OPTIMIZATION OF AN ORGANIC RANKINE CYCLE IN ENERGY RECOVERY FROM EXHAUST GASES OF A DIESEL ENGINE

International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),
ISSN 0976 – 6359(Online), Volume 5, Issue 12, December (2014), pp. 97-109 © IAEME
97
OPTIMIZATION OF AN ORGANIC RANKINE CYCLE IN
ENERGY RECOVERY FROM EXHAUST GASES OF A
DIESEL ENGINE
Zunaid Ahmed
Department of Mechanical Engineering, Royal School of Engineering & Technology,
Gauhati University, Guwahati, India,
Dimbendra K. Mahanta
Department of Mechanical Engineering, Assam Engineering College,
Gauhati University, Guwahati, India
ABSTRACT
This paper describes thermal analysis and optimization of an organic Rankine cycle (ORC)
integrated with a power generating stationary diesel engine. A simple ORC, with a regenerator, is
considered here as a bottoming cycle for producing additional power by recovering waste energy
from the exhaust gases of the engine. Taking evaporation pressure andcondensation
temperatureastwodecision variables, a genetic algorithm is used for simultaneously maximizing three
objective functions - exergy efficiency, thermal efficiency, and specific network.The optimization of
the ORC is performed for three different working fluids (n-hexane, isopentane and isobutane)with
their dry expansion after taking saturated vapour at the inlet of the turbine.On analysing and
comparing the performance of the optimized ORC systemunder the same waste energy condition,
several notable aspects are observed among the considered decision variables and objective
functions.The results shows that a considerable amount of waste energy can be recovered by the
ORC.
Keywords: Waste Energy Recovery, Organic Rankine Cycle, Exergy Analysis, Optimization,
Genetic Algorithm
INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING AND
TECHNOLOGY (IJMET)
ISSN 0976 – 6340 (Print)
ISSN 0976 – 6359 (Online)
Volume 5, Issue 12, December (2014), pp. 97-109
© IAEME: www.iaeme.com/IJMET.asp
Journal Impact Factor (2014): 7.5377 (Calculated by GISI)
www.jifactor.com
IJMET
© I A E M E

98
NOMENCLATURE
‫ܧ‬ሶ exergy rate (݇‫)ݏ/ܬ‬
ℎ specific enthalpy (݇‫)݃݇/ܬ‬
‫ܫ‬ሶ rate of exergy loss (kJ/s)
݉ሶ mass flow rate (݇݃/‫)ݏ‬
‫݌‬ pressure (݇ܲܽ)
‫ݏ‬ specific entropy (݇‫))ܭ ݃݇(/ܬ‬
ܶ temperature (℃ ‫)ܭ ݎ݋‬
‫ݒ‬ specific volume (݉ଷ
/݇݃)
ܳሶ energy rate (kg/s)
ܹሶ power (݇‫)ݏ/ܬ‬
‫ݓ‬ specific work output (݇‫)݃݇/ܬ‬
% percentage
∆‫ܪ‬௜௦ isentropic enthalpy difference (kW)
‫ܨ‬ weighted sum
Mol.wt molecular weight (g/mol)
Greek symbols
ߟ efficiency
ߝ effectiveness
Σ total
∆ difference
Subscripts
݃ exhaust gases
݂ fluid
w water
݅ each state point
݅݊ inlet
out outlet
݉݅݊ minimum
s isentropic
o dead state
crit critical
‫,݌‬ ݊݁‫ݐ‬ specific net
݁‫݃ݔ‬ exergy
‫ݐ‬ℎ݉ thermal
݁‫݌ܽݒ‬ evaporator/evaporation
‫ܾݎݑݐ‬ turbine
‫݊݁݃݁ݎ‬ regenerator
ܿ‫݀݊݋‬ condenser/condensation
‫݌݉ݑ݌‬ pump
1,2,2ܽ, 3,4,4ܽ, ‫,ܣ‬ ‫ܤ‬state points

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1. INTRODUCTION
Diesel engine (DE), that converts energy from heat to work, has vast applications in road
vehicles, marine transport, and power plants. Diesel engines are preferred for their reliability, low
specific cost and high electrical efficiency, especially in the power range of hundreds of kW to few
MW [1 - 3]. In a diesel engine, all the energy released during combustion of the fuel cannot be
converted into useful work because of some thermodynamic limitations. The balance is released
through exhaust gases and cooling systems as low grade energy (heat), and thus it is simply wasted.
An organic Rankine cycle (ORC) can be used to produce useful work by exploiting such low grade
energy sources. In ORCs, organic fluids are preferred to water when the required power is limited
and the energy source temperature is low. This is because of the fact that organic fluids often have
lower heat of vaporization and they can better follow the heat source to be cooled, and thus
temperature differences and irreversibility at the evaporator are reduced [4-6]. Many works have
focused on performance optimization of ORCs. Donghonget al. [7] optimized an ORC, driven by
exhaust heat of a gas turbine, using Modelica/Dymola software package as a modeling tool.
Chacarteguiet al. [8] studied an ORC as the bottoming cycle for large and medium recuperated gas
turbine. Yipinget al. [9] applied a genetic algorithm (GA) for parametric optimization of an ORC for
low grade waste energy recovery. Vajaet al. [10] studied the performance of an ORC as bottoming
system. Rashidiet al. [11] investigated another GA for optimization of a trans-critical power cycle
with regenerator.Amir [12] also optimized an ORC using a GA.Shuet al. [13], performed parametric
optimization of a combined system of diesel engine with bottoming ORC on alkane-based working
fluids.
The main objective of this study is to analyze and compare three different dry working fluids
for better ORC performance in waste energy recovery from the exhaust gases of a stationary diesel
engine. The dry working fluids considered arehydrocarbons (HCs). The thermodynamic
characteristics of these HCs are shown in Table 1. These hydrocarbons are attractive since some of
them have near-ambient boiling points to enable condensation near atmospheric pressure. For the
high-temperature ORC, HCs are selected as working fluids because of their appropriate critical
temperature and pressure. In addition, HCs are also environment friendly working fluids with a zero
ozone depletion potential (ODP) and relatively low global warming potential (GWP) values [16].
Table 1: Thermodynamic characteristic of fluids
Name ܶ௖௥௜௧
(K)
ܲ௖௥௜௧
(kPa)
Type Mol.wt.
(g/mol)
ODP GWP
100 yrs.
n-hexane 507.85 3058 dry 86.175 0 3
isopentane 460.39 3370 dry 72.149 0 2
isobutane 407.82 3640 dry 58.1 0 2
The fluids are taken here in the form of saturated vapour at the inlet of the turbine and then
their dry expansion is performed. A GA is used to optimize the performance of the ORC system for
each working fluid under the same waste heat condition. The optimization is performed for
simultaneously maximizing three objective functions, which are exergy efficiency, thermal
efficiency and specific network output.Multi-objective optimization is performed using the weighted
sum method.The performances of each of the working fluids are compared.

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2. SYSTEM MODELING
2.1 Description of the ORC system
The components of a simple ORC system are evaporator, turbine, condenser and pump. A
regenerator may also be used if the temperature of the working fluid leaving the turbine is markedly
higher than that leaving the pump. The fluid streams allowed in the ORC systems include exhaust
gases, working fluid and cooling water.
Fig.1: ORC-diesel engine system with a regenerator
Fig.2: T-s diagram of a simple ORC with a regenerator
The schematic of a simple ORC system with a regenerator is shown in Fig.1. The cycle is
essentially a rankine cycle. The exhaust gases enter the evaporator at state point ݃௜௡ and exit at ݃௠௜௡.
The working fluid in liquid phase enters the evaporator at state point ‫ܣ‬ and exits in the gaseous phase
at state point 3while absorbing energy from the exhaust gases. The working fluid in the gaseous

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phase then enters the turbine atstate point 3 and exitsat point 4 while producing work. Ideal
expansion of working fluid in the turbine is defined by the process 3-4‫.ݏ‬ In the regenerator the
energy exchange takes place between working fluids, i.e. hot gaseous stream (state points4-‫)ܤ‬ and
cold liquid stream (state points2-‫.)ܣ‬ The working fluid atstate point ‫ܤ‬is condensed in the condenser
tostate point1 while rejecting energy. The cooling water enters and exits condenser at state
points‫ݓ‬௜௡ and ‫ݓ‬௢௨௧. The working fluid in liquid phase is then pumped to a higher pressure level by
the pump from state point 1 to 2 while absorbing work. Ideal process in the pump is defined by the
process 1-2‫.ݏ‬The working fluid then enters the regenerator. The cyclic processes are shown in the T-
s diagram in Fig.2. The ORC operates on two pressure levels i.e. evaporation pressure ܲ௘௩௔௣ (2-‫)3-ܣ‬
and condensation pressure ܲ௖௢௡ௗ (4-‫.)1-ܤ‬
In the present study, the sub-critical ORCs with dry expansion are investigated. These fluids
are considered to ensure their dry expansion. As depicted in Fig.2, the slope of the saturated vapour
curve of a dry fluid is non-negative, which allows dry saturated vapour at the inlet of the turbine and
ensures its dry expansion inside the turbine. The considered energy source is the exhaust gases
released from a stationarydiesel engine.
For performing the simulation of the ORC, it is assumed that the system reaches a steady
state, as well as pressure drop in the pipes and heat loss to the environment from the evaporator,
condenser, turbine, pump and regenerator are negligible. The specified dead reference state is
considered with ܲ௢ and ܶ௢ as the ambient pressure and temperature, respectively. Because of the
thermodynamic irreversibility occurring in each of the components, such as non-isentropic
expansion, and compression as well as heat transfer over a finite temperature difference, the exergy
analysis is also employed to evaluate the performance of the ORC system in low grade waste energy
recovery.
2.2. Thermodynamic model
With reference to Fig. 2, a mathematical model of the ORC system is presented below on the
basis of the first and second laws of thermodynamics.
2.2.1. Energy analysis
(i) Processes ݃௜௡-݃௠௜௡and ‫:4-ܣ‬ For the evaporator, the energy absorbed from the exhaust gasesis:
ܳሶ௚ = ݉ሶ ௚൫ℎ௚,௜௡ − ℎ௚,௠௜௡൯ = ݉ሶ ௙(ℎଷ − ℎ஺) (1)
(ii) Process 3-4: For the turbine, the energy converted to work output and isentropic turbine
efficiency are:
ܹሶ ௧ = ݉ሶ ௙(ℎଷ − ℎସ) (2)
ߟ௧ =
ℎଷ − ℎସ
ℎଷ − ℎସ௦
(3)
(iii) Processes 5-‫ܤ‬ and 2-‫:ܣ‬ For the regenerator, the energy exchangeis:
(ℎହ − ℎ஻) = (ℎ஺ − ℎଶ)
(4)

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(iv) Process ‫:1-ܤ‬ For the condenser, the energy rejected to cooling wateris:
ܳሶ௪ = ݉ሶ ௙(ℎ஻ − ℎଵ) = ݉ሶ ௪൫ℎ௪,௢௨௧ − ℎ௪,௜௡൯ (5)
(v) Process 1-2: For the pump, the work input and isentropic efficiency of pump are:
ܹሶ௣ = ݉ሶ ௙(ℎଶ − ℎଵ) (6)
ߟ௣ =
ℎଶ௦ − ℎଵ
ℎଶ − ℎଵ
(7)
(vi) The net work output of the system is:
ܹሶ ௡௘௧ = ܹሶ ௧ − ܹሶ௣ (8)
(vii)The specific net work (net work output per kg of working fluid) of the system:
‫ݓ‬௦௣,௡௘௧ =
ܹሶ ௧ − ܹሶ௣
݉ሶ ௙
(9)
(viii) Thermal efficiency of the cycle can be calculated as:
ߟ௧௛ =
ܹሶ ௡௘௧
ܳሶ௚
(10)
2.2.2. Exergy analysis
(i) The exergy of the state point ݅:
The ‘o’ subscripts are used to denote the specified dead reference state under ambient pressure and
temperature conditions. In the present work, the dead state is specified
by To = 298 K ܲ௢ = 100 ݇ܲܽ.
(ii) The exergy entering into ORC (from exhaust gases) in the evaporator is:
(iii) The exergy leaving the ORC (to cooling water) at condenser is:
(iv) The exergy balance in k-the component of an open thermodynamic system is:
‫ܧ‬ሶ௜ = ݉ሶ ௙[(ℎ௜ − ℎ௢) − ܶ௢(‫ݏ‬௜ − ‫ݏ‬௢)] (11)
‫ܧ‬ሶ௚ = ‫ܧ‬ሶ௚,௜௡ − ‫ܧ‬ሶ௚,௢௨௧ (12)
‫ܧ‬ሶ௪ = ‫ܧ‬ሶ௪,௢௨௧ − ‫ܧ‬ሶ௪,௜௡ (13)
෍ ‫ܧ‬ሶ௞,௜௡ − ෍ ‫ܧ‬ሶ௞,௢௨௧ = ‫ܫ‬ሶ௞
(14)

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(v) The exergy loss in the evaporator is:
‫ܫ‬ሶ௘௩௔௣ = ‫ܧ‬ሶ௚,௠௜௡ + ‫ܧ‬ሶ஺ − ‫ܧ‬ሶ௚,௢௨௧ + ‫ܧ‬ሶଷ
(15)
(vi) The exergy loss in the turbine:
‫ܫ‬ሶ௧௨௥௕ = ‫ܧ‬ሶଷ − ܹሶ ௧ − ‫ܧ‬ሶସ
(16)
(vii) The exergy loss in the condenser is:
‫ܫ‬ሶ௖௢௡ௗ = ‫ܧ‬ሶ஻ + ‫ܧ‬ሶ௪,௜௡ − ‫ܧ‬ሶଵ − ‫ܧ‬ሶ௪,௢௨௧
(17)
(viii) The exergy loss in the pump is:
‫ܫ‬ሶ௣௨௠௣ = ܹሶ௣௨௠௣ + ‫ܧ‬ሶଵ − ‫ܧ‬ሶଶ
(18)
(ix) The exergy loss in the regenerator is:
‫ܫ‬ሶ௥௘௚௘௡ = ‫ܧ‬ሶସ + ‫ܧ‬ሶଶ − ‫ܧ‬ሶ஻ − ‫ܧ‬ሶ஺ (19)
(x) The total exergy loss due irreversibility:
‫ܫ‬ሶஊ = ‫ܫ‬ሶ௘௩௔௣ + ‫ܫ‬ሶ௧௨௥௕ + ‫ܫ‬ሶ௥௘௚௘௡ + ‫ܫ‬ሶ௖௢௡ௗ + ‫ܫ‬ሶ௣௨௠௣
(20)
(xi) The exergy balance of the ORC system is:
‫ܧ‬ሶ௚ + ܹሶ௣௨௠௣ = ‫ܧ‬ሶ௪ + ܹሶ ௧௨௥௕ + ‫ܫ‬ሶஊ (21)
The second law efficiency or the exergy efficiency expresses the capability to produce work
and it indicates how well the processes in the system perform relative to ideal (and reversible)
processes. It reflects the ability to convert energy into usable work [10]. Therefore, to evaluate the
performance of the cycle, exergy efficiency is considered.
(xii) The overall exergy efficiency of the ORC system:
ߟ௘௫௚ =
‫ܧ‬ሶ௪ + ܹሶ ௧௨௥௕
‫ܧ‬ሶ௚ + ܹሶ௣௨௠௣
= 1 −
‫ܫ‬ሶஊ
‫ܧ‬ሶ௚ + ܹሶ௣௨௠௣
(22)
3. OPTIMIZATION METHODOLOGY
3.1. Optimization of the ORC system
The optimization of the ORC system is performed for three different working fluids of dry
type. The ORC has many parameters (variables), which may affect its performance. In the present
study, the effects of the evaporation pressure (ܲ௘௩௔௣) andcondensation temperature (ܶ௖௢௡ௗ). The
exergy efficiency, thermal efficiency and specific net-work, which can evaluate the ORC
performance, are selected as the distinct objective functions for optimization of the ORC system.

104
The genetic algorithm (GA), first presented by Holland [14], is a robust optimization
algorithm, which is designed to reliably locate a global optimum even in the presence of local
optima. Further, GAs provides a great flexibility to hybridize with domain-dependent heuristics to
make an efficient implementation for a specific problem [12].
The GA starts by initializing a set of individuals that form a population. Then, the population
is evolved over generations (iterations) through the repeated application of some GA operators,
namely selection, crossover and mutation operators. In each generation, the GA evaluates the
individuals according to some objective functions and then selects the above-average individuals
through a selection operator. The selected above-average individuals have a higher possibility for
participating in the crossover operation for recombining genetic exchange among individuals.
Mutation, which periodically changes parts of the individuals, is the main operator for protecting the
algorithm from permanently losing genetic materials in the evolution process. Another operator used
in GA is migration, which allows movement of individuals among sub-populations of existing
individuals, with the best individuals from one sub-population replacing the worst individuals in
another sub-population [11].
3.2. Multi-objective optimization using weighted sum method
The weighted sum strategy converts a multi-objective problem of maximizing the vector of
criteria functions, into a scalar problem by constructing a weighted sum F(x) of all the objectives.
Maximize ‫)ݔ(ܨ‬ = ∑ ‫ݓ‬௜݂௜(‫)ݔ‬௞
௜ୀଵ (23)
where ݂௜(‫)ݔ‬ is the ݅-th normalized objective function and ‫ݓ‬௜is the weight of the ݅-th objective
function. Initially, each objective function is independently maximized. The maximum value is used
to normalize the corresponding objective function. Then, the value of ‫ݓ‬௜ has a range between zero
and one. The weights are set such that they are significant relative to each other and relative to the
objective values. The weights are chosen such that their sum equals unity (∑ ‫ݓ‬௜ = 1). F(x) can then
be optimized using a standard optimization algorithm. This weighted sum method suggests that the
solution can be found for a convex (minimization) or concave (maximization) multi-objective
optimization problem [15, 17].
4. NUMERICAL EXPERIMENTS
4.1 Simulation and optimization
The simulation of the thermodynamic model for the ORC system, incorporating equations (1)
to (22) and input parameters (Table 2 and Table 3), is done first in the EES software [19]. The
thermodynamic properties of the selected three working fluids are calculated using the fundamental
equation of state developed by Wagner and Pruss [18] as incorporated in the EES software. The
thermodynamic properties of the exhaust gases are calculated using equations of state in RefProp
[20]. The ORC system is then optimized through the GA module of the software to separately
maximize each of the objective functions, namely, exergy efficiency (ߟ௘௫௚), thermal efficiency
(ߟ௧௛௠) and specific net work output (‫ݓ‬௦௣,௡௘௧) within the specified bounds of operating parameters
evaporation pressure (ܲ௘௩௔௣) and condensation temperature (ܶ௖௢௡ௗ) mentioned in section 4.3.
In order to evaluate the average performance of the GA, 30 independent runs are performed,
for each working fluid against each objective function, with different sets of GA parameter values. In
all the runs, the initial solutions are generated randomly satisfying the variable bounds, and the
crossover and mutation probabilities are also taken randomly within the ranges of [0.80,0.90] and

105
[0.01,0.05], respectively. For the purpose of comparison, however, the population size and the
maximum number of generations to be performed are fixed as 40 and 100, respectively, in all runs.
In the case of multi-objective optimizing of three objective functions, a scalar function ‫ܨ‬ is
constructed with weighted sum of the three normalized objectives (Equation 23). Since all the three
objectives are considered to be equally important, equal weight is assigned to each, i.e. ‫ݓ‬ଵ = ‫ݓ‬ଶ =
‫ݓ‬ଷ. This function is then maximized using GA within the same bounds of independent variable to
achieve a solution which is a trade-off between maxima of the objective functions.
4.2Initial conditions
In the present study, power output, parameters of exhaust gases and exhaust compositions of
commercial V16 turbocharged diesel engine is employed in the simulations.The important
parameters of the diesel engine, under full load conditions, are listed in Table 2.
Table 2 Important parameters of the diesel engine
Parameter Value
Power Output 1000 kW
Exhaust gas temperature 868 K
Exhaust gas mass flow 1.165 kg/s
The mass-based typical compositions of the considered exhaust gases are as follows: N2 =
73.04%, H2O = 5.37%, O2 = 6.49%, CO2 = 15.10% [13]. The exhaust gases enter the evaporator at
temperature ܶ௚,௜௡. In order to avoid any corrosive effect, the minimum temperature ܶ௚,௠௜௡ at the exit
of the evaporator is constrained by the dew point temperature of the exhaust gases, which is 403K
[11]. The detail input parameters of the ORC system are shown is Table 3 where PPTD is the pinch
point temperature difference.
4.3 Boundary conditions
The decision variables which are identified as independent variables are evaporation pressure
(ܲ௘௩௔௣), and condensation temperature (ܶ௖௢௡ௗ). These are also referred to as operating parameters.
These three variables have effect on the exergy efficiency (ߟ௘௫௚), thermal efficiency (ߟ௧௛௠) and
specific net work output (‫ݓ‬௦௣,௡௘௧) [12]. The evaporation pressure (ܲ௘௩௔௣) and the corresponding inlet
temperature (ܶଷ) of the turbine are the maximum pressure and temperature of the ORC. The
minimum pressure of the ORC is the condensation pressure (ܲ௖௢௡ௗ) corresponding to the
condensation temperature (ܶ௖௢௡ௗ). The variable bounds of the independent variable (operating
parameters) are: 500‫ ܭ‬ ≤ ܲ_݁‫ ݌ܽݒ‬ ≤ critical pressure and 308‫ ܭ‬ ≤ ܶ௖௢௡ௗ ≤ 323‫.ܭ‬
Table 3: Input parameters of the ORC system
Parameter Value
Inlet temperature of exhaust gases (ܶ௚,௜௡) 868 K
Outlet temperature of exhaust gas (ܶ௚,௠௜௡) 403 K
Inlet temperature of cooling water (ܶ௪,௜௡) 298 K
PPTD Evaporator (ܶ௚,௠௜௡ − ܶ஺) 10 K
PPTD Condenser (ܶ௖,௢௨௧ − ܶ௖௢௡ௗ) 5 K
PPTD Regenerator (ܶ஻ − ܶଶ) 10 K
Isentropic efficiency of pump (ߟ௣) 0.65
Isentropic efficiency of turbine (ߟ௧) 0.85

106
5. RESULTS AND DISCUSSION
5.1. Optimum operating parameters
For each working fluid, several runs of the GA are performed to maximize each of objective
functions separately. The best, worst, mean and standard deviations of the objective values and
independent variables (operating parameters) are obtained. The mean values are presented in Table 4
for maximizing exergy efficiency (ߟ௘௫௚); Table 5 for maximizing thermal efficiency (ߟ௧௛௠); and
Table 6 for maximizing specific net work (‫ݓ‬௦௣,௡௘௧). It observed that the maximum ߟ௘௫௚,
maximumߟ௧௛௠ and maximum ‫ݓ‬௦௣.௡௘௧ for all the fluids do not occur at the same evaporation
pressure. This indicates that all the three objectives cannot be maximized simultaneously for the
ORC system operating at the same values of the operating parameters. The values of ܲ௘௩௔௣ and ܶଷ are
the highest when maximizing ߟ௘௫௚ and the lowest when maximizing ߟ௧௛௠. It is also observed that
ܶ௖௢௡ௗ for all the fluids is the almost the same when maximizing each of the objective functions. The
ORC system is expected to have maximum of any of the three objectives within the ranges of
operating parameters indicated in Table 7.
Table 4: Parameters of ORC for maximizing exergy efficiency
Working
Fluid
ܲ௘௩௔௣
(kPa)
ܶ௖௢௡ௗ
(K)
ܶଷ
(K)
Maximum
ߟ௘௫௚
(%)
n-hexane 2756.777 308.022 501.142 53.903
isopentane 3056.617 308.026 454.011 43.819
isobutane 3244.306 308.010 401.003 30.730
Table 5: Parameters of ORC for maximizing thermal efficiency
Working
Fluid
ܲ௘௩௔௣
(kPa)
ܶ௖௢௡ௗ
(K)
ܶଷ
(K)
Maximum
ߟ௧௛௠
(%)
n-hexane 2600.519 308.000 497.300 26.216
isopentane 2989.490 308.000 452.560 20.694
isobutane 3125.620 308.000 398.804 13.510
The need of multi-objective optimization is to ascertain particular values of the operating
parameters that will give a adequate trade-off among the maxima of the objective functions. For each
working fluid, several runs of the GA are performed to maximize‫ܨ‬ (weighted sum). The best, worst,
mean and standard deviations of independent variables (operating parameters) are obtained.For all
the fluids the standard deviations of ܲ௘௩௔௣ is less than 10.767 ݇ܲܽand standard deviations ofܶ௖௢௡ௗis
less than 0.01 ‫.ܭ‬ Since the standard deviations are small the mean values are considered to be the
optimum values of operating parameters. The simulation of the ORC system is run with these mean
values of operating parameters and the corresponding trade-off values of objective functions are
presented in Table 8.
Table 6: Parameters of ORC for maximizing specific net work
Working
Fluid
ܲ௘௩௔௣
(kPa)
ܶ௖௢௡ௗ
(K)
ܶଷ
(K)
Maximum
‫ݓ‬௦௣,௡௘௧
(kJ/kg)
n-hexane 2748.006 308.000 500.957 129.975
isopentane 3007.837 308.000 452.960 91.289
isobutane 3143.736 308.000 399.144 51.874

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These operating parameters that give the trade-off between the maxima lie within the ranges
as indicated in Table7.From the analysis it is observed that the trade-off is most for exergy efficiency
and least for specific net work, implying that for the optimum operating parameters, the ORC system
will run to produce specific net work close to the its maximum. The trade-off from the maxima for
each fluid when ORC is running at optimum parameters is shown in Table 9.It is also seen that the
trade-off is most for working fluid isobutaneand minimum forn-hexane.On comparing the working
fluids for the ORC system (running under the same given energy source conditions) n-hexane gives
the largest values of objective functions with least trade-off and its performance is the highest. The
ORC system running within the ranges of operating parameters (Table 7) will have good
performance, but best performance can be achieved when ORC system is running at optimum values
of operating parameters (Table 8).
Table 7: Ranges of operating parameters
Working
Fluid
Range of
ܲ௘௩௔௣ (kPa)
Range of
ܶ௖௢௡ௗ (K)
n-hexane 2600.519 - 2756.777 308.000 - 308.022
isopentane 2989.490 - 3056.617 308.000 – 308.026
isobutane 3125.620 - 3244.306 308.000 - 308.010
Table 8: Optimum parameters of ORC
Working
Fluid
Optimum operating
parameters
Corresponding objective functions
ܲ௘௩௔௣
(kPa)
ܶ௖௢௡ௗ
(K)
ߟ௘௫௚
(%)
ߟ௧௛௠
(%)
‫ݓ‬௦௣,௡௘௧
(kJ/kg)
n-hexane 2753.299 308.006 53.899 26.215 129.974
isopentane 3015.617 308.005 43.812 20.692 91.287
isobutane 3168.010 308.002 30.704 13.506 51.869
Table 9: rade-off between maxima
Working
Fluid
ߟ௘௫௚
(%)
ߟ௧௛௠
(%)
‫ݓ‬௦௣,௡௘௧
(kJ/kg)
Max. Opti. Diff. Max. Opti. Diff. Max. Opti. Diff.
n-hexane 53.903 53.899 -0.004 26.216 26.215 -0.001 129.975 129.974 -0.001
isopentane 43.819 43.812 -0.007 20.694 20.692 -0.002 91.289 91.287 -0.002
isobutane 30.730 30.704 -0.026 13.510 13.506 -0.004 51.874 51.869 -0.005
The comparative summary of the ORC systems operating at optimum parameters with the
three different working fluids is shown in Table 10. The DE under consideration produces 1000 kW
of power. For optimal conditions of ORC system, additional 149.872 kW net power can be recovered
using n-hexane as the working fluid with highest thermal and exergy efficiencies (26.215% & 53.899
%) but at the lowest condenser pressure of 30.408kPa and highest turbine inlet temperature of 501.06
K.It should be noted that for n-hexane the condenser requires low vacuum pressure. A condenser
pressure near the atmospheric pressure would be suitable from the view point of condenser design. In
this aspect, isopentane has condenser pressure closer to the atmospheric pressure. The mass flow rate
of each fluid stream of the ORC system is least for n-hexane.

108
Table 10: Comparative summary of ORC system at optimum operating parameters
Working Fluid n-hexane isopentane isobutane
ܲ௘௩௔௣ (kPa) 2753.299 3015.617 3168.010
ܲ௖௢௡ௗ (kPa) 30.408 128.190 462.587
ܶ௖௢௡ௗ (K) 308.006 308.005 308.002
ܶଷ (K) 501.060 453.128 399.598
݉ሶ ௚ (kg/s) 1.165 1.165 1.165
݉ሶ ௙ (kg/s) 1.153 1.296 1.489
݉ሶ ௪ (kg/s) 8.525 8.901 9.383
ܳሶ௚ (kW) 571.875 571.875 571.875
ܳሶ௪ (kW) 422.004 453.538 494.634
ܹሶ௣ (kW) 7.438 9.502 11.493
ܹሶ ௧ (kW) 157.310 127.839 88.734
ܹሶ ௡௘௧ (kW) 149.872 118.337 77.241
‫ܫ‬ሶΣ (kW) 132.839 163.513 203.459
ߟ௘௫௚ (%) 53.899 43.812 30.704
ߟ௧௛௠ (%) 26.215 20.692 13.506
‫ݓ‬௦௣.௡௘௧ (kJ/k) 129.974 91.287 51.869
6. CONCLUSION
It is observed that for each fluid, the maxima of exergy efficiency, thermal efficiency and
specific net work outputoccurs at different values of operating parameters.The optimum values of
operating parameters are obtained for the ORC system using each of the working fluid for achieving
adequate trade-off between the objectives. The optimum operating parameters are closer to the
parameters for obtaining maximum work output (maximum energy recovery).
The ORC system using n-hexanerecover more amountof energy (ܹሶ ௡௘௧ =149 kW) with higher
thermal efficiencies(ߟ௧௛௠ =26.215% &ߟ௘௫௚ =53.899 %) as compared to the others.The fluids n-
hexane and isopentane are found to be suitable for ORC system with regenerator in recovering waste
energy from exhaust gases of the diesel engine, from the view point of thermal aspects. However,
economic aspects need to be also taken into consideration. Other such drawbacks as higher
flammability of these fluids and their toxicity also have to be taken into account.
This analysis and optimization of ORC system is done only from the view point of thermal
considerations. There is much scope in performing economic and environmental analysis also. The
use of other effective multi-objective method to optimize thermal, economic and environmental
objectives is quite necessary for such systems.
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OPTIMIZATION OF AN ORGANIC RANKINE CYCLE IN ENERGY RECOVERY FROM EXHAUST GASES OF A DIESEL ENGINE

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