Techno-economic feasibility analysis of zeotropic mixtures and pure fluids for organic Rankine cycle systems

In this paper the techno-economic feasibility of employing zeotropic mixtures as working fluids in organic Rankine cycle power systems is assessed. For an application case based on the utilization of low temperature geothermal heat at 135 degrees C, the net present value of the organic Rankine cycle system was maximized by optimizing the process variables and heat transfer equipment design parameters. Parametric studies detailing the effect of the assumed electricity price and the employed equipment cost estimation models were carried out. The working fluids propane, i-butane, i-pentane, R1234yf, and their mixtures were selected for investigation, since they have shown promising performance in previous studies. The results suggest that the highest net present value (43.1 M(sic)) is reached by the mixture R1234yf/i-butane (53.3/46.7), which is 2.1 M(sic) higher compared to the most economically feasible pure fluid R1234yf for an electricity price of 0.252 (sic)/kWh. The results of the sensitivity analyses suggest that the techno-economic benefit of using the mixture is robust to variations in the electricity price, the cost of the working fluid, and the equipment cost estimation models. In the comparison of R1234yf based mixtures to propane based mixtures, the inclusion of model for estimating the turbine efficiency is assessed to have a relevant influence on the results. The results also indicate that the R1234yf/propane, R1234yf/i-butane, and R1234yf/i-pentane result in lower cooling water flows and thereby lower cooling tower costs compared with R1234yf.

Automated summary

This paper evaluates the techno-economic feasibility of using zeotropic mixtures as working fluids in organic Rankine cycle power systems, finding that the mixture R1234yf/i-butane has the highest net present value. The techno-economic benefit of using mixtures is robust to variations in electricity price, working fluid cost, and equipment cost estimation models.