An Efficient Investigation and Machine Learning-Based Prediction of Decolorization of Wastewater by Using Zeolite Catalyst in Electro-Fenton Reaction

Abstract

The shortage of water resources has caused extensive research to be conducted in this field to develop effective, rapid, and affordable wastewater treatment methods. For the treatment of wastewater, modern oxidation techniques are desirable due to their excellent performance and simplicity of implementation. In this project, wet impregnation and the hydrothermal technique were applied to synthesize a modified catalyst. Different analysis methods were used to determine its characteristics, including XRD, BET, FT-IR, NH3−TPD, and FE-SEM. The catalyst features a spherical shape, large surface area, high crystallinity, and uniform active phase dispersion. In order to eliminate the methylene blue dye as a modeling effluent, the catalyst’s performance was examined in a heterogeneous quasi-electro-Fenton (EF) reaction. The impact of various performance characteristics, such as catalyst concentration in the reaction medium, solution pH, and current intensity between the two electrodes, was elucidated. According to the results, the best operational circumstances included a pH level of 2, a catalyst concentration of 0.15 g/L, and a current of 150 mA, resulting in the greatest elimination efficiency of 101%. The catalyst’s performance was stable during three consecutive tests. A pseudo-first-order model for the elimination reaction’s kinetics was developed, which showed acceptable agreement with the experimental results. This study’s findings help clarify how well the heterogeneous zeolite catalyst functions in the pseudo-EF reaction. The results revealed the method’s potential to be implemented in wastewater treatment. An artificial neural network model is utilized to predict the removal percentage. The hyperparameter tuning is used to find the best model, and the model achieved an MAE of 1.26% and the R2 was 0.99.

Photocatalytic degradation of drugs and dyes using a maching learning approach

“…Furthermore, the fundamental association between catalyst fingerprint properties and pollutant degradation is difficult to quantify. 40 Researchers can utilize advanced analytical techniques, such as simulations and tests, to quantify the fingerprint properties of catalysts and their impact on pollutant degradation, thus addressing this problem. When combined with experimental data, data-driven machine learning models can help researchers identify natural links between the catalyst properties and the rate at which contaminants in water resources break down.…”

Electrocatalytic Treatment of Pharmaceutical Wastewater by Transition Metals Encapsulated by B, N-Doped CNTs

“…The SMT removal by the M@BN-C cathode was measured at different pH conditions, and it was observed that the SMT removal decreased as the pH increased (Figure 7a-c). This decline could be attributed to the favorable production of • OH under acidic conditions, which facilitated pollutant degradation [43,44]. In addition, higher leaching of transition metal resulted in the generation of more free • OH and less 1 O 2 .…”

An Application of Ultrasonic Waves in the Pretreatment of Biological Sludge in Urban Sewage and Proposing an Artificial Neural Network Predictive Model of Concentration

“…Also, in order to further prevent overfitting, dropouts were utilized in some hidden layers. These techniques help prevent the model from overfitting on certain features and enhance its ability to generalize to new data [84][85][86][87][88][89][90][91]. To add more insight on the selection of the ANN model, a comparison between the predictive capabilities of the ANN and random forest (RF) is performed.…”