Toward estimation of upgrading of n-heptane over catalysts using robust technique. (3rd March 2020)

• Record Type: Journal Article
• Authors: Sun, Xiuling; Sun, Haifeng; Du, Gaili
• Is Part Of: Petroleum science and technology. Volume 38:Number 5(2020)
• Page Start: 486
• Page End: 492
• Publication Date: 2020-03-03
• Subjects: ANN-MLP -- hydrocarbon upgrading -- hydroconversion -- mesoporous catalysts -- n-heptane; Liquid fuels -- Periodicals; Petroleum -- Periodicals; 665.505
• DOI: 10.1080/10916466.2019.1702686
• Languages: English
• ISSNs: 1091-6466

Abstract

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 (R 2 ) 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 R 2 are 0.97915, 0.9334, and 0.9746, respectively.