Robust Predictions of Catalyst Deactivation of Atmospheric Residual Desulfurization

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• 作者：Hamza Al Bazzaz ; Jia-Lin Kang ; Dduha Chehadeh ; Dawoud Bahzad ; David Shan-Hill Wong ; Shi-Shang Jang
• 刊名：Energy & Fuels
• 出版年：2015
• 出版时间：November 19, 2015
• 年：2015
• 卷：29
• 期：11
• 页码：7089-7100
• 全文大小：741K
• ISSN：1520-5029

文摘

A robust parameter estimation procedure was proposed to obtain accurate, confident parameters for the atmospheric residue desulfurization (ARDS) model in this work. A comprehensive ARDS model includes too many parameters such as deactivation and loading parameters. However, past studies have used data fitting in the early phases of reactor system life testing to obtain the parameters. This procedure was found to be too sensitive to the initial estimates used in fitting. In order to use the ARDS model for designing reactor systems and evaluating operating strategies, a robust parameter estimation procedure must be available. In this work, we demonstrate that the kinetic and deactivation parameters should be obtained from kinetic tests and accelerated test data, respectively for individual catalysts. Only loading parameters such as contact efficiencies should be obtained using early life test data. The procedure was validated with long-term experimental data. The results showed that the procedure could be applied on different catalyst systems as well, which allows the model to predict mid-term and long-term performance well during a life test. The procedure described here will substantially improve the robustness of model predictions in the refining industry.