Effect of Working Fluids on the Performance of Ocean Thermal Energy Conversion Based Hybrid Systems Using Machine Learning Approach

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 R۲۲۷ea working fluid achieves the highest power output and exergy efficiency, ۴۳۳.۵ kW and ۸.۶ % in Plan B, and ۳۸۰.۶ kW and ۷.۴۴ % in Plan C, respectively. Conversely, R۱۲۵ working fluid exhibits the lowest performance, with an output power of ۲۹۷.۷ kW and an efficiency of ۵.۸۲% in Plan B, and ۱۸۸.۳ kW and an efficiency of ۳.۶۸% 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 R۲۲۷ea and hybrid system design Plan B significantly improve efficiency and cost-effectiveness, offering a practical pathway for sustainable energy systems.

کلیدواژه ها:

Thermal efficiency ، Exergy efficiency ، Working fluids ، Machine Learning ، Ocean thermal energy conversion