Victor A. Mazur
Academy of Refrigeration, Odessa, Ukraine
Dmytro Nikitin
Academy of Refrigeration, Odessa, Ukraine
Honghee Park
Graduate School of Division of Mechanical Engineering, Korea University, Seoul, Korea
Wonuk Kim
Graduate School of Division of Mechanical Engineering, Korea University, Seoul, Korea
Joo Seoung Lee
Graduate School of Division of Mechanical Engineering, Korea University, Seoul, Korea
Yongchan Kim
School of Mechanical Engineering, Korea University, Seoul, Korea
Makato Tamura
Ibaraki University, Mito-city, Japan
Shinichiro Okushima
University of Tsukuba, Tsukuba-science-city, Japan
Hoy-Yen Chan
Department of Architecture and Built Environment, University of Nottingham, Nottingham, UK
Saffa Riffat
Department of Architecture and Built Environment, University of Nottingham, Nottingham, UK
Jie Zhu
Department of Architecture and Built Environment, University of Nottingham, Nottingham, UK
Reza Ahmadian
Hydro-environmental Research Centre, Cardiff School of Engineering, Cardiff University, UK
Roger Falconer
Hydro-environmental Research Centre, Cardiff School of Engineering, Cardiff University, UK
Bettina Bockelmann-Evans
Hydro-environmental Research Centre, Cardiff School of Engineering, Cardiff University, UK
K. Nagy
Special Advisor, Triones Institute of Technology, Budapest, Hungary
K. Körmendi
Phd student, Zrínyi Miklós National Defense University, Budapest, Hungary
Gerrit Boschloo
Uppsala University, Uppsala, Sweden
Anders Hagfeldt
Uppsala University, Uppsala, Sweden
Håkan Rensmo
Uppsala University, Uppsala, Sweden
Lars Kloo
Royal Institute of Technology, Stockholm, Sweden
Licheng Sun
Royal Institute of Technology, Stockholm, Sweden
Henrik Pettersson
Swerea IVF, Mölndal, Sweden
A. K. Singh
Department of Electrical Engineering, Indian Institute of Technology, Patna, India
S. K. Parida
Department of Electrical Engineering, Indian Institute of Technology, Patna, India
Jonathan D. Leaver
Unitec NZ, Auckland, New Zealand
Luke HT. Leaver
Asia Pacific Energy Research Centre, Tokyo, Japan
J. F. Song
The New and Renewable Energy of Beijing Key Laboratory, North China Electric Power University, China
Y. P. Yang
The New and Renewable Energy of Beijing Key Laboratory, North China Electric Power University, China
H. J. Hou
The New and Renewable Energy of Beijing Key Laboratory, North China Electric Power University, China
M. X. Zhang
The New and Renewable Energy of Beijing Key Laboratory, North China Electric Power University, China
Ian D. Bishop
University of Melbourne, Melbourne, Australia
Eric P. Johnson
Atlantic Consulting, Gattikon, Switzerland
José Renato de O. Lima
CEMPEQC - Organic Chemistry Department, Institute of Chemistry, São Paulo State University, Brazil
Fabricia Gasparini
CEMPEQC - Organic Chemistry Department, Institute of Chemistry, São Paulo State University, Brazil
Nadia de L. Camargo
CEMPEQC - Organic Chemistry Department, Institute of Chemistry, São Paulo State University, Brazil
Yussra A. Ghani
CEMPEQC - Organic Chemistry Department, Institute of Chemistry, São Paulo State University, Brazil
Rondenelly B. da Silva
Chemistry Department, Piauí Federal University, Brazil
José Eduardo de Olivieira
CEMPEQC - Organic Chemistry Department, Institute of Chemistry, São Paulo State University, Brazil
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http://dx.doi.org/10.3384/ecp11057859Published in: World Renewable Energy Congress - Sweden; 8-13 May; 2011; Linköping; Sweden
Linköping Electronic Conference Proceedings 57:15, p. 859-866
The sustainable working media selection is one of the most important stages in renewable energy technologies. The compromise among such properties as contribution to greenhouse effect; flammability; toxicity; thermodynamic behaviour; performance specifications; and the others defines a sustainable decision. The aim of present work is to apply a fuzzy set methodology providing sustainability among thermodynamic; economic; and environmental requirements. The organic Rankine cycle (ORC) for the class of working fluids based on the hydrofluoroethers (HFE) is considered to demonstrate a proposed approach. To select new working fluids; which have no information on thermodynamic behavior; artificial neural networks (ANN) approach is offered to forecast the ORC energy efficiency. The ANN correlations for coefficient of performance (COP) and pressure ratio (output) as functions of critical temperature; critical pressure and normal boiling temperature (input) are built via REFPROP database. The validation set has been used to estimate the ORC energy efficiency without of thermodynamic property calculations. The accuracy of ANN prediction for the cycle performances does not exceed 4% relative to the training set values. The Bellman – Zadeh model as the intersection of membership functions (fuzzy criteria mappings) is applied to sustainable selection of working media.
Working Fluids; Organic Rankine Cycle; Coefficient of Performance; Artificial Neural Networks
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