Approximation by Neural Network of the Effectiveness Factor in a Catalyst with Deactivation

DANIEL R. PARISI; MAURICIO CHOCRON; NORMA AMADEO; MIGUEL A. LABORDE

Chemical Engineering & Technology

Wiley-VCH Verlag GmbH & Co

Año: 2002 vol. 25 p. 1183 - 1186

1521-4125

A method for estimating the effectiveness factor in a catalytic pellet submitted to deactivation using neural networks is proposed. When a catalyst is deactivated by poisoning, the function h = h (t, F) presents a minimum when strong diffusional resistances exist. In this particular case, the few methods published in the literature are not able to calculate h. A feed forward neural network trained with the back propagation algorithm was used to estimate the effectiveness factor. This methodology is especially useful when the function h = h (t, F) presents a minimum. The predicted values using the neural network successfully fit with those obtained solving the differential equations system.  An extrapolation using temperatures outside the training range can be satisfactory performed.