DSpace logo

Please use this identifier to cite or link to this item:
Title: Virtual sensing of catalytic naphtha reforming process under uncertain feed conditions
Authors: Ahmad, Iftikhar
Ali, Gul Sayyar
Bilal, Muhammad
Hussain, Arshad
Keywords: Engineering and Technology
Soft sensor
Ensemble learning
Naphtha reforming process
Sensitivity analysis
Issue Date: 3-Mar-2018
Publisher: IEEE International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)
Abstract: Stable operation of a naphtha reforming process is always desired to get high Research Octane Number (RON) of its end product, gasoline. Uncertainty in process variables, i.e. temperature, pressure, feed composition, makes the process unstable and result in lower RON of gasoline. Soft sensors (virtual sensors) have been used to get stable process operation. In this study, a soft sensor is developed through the ensemble learning method, i.e., boosting, for prediction of RON value of the naphtha reforming process. Prediction performance of the boosted model is compared with an Artificial Neural Networks (ANN) model; the boosted model outperformed the ANN model. For analyzing the effect of process uncertainty, sensitivity analysis is performed using Fourier amplitude sensitivity test (FAST) and Sobol technique. In addition, Polynomial Chaos Expansion (PCE) is used to analyze the collective effect of inputs uncertainty on the model output. The proposed methods of soft sensors development, sensitivity analysis and uncertainty analysis are validated through real process data of a petroleum refinery. The results are highly accurate and suitable for industrial applications.
ISBN: 978-1-5386-1370-2
Appears in Collections:Proceedings

Files in This Item:
File Description SizeFormat 
8346447.htm115 BHTMLView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.