International Journal of Chemical Reactor Engineering
Ed. by de Lasa, Hugo / Xu, Charles Chunbao
12 Issues per year
IMPACT FACTOR 2016: 0.623
5-year IMPACT FACTOR: 0.761
CiteScore 2016: 0.58
SCImago Journal Rank (SJR) 2016: 0.224
Source Normalized Impact per Paper (SNIP) 2016: 0.297
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.
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