Document
type: Class

Application of artificial intelligence for the optimization of advanced oxidation processes to improve the water quality polluted with pharmaceutical compounds
e iscopus: 2-s2.0-85182906007
freetext keyword: Binary degradation; Machine learning; Metronidazole; Ozonization; Photolysis; Sulfamethoxazole; UV/H2O2 Artificial Intelligence; Hydrogen Peroxide; Oxidation-Reduction; Ozone; Pharmaceutical Preparations; Ultraviolet Rays; Water Pollutants, Chemical; Water Purification; Water Quality; drug; hydrogen peroxide; ozone; artificial intelligence; chemistry; oxidation reduction reaction; procedures; ultraviolet radiation; water management; water pollutant; water quality
most specific type: Document
artnum: 141216
crisid: 12546
volume: 351
abstract: Sulfamethoxazole and metronidazole are emerging pollutants commonly found in surface water and wastewater. These compounds have a significant environmental impact, being necessary in the design of technologies for their removal. Recently, the advanced oxidation process has been proven successful in the elimination of this kind of compounds. In this sense, the present work discusses the application of UV/H2O2 and ozonation for the degradation of both molecules in single and binary systems. Experimental kinetic data from O3 and UV/H2O2 process were adequately described by a first and second kinetic model, respectively. From the ANOVA analysis, it was determined that the most statistically significant variables were the initial concentration of the drugs (0.03 mmol L−1) and the pH = 8 for UV/H2O2 system, and only the pH (optimal value of 6) was significant for degradation with O3. Results showed that both molecules were eliminated with high degradation efficiencies (88–94%25 for UV/H2O2 and 79–98%25 for O3) in short reaction times (around 30–90 min). The modeling was performed using a quadratic regression model through response surface methodology representing adequately 90 %25 of the experimental data. On the other hand, an artificial neural network was used to evaluate a non-linear multi-variable system, a 98%25 of fit between the model and experimental data was obtained. The identification of degradation byproducts was performed by high-performance liquid chromatography coupled to a time mass detector. After each process, at least four to five stable byproducts were found in the treated water, reducing the mineralization percentage to 20%25 for both molecules.
related by: authorship_75688
related by: authorship_75685
type: Thing
chemicalscas: hydrogen peroxide, 7722-84-1; ozone, 10028-15-6; Hydrogen Peroxide, ; Ozone, ; Pharmaceutical Preparations, ; Water Pollutants, Chemical,
estado: published
related by: authorship_75687
related by: authorship_76733
date time value: article date 12546
type: Document
pmid: 38224748
related by: authorship_75689
type: Article
has publication venue: Chemosphere
type: Continuant
type: Entity
type: Generically Dependent Continuant
doi: 10.1016/j.chemosphere.2024.141216
has funding vehicle: Consejo Nacional de Ciencia y Tecnología, CONACYT
type: Information Content Entity
related by: authorship_75684
most specific type: Article
related by: authorship_75686

Continuant
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Chemosphere
type: Entity
type: Periodical
type: Document
publication venue for: Application of artificial intelligence for the optimization of advanced oxidation processes to improve the water quality polluted with pharmaceutical compounds
type: Continuant
type: Collection
type: Thing
type: Information Content Entity
type: Generically Dependent Continuant
type: Journal

Entity
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Generically Dependent Continuant
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individual
rights: termsOfUse
date: 2026-08-26T01:53:18
publisher: vivo
type: Document
label: RDF description of Application of artificial intelligence for the optimization of advanced oxidation processes to improve the water quality polluted with pharmaceutical compounds - http://orbis.uaslp.mx/vivo/individual/scopus12545

Article
type: Class

Information Content Entity
type: Class

Consejo Nacional de Ciencia y Tecnología, CONACYT
type: Relationship
type: BFO_0000020
type: Entity
type: Thing
type: Grant
type: Continuant
funding vehicle for: Application of artificial intelligence for the optimization of advanced oxidation processes to improve the water quality polluted with pharmaceutical compounds