posted on 2024-01-06, 14:10authored byJing Zhang, Xiaolong Yao, Yue Zhao, Rengui Li, Xiaofei Chen, Haibo Jin, Huangzhao Wei, Lei Ma, Zhao Mu, Xiaowei Liu
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, Fe2+ dosage, H2O2 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: Fe2+ dosage at 16.09
mg/L, H2O2 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.