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.

Keywords:

• ANN-MLP
• hydrocarbon upgrading
• hydroconversion
• mesoporous catalysts
• n-heptane