Application of Artificial Neural Network for Modeling of Acetylene Hydrogenation overPd-Ag/ -Al2O3 Nano Bimetallic Catalyst

The application of neural networks to model a full-scale industrial acetylene hydrogenation on the Pd-Ag/ γ-Al2O3 nano bimetallic catalyst in olefin plant has been studied. The operating variables studied are the, input temperature of reactor, output-temperature of reactor, hydrogen ratio of reactor, [C2H2]input and [C2H6]input. The studied operating variables were used as the input to the constructed neural network to predict the [C2H6]output at any time as the output or the target. The constructed neural network was found to be highly precise in predicting the quantity of [C2H6]Output for the new input data, which are kept unaware of the trained neural network showing its applicability to determine the [C2H6]Output for any operating conditions. The enhancement of [C2H6]Output as compared with [C2H6]Input was a consequence of an low selective acetylene hydrogenation to ethylene.

Keywords:

Acetylene hydrogenation , Pd-Ag/ γ-Al2O3 , nano bimetallic catalyst , ANN Modeling