Development of artificial nueral network model for the analysis of wastewater treatment

A statistical modeling tool called artificial neural network (ANN) is used in this work to predict the performance of wastewater treatment plant (WWTP). Extensive influent and effluent parameters database containing measured data spanning over two years of period was used to develop and train ANN us...

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Main Authors: Jami, Mohammed Saedi, Kabbashi, Nassereldeen Ahmed
Format: Monograph
Language:English
English
Published: [s.n] 2012
Subjects:
Online Access:http://irep.iium.edu.my/31218/
http://irep.iium.edu.my/31218/1/Full_Report.pdf
http://irep.iium.edu.my/31218/2/EndofProjectReport_14_03_12.pdf
id iium-31218
recordtype eprints
spelling iium-312182015-09-08T09:58:07Z http://irep.iium.edu.my/31218/ Development of artificial nueral network model for the analysis of wastewater treatment Jami, Mohammed Saedi Kabbashi, Nassereldeen Ahmed TC Hydraulic engineering. Ocean engineering A statistical modeling tool called artificial neural network (ANN) is used in this work to predict the performance of wastewater treatment plant (WWTP). Extensive influent and effluent parameters database containing measured data spanning over two years of period was used to develop and train ANN using ANN toolbox in commercially available software, MATLAB. The data were obtained from one of Sewage Treatment Plant in Malaysia. The input parameters for the ANN were BOD, SS, and COD of the influent, while the output parameters were combination of the effluent characteristics. The networks for single input-single output were compared with those of single input-multiple output. The ANN was developed for raw and screened data and the results were compared for both networks. It was found that the use of data screening is essential to come up with a better ANNs model. From the regression analysis, networks with one hidden layer and 20 neurons were found to be the best one for single input-single output approach. While the best network for the multiple inputs-single output approach was with BOD as outputs and 30 neurons. The second approach which showed a lower RMSE and higher R values was selected. The results show that hybrid (PCA+BODinf) model outperformed its corresponding normal BODinf net and recorded a higher correlation coefficients (R) values for training (0.7362), testing (0.7678) and verification (0.7699) datasets with their respective mean absolute errors (MAE) of 13.75,11.29 and 12.76. [s.n] 2012-03-14 Monograph NonPeerReviewed application/pdf en http://irep.iium.edu.my/31218/1/Full_Report.pdf application/pdf en http://irep.iium.edu.my/31218/2/EndofProjectReport_14_03_12.pdf Jami, Mohammed Saedi and Kabbashi, Nassereldeen Ahmed (2012) Development of artificial nueral network model for the analysis of wastewater treatment. Research Report. [s.n]. (Unpublished)
repository_type Digital Repository
institution_category Local University
institution International Islamic University Malaysia
building IIUM Repository
collection Online Access
language English
English
topic TC Hydraulic engineering. Ocean engineering
spellingShingle TC Hydraulic engineering. Ocean engineering
Jami, Mohammed Saedi
Kabbashi, Nassereldeen Ahmed
Development of artificial nueral network model for the analysis of wastewater treatment
description A statistical modeling tool called artificial neural network (ANN) is used in this work to predict the performance of wastewater treatment plant (WWTP). Extensive influent and effluent parameters database containing measured data spanning over two years of period was used to develop and train ANN using ANN toolbox in commercially available software, MATLAB. The data were obtained from one of Sewage Treatment Plant in Malaysia. The input parameters for the ANN were BOD, SS, and COD of the influent, while the output parameters were combination of the effluent characteristics. The networks for single input-single output were compared with those of single input-multiple output. The ANN was developed for raw and screened data and the results were compared for both networks. It was found that the use of data screening is essential to come up with a better ANNs model. From the regression analysis, networks with one hidden layer and 20 neurons were found to be the best one for single input-single output approach. While the best network for the multiple inputs-single output approach was with BOD as outputs and 30 neurons. The second approach which showed a lower RMSE and higher R values was selected. The results show that hybrid (PCA+BODinf) model outperformed its corresponding normal BODinf net and recorded a higher correlation coefficients (R) values for training (0.7362), testing (0.7678) and verification (0.7699) datasets with their respective mean absolute errors (MAE) of 13.75,11.29 and 12.76.
format Monograph
author Jami, Mohammed Saedi
Kabbashi, Nassereldeen Ahmed
author_facet Jami, Mohammed Saedi
Kabbashi, Nassereldeen Ahmed
author_sort Jami, Mohammed Saedi
title Development of artificial nueral network model for the analysis of wastewater treatment
title_short Development of artificial nueral network model for the analysis of wastewater treatment
title_full Development of artificial nueral network model for the analysis of wastewater treatment
title_fullStr Development of artificial nueral network model for the analysis of wastewater treatment
title_full_unstemmed Development of artificial nueral network model for the analysis of wastewater treatment
title_sort development of artificial nueral network model for the analysis of wastewater treatment
publisher [s.n]
publishDate 2012
url http://irep.iium.edu.my/31218/
http://irep.iium.edu.my/31218/1/Full_Report.pdf
http://irep.iium.edu.my/31218/2/EndofProjectReport_14_03_12.pdf
first_indexed 2023-09-18T20:45:28Z
last_indexed 2023-09-18T20:45:28Z
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