Intelligent collaborative attainment of structure configuration and fluid selection for the Organic Rankine cycle

The feasibility of a 3D cycle construction method (adding the dimension of zeotropic component) for improvement of the Organic Rankine Cycle (ORC) performance has been proven in previous studies. However, 3D cycle construction and optimization are difficult for both the human brain and conventional analytical method; therefore, it requires intelligent realization with the help of computer. Starting from a 2D intelligent cycle construction and optimization, and using the ORC as starting point, this paper proposes a three-level nested algorithm to attain the ORC structure construction and fluid selection intelligently and collaboratively. The nested algorithm takes net power output as the objective function and employs computational intelligence utilizing an evolution algorithm. Verification of the algorithm is performed using the data from references, followed by case studies for pure and mixture fluids in an application scenario of liquefied natural gas cold energy recovery. The verification results prove reliability and feasibility of the algorithm with a relative error of net power output of 2.5%. The results of the case studies show that the optimal pure fluid is R116 and optimal mixtures are R290 and R600a with a mass ratio of 53 to 47. Thermal efficiencies of the pure fluid and mixture ORC systems are 16.89% and 26.07%, respectively, which are improved compared with the reference. The intelligent and collaborative attainment of the ORC structure and fluid selection is achieved by the proposed nested algorithm, which not only lays the foundation for 3D intelligent cycle construction, but also makes it convenient to explore an ORC with better performance for application purposes.