Modeling cyclic volatile methylsiloxanes removal efficiency from wastewater by ZnO-coated aluminum anode using artificial neural networksopen access
- Reddy, B. S.; Narayana, P. L.; Maurya, A. K.; Gupta, V; Reddy, Y. H.; Alrefaei, Abdulwahed F.; Alkhamis, Hussein H.; Cho, Kwon-Koo; Reddy, N. S.
- Issue Date
- cVMSs removal efficiency; Artificial neural networks; Quantitative; Wastewater; Photo-electrocatalysis
- JOURNAL OF KING SAUD UNIVERSITY SCIENCE, v.33, no.2
- Journal Title
- JOURNAL OF KING SAUD UNIVERSITY SCIENCE
- Usage of cyclic volatile methyl siloxanes (cVMSs) in the industrial process is unavoidable due to their superior properties; however, it is hazardous to human health. Photocatalytic zinc oxide coated aluminum anode is used to degrade the cVMSs in wastewater. In this work, we investigated the relationship among degradation process parameters such as current density (4-20 mA/cm(2)), initial pH (5-9), plate distance (8-24 cm), UV intensity (0-120 W), and reaction time (30-100 min) vis-a-vis cVMSs removal efficiency by using data-driven artificial neural networks(ANN) model. The ANN model was trained using a backpropagation algorithm with the sigmoid activation function between input, hidden, and the output layers. Two hidden layers with eight neurons in each layer presented the minimum average training error (0.24) and higher (0.99) correlation coefficient values (both Pearson's r and Adj. R-2) as compared with the conventional regression model. The effect and relationship between the parameters and cVMSs removal efficiency were analyzed by single, two variable sensitivity analysis, qualitative and quantitative estimation. (C) 2021 The Authors. Published by Elsevier B.V. on behalf of King Saud University.
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- 공과대학 > 나노신소재공학부금속재료공학전공 > Journal Articles
- 공학계열 > Dept.of Materials Engineering and Convergence Technology > Journal Articles
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