caa a22 4500 606159304 CHVBK 20210128100624.0 cr unu---uuuuu 210128e20151001xx s 000 0 eng 10.1007/s00521-014-1781-z doi (NATIONALLICENCE)springer-10.1007/s00521-014-1781-z Modeling of catalyst composition-activity relationship of supported catalysts in NH3-NO-SCR process using artificial neural network [Elektronische Daten] [Parvaneh Nakhostin Panahi, Aligholi Niaei, Hui-Hsin Tseng, Darush Salari, Seyed Mousavi] 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. The Natural Computing Applications Forum, 2015 Artificial neural networks nationallicence Modeling nationallicence NO nationallicence Selective catalytic reduction nationallicence Supported catalysts nationallicence Nakhostin Panahi Parvaneh Department of Chemistry, Faculty of Science, University of Zanjan, Zanjan, Iran aut Niaei Aligholi Reactor & Catalyst Research Lab., Department of Chemical Engineering and Applied Chemistry, University of Tabriz, Tabriz, Iran aut Tseng Hui-Hsin School of Occupational Safety and Health, Chung Shan Medical University, 402, Taichung, Taiwan, ROC aut Salari Darush Reactor & Catalyst Research Lab., Department of Chemical Engineering and Applied Chemistry, University of Tabriz, Tabriz, Iran aut Mousavi Seyed Faculty of Chemistry, University of Kashan, Kashan, Iran aut Neural Computing and Applications Springer London 26/7(2015-10-01), 1515-1523 0941-0643 26:7<1515 2015 26 521 https://doi.org/10.1007/s00521-014-1781-z text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-014-1781-z text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Nakhostin Panahi Parvaneh Department of Chemistry, Faculty of Science, University of Zanjan, Zanjan, Iran aut NATIONALLICENCE 700 1- Niaei Aligholi Reactor & Catalyst Research Lab., Department of Chemical Engineering and Applied Chemistry, University of Tabriz, Tabriz, Iran aut NATIONALLICENCE 700 1- Tseng Hui-Hsin School of Occupational Safety and Health, Chung Shan Medical University, 402, Taichung, Taiwan, ROC aut NATIONALLICENCE 700 1- Salari Darush Reactor & Catalyst Research Lab., Department of Chemical Engineering and Applied Chemistry, University of Tabriz, Tabriz, Iran aut NATIONALLICENCE 700 1- Mousavi Seyed Faculty of Chemistry, University of Kashan, Kashan, Iran aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/7(2015-10-01), 1515-1523 0941-0643 26:7<1515 2015 26 521