Optimization and analysis of a power generation cascade cycle with the Genetic algorithm

In this study, the thermodynamic analysis and optimization of a power generation cascade cycle by the Genetic algorithm are conducted by EES software. The power generation cascade cycle consists of two temperature levels. The high-temperature consists of the steam Rankine cycle, and the low one is the organic Rankine cycle, which is generated by the condenser of the high-temperature cycle. The effect of several refrigerants for the low-temperature cycle is examined. These refrigerants are R134a, R11, R123, R245fa, R141b, and R600a in which R134a and R141b had the best and worst performance, respectively. The results show that the thermal efficiency becomes 46.9% and 36.94% for R134a and R141b, respectively, after the Genetic algorithm optimization. Also, several methods of optimization in EES are compared to each other. These methods are conjugate direction method, variable metric method, Nelder-mead simplex method, and direct algorithm which their results are almost similar to the Genetic algorithm.

Keywords:

Cascade cycle , Optimization , Rankine cycle , Genetic algorithm