Artificial neural network model for predicting wet scrubber performance

Increased public awareness posed for global climate change has led to greater concern over the impact of environmental changes due to constant emissions of air pollutants from industrial production. Wet scrubbers have important advantages when compared to other air pollution control devices. They ca...

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Main Authors:	Danzomo, Bashir Ahmed, Salami, Momoh Jimoh Eyiomika, Khan, Md. Raisuddin
Format:	Article
Language:	English
Published:	IJSER 2012
Subjects:	TA168 Systems engineering
Online Access:	http://irep.iium.edu.my/26859/ http://irep.iium.edu.my/26859/ http://irep.iium.edu.my/26859/1/Artificial-Neural-Network-Model-for-Predicting-Wet-Scrubber-Performance.pdf

id	iium-26859
recordtype	eprints
spelling	iium-268592014-06-17T07:23:28Z http://irep.iium.edu.my/26859/ Artificial neural network model for predicting wet scrubber performance Danzomo, Bashir Ahmed Salami, Momoh Jimoh Eyiomika Khan, Md. Raisuddin TA168 Systems engineering Increased public awareness posed for global climate change has led to greater concern over the impact of environmental changes due to constant emissions of air pollutants from industrial production. Wet scrubbers have important advantages when compared to other air pollution control devices. They can collect particulates like flammable and explosive dusts, foundry dusts, cement dusts, large volume of gaseous pollutants, acid mists and furnace fumes. In this study, a three layer feed forward neural network has been used to predict the performance of wet scrubber system for air pollution control. The theoretical performance, ηperf of the system was calculated using 206 scenarios for 8 data sets for the operating variables with nonlinear and complex characteristics. The performance fitness of the neural network (MSE = 0.00000107 and R-value = 0.9979) describes the effectiveness of the ANN model in predicting the performance of the scrubber system and the model follows the pattern of the theoretical data describing the scrubber performance at a higher efficiency range. IJSER 2012-11 Article PeerReviewed application/pdf en http://irep.iium.edu.my/26859/1/Artificial-Neural-Network-Model-for-Predicting-Wet-Scrubber-Performance.pdf Danzomo, Bashir Ahmed and Salami, Momoh Jimoh Eyiomika and Khan, Md. Raisuddin (2012) Artificial neural network model for predicting wet scrubber performance. International Journal of Scientific & Engineering Research, 3 (11). pp. 1-10. ISSN 2229-5518 http://www.ijser.org/onlineResearchPaperViewer.aspx?Artificial-Neural-Network-Model-for-Predicting-Wet-Scrubber-Performance.pdf
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	TA168 Systems engineering
spellingShingle	TA168 Systems engineering Danzomo, Bashir Ahmed Salami, Momoh Jimoh Eyiomika Khan, Md. Raisuddin Artificial neural network model for predicting wet scrubber performance
description	Increased public awareness posed for global climate change has led to greater concern over the impact of environmental changes due to constant emissions of air pollutants from industrial production. Wet scrubbers have important advantages when compared to other air pollution control devices. They can collect particulates like flammable and explosive dusts, foundry dusts, cement dusts, large volume of gaseous pollutants, acid mists and furnace fumes. In this study, a three layer feed forward neural network has been used to predict the performance of wet scrubber system for air pollution control. The theoretical performance, ηperf of the system was calculated using 206 scenarios for 8 data sets for the operating variables with nonlinear and complex characteristics. The performance fitness of the neural network (MSE = 0.00000107 and R-value = 0.9979) describes the effectiveness of the ANN model in predicting the performance of the scrubber system and the model follows the pattern of the theoretical data describing the scrubber performance at a higher efficiency range.
format	Article
author	Danzomo, Bashir Ahmed Salami, Momoh Jimoh Eyiomika Khan, Md. Raisuddin
author_facet	Danzomo, Bashir Ahmed Salami, Momoh Jimoh Eyiomika Khan, Md. Raisuddin
author_sort	Danzomo, Bashir Ahmed
title	Artificial neural network model for predicting wet scrubber performance
title_short	Artificial neural network model for predicting wet scrubber performance
title_full	Artificial neural network model for predicting wet scrubber performance
title_fullStr	Artificial neural network model for predicting wet scrubber performance
title_full_unstemmed	Artificial neural network model for predicting wet scrubber performance
title_sort	artificial neural network model for predicting wet scrubber performance
publisher	IJSER
publishDate	2012
url	http://irep.iium.edu.my/26859/ http://irep.iium.edu.my/26859/ http://irep.iium.edu.my/26859/1/Artificial-Neural-Network-Model-for-Predicting-Wet-Scrubber-Performance.pdf
first_indexed	2023-09-18T20:39:57Z
last_indexed	2023-09-18T20:39:57Z
_version_	1777409297982947328

Artificial neural network model for predicting wet scrubber performance

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