Prediction of optimal adsorption of aqueous phenol removal with oil palm empty fruit bunch activated carbon using Artificial Neural Network (ANN)

Adsorption process has an edge in wastewater treatment over other techniques due to low initial cost, sludge free, ease of operation and insensitivity to toxic substance. It is a very essential part in the wastewater treatment process chain. It involves both physical and chemical phenomena and hence...

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Main Authors:	Olanrewaju, Rashidah Funke, Muyibi, Suleyman Aremu
Format:	Article
Language:	English English
Published:	American-Eurasian Network for Scientific Information 2014
Subjects:	TK5101 Telecommunication. Including telegraphy, radio, radar, television
Online Access:	http://irep.iium.edu.my/57927/ http://irep.iium.edu.my/57927/ http://irep.iium.edu.my/57927/1/57927_Prediction%20of%20optimal%20adsorption%20of%20aqueous%20phenol%20removal.pdf http://irep.iium.edu.my/57927/2/57927_Prediction%20of%20optimal%20adsorption%20of%20aqueous%20phenol%20removal_SCOPUS.pdf

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spelling	iium-579272017-09-14T08:51:09Z http://irep.iium.edu.my/57927/ Prediction of optimal adsorption of aqueous phenol removal with oil palm empty fruit bunch activated carbon using Artificial Neural Network (ANN) Olanrewaju, Rashidah Funke Muyibi, Suleyman Aremu TK5101 Telecommunication. Including telegraphy, radio, radar, television Adsorption process has an edge in wastewater treatment over other techniques due to low initial cost, sludge free, ease of operation and insensitivity to toxic substance. It is a very essential part in the wastewater treatment process chain. It involves both physical and chemical phenomena and hence susceptible to high percentage of errors due to human factor, variation in the quality as well as chemical/physical characteristics of raw materials used. In order to reduce this percentage error and obtain optimal treatment efficiency, an intelligent method of predicting optimal adsorption capacity based on Artificial Neural Network (ANN) was proposed. Production of Powdered Activation Carbon PAC from processed oil palm empty fruit bunches, EFB was used as adsorbent. Since production of PAC is affected by many parameters, such as CO2 gas flow rate, activation time and activation temperature. Adsorption design was carried out using all these parameters and production results were analyzed. ANN was used to forecast optimal adsorption capacity for aqueous phenol removal. Such ANN based system will be a useful method to address most errors common in wastewater treatment cause by human factors. Experimental results on real data show that the newly developed system is able to accurately predict the optimal adsorption capacity needed in wastewater treatment plant. The Regression and correlation between of optimal adsorption capacity for experimental result and ANN estimation model is 0.9999 of 1.000. This high Correlation of coefficient indicates that the ANN model is a perfect match. © 2014 AENSI Publisher All rights reserved. American-Eurasian Network for Scientific Information 2014 Article PeerReviewed application/pdf en http://irep.iium.edu.my/57927/1/57927_Prediction%20of%20optimal%20adsorption%20of%20aqueous%20phenol%20removal.pdf application/pdf en http://irep.iium.edu.my/57927/2/57927_Prediction%20of%20optimal%20adsorption%20of%20aqueous%20phenol%20removal_SCOPUS.pdf Olanrewaju, Rashidah Funke and Muyibi, Suleyman Aremu (2014) Prediction of optimal adsorption of aqueous phenol removal with oil palm empty fruit bunch activated carbon using Artificial Neural Network (ANN). Advances in Environmental Biology, 8 (3 (Special)). pp. 582-589. ISSN 1995-0756 http://www.aensiweb.com/old/aeb/2014/582-589.pdf
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	TK5101 Telecommunication. Including telegraphy, radio, radar, television
spellingShingle	TK5101 Telecommunication. Including telegraphy, radio, radar, television Olanrewaju, Rashidah Funke Muyibi, Suleyman Aremu Prediction of optimal adsorption of aqueous phenol removal with oil palm empty fruit bunch activated carbon using Artificial Neural Network (ANN)
description	Adsorption process has an edge in wastewater treatment over other techniques due to low initial cost, sludge free, ease of operation and insensitivity to toxic substance. It is a very essential part in the wastewater treatment process chain. It involves both physical and chemical phenomena and hence susceptible to high percentage of errors due to human factor, variation in the quality as well as chemical/physical characteristics of raw materials used. In order to reduce this percentage error and obtain optimal treatment efficiency, an intelligent method of predicting optimal adsorption capacity based on Artificial Neural Network (ANN) was proposed. Production of Powdered Activation Carbon PAC from processed oil palm empty fruit bunches, EFB was used as adsorbent. Since production of PAC is affected by many parameters, such as CO2 gas flow rate, activation time and activation temperature. Adsorption design was carried out using all these parameters and production results were analyzed. ANN was used to forecast optimal adsorption capacity for aqueous phenol removal. Such ANN based system will be a useful method to address most errors common in wastewater treatment cause by human factors. Experimental results on real data show that the newly developed system is able to accurately predict the optimal adsorption capacity needed in wastewater treatment plant. The Regression and correlation between of optimal adsorption capacity for experimental result and ANN estimation model is 0.9999 of 1.000. This high Correlation of coefficient indicates that the ANN model is a perfect match. © 2014 AENSI Publisher All rights reserved.
format	Article
author	Olanrewaju, Rashidah Funke Muyibi, Suleyman Aremu
author_facet	Olanrewaju, Rashidah Funke Muyibi, Suleyman Aremu
author_sort	Olanrewaju, Rashidah Funke
title	Prediction of optimal adsorption of aqueous phenol removal with oil palm empty fruit bunch activated carbon using Artificial Neural Network (ANN)
title_short	Prediction of optimal adsorption of aqueous phenol removal with oil palm empty fruit bunch activated carbon using Artificial Neural Network (ANN)
title_full	Prediction of optimal adsorption of aqueous phenol removal with oil palm empty fruit bunch activated carbon using Artificial Neural Network (ANN)
title_fullStr	Prediction of optimal adsorption of aqueous phenol removal with oil palm empty fruit bunch activated carbon using Artificial Neural Network (ANN)
title_full_unstemmed	Prediction of optimal adsorption of aqueous phenol removal with oil palm empty fruit bunch activated carbon using Artificial Neural Network (ANN)
title_sort	prediction of optimal adsorption of aqueous phenol removal with oil palm empty fruit bunch activated carbon using artificial neural network (ann)
publisher	American-Eurasian Network for Scientific Information
publishDate	2014
url	http://irep.iium.edu.my/57927/ http://irep.iium.edu.my/57927/ http://irep.iium.edu.my/57927/1/57927_Prediction%20of%20optimal%20adsorption%20of%20aqueous%20phenol%20removal.pdf http://irep.iium.edu.my/57927/2/57927_Prediction%20of%20optimal%20adsorption%20of%20aqueous%20phenol%20removal_SCOPUS.pdf
first_indexed	2023-09-18T21:21:54Z
last_indexed	2023-09-18T21:21:54Z
_version_	1777411937281245184

Prediction of optimal adsorption of aqueous phenol removal with oil palm empty fruit bunch activated carbon using Artificial Neural Network (ANN)

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