Parametric Optimization of Organic Rankine Cycle by Genetic Algorithm

Abstract

Organic Rankine Cycle (ORC) is widely used in the field of low temperature waste heat recovery, including solar, biomass and geothermal energy, among others. Based on the thermodynamic model of ORC system built up in Matlab, this study employ Genetic Algorithm (GA) on ORC system for parametric optimization and select a ratio of heat transfer area to total net power output as the performance evaluation criterion to predict the economy of system. R11, R113, R123 and isopentane are choosed as the working medium. The results show that the ORC system with isopentane has the minimum objective function value of 0.429m2/kw. The corresponding condensing temperature and degree of supercooling are generally located at lower boundary over their parametric design ranges, and the corresponding pinch point temperature difference are located at upper boundary. For different working fluids, there exist an optimum evaporating temperature and degree of superheat.

Determination of Optimum Pinch Point Temperature Difference Depending on Heat Source Temperature and Organic Fluid with Genetic Algorithm

“…𝜂 𝑡ℎ = 𝑊 𝑛𝑒𝑡 /𝑄 𝑒 (10) The irreversibility equations used for the pump (11), evaporator (12), turbine (13) and condenser (14) in the exergy analysis of the components in the system are given below. The average temperatures of the heat source and cooling water are given in Equation 15-16.…”

“…In some studies, the objective function was determined through a single parameter in GA optimization. The objective functions; Bian et al [11] determined the heat transfer area as the ratio of the total net power output, and Long et al [12] decided the total exergy efficiency. Gutierrez et al [13] accepted gross annual profit as an objective function, Han et al [14] as a total irreversibility loss, Pierobon et al [15] as a thermal efficiency, Agromayor et al [16] as a second law efficiency.…”