Exergy Analysis and Thermodynamic Optimization of a steam power plant- Based Rankine cycle system Using Intelligent Optimization Algorithms

ABSTRACT

In this paper, exergy analysis of a steam power plant located in southern Iran named Zarand power plant has been studied. In order to optimize the performance of the Rankine cycle and achieve higher exergy efficiency, several parameters have been considered as decision variables. Knowing that there is the ability to change some of the parameters in the specific range in the process of electricity production in power plant, temperature and output pressure of the boiler and output pressure of four steps of extraction turbine have been selected as six decision variables. Also, exergy efficiency has been considered as the objective function. For this purpose, the exergy efficiency of the system is optimized using intelligent algorithms including bees, fireflies, and algorithm based on teaching and learning and they are compared with each other. The results show that in the case of suitable changes of decision variables and applying appropriate algorithms, exergy efficiency of the studied thermal power plant can be increased from 30.1% to 30.68047%, 30.70368%, and 30.70369%, respectively. It means using optimization algorithms of bees, fireflies, training-learning, exergy efficiency of the Rankine cycle of the studied power plant can be increased by 0.58047%, 0.60368%, and 0.60369%, respectively.

KEYWORDS:

• Exergy efficiency
• Intelligent algorithms
• Rankine cycle
• thermal power plant

Nomenclature

${\dot{E}}_x$	=	exergy flow rate (kW)
$\dot{E}{x}^D$	=	exergy destruction
$\dot{E}{x}^Q$	=	exergy associated with heat
$\dot{E}{x}^W$	=	exergy associated with work
ex	=	specific exergy (kJ/kg)
h	=	specific enthalpy (kJ/kg)
LHV	=	lower heating value of fuel (kJ/kg)
$\dot{m}$	=	mass flow rate (kg/s)
P	=	pressure (bar)
$\dot{Q}$	=	heat transferred (kW)
r	=	random number in PSO optimisation
R	=	gas constant (kJ/kg K)
s	=	specific entropy (kJ/kg K)
T	=	temperature (C)
$\dot{w}$	=	work rate (kW)
y	=	mole fraction
BA	=	Bees Algorithm
FA	=	Firefly Algorithm
TLBO	=	Teaching Learning Based Optimisation

Greek symbols

ψ	=	exergy efficiency
$\xi$	=	chemical exergy/energy ratio
$\eta$	=	energy efficiency

Subscripts

0	=	reference environment condition
ch	=	chemical
cond	=	condenser
c.v.	=	control volume
f	=	saturated liquid
FWH	=	feedwater heater
g	=	saturated vapour
in	=	inlet stream
ke	=	kinetic
ph	=	physical
po	=	potential
out	=	outlet stream
st	=	steam turbine

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. International Association for the Properties of Water and Steam.

Additional information

Notes on contributors

Samad Elahifar

Samad Elahifar is an expert in Conditions monitoring and mechanical parts. He graduated high school in mathematics and physics field in 2001. He graduated in mechanical engineering at the Azad University of Dezful in 2006 (Bachelor degree). He also got his master's degree at the Azad University of Dezful in 2016. He has been working as a mechanical engineer at Khuzestan Steel Company for 11 years now.

Ehsanolah Assareh

Ehsanolah Assareh is a researcher in mechanical engineering, thermodynamic engineering and active member of Iran's National Elites Foundation where he has been recognized as a very talented Iranian student. He has published more than 40 research articles in authentic international journals and conferences (indexed by Thomson Reuters) and more than 30 scientific articles in other national and international journals and conferences. currently his main research interest is working on multi-objective optimization for thermodynamics cycles with renewable energy systems & multi-objective optimization coupled simulation analysis.

Rahim Moltames

Rahim Moltames is an M.Sc. in energy systems engineering from Sharif University of Technology in Iran. His research interests are in the areas of photovoltaics, solar thermal, CFD, monitoring system design, AVR microcontroller programming, economics, heat transfer, greenhouse energy management, and energy systems modeling.