Metaheuristic optimization of an organic rankine cycle using advanced exergy analysis and artificial bee colony algorithm

Abstract

In optimizing thermodynamic cycles, selecting the objective function is crucial, and including advanced methods in addition to classical approaches can provide significant advantages to the optimization process. In this study, the condenser temperature, evaporator temperature, and turbine inlet pressure are considered as variables to be optimized in an organic Rankine cycle that extracts heat from a low-temperature geothermal water source. Total unavoidable exergy destruction, thermal efficiency, second-law efficiency, and network output are optimized individually. The artificial bee colony algorithm, a metaheuristic approach, is employed as the optimization method. R123, R11, and R245ca are considered to be the working fluids, and each objective function is applied individually. A total of 12 different optimization processes are conducted, and the achieved objective values are compared. Thus, not only identifying the fluid with the best potential, but also the selection of the most advantageous objective function is determined. In this study, it is observed that selecting R11 as the working fluid and applying total unavoidable exergy minimization optimization result in the best values for all objectives. While other fluids show relatively successful outcomes under different objectives, choosing total unavoidable exergy destruction as the objective function has consistently led to successful results in almost all cases. Maximum work output value was obtained with R11 as 298.45 kW.