Uncertainty analysis of the CPA and a quadrupolar CPA equation of state - With emphasis on CO₂

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• 作者：Martin G. Bjø ; rner^a ; ^{mgabj@kt.dtu.dk" class="auth_mail" title="E-mail the corresponding author} ; Gü ; rkan Sin^b ; Georgios M. Kontogeorgis^a
• 关键词：Equation of state ; CPA ; Quadrupole ; CO2 ; Parameter estimation ; Propagation of uncertainty
• 刊名：Fluid Phase Equilibria
• 出版年：2016
• 出版时间：25 April 2016
• 年：2016
• 卷：414
• 期：Complete
• 页码：29-47
• 全文大小：3237 K

文摘

The parameters of thermodynamic models, such as the cubic plus association (CPA) equation of state, are subject to uncertainties due to measurement errors in the experimental data that the models are correlated to. More importantly as the number of adjustable parameters increase, the parameter estimation problem becomes more complicated due to parameter identifiability issues. In this work the uncertainties in the pure compound parameters of CO₂ are investigated using several different CPA approaches, including a new quadrupolar CPA. The uncertainties are estimated using both least squares estimation and the bootstrap method for parameter estimation. The uncertainties in the parameters estimated from the bootstrap method are propagated to physical property and vapor liquid equilibrium predictions using Monte Carlo simulations.

The results indicate that both the pure compound parameter uncertainty and the propagated uncertainty are negligible for the modeling approaches which employ three adjustable parameters. For modeling approaches with more than three adjustable parameters, however, there may be significant uncertainties in the pure compound parameters, as well as a high degree of correlation between the adjustable parameters. This results in significant propagated errors for certain output properties. To reduce the uncertainty in the adjustable model parameters the heat of vaporization was included as additional correlation data. This resulted in parameter distributions which followed a normal distribution more closely, however, the correlation between the adjustable parameters remained high. Overall the results indicate, that it is important to report parameter uncertainties together with their correlation matrix when a model is developed, so that better informed decisions can be made, for instance about which model extension, or association scheme should be employed.