مشخصات پژوهش

Climate change and fossil fuel depletion highlight the need for efficient renewable-based power generation. This study analyzes a molten-salt-driven Kalina cycle using integrated technoeconomic, conventional, and advanced exergy methods under both full-load and part-load conditions. In the baseline full-load case, the system achieves energy and exergy efficiencies of 30.2% and 36.72%, with an annual income of 17.34 M$, net present value of 52.03 M$, and a payback period of 7.70 years. Across part-load conditions of 0.9, 0.8, 0.7, 0.6, and 0.5, the PBP progressively increases to 7.83, 8.10, 8.49, 8.99, and 9.99 years, respectively, indicating reduced economic attractiveness at lower loads. Conventional exergy analysis indicates that the TES heat exchanger has the highest exergy destruction rate, whereas advanced exergy analysis identifies the turbine as the primary optimization target, with most of its exergy destruction being avoidable and endogenous. The TES heat exchanger ranks second in all scenarios except under full-load operation, where condenser 1 dominates. A machine learning-based multi-objective optimization framework is applied to enhance system performance. At full load, optimization improves energy and exergy efficiencies by 21.6% and 18.3%, respectively, while reducing total exergy destruction by 20.4%. From an economic perspective, the payback period decreases to 6.36 years, while annual income and net present value increase by 1.27% and 20.5%, respectively.