Artificial intelligence-aided preparation of perovskite SrFexZr1-xO3-δ catalysts for ozonation degradation of organic pollutant concentrated water after membrane treatment

Wang, Xu; Zhang, Yanan; Zhang, Cheng; Wei, Huangzhao; Jin, Haibo; Zhao, M. G.; Xiaofei, Chen; Chen, Xinru; Wang, Pin; Guo, Xiaojun; Ding, Fang; Liu, Xiaowei; Ma, Lei

doi:10.1016/j.chemosphere.2023.137825

Cited by 6 publications

(4 citation statements)

References 63 publications

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“…Ultimately, the testing set gauges the resulting model's prowess. 28 A systematic approach is imperative in ANN model development, encompassing factors like data preprocessing, input selection, network architecture, parameter optimization, and model validation. 44 Overfitting, a potential pitfall, may occur when the error on the training set is driven to an extremely small value, while the error for the test data presented to the network is high.…”

Section: Basic Principles Of Neural Networkmentioning

confidence: 99%

“…While the validation set refines parameter choice, the complete training is executed on both training and validation data sets. Ultimately, the testing set gauges the resulting model’s prowess . A systematic approach is imperative in ANN model development, encompassing factors like data preprocessing, input selection, network architecture, parameter optimization, and model validation .…”

Section: Ann Modelmentioning

confidence: 99%

“…Advanced oxidation processes (AOPs), compared to conventional technologies like biological treatment, exhibit intriguing properties in treating organic wastewater. Featured by robust treatment capacity, cost-effectiveness, and excellent treatment efficiency, AOPs do not dismiss new pollution to water bodies, drawing significant attention in environmental science and technology. − In this milieu, catalysts, pivotal for efficient generation and utilization of free radicals, dictate wastewater treatment outcomes. − ANN’s prowess in forecasting optimal conditions for oxidation systems , and in refining catalyst preparations underscores their tremendous potential to economize reagents and labor . Our earlier research also utilized artificial intelligence to predict the outcomes for different advanced oxidation reactions, finding that the ANN is more accurate than the response surface model (RSM) with 50 data sets and 5 input variables .…”

Section: Introductionmentioning

confidence: 99%

“…21−23 In this milieu, catalysts, pivotal for efficient generation and utilization of free radicals, dictate wastewater treatment outcomes. 24−26 ANN's prowess in forecasting optimal conditions for oxidation systems 17,27 and in refining catalyst preparations 28 underscores their tremendous potential to economize reagents and labor. 29 research also utilized artificial intelligence to predict the outcomes for different advanced oxidation reactions, finding that the ANN is more accurate than the response surface model (RSM) with 50 data sets and 5 input variables.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Basic Principles Of Neural Networkmentioning

confidence: 99%

Section: Ann Modelmentioning

confidence: 99%