Computing thermodynamic properties of ammonia–water mixtures using artificial neural networks

•ANN are used to compute thermodynamic properties of ammonia–water mixture.•Error in predictions is observed to be less than 0.5%.•ANN based approach offers ∼60% computational speedup in calculating properties.•Thermodynamically consistent and accurate predictions of transient response.•Transient simulation speeds improved by ∼80%. Artificial neural networks (ANN) provide a computationally efficient pathway for solving complex non-linear problems. Cascaded ANN are used to compute thermodynamic properties of the ammonia–water mixture working-pair commonly used in vapor absorption heat pumps. Thermodynamic property routines can affect the accuracy and pose a significant computational bottleneck for steady-state and transient cycle simulations of ammonia–water. The properties computed using the proposed method agree within 0.5% of equation of state data over a wide range of operating parameters. It is observed that the ANN based property routines, developed as explicit functions, offer ∼60% decrease in computational time over currently used property routines. As a case study, the property calculation modules developed using ANN are employed in simulating the dynamic response of a representative ammonia-water condenser for a vapor absorption cycle. The models predict the transient behavior accurately with an ∼80% computational speedup compared to conventional property routines.