Search

In this research, an upgraded and environmentally friendly process involving WO3/Co-ZIF nanocomposite was used for the removal of Cefixime from the aqueous solutions. Intelligent decision-making was employed using various models including Support Vector Regression (SVR), Genetic Algorithm (GA), Artificial Neural Network (ANN), Simulation Optimization Language for Visualized Excel Results (SOLVER), and Response Surface Methodology (RSM). SVR, ANN, and RSM models were used for modeling and predicting results, while GA and SOLVER models were employed to achieve the optimal conditions for Cefixime degradation. The primary goal of applying different models was to achieve the best conditions with high accuracy in Cefixime degradation. Based on R analysis, the quadratic factorial model in RSM was selected as the best model, and the regression coefficients obtained from it were used to evaluate the performance of artificial intelligence models. According to the quadratic factorial model, interactions between pH and time, pH and catalyst amount, as well as reaction time and catalyst amount were identified as the most significant factors in predicting results. In a comparison between the different models based on Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R2 Score) indices, the SVR model was selected as the best model for the prediction of the results, with a higher R2 Score (0.98), and lower MAE (1.54) and RMSE (3.91) compared to the ANN model. Both ANN and SVR models identified pH as the most important parameter in the prediction of the results. According to the Genetic Algorithm, interactions between the initial concentration of Cefixime with reaction time, as well as between the initial concentration of Cefixime and catalyst amount, had the greatest impact on selecting the optimal values. Using the Genetic Algorithm and SOLVER models, the optimum values for the initial concentration of Cefixime, pH, time, and catalyst amount were determined to be (6.14 mg L−1, 3.13, 117.65 min, and 0.19 g L−1) and (5 mg L−1, 3, 120 min, and 0.19 g L−1), respectively. Given the presented results, this research can contribute significantly to advancements in intelligent decision-making and optimization of the pollutant removal processes from the environment. [ABSTRACT FROM AUTHOR]

This study aimed at photocatalytic degradation of biochemical oxygen demand (BOD) from the effluent of the Baluch textile factory in Iranshahr (Iran) using copper oxide nanoparticles (CuONPs). The characteristics of CuONPs were analyzed using a scanning electron microscope (SEM), transmission electron microscope (TEM), X‐ray diffraction (XRD) and X‐ray fluorescence (XRF). The results showed that the maximum removal efficiency (90.03%) was related to the state in which the intensity of ultraviolet (UV) was at the highest value (30 W). In this study, high values of coefficient of determination (R2) (99.2%) and adjusted coefficient of determination (adjusted R2) (98.11%) indicated that the very large share of changes in response variables was determined by independent factors. According to the results of analysis of variance, the order of the effect of the factors used in the study were as follows: pH > UV > time > Copper(II) oxide (CuO) dose. The BOD removal efficiency increased when pH was increased from 3 to 7 and contact time was increased from 10 to 60 min. More than 89% of BOD was removed at pH 7 and contact time 60 min. As such, when nanoparticle dose (from 0.02 to 0.05 g/L and contact time from 10 to 60 min) increased, so that the highest removal efficiency (89%) was reported at a dose of 0.05 g/L nanoparticles and a contact time of 60 min. In general, the Langmuir isotherm had a much better fit (R2 = 0.991) than the Freundlich (R2 = 0.9712) and Temkin (R2 = 0.7545) isotherms. [ABSTRACT FROM AUTHOR]

High concentrations of antibiotics have been identified in aqueous media, which has diminished the quality of water resources. These compounds are usually highly toxic and have low biodegradability, and there have been reports about their mutagenic or carcinogenic effects. The aim of this study was to apply zero-valent iron-oxide nanoparticles in the presence of hydrogen peroxide and the sonolysis process for the removal of the amoxicillin antibiotic from aqueous media. In this study, zero-valent iron nanoparticles were prepared by an iron chloride reduction method in the presence of sodium borohydride (NaBH4), and the obtained nanoparticles were characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), and vibrating-sample magnetometry (VSM). Then, using a Fenton-like process, synthetic wastewater containing 100 to 500 mg/L amoxicillin antibiotic was investigated, and the effects of different parameters, such as the frequency (1 and 2 kHz), contact time (15 to 120 min), the concentration of hydrogen peroxide (0.3%, 0.5%, and 6%), the dose of zero-valent iron nanoparticles (0.05, 0.1, 0.5 g/L), and pH (3, 5, 10) were thoroughly studied. A pH of 3, hydrogen peroxide concentration of 3%, ultrasonic-wave frequency of 130 kHz, zero-valent iron nanoparticles of 0.5 g/L, and contaminant concentration of 100 mg/L were obtained as the optimal conditions of the combined US/H2O2/nZVI process. Under the optimal conditions of the combined process of zero-valent iron nanoparticles and hydrogen peroxide in the presence of ultrasonic waves, a 99.7% removal efficiency of amoxicillin was achieved in 120 min. The results show that the combined US/H2O2/nZVI process could be successfully used to remove environmental contaminants, including antibiotics such as amoxicillin, with a high removal percentage. [ABSTRACT FROM AUTHOR]

In this work, a photocatalytic process was used for removing metformin as a widely prescribed drug to reduce blood sugar in type 2 diabetes. Photocatalytic processes are one of the most common oxidation processes in degrading organic compounds. This study aimed to investigate the efficiency of Fe3+ doped TiO2 nanoparticles in degrading metformin in synthetic wastewater. Therefore, in the first step, Fe3+ doped TiO2 nanoparticles were synthesized by the sol–gel method and characterized using X-ray diffraction, energy-dispersive X-ray spectrometry, scanning electron microscopy, and pH(PZC) techniques. Then, the effects of parameters, such as the initial concentration of metformin (10–45 mg/L), catalyst dose (25–75 mg/L), pH (3–11), and time (30–150 min), were studied on the photocatalytic degradation of metformin. Results showed that by increasing the experimental parameters, including pH, catalyst dose, and contact time, the nanoparticle removal efficiency increased. Overall, these findings indicated that under optimum conditions, including pH = 11, nanoparticle dose of 75 mg/L, and contact time of 150 min, photocatalytic degradation can remove 93.8% of metformin with an initial concentration of 35 mg/L. Moreover, Fe3+ doped TiO2 nanoparticles with UV light (as a photocatalytic treatment system) have good efficiency in degrading metformin. [ABSTRACT FROM AUTHOR]