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

Muyibi, Suleyman Aremu and Olanrewaju, Rashidah Funke (2013) Prediction of optimal adsorption of aqueous phenol removal with oil palm empty fruit bunch activated carbon using Artificial Neural Network (ANN). In: 3rd International Conference on Biotechnology (ICBioE'13), 2-4 July, 2013, Berjaya Timesquare Hotel, Kuala Lumpur.

Preview	PDF Download (355kB) | Preview
Preview	PDF (Tentative Programme) - Supplemental Material Download (386kB) | Preview

Abstract

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, 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.

Item Type:	Conference or Workshop Item (Full Paper)
Additional Information:	4164/30641
Subjects:	Q Science > Q Science (General) Q Science > QA Mathematics
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Biotechnology Engineering
Depositing User:	Dr. Rashidah Funke Olanrewaju
Date Deposited:	20 Sep 2013 15:56
Last Modified:	20 Sep 2013 16:10
URI:	http://irep.iium.edu.my/id/eprint/30641

Actions (login required)

View Item