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

Artificial neural networks (ANN) provide a computationally efficient pathway for solving complex nonlinear 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 similar to 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 similar to 80% computational speedup compared to conventional property routines. (C) 2019 Elsevier Ltd and IIR. All rights reserved.