Toward estimation of upgrading of n-heptane over catalysts using robust technique

Abstract

Catalytic reforming in the presence of metal-acid bifunctional catalysts is a widely used reaction in refinery industry to improve some properties of products like temperature performance of diesel and octane number of gasoline. So the ability of the prediction of Iso-C7 selectivity during n-heptane hyroconversion is a key issue. In this study, a data set which was collected from previous publications are put in an artificial neural network-multi layer perceptron (ANN-MLP) model. Properties used as input parameters are: temperature, pressure, WHSV (weight hourly space velocity), catalysts acidity and pore volume of the catalysts, and Iso-C7 selectivity used as the output parameter. Based on results, the MLP-ANN has great ability to estimate n-heptane hydroconversion. Root mean squared error (RMSE) and R-squared (R2) error were calculated for training, test and total set of data. For training set, test set and total set RMSE are 97915, 5.1607, and 3.9441, respectively and corresponding R2 are 0.97915, 0.9334, and 0.9746, respectively.