(83c) Integration of Advanced Physical Property Models in the Multi-Scale Simulation of Refinery Heat Exchangers Undergoing Crude Oil Fouling

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Improving energy efficiency in oil refineries is becoming increasingly important, especially in the crude distillation unit where energy consumption is excessively high. In that unit, energy from the hot distillation products is recovered in the pre-heat train, an extensive network of heat exchangers, to heat the crude oil from storage temperature before entering the distillation column. This process is however hindered by fouling, thereby resulting in gradually lower energy efficiencies as the deposits build up on the heat transfer surfaces. In this context, modelling of heat transfer equipment and fouling has emerged as a promising tool to assist in understanding the complex phenomena involved in fouling, monitoring of heat exchangers, prediction of fouling behaviour and proposition of mitigation options.

Recent work in this area has led to the development of a dynamic, distributed model of a shell and tube heat exchanger [1], implemented in Hexxcell StudioTM [2], which has been successful in predicting fouling in heat exchanger units. The model assesses deposition and accounts for the variation of the physical properties of the fluids involved as function of local conditions.

Thermo-physical properties are involved directly in heat and mass balances and heat transfer rates, and thus an accurate prediction of those properties is important for monitoring, performance assessment and design purposes. Much work over the past two decades has led to the development of the Statistical Association Fluid Theory (SAFT) into an equation-of-state (EOS) form. Due its physical basis, this EOS has found good use in describing the fluid phase behaviour and thermodynamic properties of complex mixtures [3].

In this work, part of the UNIHEAT project [4], advanced SAFT models are incorporated into the above heat exchanger model.  A group contribution version of the SAFT-γ theory is used employing a coarse grained (CG) approach [5]. The thermodynamic models are used to calculate properties such as heat capacity and density as function of local conditions (temperature, pressure) and composition. Crude oil is modelled as a mixture of a finite number of pseudo-components with composition characterised based on their true boiling point (TBP) curve, using Multiflash [6]. Using critical properties and acentric factors of the pseudo-component fractions, the required Mie force-field parameters were obtained from a corresponding states treatment. Once the mixture representing the crude oil is fully defined and the parameters for each pseudo-component are determined, the thermo-physical properties can be estimated as function of local operating conditions. This multi-scale approach is demonstrated for two crude oils in a typical industrial heat exchanger at refinery conditions. Finally, results are compared to those obtained using other physical properties modelling methods, such as cubic EoS and correlations from oil industry.

Acknowledgments

This research was performed under the UNIHEAT project. The authors wish to acknowledge the Skolkovo Foundation and BP for financial support. The support of Hexxcell Ltd, through provision of Hexxcell Studio™, is also acknowledged.

[1] Coletti F, Macchietto S. A Dynamic, Distributed Model of Shell-and-Tube Heat Exchangers Undergoing Crude Oil Fouling. Ind Eng Chem Res. 2011;50(8):4515–4533.

[2] Hexxcell Ltd., 2015. Hexxcell StudioTM. http://www.hexxcell.com.

[3] Müller, E. A. and Jackson, G. Force-Field Parameters from the SAFT-γ Equation of State for Use in Coarse-Grained Molecular Simulations. Annual review of chemical and biomolecular engineering5, 405-427.

[4] Macchietto, S., 2015. Energy Efficient Heat Exchange in Fouling Conditions: the UNIHEAT Project. Proceedings, Heat Exchanger Fouling and Cleaning XI, Enfield (Ireland), June 7-11.

[5] Mejía, A., C. Herdes, and E.A. Müller, Force Fields for Coarse-Grained Molecular Simulations from a Corresponding States Correlation. Industrial & Engineering Chemistry Research, 2014. 53(10): p. 4131-4141.

[6] Infochem, Multiflash. http://www.kbcat.com/technology/infochem-software.