Effect of Working Fluids on the Performance of Ocean Thermal Energy Conversion Based Hybrid Systems Using Machine Learning Approach
نویسندگان
1 Department of Energy Engineering, Hamedan University of Technology, Hamedan, Iran.
2 Department of Chemical Engineering, Hamedan University of Technology, Hamedan, Iran.
3 Department of Chemical Engineering, Hamedan University of Technology, Hamedan, Iran.
4 Department of Mechanical Engineering, Hamedan University of Technology, Hamedan, Iran.
doi
10.30501/jree.2025.491739.2193چکیده
This study compares the performance of seven working fluids in hybrid ocean thermal energy conversion (OTEC) systems integrated with solar and wind energy. Plan B integrates thermoelectric technology with a wind turbine, and Plan C relies solely on a standalone wind turbine. Machine learning techniques were applied for performance prediction and multi-objective optimization. The results show that R227ea working fluid achieves the highest power output and exergy efficiency—433.5 kW and 8.6 % in Plan B, and 380.6 kW and 7.44 % in Plan C, respectively. Conversely, R125 working fluid exhibits the lowest performance, with an output power of 297.7 kW and an efficiency of 5.82% in Plan B, and 188.3 kW and an efficiency of 3.68% in Plan C. Also, Plan B outperforms Plan C in all performance metrics such as efficiency, power output, and cost-effectiveness, due to the type of hybrid configuration. Overall, the results show that optimal fluid selection R227ea and hybrid system design Plan B significantly improve efficiency and cost-effectiveness, offering a practical pathway for sustainable energy systems.
