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Neural Computing and Applications

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01.10.2015 | Original Article

Modeling of catalyst composition–activity relationship of supported catalysts in NH₃–NO-SCR process using artificial neural network

verfasst von: Parvaneh Nakhostin Panahi, Aligholi Niaei, Hui-Hsin Tseng, Darush Salari, Seyed Mahdi Mousavi

Erschienen in: Neural Computing and Applications | Ausgabe 7/2015

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Abstract

This paper presents an artificial neural network (ANN) for modeling the relationship between catalyst composition and catalytic performance in the NH₃-SCR of NO process. The supported catalysts with different transition metals (Mn, Fe, Co and Cu) and (γ-Al₂O₃, ZSM5 and SAPO-34) supports were prepared and tested in NH₃–NO-SCR reaction to generate required data for neural network development. The ANN was constructed using the experimental dataset, and all the data were integrated using support and metal atomic descriptors for the construction of general catalyst design model. The statistical analysis of the results indicated that the R ² values for the training and test data were high, more than 0.9, and this indicates that ANN-based model developed in this work can predict catalyst performance correctly. More evaluation of the obtained model revealed that metal has more influence than support on catalyst activity at supported catalysts.

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Titel: Modeling of catalyst composition–activity relationship of supported catalysts in NH3–NO-SCR process using artificial neural network
verfasst von: Parvaneh Nakhostin Panahi
Aligholi Niaei
Hui-Hsin Tseng
Darush Salari
Seyed Mahdi Mousavi
Publikationsdatum: 01.10.2015
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 7/2015
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-014-1781-z

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