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A New Catalytic System for the Photodegradation of Endocrinal Disruptors: Synthesis and Efficiency Modeling and Optimization

dc.contributor.authorJasso Salcedo, Alma Berenice
dc.contributor.authorMeimaroglou, Dimitrios
dc.contributor.authorCamargo, Mauricio
dc.contributor.authorHoppe, Sandrine
dc.contributor.authorPla, Fernand
dc.contributor.authorEscobar Barrios, Vladimir Alonso
dc.contributor.editorThe Italian Association of Chemical Engineering
dc.date.accessioned2018-12-13T21:09:20Z
dc.date.available2018-12-13T21:09:20Z
dc.date.issued2015
dc.identifier.citationJasso Salcedo A., Meimaroglou D., Camargo M., Hoppe S., Pla F., Escobar Barrios V., 2015, A new catalytic system for the photodegradation of endocrinal disruptors: synthesis and efficiency modeling and optimization, Chemical Engineering Transactions, 43, 937-942 DOI: 10.3303/CET1543157
dc.identifier.urihttp://hdl.handle.net/11627/4814
dc.description.abstract"Nowadays, the challenge of understanding relationships between catalysts properties and performance in the context of heterogeneous catalysis is a hot topic. Indeed, catalytic processes are generally affected by many different operational parameters that need to be modeled and optimized. The challenge can be addressed using artificial neural networks due to their flexibility to work without mathematical description of the process. The present work enters within the framework of the photodegradation of water contaminants using ZnObased catalysts. ZnO is a non-toxic cheap material with an interesting photocatalytic potential. However, its application is reduced because of its poor efficiency, photocorrosion and difficulties for recovery. The objective of this work is to improve this efficiency, regarding particularly the photodegradation of an endocrinal disruptor: bisphenol-A (BPA), via the synthesis of a new catalytic system based on ZnO and the modeling of both the synthesis process and photocatalytic performance of this new catalytic system. Modeling and optimization will be carried out using artificial neural network tools coupled to an evolutionary algorithm. The connection between the two artificial neural network models will make it possible to identify the optimal synthesis parameters that lead to the maximum photocatalytic efficiency (within the studied domain), thus shedding light on the association of the system structure with its photocatalytic performance."
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject.classificationCIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA
dc.titleA New Catalytic System for the Photodegradation of Endocrinal Disruptors: Synthesis and Efficiency Modeling and Optimization
dc.typearticle
dc.identifier.doihttps://www.aidic.it/cet/15/43/157.pdf
dc.rights.accessAcceso Abierto


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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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