Atenolol sequestration using activated carbon derived from gasified Glyricidia sepium

ASPAD dynamic simulation and artificial neural network for atenolol adsorption in GGSWAC packed bed column

This study aimed to assess the dynamic simulation models provided by Aspen adsorption (ASPAD) and artificial neural network (ANN) in understanding the adsorption behavior of atenolol (ATN) on gasified Glyricidia sepium woodchips activated carbon (GGSWAC) within fixed bed columns for wastewater treatment. The findings demonstrated that increasing the bed height from 1 to 3 cm extended breakthrough and exhaustion times while enhancing adsorption capacity. Conversely, higher initial ATN concentrations resulted in shorter breakthrough and exhaustion times but increased adsorption capacity. Elevated influent flow rates reduced breakthrough and exhaustion times while maintaining constant adsorption capacity. The ASPAD software demonstrated competence in accurately modeling the crucial exhaustion points. However, there is room for enhancement in forecasting breakthrough times, as it exhibited deviations ranging from 6.52 to 239.53% when compared to the actual experimental data. ANN models in both MATLAB and Python demonstrated precise predictive abilities, with the Python model (R2 = 0.985) outperforming the MATLAB model (R2 = 0.9691). The Python ANN also exhibited superior fitting performance with lower MSE and MAE. The most influential factor was the initial ATN concentration (28.96%), followed by bed height (26.39%), influent flow rate (22.43%), and total effluent time (22.22%). The findings of this study offer an extensive comprehension of breakthrough patterns and enable accurate forecasts of column performance.

A mini review of recent progress in the removal of emerging contaminants from pharmaceutical waste using various adsorbents

The presence of emerging contaminants (ECs) originating from pharmaceutical waste in water, wastewater, and marine ecosystems at various geographical locations has been clearly publicised. This review paper presents an overview of current monitoring data on the occurrences and distributions of ECs in coastal ecosystem, tap water, surface water, ground water, treated sewage effluents, and other sources. Technological advancements for EC removal are also presented, which include physical, chemical, biological, and hybrid treatments. Adsorption remains the most effective method to remove ECs from water bodies. Various types of adsorbents, such as activated carbons, biochars, nanoadsorbents (carbon nanotubes and graphene), ordered mesoporous carbons, molecular imprinting polymers, clays, zeolites, and metal-organic frameworks have been extensively used for removing ECs from water sources and wastewater. Extensive findings on adsorptive performances, process efficiency, reusability properties, and other related information are thoroughly discussed in this mini review.

Using Sono-Electro-Persulfate Process for Atenolol Removal from Aqueous Solutions: Prediction and Optimization with the ANFIS Model and Genetic Algorithm

Atenolol (ATN) is a drug that is widely used to treat some heart diseases, and since it cannot be completely decomposed in the human body, some amounts of it are found in surface water. These amounts may bring risks to the environment and humans, and for this reason, its removal is a must. In the present study, the combined sono-electro-persulfate method was used for ATN removal. Based on the design of the experiment conducted by response surface methodology (RSM), the effects of 5 main factors (pH, time, PS concentration, current intensity, and initial ATN concentration) have been investigated at 5 levels. After passing the test steps in different conditions, the remaining amount of ATN has been measured by high-performance liquid chromatography (HPLC). Finally, an adaptive neuro-fuzzy inference system (ANFIS) with 99.63% accuracy and a genetic algorithm (GA) were used to analyze and interpret data and predict optimal conditions. The obtained results indicate the possibility of a maximum efficiency of 99.8% in the mentioned conditions (Ph of 7.4, time of 18 min, PS concentration of 2000 mg/L, current intensity of 3.35 A, and initial ATN concentration of 11.2 mg/L). According to the obtained results, the initial concentration of ATN can be considered as the most effective factor in this process, and the best Ph range for this experiment was the neutral range. The sono-electro persulfate process can be mentioned as a new and effective method for removing ATN from water sources.