scholarly journals Combination of Gibbs and Helmholtz Energy Equations of State in a Multiparameter Mixture Model Using the IAPWS Seawater Model as an Example

2022 ◽  
Vol 43 (3) ◽  
Author(s):  
Benedikt Semrau ◽  
Sebastian Hielscher ◽  
Monika Thol ◽  
Roland Span

AbstractFor carbon capture and storage (CCS) applications different sets of equations of state are used to describe the whole CCS-chain. While for the transport and pipeline sections highly accurate equations of state (EOS) explicit in the Helmholtz energy are used, properties under typical geological storage conditions are described by more simple, mostly cubic EOS, and brines are described by Gibbs energy models. Combining the transport and storage sections leads to inconsistent calculations. Since the used models are formulated in different independent variables (temperature and density versus temperature and pressure), mass and energy balances are challenging and equilibria in the injection region are difficult to model. To overcome these limitations, a predictive combination of the Gibbs energy-based IAPWS seawater model (IAPWS R13-08, 2008) with Helmholtz energy-based multi-parameter EOS is presented within this work. The Helmholtz energy model used in this work is based on the EOS-CG-2016 of Gernert and Span (J Chem Thermodyn 93:274–293, 10.1016/j.jct.2015.05.015, 2016). The results prove that a consistent combination of the two different models is possible. Furthermore, it is shown, that a more complex brine model needs to be combined with Helmholtz energy EOS for calculations at storage conditions.

2021 ◽  
Vol 13 (5) ◽  
pp. 2527
Author(s):  
George Truc ◽  
Nejat Rahmanian ◽  
Mahboubeh Pishnamazi

Carbon capture and storage (CCS) has attracted renewed interest in the re-evaluation of the equations of state (EoS) for the prediction of thermodynamic properties. This study also evaluates EoS for Peng–Robinson (PR) and Soave–Redlich–Kwong (SRK) and their capability to predict the thermodynamic properties of CO2-rich mixtures. The investigation was carried out using machine learning such as an artificial neural network (ANN) and a classified learner. A lower average absolute relative deviation (AARD) of 7.46% was obtained for the PR in comparison with SRK (AARD = 15.0%) for three components system of CO2 with N2 and CH4. Moreover, it was found to be 13.5% for PR and 19.50% for SRK in the five components’ (CO2 with N2, CH4, Ar, and O2) case. In addition, applying machine learning provided promise and valuable insight to deal with engineering problems. The implementation of machine learning in conjunction with EoS led to getting lower predictive AARD in contrast to EoS. An of AARD 2.81% was achieved for the three components and 12.2% for the respective five components mixture.


2014 ◽  
Vol 12 (9) ◽  
pp. 918-927 ◽  
Author(s):  
Sergiu Sima ◽  
Julia Cruz-Doblas ◽  
Martin Cismondi ◽  
Catinca Secuianu

AbstractThe phase behavior of the carbon dioxide + cycloalkane mixtures usually receives low attention, though these systems are important for many industries, e.g. the carbon capture and storage. In this paper calculations results for the carbon dioxide + cyclopentane binary system are presented, based on SRK and PR cubic equations of state with classical van der Waals mixing rules. A single set of binary parameters for each model was proposed to predict the global phase behavior of the system in a wide range of pressure and temperature. Albeit the thermodynamic models used are simple, they are able to represent fairly well the phase behavior of the system analyzed in this paper.


2012 ◽  
Vol 23 ◽  
pp. 236-245 ◽  
Author(s):  
Øivind Wilhelmsen ◽  
Geir Skaugen ◽  
Oddvar Jørstad ◽  
Hailong Li

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