Multiple regression models for the prediction of the maximum obtainable thermal efficiency of organic Rankine cycles

• The maximum thermal efficiency of ORCs in hundreds of cases was analysed.
• Multiple regression models were derived to predict the maximum obtainable efficiency of ORCs.
• Using only key design parameters, the maximum obtainable efficiency can be evaluated.
• The regression models decrease the resources needed to evaluate the maximum potential.
• The models are statistically strong and in good agreement with the literature.

Much attention is focused on increasing the energy efficiency to decrease fuel costs and CO2 emissions throughout industrial sectors. The ORC (organic Rankine cycle) is a relatively simple but efficient process that can be used for this purpose by converting low and medium temperature waste heat to power. In this study we propose four linear regression models to predict the maximum obtainable thermal efficiency for simple and recuperated ORCs. A previously derived methodology is able to determine the maximum thermal efficiency among many combinations of fluids and processes, given the boundary conditions of the process. Hundreds of optimised cases with varied design parameters are used as observations in four multiple regression analyses. We analyse the model assumptions, prediction abilities and extrapolations, and compare the results with recent studies in the literature. The models are in agreement with the literature, and they present an opportunity for accurate prediction of the potential of an ORC to convert heat sources with temperatures from 80 to 360 °C, without detailed knowledge or need for simulation of the process.