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dc.contributor.author
Alvarez, Dolores María Eugenia
dc.contributor.author
Gerbaldo, María Verónica
dc.contributor.author
Mario Roberto, Modesti
dc.contributor.author
Mendieta, Silvia Nazaret
dc.contributor.author
Crivello, Mónica Elsie
dc.date.available
2023-07-12T13:30:49Z
dc.date.issued
2022-09-18
dc.identifier.citation
Alvarez, Dolores María Eugenia; Gerbaldo, María Verónica; Mario Roberto, Modesti; Mendieta, Silvia Nazaret; Crivello, Mónica Elsie; Artificial neural networks for mnodelling the degradation of emerging contaminants Process; Springer/Plenum Publishers; Topics In Catalysis; 65; 13-16; 18-9-2022; 1440-1446
dc.identifier.issn
1022-5528
dc.identifier.uri
http://hdl.handle.net/11336/203419
dc.description.abstract
Diclofenac sodium is an emerging contaminant that can be harmful for ecology and human health. This substance can be degraded by a heterogeneous Photo-Fenton process, CoFe2O4 as catalyst, H2O2 as oxidant and UV radiation. The aims of the work are the comparison of different artificial neural networks to characterize the relationship between diclofenac degradation and H2O2 consumption, with the Total Organic Carbon achieved in the mineralization of the drug and the testing of the selected model capacity to predict the Total Organic Carbon concentration, by employing the reused catalyst. The best performing backpropagation neural network was constituted with a ten neurons hidden layer with sigmoid transfer function and one linear neuron, as output. It was determined that the model can approximate the trend between the input data (Absorbance and H2O2 concentration) and output ones (Total Organic Carbon concentration) when it was validated with data from reactions employing CoFe2O4 for second and third time. The development of these models is of interest due to the consequent reduction of time and costs in experimental work. It represents a study of the evolution of chemical indicators in the treatment of emerging contaminants.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Springer/Plenum Publishers
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
ARTIFICIAL NEURAL NETWORKS
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CO-FERRITES
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EMERGING CONTAMINANTS
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MODELS
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VALIDATION
dc.subject.classification
Ingeniería de Procesos Químicos
dc.subject.classification
Ingeniería Química
dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS
dc.title
Artificial neural networks for mnodelling the degradation of emerging contaminants Process
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2023-07-07T17:32:11Z
dc.journal.volume
65
dc.journal.number
13-16
dc.journal.pagination
1440-1446
dc.journal.pais
Estados Unidos
dc.journal.ciudad
Nueva York
dc.description.fil
Fil: Alvarez, Dolores María Eugenia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Tecnología Química. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación y Tecnología Química; Argentina
dc.description.fil
Fil: Gerbaldo, María Verónica. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Tecnología Química. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación y Tecnología Química; Argentina
dc.description.fil
Fil: Mario Roberto, Modesti. Universidad Tecnológica Nacional. Facultad Regional Córdoba; Argentina
dc.description.fil
Fil: Mendieta, Silvia Nazaret. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Tecnología Química. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación y Tecnología Química; Argentina
dc.description.fil
Fil: Crivello, Mónica Elsie. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Tecnología Química. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación y Tecnología Química; Argentina
dc.journal.title
Topics In Catalysis
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1007/s11244-022-01674-7
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007/s11244-022-01674-7
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