Enhancing sodium percarbonate catalytic wet peroxide oxidation with artificial intelligence-optimized swirl flow: Ni single atom sites on carbon nanotubes for improved reactivity and silicon resistance

Yao, Chenxing; Zhang, Jing; Gao, Liansong; Jin, Chengyu; Wang, Shengzhe; Jiang, Wenshuo; Liang, Hanrui; Feng, Pan; Li, Xianru; Ma, Lei; Wei, Huangzhao; Sun, Chenglin

doi:10.1016/j.chemosphere.2023.140606

Cited by 3 publications

(3 citation statements)

References 51 publications

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“…This paper has established an early warning model based on the BP neural network to predict the total number of preschool children affected in a given month based on the monthly reported counts of each symptom. Through experiments, the feasibility of the BP neural network prediction model in forecasting the overall health condition of preschool children, specifically the total monthly reported counts, has been confirmed [29] . The establishment of this predictive model holds significant importance for the prevention of infectious diseases in a border region of a certain province.…”

Section: Discussionmentioning

confidence: 84%

Research on an intelligent assessment method for the physical health of preschool children based on the Artificial Neural Network (ANN) model

Li,

Malek,

Awang

2024

Environ. Soc. Psychol.

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This study aims to use the BP neural network model to predict the monthly total number of reported symptoms in preschool children, thereby achieving early warning for the total number of cases in the current month. By analyzing the training results of the neural network and the fitting of the model, the feasibility and effectiveness of the BP neural network in monitoring the physical health of preschool children are validated. The predictive model is of great significance for the prevention of infectious diseases in a border region of a certain province, providing important information for the local incidence situation. The research results indicate that the established BP neural network model demonstrates good accuracy and practicality in predicting the monthly total number of reported symptoms in preschool children’s physical health.

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Section: Discussionmentioning

confidence: 84%

Research on an intelligent assessment method for the physical health of preschool children based on the Artificial Neural Network (ANN) model

Li,

Malek,

Awang

2024

Environ. Soc. Psychol.

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“…As in liquid chromatography, m -cresol becomes undetectable after undergoing chemical changes, such as ring-opening or forming intermediates, even though it is still harmful. This leads to the reported removal rates of m -cresol often reaching 100% . Therefore, for a thorough and accurate assessment of water quality, the TOC removal rate is employed as the principal metric for organic matter removal, offering a complete view of the treatment process’s effectiveness.…”

Section: Introductionmentioning

confidence: 99%

“…This leads to the reported removal rates of m-cresol often reaching 100%. 33 Therefore, for a thorough and accurate assessment of water quality, the TOC removal rate is employed as the principal metric for organic matter removal, offering a complete view of the treatment process's effectiveness. By harnessing ANN, we aim to evaluate TOC removal in the treatment, converging UV-Fenton processes, and ANN for optimal results.…”

Section: Introductionmentioning

confidence: 99%

Optimizing the UV-Fenton Degradation of m-Cresol Wastewater: An Experimental and Artificial Intelligence Modeling Approach

Zhang,

Yao,

Zhao

et al. 2024

Ind. Eng. Chem. Res.

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Wastewater treatment, especially the efficient degradation of contaminants such as m-cresol, remains a pivotal challenge. This study investigates the application of artificial neural networks (ANN) in predicting total organic carbon (TOC) removal rates from m-cresol-contaminated wastewater by using the ultraviolet (UV)-Fenton oxidation process. Six key variables, namely, Fe 2+ dosage, H 2 O 2 dosage, catalyst quantity, reaction time, pH, and substrate concentration, were employed as inputs to the ANN model. Leveraging this multivariable input and a comprehensive data set, the ANN model projected a maximum TOC removal rate of 87.12%, validated by an efficiency of 86.26% achieved through experiments under the derived optimal conditions: Fe 2+ dosage at 16.09 mg/L, H 2 O 2 dosage at 1.40 mg/L, catalyst quantity at 0.11 g/L, reaction time of 29.80 min, initial pH of 3.66, and substrate concentration of 50 mg/L. Comparative analysis with other machine learning algorithms further revealed that the ANN model notably outperformed linear regression, support vector regression, and random forest in terms of precision. This work paves the way for resource-optimized experimental designs, fostering real-time wastewater monitoring and refining advanced oxidation process proficiency in industrial applications.

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Bimetal-organic framework derived single-atom-like Co/Fe catalysts for peroxydisulfate activation: The dual amplification of radical and nonradical pathways

Deng,

Wei,

Zhang

et al. 2025

Colloids and Surfaces A: Physicochemical and Engineering Aspect

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Enhancing sodium percarbonate catalytic wet peroxide oxidation with artificial intelligence-optimized swirl flow: Ni single atom sites on carbon nanotubes for improved reactivity and silicon resistance

Cited by 3 publications

References 51 publications

Research on an intelligent assessment method for the physical health of preschool children based on the Artificial Neural Network (ANN) model

Research on an intelligent assessment method for the physical health of preschool children based on the Artificial Neural Network (ANN) model

Optimizing the UV-Fenton Degradation of m-Cresol Wastewater: An Experimental and Artificial Intelligence Modeling Approach

Bimetal-organic framework derived single-atom-like Co/Fe catalysts for peroxydisulfate activation: The dual amplification of radical and nonradical pathways

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