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

• Published in: 2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT) pp. 1 - 5
• Main Authors: Mohan, Varun Geetha, Ali, Al-Fahim Mubarak, Vijayan, Bincy Lathakumary, Azad, Saiful, Bin Ameedeen, Mohamed Ariff
• Format: Conference Proceeding
• Language: English
• Published: IEEE 16.02.2022
• Subjects: Artificial intelligence; Backpropagation; Backpropagation neural network; Chemical Oxygen Demand; Machine learning; Neural networks; Oxygen; Petrochemical wastewater treatment; Petrochemicals; Pollutant Removal Efficiency; Predictive models; Principal component analysis; Water conservation
• DOI: 10.1109/ICEEICT53079.2022.9768566

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.