An improved method of intelligence construction for subcritical thermodynamic cycle

With the increase of the consumed proportion of renewable energy, energy systems with renewable energy and multi-energy complementary would become more diverse. As the core of energy systems, the existing thermodynamic cycles cannot meet the diverse scenarios of energy utilization in the future. Therefore, it is urgent to develop intelligent methods to rapidly design new forms of cycles. In this study, an intelligent algorithm is designed to perform the construction and optimization of the cycle. Databases on structures and working fluids are established for the comprehensive optimization of the cycle. The four-process, five-process, and six-process cycles are constructed, and ten cycle structures are obtained. The parameters of the cycles are optimized by the genetic algorithm, and the influence of temperature range on cycle efficiency is also analyzed. The maximum efficiencies of the cycles are 21.17%, 26.48%, 30.22%, and 31.39% respectively for four temperature ranges. The efficiency of the new cycle built through different processes can be improved by up to 9.96% compared to that of the classical Rankine cycle. The influence of the structure and working fluid on the thermodynamic cycle is also analyzed. The results show that the proposed method has strong ability to develop new cycles, which is of great significance for the future development of energy systems.

Automated summary

An intelligent algorithm is developed to rapidly design new forms of energy cycles and optimize their parameters to improve efficiency. The influence of temperature range, structure, and working fluid on the cycle is analyzed. The results demonstrate the significance of this method for the future development of energy systems.