Multi-level Design and Selection of Optimum Working Fluids and ORC Systems for Power and Heat Cogeneration from Low Enthalpy Renewable Sources

Athanasios I. Papadopoulos, Mirko Stijepovic, Patrick Linke, Panos Seferlis, Spyros Voutetakis

Research output: Chapter in Book/Report/Conference proceedingChapter

24 Citations (Scopus)

Abstract

This work presents a multi-level method for the design and selection of heat exchange working fluids tailored for Organic Rankine Cycle (ORC) systems used in power and/or heat cogeneration from renewable, low enthalpy sources. A systematic methodology is employed supporting the design of optimum working fluid candidates using Computer Aided Molecular Design (CAMD). The performance of the designed fluids is evaluated using ORC models that enable simulation and economic design optimization. In addition to chemical/physical properties the performed evaluation considers working fluid characteristics such as safety (toxicity and flammability) and environmental properties (ozone depletion potential and global warming potential) that are equally important to economic efficiency. The proposed approach is illustrated through a case study involving varying geothermal field conditions employed as energy sources for greenhouse power and heat co-generation.

Original languageEnglish
Title of host publicationComputer Aided Chemical Engineering
PublisherElsevier B.V.
Pages66-70
Number of pages5
DOIs
Publication statusPublished - 2012

Publication series

NameComputer Aided Chemical Engineering
Volume30
ISSN (Print)1570-7946

    Fingerprint

Keywords

  • CAMD
  • Rankine cycle
  • Renewable energy
  • Working fluids synthesis

ASJC Scopus subject areas

  • Chemical Engineering(all)
  • Computer Science Applications

Cite this

Papadopoulos, A. I., Stijepovic, M., Linke, P., Seferlis, P., & Voutetakis, S. (2012). Multi-level Design and Selection of Optimum Working Fluids and ORC Systems for Power and Heat Cogeneration from Low Enthalpy Renewable Sources. In Computer Aided Chemical Engineering (pp. 66-70). (Computer Aided Chemical Engineering; Vol. 30). Elsevier B.V.. https://doi.org/10.1016/B978-0-444-59519-5.50014-9