International Journal of Chemical Reactor Engineering
Ed. by de Lasa, Hugo / Xu, Charles Chunbao
IMPACT FACTOR 2017: 0.881
5-year IMPACT FACTOR: 0.908
CiteScore 2017: 0.86
SCImago Journal Rank (SJR) 2017: 0.306
Source Normalized Impact per Paper (SNIP) 2017: 0.503
A Neural Network Approach for Prediction of the CuO-ZnO-Al2O3 Catalyst Deactivation
In this work an Artificial Neural Networks (ANN) approach for estimation of catalyst deactivation during methanol synthesis has been proposed. The approach predicts deactivation of the catalyst at different operating conditions as a function of time. Experimental data of a typical differential reactor were pre-scaled and used for training. Among the various training algorithms, Exact Radial Basis (RBE) method had the best prediction performance and was used for simulation of the reactor. The proposed approach interprets deactivation data, while there are not enough data versus time at different inlet conditions. By using the model, sufficient data were generated which vary with time and agree very well with experimental data. Capability of the model in generating deactivation data at different temperatures, pressures and feed compositions was excellent. The proposed method has great potential as a means to compensate for lack of the phenomenological kinetic modeling techniques.
Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.