39543/
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spelling my.utm.395432017-09-25T07:36:43Z http://eprints.utm.my/id/eprint/39543/ ANFIS modelling of carbon removal in domestic wastewater treatment plant Gaya, Muhammad Sani Abdul Wahab, Norhaliza Md. Sam, Yahaya Nor Anuar, Aznah Samsuddin, Sharatul Izah TK Electrical engineering. Electronics Nuclear engineering Modelling of an ill-defined system such as the wastewater treatment plant is quite tedious and difficult. However, successful and optimal operation of the system relied upon a suitable model. Most of the available developed models were applied to industrial wastewater treatment plants. This paper presents adaptive neuro fuzzy inference system (ANFIS) model for carbon removal in the Bunu domestic wastewater treatment plant in Kuala Lumpur, Malaysia. For comparison feed-forward neural network (FFNN) was used. Simulation results revealed that ANFIS model is slightly better than the FFNN model, thus proving that the model is a reliable and valuable tool for the wastewater treatment plant. 2013 Conference or Workshop Item PeerReviewed Gaya, Muhammad Sani and Abdul Wahab, Norhaliza and Md. Sam, Yahaya and Nor Anuar, Aznah and Samsuddin, Sharatul Izah (2013) ANFIS modelling of carbon removal in domestic wastewater treatment plant. In: The 2nd International Conference on Advance Materials Design and Mechanics (ICAMDM2013), 2013.
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
Gaya, Muhammad Sani
Abdul Wahab, Norhaliza
Md. Sam, Yahaya
Nor Anuar, Aznah
Samsuddin, Sharatul Izah
ANFIS modelling of carbon removal in domestic wastewater treatment plant
description Modelling of an ill-defined system such as the wastewater treatment plant is quite tedious and difficult. However, successful and optimal operation of the system relied upon a suitable model. Most of the available developed models were applied to industrial wastewater treatment plants. This paper presents adaptive neuro fuzzy inference system (ANFIS) model for carbon removal in the Bunu domestic wastewater treatment plant in Kuala Lumpur, Malaysia. For comparison feed-forward neural network (FFNN) was used. Simulation results revealed that ANFIS model is slightly better than the FFNN model, thus proving that the model is a reliable and valuable tool for the wastewater treatment plant.
format Conference or Workshop Item
author Gaya, Muhammad Sani
Abdul Wahab, Norhaliza
Md. Sam, Yahaya
Nor Anuar, Aznah
Samsuddin, Sharatul Izah
author_facet Gaya, Muhammad Sani
Abdul Wahab, Norhaliza
Md. Sam, Yahaya
Nor Anuar, Aznah
Samsuddin, Sharatul Izah
author_sort Gaya, Muhammad Sani
title ANFIS modelling of carbon removal in domestic wastewater treatment plant
title_short ANFIS modelling of carbon removal in domestic wastewater treatment plant
title_full ANFIS modelling of carbon removal in domestic wastewater treatment plant
title_fullStr ANFIS modelling of carbon removal in domestic wastewater treatment plant
title_full_unstemmed ANFIS modelling of carbon removal in domestic wastewater treatment plant
title_sort anfis modelling of carbon removal in domestic wastewater treatment plant
publishDate 2013
url http://eprints.utm.my/id/eprint/39543/
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score 13.160551