Predictive models using supervised neural network for pollutant removal efficiency in petrochemical wastewater treatment

Mohan, Varun Geetha and Al-Fahim, Mubarak-Ali and Ameedeen, Mohamed Ariff and Vijayan, Bincy Lathakumary and Aminuddin, Afrig and Widayani, Wiwi (2022) Predictive models using supervised neural network for pollutant removal efficiency in petrochemical wastewater treatment. In: 2022 5th International Conference on Information and Communications Technology (ICOIACT) , 24-25 August 2022 , Yogyakarta, Indonesia. pp. 1-6.. ISSN 2770-4661 ISBN 978-1-6654-5140-6

Pdf
Predictive Models Using Supervised Neural_FULL.pdf
Restricted to Repository staff only
Download (1MB) | Request a copy

Preview

Pdf
Predictive models using supervised neural network for pollutant removal .pdf
Download (107kB) | Preview

DOI/Official URL: https://doi.org/ 10.1109/ICOIACT55506.2022.9971929

Abstract

The important process in wastewater treatment is the removal of pollutants, and the dataset having so many features may cause difficulty training the data and predicting key variables. This work aims to propose set parameters through normalization techniques, feature selection techniques, and AI techniques. The datasets have 36 features and a key parameter, and experimental datasets contain 628. Constant factor, Z-score, and Min-max normalization are the normalization techniques used to normalize the petrochemical wastewater dataset. SelectKBest, ExtraTreeClassifier, PCA, and RFE are the feature selection techniques for data mining. Then finally done with AI implementation with the help of a supervised neural network technique called backpropagation neural network (BPNN).

Item Type:	Conference or Workshop Item (Lecture)
Uncontrolled Keywords:	Backpropagation neural network; Feature selection; Normalization; Petrochemical wastewater treatment processes
Subjects:	Q Science > QA Mathematics > QA76 Computer software Q Science > QD Chemistry T Technology > TA Engineering (General). Civil engineering (General) T Technology > TP Chemical technology
Faculty/Division:	Faculty of Industrial Sciences And Technology Institute of Postgraduate Studies Faculty of Computing
Depositing User:	Mrs Norsaini Abdul Samat
Date Deposited:	21 Dec 2022 01:56
Last Modified:	21 Dec 2022 01:56
URI:	http://umpir.ump.edu.my/id/eprint/35990
Download Statistic:	View Download Statistics

Actions (login required)

View Item