A generalized predictive model for TiO2–Catalyzed photo-degradation rate constants of water contaminants through artificial neural network

Jiang, Zhuoying; Hu, Jiajie; Zhang, Xijin; Zhao, Yihang; Fan, Xudong; Zhong, Shifa; Zhang, Huichun; Yu, Xiong

doi:10.1016/j.envres.2020.109697

Cited by 35 publications

(16 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the case of solution pH, it affects the overall charge of the catalyst and pollutant, and consequently, the adsorption of pollutants on the surface and degradation could be influenced. Thus, it is not necessarily correct that the pollutant degrades faster at higher pH because several MOFs with various structures have different responses to pH 51 . For instance, Zhang et al 57 reported that the pH 3 is the best value to degrade TC on MIL-88A, while Pan et al 61 claimed that the TC was degraded over AgI/UiO-66-NH 2 more rapidly in pH 4.3.…”

Section: Resultsmentioning

confidence: 99%

An insight into tetracycline photocatalytic degradation by MOFs using the artificial intelligence technique

Gheytanzadeh

Baghban

Habibzadeh

et al. 2022

Sci Rep

View full text Add to dashboard Cite

Tetracyclines (TCs) have been extensively used for humans and animal diseases treatment and livestock growth promotion. The consumption of such antibiotics has been ever-growing nowadays due to various bacterial infections and other pathologic conditions, resulting in more discharge into the aquatic environments. This brings threats to ecosystems and human bodies. Up to now, several attempts have been made to reduce TC amounts in the wastewater, among which photocatalysis, an advanced oxidation process, is known as an eco-friendly and efficient technology. In this regard, metal organic frameworks (MOFs) have been known as the promising materials as photocatalysts. Thus, studying TC photocatalytic degradation by MOFs would help scientists and engineers optimize the process in terms of effective parameters. Nevertheless, the costly and time-consuming experimental methods, having instrumental errors, encouraged the authors to use the computational method for a more comprehensive assessment. In doing so, a wide-ranging databank including 374 experimental data points was gathered from the literature. A powerful machine learning method of Gaussian process regression (GPR) model with four kernel functions was proposed to estimate the TC degradation in terms of MOFs features (surface area and pore volume) and operational parameters (illumination time, catalyst dosage, TC concentration, pH). The GPR models performed quite well, among which GPR-Matern model shows the most accurate performance with R2, MRE, MSE, RMSE, and STD of 0.981, 12.29, 18.03, 4.25, and 3.33, respectively. In addition, an analysis of sensitivity was carried out to assess the effect of the inputs on the TC photodegradation by MOFs. It revealed that the illumination time and the surface area play a significant role in the decomposition activity.

show abstract

Section: Resultsmentioning

confidence: 99%

An insight into tetracycline photocatalytic degradation by MOFs using the artificial intelligence technique

Gheytanzadeh

Baghban

Habibzadeh

et al. 2022

Sci Rep

View full text Add to dashboard Cite

show abstract

“…The importance factor of water contaminant is obtained by adding the SHAP values of contaminant feature vector (vector of 1280 generated by EfficientNet). Figure 4 also compares the importance factors of the CNN_Aug model in this work and the previous ANN model [15]. The feature importances of the two models share the same orders: contaminant type > initial concentration > temperature > TiO2 dosage > pH > light intensity.…”

Section: Feature Importancementioning

confidence: 85%

“…The coefficient of determination (R 2 ), root-mean-square error (RMSE), and mean absolute error (MAE) are used to assess the model performance. Three models are trained and compared: CNN model trained with augmented dataset (CNN_Aug), CNN model trained with original dataset (CNN_Ori), and ANN model trained using molecule fingerprints (ANN) published in ref [15]. The scatter plots of the predicted vs. experimental photocatalytic degradation-rate constants −log(k) are shown in Figure 1.…”

Section: Model Performance and Comparisonmentioning

confidence: 99%

“…Machine-learning models have become an inexpensive but effective tool for the investigation and prediction of features of different environmental chemicals [7][8][9][10]. Particularly in the catalysis field, to accelerate new catalyst-discovery practice, data-driven machine-learning models have been used to predict catalyst properties, e.g., band gaps, and performance, e.g., adsorption energy, water-splitting efficiency, and water-contaminant degradation efficiency [11][12][13][14][15]. However, the "black box" character in machine learning makes the models indescribable and intractable on the relationship between the input and output.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Predicting Active Sites in Photocatalytic Degradation Process Using an Interpretable Molecular-Image Combined Convolutional Neural Network

Jiang

2022

Catalysts

Self Cite

View full text Add to dashboard Cite

Machine-learning models have great potential to accelerate the design and performance assessment of photocatalysts, leveraging their unique advantages in detecting patterns and making predictions based on data. However, most machine-learning models are “black-box” models due to lack of interpretability. This paper describes the development of an interpretable neural-network model on the performance of photocatalytic degradation of organic contaminants by TiO2. The molecular structures of the organic contaminants are represented by molecular images, which are subsequently encoded by feeding into a special convolutional neural network (CNN), EfficientNet, to extract the critical structural features. The extracted features in addition to five other experimental variables were input to a neural network that was subsequently trained to predict the photodegradation reaction rates of the organic contaminants by TiO2. The results show that this machine-learning (ML) model attains a higher accuracy to predict the photocatalytic degradation rate of organic contaminants than a previously developed machine-learning model that used molecular fingerprint encoding. In addition, the most relevant regions in the molecular image affecting the photocatalytic rates can be extracted with gradient-weighted class activation mapping (Grad-CAM). This interpretable machine-learning model, leveraging the graphic interpretability of CNN model, allows us to highlight regions of the molecular structure serving as the active sites of water contaminants during the photocatalytic degradation process. This provides an important piece of information to understand the influence of molecular structures on the photocatalytic degradation process.

show abstract

“…It stores the atomic and structural information of molecules in a binary digit vector, where "1" represents presence and "0" represents the absence of a particular substructure. It has shown the potentials to encode organic materials for machine learning models [34][35][36][37]. The advantages of MF representation include that the properties of small molecules can be predicted at high accuracy and with low computational time at the same time [36].…”

mentioning

confidence: 99%

A Novel Machine Learning Model to Predict the Photo-Degradation Performance of Different Photocatalysts on a Variety of Water Contaminants

Jiang

Tong

et al. 2021

Catalysts

Self Cite

View full text Add to dashboard Cite

This paper describes an innovative machine learning (ML) model to predict the performance of different metal oxide photocatalysts on a wide range of contaminants. The molecular structures of metal oxide photocatalysts are encoded with a crystal graph convolution neural network (CGCNN). The structure of organic compounds is encoded via digital molecular fingerprints (MF). The encoded features of the photocatalysts and contaminants are input to an artificial neural network (ANN), named as CGCNN-MF-ANN model. The CGCNN-MF-ANN model has achieved a very good prediction of the photocatalytic degradation rate constants by different photocatalysts over a wide range of organic contaminants. The effects of the data training strategy on the ML model performance are compared. The effects of different factors on photocatalytic degradation performance are further evaluated by feature importance analyses. Examples are illustrated on the use of this novel ML model for optimal photocatalyst selection and for assessing other types of photocatalysts for different environmental applications.

show abstract

A generalized predictive model for TiO2–Catalyzed photo-degradation rate constants of water contaminants through artificial neural network

Cited by 35 publications

References 50 publications

An insight into tetracycline photocatalytic degradation by MOFs using the artificial intelligence technique

An insight into tetracycline photocatalytic degradation by MOFs using the artificial intelligence technique

Predicting Active Sites in Photocatalytic Degradation Process Using an Interpretable Molecular-Image Combined Convolutional Neural Network

A Novel Machine Learning Model to Predict the Photo-Degradation Performance of Different Photocatalysts on a Variety of Water Contaminants

Contact Info

Product

Resources

About