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)
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Abstract
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.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Uncontrolled Keywords: | Petrochemical wastewater treatment; artificial intelligence; backpropagation neural network; principal component analysis; chemical oxygen demand; pollutant removal efficiency |
| Subjects: | T Technology > TP Chemical technology T Technology > TS Manufactures |
| Faculty/Division: | Faculty of Industrial Sciences And Technology Institute of Postgraduate Studies Faculty of Computing |
| Depositing User: | Miss. Ratna Wilis Haryati Mustapa |
| Date Deposited: | 14 Sep 2022 06:52 |
| Last Modified: | 14 Sep 2022 06:54 |
| URI: | http://umpir.ump.edu.my/id/eprint/35203 |
| Download Statistic: | View Download Statistics |
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