Knowledge extraction of sonophotocatalytic treatment for acid blue 113 dye removal by artificial neural networks

UV-activated persulfates oxidation of anthraquinone dye: Kinetics and ecotoxicological assessment

Sulfate radical-based advanced oxidation processes (SR-AOPs) are gaining popularity as a feasible alternative for removing recalcitrant pollutants in an aqueous environment. Persulfates, namely peroxydisulfate (PDS) and peroxymonosulfate (PMS) are the most common sulfate radical donors. Persulfates activation by ultraviolet (UV) irradiation is considered feasible due to the high concentration of radicals produced as well as the lack of catalysts leaching. The research focuses on determining the impact of activated PDS and PMS on the degradation of anthraquinone dye, i.e., Acid Blue 129 (AB129). UV-activated PDS and PMS can quickly degrade the AB129 as well as restrict the formation of by-products. This could explain the reduced ecotoxicity levels of the treated water after degradation, using an aquatic plant (Lemna minor) and a crustacean (Daphnia magna). This, on the other hand, can ensure that the sulfate radical-based processes can be an environmentally friendly technology.

An insight into tetracycline photocatalytic degradation by MOFs using the artificial intelligence technique

Tetracyclines (TCs) have been extensively used for humans and animal diseases treatment and livestock growth promotion. The consumption of such antibiotics has been ever-growing nowadays due to various bacterial infections and other pathologic conditions, resulting in more discharge into the aquatic environments. This brings threats to ecosystems and human bodies. Up to now, several attempts have been made to reduce TC amounts in the wastewater, among which photocatalysis, an advanced oxidation process, is known as an eco-friendly and efficient technology. In this regard, metal organic frameworks (MOFs) have been known as the promising materials as photocatalysts. Thus, studying TC photocatalytic degradation by MOFs would help scientists and engineers optimize the process in terms of effective parameters. Nevertheless, the costly and time-consuming experimental methods, having instrumental errors, encouraged the authors to use the computational method for a more comprehensive assessment. In doing so, a wide-ranging databank including 374 experimental data points was gathered from the literature. A powerful machine learning method of Gaussian process regression (GPR) model with four kernel functions was proposed to estimate the TC degradation in terms of MOFs features (surface area and pore volume) and operational parameters (illumination time, catalyst dosage, TC concentration, pH). The GPR models performed quite well, among which GPR-Matern model shows the most accurate performance with R², MRE, MSE, RMSE, and STD of 0.981, 12.29, 18.03, 4.25, and 3.33, respectively. In addition, an analysis of sensitivity was carried out to assess the effect of the inputs on the TC photodegradation by MOFs. It revealed that the illumination time and the surface area play a significant role in the decomposition activity.