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Approximation by Neural Network of the Effectiveness Factor in a Catalyst with Deactivation
Author(s) -
Parisi D. R.,
Chocrón M.,
Amadeo N. E.,
Laborde M. A.
Publication year - 2002
Publication title -
chemical engineering and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.403
H-Index - 81
eISSN - 1521-4125
pISSN - 0930-7516
DOI - 10.1002/1521-4125(20021210)25:12<1183::aid-ceat1183>3.0.co;2-g
Subject(s) - extrapolation , artificial neural network , feedforward neural network , range (aeronautics) , activation function , function (biology) , feed forward , catalysis , biological system , computer science , mathematics , mathematical optimization , control theory (sociology) , artificial intelligence , chemistry , materials science , engineering , mathematical analysis , control engineering , control (management) , biochemistry , evolutionary biology , composite material , biology
Abstract 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 η = η ( t ,ϕ) presents a minimum when strong diffusional resistances exist. In this particular case, the few methods published in the literature are not able to calculate η. A feedforward neural network trained with the back‐propagation algorithm was used to estimate the effectiveness factor. This methodology is especially useful when the function η = η ( t ,ϕ) presents a minimum. The predicted values using the neural network successfully fit those obtained solving the differential equation system. An extrapolation using temperatures outside the training range can be satisfactorily performed.