Direct neural network modeling for separation of linear and branched paraffins by adsorption process for gasoline octane number improvement

Autor: Georgina C. Laredo, Ali Bassam, J.A. Hernández, Jesus Castillo, R. A. Conde-Gutiérrez
Rok vydání: 2014
Předmět:
Zdroj: Fuel. 124:158-167
ISSN: 0016-2361
DOI: 10.1016/j.fuel.2014.01.080
Popis: An artificial neural network (ANN) approach was used to develop a new predictive model for the calculation of hydrocarbons breakthrough curves in separation of linear and branched paraffins by adsorption process. Three-layer ANN architecture was trained using an experimental database and the concentration at t time over initial concentration ( C / C o ) was calculated as output variable. Experimental temperature ( T ), times of adsorption ( t ), octane number ( ON ) and the density ( ρ ) of the hydrocarbons were considered as main input variables for the model. For the ANN optimization process, the Levenberg–Marquardt (LM) learning algorithm, the hyperbolic tangent sigmoid transfer-function and the linear transfer-function were applied. The best fitting training data set was acquired with an ANN architecture composed by 22 neurons in the hidden layer (4-22-1), which made possible to predict the C / C o with a satisfactory efficiency ( R 2 > 0.96). A suitable accuracy of the ANN model was achieved with a mean percentage error (MPE) of ∼5%. All the C / C o predicted with the ANN model were statistically analyzed and compared with the “true” C / C o experimental data reported in the experiments carried out in the lab. With all these results, we suggest that the ANN model could be used as a tool for the reliable prediction of the breakthrough curves obtained during the separation of linear and branched paraffins by adsorption processes.
Databáze: OpenAIRE
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