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A Supervised Neural Network-based predictive model for petrochemical wastewater treatment dataset

It is understood that water is the most valuable natural resource and as like wastewater treatment plants are necessary base to control the environmental balance where they are installed. To ensure good quality effluents, the dynamic and complicated wastewater treatment procedure must be handled eff...

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Main Authors: Varun, Geetha Mohan, Mubarak Ali, Al-Fahim, Vijayan, Bincy Lathakumary, Saiful, Azad, Mohamed Ariff, Ameedeen
Format: Conference or Workshop Item
Language:English
Published: IEEE Explorer 2022
Subjects:
TP Chemical technology
TS Manufactures
Online Access:http://umpir.ump.edu.my/id/eprint/35203/1/A_Supervised_Neural_Network-based_predictive_model_for_petrochemical_wastewater_treatment_dataset.pdf
http://umpir.ump.edu.my/id/eprint/35203/
http://10.1109/ICEEICT53079.2022.9768566
http://10.1109/ICEEICT53079.2022.9768566
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spelling my.ump.umpir.352032022-09-14T06:54:04Z http://umpir.ump.edu.my/id/eprint/35203/ A Supervised Neural Network-based predictive model for petrochemical wastewater treatment dataset Varun, Geetha Mohan Mubarak Ali, Al-Fahim Vijayan, Bincy Lathakumary Saiful, Azad Mohamed Ariff, Ameedeen TP Chemical technology TS Manufactures It is understood that water is the most valuable natural resource and as like wastewater treatment plants are necessary base to control the environmental balance where they are installed. To ensure good quality effluents, the dynamic and complicated wastewater treatment procedure must be handled efficiently. A global interest has been prompted in conservation, reuse, and alternative water sources due to growing treats over water supply scarcity. Water utilities are searching for more efficient ways to maintain their resources globally. The development of machine learning techniques is starting to offer real opportunities to operate water treatment systems in more efficient manners. This paperwork shows research as well as its development work implemented to predict the performance of petrochemical wastewater treatment. The data were used from a reputed chemical plant and the predictive models were developed by implementation of Backpropagation Neural Network using sample datasets with the parameters of wastewater dataset. IEEE Explorer 2022-05-10 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/35203/1/A_Supervised_Neural_Network-based_predictive_model_for_petrochemical_wastewater_treatment_dataset.pdf Varun, Geetha Mohan and Mubarak Ali, Al-Fahim and Vijayan, Bincy Lathakumary and Saiful, Azad and Mohamed Ariff, Ameedeen (2022) A Supervised Neural Network-based predictive model for petrochemical wastewater treatment dataset. In: 2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT), 16-18 February 2022 , Trichy, India. pp. 1-5.. ISBN 978-1-6654-3648-9 (Online); 978-1-6654-3647-2(PoD) http://10.1109/ICEEICT53079.2022.9768566 http://10.1109/ICEEICT53079.2022.9768566
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic TP Chemical technology
TS Manufactures
spellingShingle TP Chemical technology
TS Manufactures
Varun, Geetha Mohan
Mubarak Ali, Al-Fahim
Vijayan, Bincy Lathakumary
Saiful, Azad
Mohamed Ariff, Ameedeen
A Supervised Neural Network-based predictive model for petrochemical wastewater treatment dataset
description It is understood that water is the most valuable natural resource and as like wastewater treatment plants are necessary base to control the environmental balance where they are installed. To ensure good quality effluents, the dynamic and complicated wastewater treatment procedure must be handled efficiently. A global interest has been prompted in conservation, reuse, and alternative water sources due to growing treats over water supply scarcity. Water utilities are searching for more efficient ways to maintain their resources globally. The development of machine learning techniques is starting to offer real opportunities to operate water treatment systems in more efficient manners. This paperwork shows research as well as its development work implemented to predict the performance of petrochemical wastewater treatment. The data were used from a reputed chemical plant and the predictive models were developed by implementation of Backpropagation Neural Network using sample datasets with the parameters of wastewater dataset.
format Conference or Workshop Item
author Varun, Geetha Mohan
Mubarak Ali, Al-Fahim
Vijayan, Bincy Lathakumary
Saiful, Azad
Mohamed Ariff, Ameedeen
author_facet Varun, Geetha Mohan
Mubarak Ali, Al-Fahim
Vijayan, Bincy Lathakumary
Saiful, Azad
Mohamed Ariff, Ameedeen
author_sort Varun, Geetha Mohan
title A Supervised Neural Network-based predictive model for petrochemical wastewater treatment dataset
title_short A Supervised Neural Network-based predictive model for petrochemical wastewater treatment dataset
title_full A Supervised Neural Network-based predictive model for petrochemical wastewater treatment dataset
title_fullStr A Supervised Neural Network-based predictive model for petrochemical wastewater treatment dataset
title_full_unstemmed A Supervised Neural Network-based predictive model for petrochemical wastewater treatment dataset
title_sort supervised neural network-based predictive model for petrochemical wastewater treatment dataset
publisher IEEE Explorer
publishDate 2022
url http://umpir.ump.edu.my/id/eprint/35203/1/A_Supervised_Neural_Network-based_predictive_model_for_petrochemical_wastewater_treatment_dataset.pdf
http://umpir.ump.edu.my/id/eprint/35203/
http://10.1109/ICEEICT53079.2022.9768566
http://10.1109/ICEEICT53079.2022.9768566
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score 13.211869

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