Knowledge extraction of sonophotocatalytic treatment for acid blue 113 dye removal by artificial neural networks
- Reddy, B. S.; Maurya, A. K.; Narayana, P. L.; Pasha, S. K. Khadheer; Reddy, M. R.; Hatshan, Mohammad Rafe; Darwish, Noura M.; Kori, S. A.; Cho, Kwon-Koo; Reddy, N. S.
- Issue Date
- ACADEMIC PRESS INC ELSEVIER SCIENCE
- Artificial neural networks; Index of relative importance; Prediction; Sensitivity analysis; Sonophotocatalytic; Textile wastewater
- ENVIRONMENTAL RESEARCH, v.204
- Journal Title
- ENVIRONMENTAL RESEARCH
- Removing decolorizing acid blue 113 (AB113) dye from textile wastewater is challenging due to its high stability and resistance to removal. In this study, we used an artificial neural network (ANN) model to estimate the effect of five different variables on AB113 dye removal in the sonophotocatalytic process. The five variables considered were reaction time (5-25 min), pH (3-11), ZnO dosage (0.2-1.0 g/L), ultrasonic power (100-300 W/L), and persulphate dosage (0.2-3 mmol/L). The most effective model had a 5-7-1 architecture, with an average deviation of 0.44 and R-2 of 0.99. A sensitivity analysis was used to analyze the impact of different process variables on removal efficiency and to identify the most effective variable settings for maximum dye removal. Then, an imaginary sonophotocatalytic system was created to measure the quantitative impact of other process parameters on AB113 dye removal. The optimum process parameters for maximum AB 113 removal were identified as 6.2 pH, 25 min reaction time, 300 W/L ultrasonic power, 1.0 g/L ZnO dosage, and 2.54 mmol/L persulfate dosage. The model created was able to identify trends in dye removal and can contribute to future experiments.
- Files in This Item
- There are no files associated with this item.
- Appears in
- 공학계열 > Dept.of Materials Engineering and Convergence Technology > Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.