A Thermodynamic Library for Simulation and Optimization of Dynamic Processes

Abstract Process system tools, such as simulation and optimization of dynamic systems, are widely used in the process industries for development of operational strategies and control for process systems. These tools rely on thermodynamic models and many thermodynamic models have been developed for different compounds and mixtures. However, rigorous thermodynamic models are generally computationally intensive and not available as open-source libraries for process simulation and optimization. In this paper, we describe the application of a novel open-source rigorous thermodynamic library, ThermoLib, which is designed for dynamic simulation and optimization of vapor-liquid processes. ThermoLib is implemented in Matlab and C and uses cubic equations of state to compute vapor and liquid phase thermodynamic properties. The novelty of ThermoLib is that it provides analytical first and second order derivatives. These derivatives are needed for efficient dynamic simulation and optimization. The analytical derivatives improve the computational performance by a factor between 12 and 35 as compared to finite difference approximations. We present two examples that use ThermoLib routines in their implementations: (1) simulation of a vapor-compression cycle, and (2) optimal control of an isoenergetic-isochoric flash separation process. The ThermoLib software used in this paper is distributed as open-source software at www.psetools.org.

[1]  G. Soave Equilibrium constants from a modified Redlich-Kwong equation of state , 1972 .

[2]  Li Zhao,et al.  Theoretical and basic experimental analysis on load adjustment of geothermal heat pump systems , 2003 .

[3]  Fidel Mato,et al.  Teaching advanced equations of state in applied thermodynamics courses using open source programs , 2011 .

[4]  Jonathan S. Litt Toolbox for the Modeling and Analysis of Thermodynamic Systems Workshop , 2015 .

[5]  John Bagterp Jørgensen,et al.  An open-source thermodynamic software library , 2016 .

[6]  J. B. Jørgensen Adjoint sensitivity results for predictive control, state- and parameter-estimation with nonlinear models , 2007, 2007 European Control Conference (ECC).

[8]  Morten C. Svensson,et al.  Model-based optimizing control of a water-to-water heat pump unit , 1996 .

[9]  G. H. Thomson The DIPPR® databases , 1996 .

[10]  Stephen P. Boyd,et al.  Nonconvex model predictive control for commercial refrigeration , 2013, Int. J. Control.

[11]  Niels Kjølstad Poulsen,et al.  Economic Model Predictive Control for building climate control in a Smart Grid , 2012, 2012 IEEE PES Innovative Smart Grid Technologies (ISGT).

[12]  D. Peng,et al.  A New Two-Constant Equation of State , 1976 .

[13]  Sigurd Skogestad,et al.  Optimal operation of simple refrigeration cycles: Part II: Selection of controlled variables , 2007, Comput. Chem. Eng..

[14]  S. Sandler,et al.  Critical evaluation of equation of state mixing rules for the prediction of high-pressure phase equilibria , 1989 .

[15]  Mauro Palumbo,et al.  OpenCalphad - a free thermodynamic software , 2015, Integrating Materials and Manufacturing Innovation.

[16]  Wolfgang Dahmen,et al.  Introduction to Model Based Optimization of Chemical Processes on Moving Horizons , 2001 .