Exergy Analysis Based on AI Correlations for Seawater Properties: Case Study of Industrial MED-TVC Plant in Kuwait

Desalination is an increasingly important element in the sustainable supply of potable water. To accurately predict costs, the efficiency of such systems requires accurate knowledge of seawater’s thermodynamic properties. Four models have been proposed for determining the thermophysical properties of salt water, pure water, an ideal mixture, and an aqueous sodium chloride solution, and empirical correlations, as would be expected, provide the precision necessary for accurate exergy calculations. This research began with a study of the most recent and accurate empirical investigations of the thermodynamic properties of seawater. It then employed AI techniques to develop a simpler, more accurate model for density, Gibbs free energy, specific enthalpy, and specific entropy for pressures extending up to 12 MPa, salinities from 0 to 80 g/kg, and the temperature range of 10 °C to 120 °C. The AI-based correlations achieved absolute errors of 1.5 kg/m3 for density, 0.185 kJ/kg for specific enthalpy, 0.005 kJ/kg·K for specific entropy, and 0.214 kJ/kg for Gibbs free energy. These values demonstrated at least equivalent, and even superior, accuracy to the existing state-of-the-art formulations, with the advantage of significantly reduced computational complexity, enhanced computational efficiency, and a more user-friendly implementation. Validation against experimental data demonstrated the exceptional accuracy of the predicted values for all the stated thermodynamic properties. In addition, an exergy-based assessment was conducted of the performance of a recently commissioned desalination plant in Kuwait. This was a large-scale multi-effect distillation plant with thermal vapour compression (MED-TVC), showing a second-law efficiency of 8.9%, with the primary source of exergy destruction identified as the evaporator units. Comparative assessment with a more conventional approach showed differences of less than 0.4% in total exergy destruction and less than 5% in exergetic efficiency. This is taken as a validation of the accuracy, reliability, and practical usefulness of the proposed AI framework for the performance evaluation of desalination systems.