A generalized predictive model for TiO-Catalyzed photo-degradation rate constants of water contaminants through artificial neural network.

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ID: 107131
2020
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Abstract
Titanium dioxide (TiO) is a well-known photocatalyst in the applications of water contaminant treatment. Traditionally, the kinetics of photo-degradation rates are obtained from experiments, which consumes enormous labor and experimental investments. Here, a generalized predictive model was developed for prediction of the photo-degradation rate constants of organic contaminants in the presence of TiO nanoparticles and ultraviolet irradiation in aqueous solution. This model combines an artificial neural network (ANN) with a variety of factors that affect the photo-degradation performance, i.e., ultraviolet intensity, TiO dosage, organic contaminant type and initial concentration in water, and initial pH of the solution. The molecular fingerprints (MF) were used to interpret the organic contaminants as binary vectors, a format that is machine-readable in computational linguistics. A dataset of 446 data points for training and testing was collected from the literature. This predictive model shows a good accuracy with a root mean square error (RMSE) of 0.173.
Reference Key
jiang2020aenvironmental Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Jiang, Zhuoying;Hu, Jiajie;Zhang, Xijin;Zhao, Yihang;Fan, Xudong;Zhong, Shifa;Zhang, Huichun;Yu, Xiong;
Journal Environmental research
Year 2020
DOI
S0013-9351(20)30590-9
URL
Keywords
Machine learning artificial neural network titanium dioxide molecular fingerprint photo-degradation of water contaminants reaction rate constant

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