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

Ulrik Larsen, Leonardo Pierobon, Jorrit Wronski, Fredrik Haglind

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Abstract

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. © 2013 Elsevier Ltd. All rights reserved
Original languageEnglish
JournalEnergy
Volume65
Issue number1
Pages (from-to)503-510
Number of pages8
ISSN0360-5442
DOIs
Publication statusPublished - 2014

Keywords

  • Organic Rankine cycle
  • Performance prediction
  • Waste heat recovery
  • Multiple regression analysis

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