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Journal of Thermal Analysis and Calorimetry

, Volume 136, Issue 4, pp 1645–1665 | Cite as

Performance analysis and multi-objective optimization of an organic Rankine cycle with binary zeotropic working fluid employing modified artificial bee colony algorithm

  • Sadegh SadeghiEmail author
  • Peyman Maghsoudi
  • Bahman Shabani
  • Hamid Haghshenas Gorgani
  • Negar Shabani
Article

Abstract

From a thermal point of view, zeotropic mixtures are likely to be more efficient than azeotropic fluids in low-temperature power cycles for reduction in exergy destruction occurring during heat absorption/rejection processes due to their suitable boiling characteristics. In this study, comprehensive energetic and exergetic analyses are mathematically performed for an organic Rankine cycle (ORC) system employing a potential binary zeotropic working fluid, namely R717/water. For this purpose, initially mass, energy, and exergy balance equations are derived. With regard to the similarity in molar mass of R717 (17.03 g mol−1) and water (18.01 g mol−1), there is no need to alter the size of the ORC components such as turbine and pump. In order to achieve the optimal thermal and exergy efficiencies of the ORC system, modified version a powerful and relatively new optimization algorithm called artificial bee colony (ABC) is used taking into account different effective constraints. The main motivation behind using ABC lies on its robustness, reliability, and convergence rate speed in dealing with complicated constrained multi-objective problems. Convergence rates of the algorithm for optimal calculation of the efficiencies are presented. Subsequently, due to the importance of exergy concept in ORC systems, exergy destructions occurring in the components are computed. Finally, the impacts of pressure, temperature, mass fraction, and mass flow rate on the ORC thermal and exergy efficiencies are discussed.

Keywords

Binary zeotropic mixture Organic Rankine cycle Thermal efficiency Exergy efficiency Multi-objective optimization Artificial bee colony algorithm 

List of symbols

eCh.

Chemical exergy (kJ kg−1)

ePh.

Physical exergy (kJ kg−1)

\(G_{\text{r}}^{\text{E}}\)

Gibbs excess energy of mixture (kJ kg−1)

h

Specific enthalpy (kJ kg−1)

hE

Excess enthalpy of mixture (kJ kg−1)

\(h_{\text{m}}^{\text{g}}\)

Enthalpy of mixture at vapor phase (kJ kg−1)

\(h_{\text{m}}^{\text{L}}\)

Enthalpy of mixture at liquid phase (kJ kg−1)

\(\dot{m}\)

Mass flow rate (kg s−1)

M

Molar mass (g mol−1)

P

Pressure (bar)

Pr

Reduced pressure (bar)

\(\dot{Q}\)

Heat transfer rate (kW)

Qu

Quality (%)

s

Entropy (kJ kg−1 K−1)

sE

Excess entropy of mixture (kJ kg−1 K−1)

\(s_{\text{m}}^{\text{g}}\)

Specific entropy of mixture at vapor phase (kJ kg−1 K−1)

\(s_{\text{m}}^{\text{L}}\)

Specific entropy of mixture at liquid phase (kJ kg−1 K−1)

T

Temperature (K)

Tr

Reduced temperature (K)

u

Specific internal energy (kJ kg−1)

v

Specific volume (m3 kg−1)

vE

Excess volume of mixture (m3 kg−1)

\(v_{\text{m}}^{\text{g}}\)

Specific volume of mixture at vapor phase (m3 kg−1)

\(v_{\text{m}}^{\text{L}}\)

Specific volume of mixture at liquid phase (m3 kg−1)

\(\dot{W}\)

Power (kW)

Xf

R717 mass fraction (%)

Greek symbols

η

Efficiency (%)

Subscripts

ABC

Artificial bee colony

Ch

Chemical

D

Number of optimization parameters

DE

Differential evolution

EES

Engineering equation solver

ES

Evolution strategy

Ex.

Exergy

EV

Expansion valve

Fit

Fitness

Fi

Value of objective function for solution i

g

Gas

GA

Genetic algorithm

GWP

Global warming potential

l

Liquid

LEC

Levelized energy cost

NP

Number of colony size

ODP

Ozone depletion potential

ORC

Organic Rankine cycle

P

Probability

Ph.

Physical

PSO

Particle swarm optimization

R

Refrigerant

SN

Food source number

SRC

Supercritical Rankine cycle

Th.

Thermal

VBA

Virtual bee algorithm

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2018

Authors and Affiliations

  • Sadegh Sadeghi
    • 1
    Email author
  • Peyman Maghsoudi
    • 2
  • Bahman Shabani
    • 3
  • Hamid Haghshenas Gorgani
    • 4
  • Negar Shabani
    • 5
  1. 1.Department of Mechanical Engineering, School of EngineeringIran University of Science and TechnologyNarmak, TehranIran
  2. 2.School of Mechanical Engineering, College of EngineeringUniversity of Tehran (UT)Amirabad, TehranIran
  3. 3.School EngineeringRMIT UniversityMelbourneAustralia
  4. 4.Engineering Graphics CenterSharif University of TechnologyTehranIran
  5. 5.School of SciencePersian Gulf UniversityBushehrIran

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