A graphical criterion for working fluid selection and thermodynamic system comparison in waste heat recovery

Abstract In the present study, we proposed a graphical criterion called CE diagram by achieving the Pareto optimal solutions of the annual cash flow and exergy efficiency. This new graphical criterion enables both working fluid selection and thermodynamic system comparison for waste heat recovery. It's better than the existing criterion based on single objective optimization because it is graphical and intuitionistic in the form of diagram. The features of CE diagram were illustrated by studying 5 examples with different heat-source temperatures (ranging between 100 °C to 260 °C), 26 chlorine-free working fluids and two typical ORC systems including basic organic Rankine cycle(BORC) and recuperative organic Rankine cycle (RORC). It is found that the proposed graphical criterion is feasible and can be applied to any closed loop waste heat recovery thermodynamic systems and working fluids.

[1]  Benedick Montreal Protocol on Substances that Deplete the Ozone Layer , 1996 .

[2]  Patrick Linke,et al.  On the systematic design and selection of optimal working fluids for Organic Rankine Cycles , 2010 .

[3]  Filip Logist,et al.  Fast Pareto set generation for nonlinear optimal control problems with multiple objectives , 2010 .

[4]  Nicolas Galanis,et al.  Parametric study and optimization of a transcritical power cycle using a low temperature source , 2010 .

[5]  Vincent Lemort,et al.  Thermo-economic optimization of waste heat recovery Organic Rankine Cycles , 2011 .

[6]  Patrick Linke,et al.  Toward Optimum Working Fluid Mixtures for Organic Rankine Cycles using Molecular Design and Sensitivity Analysis , 2013 .

[7]  Richard Turton,et al.  Analysis, Synthesis and Design of Chemical Processes , 2002 .

[8]  Chao Xu,et al.  Parametric optimization of regenerative organic Rankine cycle (ORC) for low grade waste heat recovery using genetic algorithm , 2013 .

[9]  Antonio Rovira,et al.  Thermoeconomic optimization of combined cycle gas turbine power plants using genetic algorithms , 2003 .

[10]  Bertrand F. Tchanche,et al.  Fluid selection for a low-temperature solar organic Rankine cycle , 2009 .

[11]  J. Van Roy,et al.  Parametric optimization and performance analysis of a waste heat recovery system using Organic Ranki , 2010 .

[12]  Vincent Lemort,et al.  Working fluid selection and operating maps for Organic Rankine Cycle expansion machines , 2012 .

[13]  Houjin Chen,et al.  Application of Distributed Genetic Algorithm Based on Migration Strategy in Image Segmentation , 2007, Third International Conference on Natural Computation (ICNC 2007).

[14]  Guo Tao,et al.  Performance comparison and parametric optimization of subcritical Organic Rankine Cycle (ORC) and transcritical power cycle system for low-temperature geothermal power generation , 2011 .

[15]  Markus Preißinger,et al.  Exergoeconomic optimization of an Organic Rankine Cycle for low-temperature geothermal heat sources , 2012 .

[16]  Takahisa Yamamoto,et al.  Design and testing of the Organic Rankine Cycle , 2001 .

[17]  Yiping Dai,et al.  Multi-objective optimization of an organic Rankine cycle (ORC) for low grade waste heat recovery using evolutionary algorithm , 2013 .

[18]  Chandrasekharan Rajendran,et al.  A multi-objective simulated-annealing algorithm for scheduling in flowshops to minimize the makespan and total flowtime of jobs , 2005, Eur. J. Oper. Res..

[19]  Brian Elmegaard,et al.  Multi-objective optimization of organic Rankine cycles for waste heat recovery: Application in an offshore platform , 2013 .

[20]  Mortaza Yari,et al.  Performance analysis of the different Organic Rankine Cycles (ORCs) using dry fluids , 2009 .

[21]  W. Worek,et al.  Optimum design criteria for an Organic Rankine cycle using low-temperature geothermal heat sources , 2007 .

[22]  François Maréchal,et al.  Integrated Thermo-Economic Modelling of Geothermal Resources for Optimal Exploitation Scheme Identification , 2010 .

[23]  Tao Guo,et al.  Fluids and parameters optimization for a novel cogeneration system driven by low-temperature geother , 2011 .

[24]  Yiping Dai,et al.  Exergy analyses and parametric optimizations for different cogeneration power plants in cement industry , 2009 .

[25]  Lourdes García-Rodríguez,et al.  Preliminary assessment of solar organic Rankine cycles for driving a desalination system , 2007 .

[26]  Jiangfeng Wang,et al.  Parametric optimization and comparative study of organic Rankine cycle (ORC) for low grade waste heat recovery , 2009 .

[27]  Jinliang Xu,et al.  The optimal evaporation temperature and working fluids for subcritical organic Rankine cycle , 2012 .

[28]  Oguz Arslan,et al.  ANN based optimization of supercritical ORC-Binary geothermal power plant: Simav case study , 2011 .

[29]  Ulli Drescher,et al.  Fluid selection for the Organic Rankine Cycle (ORC) in biomass power and heat plants , 2007 .

[30]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[31]  Olav Bolland,et al.  Working fluids for low-temperature heat source , 2010 .

[32]  Naijun Zhou,et al.  Fluid selection and parametric optimization of organic Rankine cycle using low temperature waste heat , 2012 .

[33]  Rambod Rayegan,et al.  A procedure to select working fluids for Solar Organic Rankine Cycles (ORCs) , 2011 .