Dynamic Modeling for Ionic Liquid-Based Absorption Refrigeration Systems

Residual heat is a common waste product of many industrial processes, and often low quality enthalpy is available from natural sources (e.g., geothermal springs, solar collectors, etc). Absorption refrigeration cycles are able to utilize such energy to produce useful cooling, with little electricity required (Srikhirin et al., 2001). In many developing areas, compression-based refrigeration may not be feasible due to weaknesses in the electrical grid, and sources of cheap residual heat may be present, both arguments for absorption refrigeration.

Certain ionic liquids (ILs) have inherent advantages as absorbents, such as negligible vapor pressure and ease of refrigerant separation, chemical inertness, and good transport properties (Shiflett and Yokozeki, 2006). In this presentation, we develop a rigorous dynamic model to study selected IL-water absorption refrigeration systems and the global sensitivity of cycle parameters to aid in design and optimization.

Traditionally, absorption refrigeration systems are rated through their coefficient of performance (COP), the ratio between evaporator cooling duty and the energy spent (generator heat duty and pump work), usually at steady state. However, when designing or optimizing absorption refrigeration systems, the transient behavior of the cycle is also important. Nevertheless, most literature is focused on steady-state modeling, and rigorous dynamic models for IL-based systems are lacking.

The challenges of creating rigorous dynamic models include the need for good quality thermodynamic and transport data (Kelkar and Maginn, 2007; Rodríguez and Brennecke, 2006), the multitude of states to be tracked and numerical stiffness. Using the basic framework of Cai et al. (2007), we set out to develop a comprehensive lumped-compartment model as a system of ordinary differential equations (ODEs) solved as an initial value problem (IVP). This structure readily lends itself to studying global sensitivity, using stochastic or deterministic tools. The states tracked are compartment masses and compositions, flow rates, and temperatures, while other transient parameters (e.g., enthalpy flows, performance, etc) are derived from these ODE states.

Systems of interest are based on 1-ethyl-3-methylimidazolium ([emim]) salts, such as trifluoroacetate ([TFA]), trifluoromethanesulfonate (or triflate) ([OTf]), and ethylsulfate ([EtSO₄]). We assume a geothermal heat source, generating 1 ton of refrigeration (12,000 BTU/h), with a space to be cooled at 10 °C. Example results for these systems include dynamic behavior of the equipment, performance, and cooling duty, as well as their sensitivity with respect to thermodynamic and transport properties. Advantages of selected approach include the use of established numerical modeling tools, such as MATLAB, and easy coupling with stochastic or deterministic sensitivity tools. Control algorithms can be also readily studied.

References

Cai, W., Sen, M. & Paolucci, S. (2007). Dynamic modeling of an absorption refrigeration system using ionic liquids. 2007 ASME International Mechanical Engineering Congress and Exposition, Proceedings of IMECE2007.

Srikhirin, P., Aphornratana, S. & Chungpaibulpatana, S. (2001). A review of absorption refrigeration technologies. Renewable & Sustainable Energy Reviews, 5, 343–372.

Kelkar, M. S. & Maginn, E. J. (2007). Effect of temperature and water content on the shear viscosity of the ionic liquid 1-ethyl-3-methylimidazolium bis(trifluoromethanesulfonyl)imide as studied by atomistic simulations. Journal of Physical Chemistry B, 111, 4867–4876.

Rodríguez, H. & Brennecke, J. F. (2006). Temperature and composition dependence of the density and viscosity of binary mixtures of water + ionic liquid. Journal of Chemical Engineering Data, 51, 2145–2155.

Shiflett, M. B. & Yokozeki, A. (2006). Absorption cycle utilizing ionic liquid as working fluid. U.S. Pat. 2006/0197053 A1.