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DOI:

https://doi.org/10.1016/j.jclepro.2020.120780

Reference:

JCLP 120780

To appear in:

Journal of Cleaner Production

Received Date: 29 July 2019 Revised Date:

20 February 2020

Accepted Date: 25 February 2020

Please cite this article as: Ehyaei MA, Ahmadi A, El Haj Assad M, Rosen MA, Investigation of an integrated system combining an Organic Rankine Cycle and absorption chiller driven by geothermal energy: Energy, exergy, and economic analyses and optimization, Journal of Cleaner Production (2020), doi: https://doi.org/10.1016/j.jclepro.2020.120780. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Ltd.

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Investigation of an integrated system combining an Organic Rankine Cycle and absorption chiller driven by geothermal energy: Energy, exergy, and economic analyses and optimization

4

M.A. Ehyaei1*, A. Ahmadi2, M. El Haj Assad3, Marc A. Rosen4

5

1

6 7

2

8

3

Department of Mechanical Engineering, Pardis Branch, Islamic Azad University, Pardis New City, Iran

Iran University of Science and Technology, School of New Technologies, Department of Energy Systems Engineering, Iran Sustainable & Renewable Energy Engineering Department, University of Sharjah, United Arab Emirates.

9 10

4 Faculty of Engineering and Applied Science, University of Ontario Institute of Technology, 2000 Simcoe Street North, Oshawa, ON, L1G 0C5, Canada

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*Corresponding author: E-mail: [email protected], Tel: +98-9123478028

12 13 14 15 16 17 18 19 20 21 22 23 24

Abstract: This study aims to investigate a novel hybrid system for combined cooling and power (CCP) driven by geothermal energy. This study is performed using energy, exergy, and economic analyses. The results of this study show that application of LiBr absorption chiller downstream of the Organic Rankine Cycle (ORC) cycle increases the energy efficiency of the system from 9.3% to 47.3%. But it decreases the exergy efficiency from 15.6% to 4.6%, mainly due to the increase in the exergy destruction of the system. By definition of a new parameter called the electricity and cooling cost for this hybrid system, the results show this parameter decreases from 0.0552 to 0.0028 $/kWh when the LiBr absorption chiller cycle is added. Moreover, a multi-objective optimization of this hybrid system is carried out by the MOPSO algorithm for two objective functions including cost of electricity and cooling (C ), and exergy efficiency (η ).. The results show that the optimum electricity and cooling cost and exergy efficiency are 0.0033 $/kWh, and 6.8%, respectively.

25 26

Key Words: Geothermal, Absorption chiller, Optimization, MOPSO, ORC

27 28

1.

Introduction

29

Human dependence on fossil fuels is increasing rapidly and therefore many suggest that the

30

use of fossil fuels has peaked. However, fossil fuels are finite and the consequences of

31

burning fossil fuels have been a challenge for human societies (Darvish et al., 2015;

32

Zeinodini and Aliehyaei, 2019).

33

The increase in need for energy and the concerns over fossil fuel resource utilization as

34

well as its harmful environmental impacts have encouraged society to move towards the 1

1

use of clean energy sources. Among the renewable energy sources, geothermal energy is a

2

beneficial option due to its relatively wide availability on the Earth (Domra Kana et al.,

3

2015).

4

Geothermal energy has a wide range of industrial and domestic applications based on its

5

heat source temperature. Geothermal energy has been applied recently in various areas,

6

including electricity generation with ORC power plants (Assad et al., 2017; Yousefi and

7

Ehyaei, 2019), cooling with absorption chillers (Zare, 2016) and fresh water production

8

from desalination plants (Abdelkareem et al., 2018).

9

Haryshat (Hrayshat, 2009) investigated the geothermal energy resources in Jordan and

10

claimed that this country has a large number of resources for temperature range from 20 to

11

62 °C. Similarly, Algieri and Sebo (Algieri and Šebo, 2017) investigated the application of

12

geothermal energy resources for small scale power production in Slovakia.

13

There are many types of research on the use of geothermal energy in electrical power

14

production. In general, the results of studies demonstrate that using geothermal energy in a

15

cogeneration cycle (such as combined power and cooling) can improve efficiency.

16

Erdeweghe et al. (Van Erdeweghe et al., 2018) compared the performance of four

17

combined heat and power (CHP) systems using different geothermal resource temperatures

18

in the northwest of Europe. In this study, four power generation configurations were

19

examined. The results of this study showed that geothermal power plants in a dual power

20

and heat generation mode always higher efficiencies compared to other plants for

21

electricity production.

22

Several other investigations have included energy and exergy analyses of systems using

23

geothermal energy sources. The most common approach is to use an Organic Rankine

24

Cycle (ORC) to take advantage of the heat for producing power and useful heat. Ozenger

25

et al. (Ozgener et al., 2005) examined a heating system driven by the geothermal energy in

26

the Gunen region of Turkey in terms of energy and exergy. The results of this study

27

showed that the exergy losses in pumps, heat exchangers, pipelines, and the injected cooled

2

1 2

liquid water into the tank were 14.8 %, 7.1 %, 1.1 %, and 12.9 % from the overall input exergy, respectively.

3

Jalilinasrabady et al. (Jalilinasrabady et al., 2012) analyzed Sabalan geothermal power

4

plant using exergy analysis. Their results showed that application of two ORC circuits,

5

with three levels of pressure (7.5, 1.1 and 0.1 bar), resulted in an output power of 49.7

6

MW. Nami et al. (Nami et al., 2017) conducted the conventional and advanced exergy

7

analyses of an ORC driven by a geothermal resource. According to the advanced exergy

8

analysis, around 72 % of the total exergy destruction was due to the internal exergy

9

destruction in components and it was higher than the external exergy destruction.

10

Kianfard et al. (Kianfard et al., 2018) carried out exergy and economic-exergy analyses of

11

systems including a polymer fuel cell, reverse osmosis water desalination unit and ORC

12

driven by geothermal energy. The payback time of this system was about 5-6 years.

13

Energy and exergy analyses of a system producing both power and cooling have been

14

investigated for a geothermal ORC-absorption chiller combination (Leveni et al., 2019). In

15

that study, the electric power was produced using an ORC and the cooling load was

16

obtained using a single effect water-lithium bromide absorption chiller to produce cooling.

17

The hot geofluid leaving the ORC was circulated in the generator of the chiller before

18

sending it to the reinjection well. The results of this study showed that the highest exergy

19

destruction (8.6%) occurred in the economizer of the ORC, followed by the generator

20

(6.3%) and the absorber (5.3%) of the absorption chiller.

21

In recent years, several studies have been reported on integrated solar and geothermal

22

energy systems to produce electricity or hydrogen (Behzadi et al., 2018; Bicer and Dincer,

23

2016; Gholamian et al., 2018; Islam and Dincer, 2017; Li et al., 2019; Yuksel et al., 2018).

24

Additional work has also been done in the area of multi-generation systems. Kanoglu et al.

25

(Kanoglu et al., 2010) used geothermal energy source to drive the electrolysis process in

26

four different ways for hydrogen production using irreversible thermodynamic analysis.

27

The results showed that preheating water of the electrolysis process yielded the highest

3

1

amount of hydrogen (0.00134 kg). The temperature of geothermal water was as high as

2

200℃.

3

In addition to using thermodynamic analysis for finding the best possible performance of

4

ORC power plants, the selection of a suitable organic fluid for the ORC plays an important

5

role in improving the ORC performance. The research on the selection of the optimal

6

working fluid for an ORC using a geothermal energy source has been significant. Zhai et

7

al. (Zhai et al., 2014) examined the effect of fluid properties of hydrocarbons and

8

hydrofluorocarbons on system performance. It was shown that fluids such as R32, R134a,

9

and propylene, with global warming potential (GWP) values less than 1500, exhibit better

10

performance than other working fluids.

11

Yang and Yeh (Yang and Yeh, 2016) conducted research on the economic optimization of

12

an ORC with geothermal energy resources. In this study, a parameter was defined, called

13

the output power index, which represents the ratio of output power to total cost. The

14

optimal value for this parameter was calculated for the ORC with various working fluids.

15

Their results show that the most suitable working fluids for the ORC were R600 followed

16

by R600a, R1233zd, R1234yf, R1234ze and R290. A more comprehensive study of ORC

17

optimization with geothermal energy sources, considering thermodynamic and economic

18

analyses, was conducted by Kazemi and Samadi (Kazemi and Samadi, 2016).

19

Heberle et al. (Heberle et al., 2016) performed a life cycle assessment (LCA) of a binary

20

geothermal power plant in Germany. For this plant, single- and two-stage ORCs were

21

considered for the sub-critical and super-critical conditions, respectively. Their results

22

show that the exergetic performance for refrigerant working fluids with low GWPs was

23

similar to that for common refrigerants.

24

Al-Mousavi et al. (Al-Mousawi et al., 2017) studied four scenarios for the integration of

25

the ORC and the absorption chiller. The working fluid of the absorption chiller was

26

AQSOA-ZO2 (a solution of water and silica gel) and the working fluids of ORC were

27

R245fa, R365mfc, and R141b. In the first three scenarios, the absorption chiller was placed

28

upstream in the cycle and for the fourth scenario, the ORC was placed upstream. The best 4

1

scenario was the case of using the waste heat of the absorption chiller as the heat source for

2

the evaporator of the ORC cycle to generate electricity. Similar studies were conducted by

3

other researchers (Jiang et al., 2014; Wang et al., 2014). Note that, in the references (Al-

4

Mousawi et al., 2017; Jiang et al., 2014; Wang et al., 2014), the chiller was of an

5

adsorption type. Although this type of chiller has some advantages over the absorption

6

chiller, it is not considered in this study due to the lack of commercialization of adsorption

7

chillers in many places, including Iran. Bianchi et al. (Bianchi et al., 2018) investigated a

8

small binary ORC using R134a as the working fluid. The water temperature of the

9 10

geothermal well was less than 60° C. The energy efficiency of this cycle was determined to be about 4.4 %.

11

The

12

thermodynamically based on energy and exergy analyses (Navongxay and Chaiyat, 2019).

13

The study considered water-lithium bromide and ammonia-water absorption chillers, in an

14

attempt to introduce the most suitable chiller for the ORC based on energy and exergy

15

efficiencies. The results revealed that water-lithium bromide chiller was a better option for

16

integration with an ORC, based on a higher energy efficiency and a higher exergy

17

coefficient of performance.

18

The combination of an ORC, an absorption refrigeration cycle (ARC) and an ejector

19

refrigeration cycle (ERC) run by industrial low-temperature flue gas were examined (Sun

20

et al., 2017). The result of that study showed that the combination of ORC and ARC

21

system had a better exergy efficiency than the basic ORC system, since the evaporation

22

temperature was above 162°C. Moreover, comprehensive results on exergy efficiency of

23

the combination of this three-part system were reported for parameters such as evaporation

24

temperature, condensation temperature, and superheat temperature.

combination

of

ORCs

with

absorption

chillers

has

been

investigated

25 26

In a novel hybrid system including an ORC, a heat pump (HP) and gas burner were

27

employed to provide cooling for a Data Center (DC) as well as to use the rejected heat to

28

provide hot water for central heating. The results showed that this system can achieve an

29 30

indoor room temperature between 18 − 25 C by absorbing 12 kW of heat to raise the hot water temperature to 80 C. Therefore, the application of such a system can improve the 5

1

thermodynamic performance of the DC center by waste heat recovery (Al-Tameemi et al.,

2

2019)

3

A combined Organic Rankine Cycle (ORC) and adsorption cycle was applied to provide

4

power, heating and a cooling load (Roumpedakis et al., 2019). In that research, first and

5

second efficiencies of the configurations were obtained for a number of organic fluids at

6

subcritical and supercritical conditions for the ORC. Also, the systems were optimized by

7

considering the pinch point values in the heat exchangers. A zeolite-water adsorption

8

chiller was considered to supply cooling capacity of 13 kW. The results of each

9

configuration were matched with that of an integrated ORC and vapor compression cycle

10

(VCC) with the same cooling capacity. The highest exergy efficiencies for the ORC-

11

adsorption chiller and the ORC-VCC systems were about 40% and 30%, respectively.

12

This paper aims to improve understanding of a novel hybrid system that combine cold and

13

power (CCP) driven by geothermal energy, through energy, exergy and economic analyses.

14

Although there are few works related to similar process configurations (Al-Mousawi et al.,

15

2017; Jiang et al., 2014; Wang et al., 2014), none of the earlier research has included

16

complete of energy, exergy and economic analyses together with Multi-Objective Particle

17

Swarm Optimization (MOPSO) of the utilization of geothermal energy with absorption

18

chillers. Additionally, the configuration of the cycle of this paper is not similar to the

19

previous papers in this field. Moreover, a new configuration of the proposed combined

20

system is proposed, which has not been considered before in all of the above-mentioned

21

references.

22

Regarding to usage of geothermal energy in Iran, statistics show that Iran has a high

23 24

potential of geothermal energy resources (1.078 × 10 kJ). Iran can produce a large

amount of electrical power from geothermal power plants which are at nine locations over

25

the country (Torshizi et al., 2018).

26

The analysis proposed in this study was conducted using data for Bandar Abbas city in the

27

south of Iran. Bandar Abbas is a city with a warm and wet climate. The geothermal system

28

in this city is hydrothermal with a temperature of about 120 °C and a pressure of 202.2 kPa 6

1

(Torshizi et al., 2018). In this paper, the Organic Rankine Cycle (ORC) is considered as an

2

upstream cycle and the absorption chiller as the downstream cycle. The energy required for

3

the evaporator of the ORC and the generator of the absorption chiller is provided by

4

geothermal energy. The optimization of system performance is performed based on two

5

objective functions namely, the cost of electricity and cooling and the exergy efficiency of

6

the overall cycle. Then appropriate decision-making variables are selected because these

7

parameters can be monitored at the power plant site. These parameters are the mass flow

8

rates of the ORC and absorption chiller working fluids, the concentration of LiBr in the

9

generator, the absorber and generator temperatures and the absorber pressure. This is due

10

to the fact the rest of parameters of the absorption chiller is supplied by chiller

11

manufacturer. For the final evaluation, a new criterion is considered, which is the cost ratio

12

of electricity and cooling to the exergy efficiency. Since the most favorable condition

13

includes the increase in exergy efficiency and the decrease in electricity and cooling cost,

14

the minimum value of this criterion on the Pareto graph is determined to be the optimal

15

point.

16

The article has several innovative aspects:

17

• Introduction of a novel combined cycle for power and cooling by the using geothermal

18

energy

19

• Assessments and optimization of energy, exergy, and economic aspects of this hybrid

20

cycle are performed by taking into account decision variables and target functions with

21

MOPSO algorithm

22 23 24 25

• A new criterion of the ratio of the cost of electricity and cooling (C ), and the exergy efficiency (η ) is defined for selecting the optimum point

2. Process description

26

Figure 1 shows aschematic and T-S diagram of the combined system. The temperature and

27

pressure of the geothermal energy resource are considered to be 120 °C and 2 bar,

28

respectively (Torshizi et al., 2018). The water mass flow rate of the geothermal system is

29

considered to be between 13 and 15 kg/s (Torshizi et al., 2018). The hot water (geofluid) is 7

1

extracted from the geothermal well to provide the required energy for the evaporator of the

2

ORC cycle and the absorption chiller (points 19, 20 and 21). After that, it is reinjected into

3

the re-injection geothermal well. In the ORC, the working fluid is refrigerant R134a

4

(tetrafluoroethane). The properties of this refrigerant are similar to those of R12, but with

5

lower values of global warming (GWP) and ozone depletion potential (ODP). The

6

properties

7

(www.coolprop.org/fluid_properties/fluids/R134a.html).

8

The working fluid enters the pump (point 1) and after being pressurized (point 2) it

9

exchanges heat with the outlet fluid of the expander in the regenerator. The fluid then

10

enters the evaporator (point 3). In the evaporator, the working fluid gains heat from the

11

geofluid and it becomes superheated steam (point 4) which is expanded in the expander

12

(turbine) at point 5 to produce electricity. The exit vapor from the turbine is passed through

13

the regenerative unit and heat is exchanged with the working fluid leaving the pump (point

14

6). The output vapor then enters the condenser. In this component, heat is rejected to the

15

surrounding and the working fluid becomes a saturated liquid.

16

The cooling production is accounted for a single effect lithium bromide (LiBr) absorption

17

chiller. In this cycle (point 11), water vapor enters the absorber and is absorbed by the

18

concentrated solution of lithium bromide. The absorber is cooled with external circulating

19

water (points 22 and 23). The LiBr and water solution (mixture) is compressed by the

20

pump (point 16) and then enters the generator. In the generator, the required heat is taken

21

from the geothermal energy and the water evaporates from the LiBr solution working fluid

22

to vapor (point 12). The water vapor enters the condenser, and after exchanging heat with

23

the circulating cooling water (points 9 and 10) it becomes a saturated liquid (point 13). The

24

pressure of the outlet liquid from the condenser is decreased in the expansion valve (point

25

14). The mixture of liquid and vapor in the evaporator is converted to saturated vapor after

26

receiving heat from the environment.

27

Specifications of the parameters of the cycle are presented in Table 2 (Bagheri et al., 2019;

28

Bianchi et al., 2018; Ghasemian and Ehyaei, 2018). In that table, P denotes pressure (kPa),

29

T temperature (oC), m mass flow rate ( " ), x concentration of LiBr and η polytropic

of

R134a

are

!

8

given

in

Table

1

1

efficiency. The numbers 1 to 23 correspond to numbers shown in Figure 1. The subscripts

2

P, T, and HX denote pump, turbine and heat exchanger, respectively.

3

4 9

1

Figure 1. Schematic diagram and T-S diagram of the hybrid cycle

2 3 4 5

Table 1. Properties of R134a (www.coolprop.org/fluid_properties/fluids/R134a.html) Fluid

R134a

Molecular mass

Boiling point

Critical temperature

ODP

GWP

(K)

Critical pressure (kPa)

(kg kmol)

(K)

102.03

246.8

374.21

4060

0

1430

6 7

Table 2. Cycle parameter specifications Parameter P$ P% P& P' P P( T m*+

Value 395 kPa 395 kPa 101.3 kPa 101.3 kPa 70.9 kPa 70.9 kPa 120 oC !

3 "

x ' T' T( m-./0

8 9

41% 43 oC 80 oC !

1 " 0.85 0.85 0.85

η1 η2 η34

3. Energy, exergy, and economic analyses

10

The following assumptions are considered in the analyses (Bagheri et al., 2019):

11

1) Steady state flow in all components

12

2) The processes in the pump and turbine are polytropic and their efficiencies are 0.85.

13

4) Specific heats are constant.

10

1

5) Potential and kinetic energies are ignored.

2

6) The energy efficiencies of the evaporator and condenser in the ORC are both 0.85.

3

7) The properties of geothermal working fluid are the same as those of pure water.

4

8) The heat exchanger efficiency of the absorption chiller generator is 0.85.

5

The mass, energy and exergy balance equations for the cycle components are listed in Table 3.

6

The mass, energy and exergy balance equations are based on the general conservation of mass,

7

energy and exergy equations as shown below (Bejan, 2016): dm = 8m − 8m dt .9

8

:;<

kg where m denotes the mass in the system and m is the mass flow rate = @sA. Q − W = 8 m Dh + :;<

9 10 11

(1)

V& V& + gZI − 8 m Dh + + gZI 2 2 :;.9<

where Q and W are heat and work transfer rate (kW), respectively, h is the specific enthalpy

kJ = @kgA, V is the velocity (m⁄s), g is the gravitational acceleration [email protected] & L and Z is the height (m).

ĖxN + ∑.9 ṁe = ∑:;< ṁe + ĖxR + ĖxS 12

14 15

(3)

where ĖxN = T1 −

13

(2)

T0

ĖxR = W

T

UQ

(4) (5)

Here, E is the exergy rate (kW) and subscripts Q, W, D are the heat, work and destruction, respectively.

16 17 11

1 2 3 4 5 Row

Table 3. Mass, energy and exergy balance equations for the system components Component

Mass conservation

Energy conservation

Exergy destruction rate

ORC 1

Pump

m = m&

2

Regenerator

m$ = m& & m' = m(

3

Evaporator

4 5

Turbine Condenser

Pump

7 8

Expansion valve 1 Absorber

9

Generator

10

Condenser

11

Expansion valve 2 Evaporative

12

= m ( & x

'

$

%

m

=m

=x

m m

$ \

= m & & x = m ] & x m

%

=m

$ \

=x =x

(

wX =

8 9

m ' (h

m& e& + m% e% − m e

QY = m \ h & ]

(

m( e( − m e − Q [ (1 −

− h ')

m ' (e

\

+ m ]h

]

− m 'h

'

+ m &h

&

− m (h

(

m & (h

&

− h $)

_____ Q = m % (h

− h %)

m e

'

m \ (e

+ m ]e

]

− e \)

− m 'e T − ) T^ m (e ( − m \e \ ± m &e T − ) TY

m &e

&

m % (e

]

− m $e m $ (e

%

T ) T[

− e ( ) − wX

%

$

'

− Q ^ (1

&

+ Q Y (1

− Q [ (1 −

− e $)

− e ) + Q (1 −

In the above table, subscripts E, C, A, and G represent the evaporator, condenser, absorber

and generator, respectively. The exergy destruction rate in this paper is denoted by ES

(kW). The notation “0” denotes the reference state, which is 1 atm and 20℃. h is the

12

− m$ e$

m% e% − m' e' − w2

η1 _____

Q^ = m h

6 7

m& e& + m' e' − m( e( − m$ e$

Absorption chiller

m ]+m =m ' m ]x ] = m 'x ' m \+m & =m ( m \x \ = m (x (

m (e − e& ) − wX

Q = m$ (h% − h$ ) = m (T − T& ) η34 w2 = m% (h% − h' )η2 Q [ = m( (h( − h )

m% = m' m = m( '

m (h& − h ) η1

m& (h$ − h& ) = m' (h' − h( ) η+Y

m$ = m%

m

6

wX =

T ) T[

T ) T

`

`

1

specific enthalpy ( !) and e ( !) is the specific exergy, which is expressed as follows

2

(Bejan, 2016): e = (h − h ) − T (s − s )

`

(6)

3

where s is the specific entropy (

4

The net generated power by the ORC cycle is calculated as follows:

5 6

!a

).

W9 < = W2 − W1

(7)

The energy and exergy efficiencies of the ORC cycle can be presented, respectively, as follows (Bejan, 2016): η

9

η

= =

7

W9 < m! : cp! : (T

d T&

W9 < ∑.9 me − ∑:;< me

(8)

)

(9)

8

The coefficient of performance (COP) and the exergy efficiency of the absorption chiller

9

are expressed, respectively, as: COP = η

Q

(10)

Q Y + W1

g Nf ( d h )

=∑

mn i

(11)

gf

d∑jkl i

10

The energy and exergy efficiencies of the overall cycle are calculated, respectively, as

11

follows:

η η

9

=

Rnol pNf

(12)

iqoj [Xqoj (2rst 2ur)

g Rnol pNf( d h )

=∑

mn i

(13)

gf

d∑jkl i

13

`

1

where cp (

2

hot water of the geothermal source, and the subscripts “en” and “ex” denote energy and

3

exergy, respectively.

4

The electricity cost has three parts: CI as the initial cost, COM as the operation and

5

maintenance cost and CF as the fuel cost. Since the energy resource is geothermal energy

6

in this paper, the fuel cost part is zero.

7

The electricity cost can be calculated as (Frangopoulos, 1987; Horngren et al., 2010):

!a

) is the specific heat at constant pressure, the subscript “geo” represents the

vm(rwm)x p yz (rwm)x tr

8

C =

9

In order to include the cooling loads produced by the absorption chiller in the electricity

10

cost, an assumed cooler is considered in this study, which consumes electricity to produce

11

cooling. The assumed cooler produces an equivalent cooling load as the absorption chiller

12

in this system. The COP of the assumed cooler is 2.8. With this method, the electricity

13

quantity that should be consumed to produce the equivalent cooling by the absorption

14

chiller is considered. In reality, Equation 15 calculates the electricity cost accounting for

15

the fact that the difference between the equivalent cooling load which is produced by the

16

absorption chiller and the load if there is no absorption chiller, should be produced by the

17

assumed heat pump. The effects of this cooling load on electricity costs are taken into

18

account. A similar procedure has been applied for a combined heat and power (CHP)

19

system considering an assumed boiler for the CHP system (Ehyaei and Mozafari, 2010).

20

Based on a modified form of Equation 14, electricity and cooling cost is calculated as: =

]\( Rnol

(14)

vm(rwm)x p yz (rwm)x tr { ]\( (Rnol p f ) vy|

21

C

22

where C is the cost of purchase and installation of equipment ($), i is the interest rate,

23

which is assumed to be 2%, and L is the life time of the system (years). The subscripts EC

24

denotes electricity and cooling and OM denotes operation and maintenance. Equation 14

(15)

14

Nf

to take into account the cost of the cooling

1

includes a term in the denominator

2

produced by the absorption chiller.

3

Table 4 shows the purchase cost and installation cost of the system components. In this

4

table, z (m) is the depth of the well, and A is the area of a component (m2). The inflation

5

rate is assumed to be 2%.

*1

6 7 8 9

Table 4. Cost of purchase and installation of cycle components Cost function ($)

Reference

Component Turbine Pump Condenser Evaporator Absorption chiller

Geothermal well

2237(w2 ) R}

ORC

.%

(Alshammari et al., 2018) (Lecompte et al., 2013)

1026( ) $ 0338.6 A 216.6+353.4 A .&'

14740.2(Q )d 3.3 16.5×z

.( \

.(]%

(Lecompte et al., 2013) (Lecompte et al., 2013; Quoilin et al., 2011) Absorption chiller (Schöpfer, 2015) + Geothermal https://www.semanticscholar.org/paper/IntroducingGEOPHIRES-v1.0%3A-Software-Package-for-ofBeckersLukawski/d2407f961222afa4d3a4243fe8cdd06e97021ad4

10 11

The flowchart of the mathematical modeling of the cycle is shown in Figure 2.

15

1 2

Figure 2. Flowchart of the mathematical modeling of the system

3

4. Multi-objective particle swarm optimization Algorithm

4

Multi-Objective Particle Swarm Optimization (MOPSO) algorithm was suggested by

5

Coello in 2004. This algorithm was based on the PSO algorithm, except it is applicable for

6

multi-objective problems. The general mathematical formula for optimization are as follow

7

(Angeline, 1998; Coello et al., 2004; Eberhart and Shi, 1998): Min/Max f. (X) for

Subject to: g … (X) † 0

i = 1,2,…N

For j = 1,2, …, J

h (X) = 0 For k = 1, 2…K X- ‰ X ‰ XŠ

16

(16)

1 2 3 4 5 6 7 8

The X solution for this problem is a vector with d dimensions X(x ,x& , …, x‹ ). Here, N is

the number of objective functions. L and U are the lower and upper limits of vector X in the search domain, respectively (Coello et al., 2004).

In a search space with d dimensions, assume the space vector and velocity vector for the Ž •• particle as X. (x . , x&. , …, x‹. ) and V. (v . , v&. , …,v‹. ), respectively. The best

performance of each particle relative to objective function value by its own in the swarm is represented by P. (p . , p&. , …,p‹. ) and it is called pbest. Also, the overall best performance of the particle concerning the swarm is called global and it is denoted by gbest. Each

9

particle in the swarm tries to modify its situation based on current position, distance

10

between the current position and the best and global performance of the particle and

11

current velocity (Reyes-Sierra and Coello, 2006):

12

The new position and velocity of the particle are governed as follows:

13

14 15 16 17 18 19 20

V.

< "< -X . )+C& r& (x!“ "<

− X.< )

(17) (18)

where, w, C and C& are specific parameters and r and r& are random numbers. X.< , V.< , X.

are the current position and velocity at iterations t and t+1,

respectively (Hosseini et al., 2015; Lalwani et al., 2013).

Figure 3 shows a flowchart of PSO algorithm (Lalwani et al., 2013). In the PSO algorithm, a single objective function should be minimized to find X(x , x& , …, x‹ ) for a search space

with d dimensions, so that it satisfies the following (Hosseini et al., 2015; Lalwani et al., 2013) Minimization/Maximization y = f(X)

(19)

17

1

Figure 3. PSO algorithm flowchart

2

In MOPSO algorithm, there are more than one objective function but the procedure of the

3

solution is the same. Figure 4 shows a flowchart for determining the dominant solution

4

(Kusiak and Xu, 2012; Lalwani et al., 2013).

18

1 2

Figure 4. MOPSO algorithm 19

1

5. Results and Discussion

2

5.1. System modeling description

3

The proposed system in this paper is based on the geothermal conditions of the city of

4

Bandar Abbas which is the capital of Hormozgan province in southern Iran (Navongxay

5 6 7

and Chaiyat, 2019). Bandar Abbas is at longitude 27.1832 ° N and latitude 56.2666 ° E. The climate of this city is hot and humid. The air temperature on the hottest day is 52 ℃ and on the coldest day is 2℃. The average rainfall in Bandar Abbas is about 200 mm

8

(Navongxay and Chaiyat, 2019). So the cooling load constitutes the main part of thermal

9

loads.

10 11

The temperature and pressure of the geothermal water are about 120 ℃ and 202.2 kPa,

respectively. Moreover, the mass flow rate of geofluid (water) is in the range of 13 to 15

12

kg/s (Torshizi et al., 2018).

13

Matlab software is applied to simulate this system. With this software, the main program is

14

written to calculate two objective functions. One of the objective functions is applied to

15

simulate the absorption chiller. Formulas from reference (Kaita, 2001) are used to compute

16

the properties of the water and lithium bromide solution. To calculate water vapor

17

properties, an Xsteam m-file is used in Matlab software. To design the ORC, another

18

function is written in Matlab software. To compute the thermodynamic properties of

19

R134a, Refprop software is used. A subroutine in the Matlab software is written to link

20

these functions.

21

5.2. Validation of model

22

Since there is no similar article to this research work, its exact validation is not possible.

23

So, we validate the ORC and absorption chiller modeling separately. To compare the

24

results of the ORC with reference (Al-Mousawi et al., 2017), the simple arrangement of the

25

ORC is considered without regeneration. Based on that reference, the working fluid is

26

considered as R245fa and the heat source temperature is 115℃. We also consider the same

27

operating conditions. 20

1

Figure 5 compares the energy efficiency of the ORC cycle considered here with that in

2

reference (Al-Mousawi et al., 2017). An acceptable level of agreement is observed with a

3

calculation error of about 3%.

4

In order to validate the results of the absorption chiller, the results are compared with those

5

from reference (H. Al-Tahaineh, 2013) for the same the input data used from that

6

reference. Figure 3 of that paper is considered based on a cooling load of 10 kW and a

7 8

generator temperature of 90 ° C. We input the same information regarding operating conditions.

9

Figure 6 compares the COP of the single effect lithium bromide absorption chiller from the

10

present study with that from reference (H. Al-Tahaineh, 2013). Again, good agreement is

11

observed, with an error of about 4%. 12 10

9.7

10

ɳen (%)

8 6 4 2 0 12 13 14

Simulation

Ref (Al-Mousawi et al., 2017)

Figure 5. Energy efficiency of the ORC cycle from the present simulation and from reference (Al-Mousawi et al., 2017)

21

0.9 0.8

0.79

0.76

0.7

COP

0.6 0.5 0.4 0.3 0.2 0.1 0 Simulation

1

Ref (H. Al-Tahaineh, 2013)

2 3

Figure 6. COP for absorption chiller from the present simulation and from reference (H. Al-Tahaineh, 2013)

4

For better validation, the data of an external reference (Ozdemir and Kilic, 2018) is

5

considered. In this paper the ORC without regenerator and with heat rejection source (250

6

o

7

cycle is displayed on page 2384 of the paper. Comparison of the thermal efficiency for

8

three working fluids (R113, R114 and R245fa) is shown in Figure 7, which is reasonable.

C) is investigated. The inlet pressure of turbine is 1000 kPa. Other inlet information of this

22

20 18

ɳen (%)

16

17

Ref

Simulation

15.3

14 12 12

10.7

10 10

8.6

8 6 4 2 0 R113

R114

R245fa

1 2 3

Figure 7. Energy efficiency for simple configuration ORC from the present simulation and from reference (Ozdemir and Kilic, 2018) for various working fluids

4

5.3. Thermo-economic results

5

Figure 8 shows the rate of exergy destruction for the absorption chiller and ORC

6

components. In that figure, the first five bars on the left-hand side are related to ORC and

7

the remaining seven columns are related to the absorption chiller cycle. As is shown, the

8

highest exergy destruction rate occurs in the evaporator of the absorption chiller. The rate

9

of exergy destruction is higher in the evaporator of the absorption chiller than the

10

evaporator of the ORC due to the higher temperature difference in the evaporator of

11

absorption chiller. The same statement can be made for the condenser components of the

12

absorption chiller relative to the condenser of the ORC.

23

1600 1,397.1

1400 1200

ĖD(kW)

1000 800 600

495.3

400 200

168.1 13.9

19.0

38.5

50.2

33.2

17.9

19.6

25.8

23.4

0 Evaporator

1

Steam Condenser turbine

Pump

Regenrator Condenser Expansion Evaporator Absorber valve 1

Pump

Generator Expansion valve 2

2

Figure 8. Exergy destruction rates of cycle components

3

Figure 9 shows the energy and exergy efficiencies of the ORC and the combination of the

4

ORC and absorption chiller (Abs). It can be seen that adding the cooling cycle to the ORC

5

cycle increases the energy efficiency from 9.6 % to 47.3 %, but decreases the exergy

6

efficiency from 21.4 % to 4.8 %. The increase in energy efficiency is consistent with the

7

results of reference (H. Al-Tahaineh, 2013)

8

The opposing trends are now explained. Since, from the energy point of view, cooling and

9

generating electricity are similar (Equations 8 and 12), as both are energy. Thus, the

10

significant cooling that is provided by the absorption chiller from the geothermal source is

11

due to this increase in energy efficiency. However, from an exergy point of view, cooling

12

and electrical power are not the same (Equations 9 and 13). That is, the exergy rate of

13 14

electrical power production is equal to power production (E™ = W). But, for the cooling and heating production, the exergy rate depends on the temperatures of the resource and 2h

15

the environment (EN = Q or Q 3 (1 − 2

16

absorption chiller in this paper) often increases the rate of exergy destruction rate. Hence

17

the exergy efficiency is reduced.

v :02š

24

). Moreover, adding a component (an

60

50

ɳen

47.3

ɳex 40

30 21.4 20

10

9.6

4.8 0 ORC

ORC+Abs

1 2 3

Figure 9. Energy and exergy efficiencies of the ORC cycle and the combined cycle of ORC and absorption chiller

4

Figure 10 shows the cost of electricity (CE) and the cost of electricity and cooling (CEC) of

5

the ORC and the combined ORC, and absorption chiller. It is clear adding the absorption

6

chiller (Abs) does not have any effect on electricity cost (Equation 14). That is because the

7

absorption chiller does not produce electricity.

8

But the addition of the absorption chiller to the ORC can significantly reduce the cost of

9

electricity and cooling (CEC). At first glance, it appears strange that the cost of electricity

10

and cooling is decreased significantly. The reasons are as follows:

11

1) The effect of cooling produced by the absorption chiller in the cost of electricity and

12

cooling. This is due to the assumption of a heat pump that produces the same amount of

13

cooling load as the absorption chiller with a COP of 2.8 (denominator of Equation 15).

14

2) CEC is not just the cost of electricity but also the cost of electricity and cooling

15

production in the system.

16

By this method, the effect of cooling is also considered in the economic evaluation. So, the

17

reason for this decrease is the existence of a cooling rate in the denominator of Equation 25

1

15. Note that a similar result is obtained with this method for the CHP system reported in

2

the literature (Equation 18 of reference (Ehyaei and Mozafari, 2010)).

CE or CEC ($/kWh)

0.07 0.06

0.0552

0.0552

CEC ($/kWh)

0.0552

CE ($/kWh) 0.05 0.04 0.03 0.02 0.01 0.0028 0 ORC

3 4 5

ORC+Abs

Figure. 10. Costs of produced electricity and cooling for the ORC and the combination of ORC and absorption chiller

6 7

5.4. System optimization

8

The two objective functions considered in this paper for optimization by the MOPSO algorithm

9

are as follows:

10

$ ›œ• , žŸž ( ) ¡¢£

11

These objective functions include the exergy efficiency, the electricity as well as cooling cost. It

12

is our objective to maximize exergy efficiency as well as to minimize the electricity and cooling

13

cost. Because, it is usual that a higher system exergy efficiency raises the system cost, the aim of

14

this optimization to select a balance point between system performance and cost.

15

In the optimization of cycle by the MOPSO algorithm, the optimization setting and values of all

16

variables are shown in Tables 5 and 6, respectively.

17 18 26

1

Table 5. MOPSO algorithm optimization settings

2

Variable Maximum iteration Number of particles Repository size Inertia weight Inertia weight damping rate

3 4 5

Value 500 50 100 1 0.95

6 7

Table 6. Optimization variables and lower and upper bounds

8 9 10 11 12 13 14 15

Variable x(1) = m*+

x(2) = m-./0 x(3) = x ' x(4) = T ' x(5) = T ( x(6) = P'

Unit kg s kg s ℃ ℃ kPa

Lower limit 2

Upper limit 3

0.2

1

40 40 70 90

50 50 90 250

16

Figure 11 shows the Pareto diagram based on the objective function of the dual power and

17

the cooling generation cycle. As is shown, the increase in cost causes the increase in

18

exergy efficiency. Table 6 lists values of objective functions and variables for points A, B,

19

and C in Figure 11.

20 21

27

1 2

Figure 11. Pareto diagram for the objective functions of the combined power

3

and cooling cycle

4

It is obvious that the increase in the exergy efficiency causes the increase in the cost of

5

electricity and cooling as well. The main reason for the increase in the cost of electricity is

6

due to the increase in the cost of purchase and installation according to Equation 15 and

7

Table 3.

8

For this reason, three optimal points are proposed namely, A, B, and C. Point A shows the

9

lowest exergy efficiency and cost of electricity and cooling, while Point C presents the

10

highest exergy efficiency and cost of electricity and cooling. Point B is located between

11

points A and C. Point B has a medium exergy efficiency and cost of produced electricity

12

and cooling. The designer can select one of these as the selected optimum based on

13

preferences or needs. The values of the target functions for points A, B, and C and the

14

corresponding decision variables are shown in Table 7.

15 16 28

1

Table 7. Values of objective functions and variables for points A, B and C in Figure 13 Variable and objective function η C

x(1) = m*+

x(2) = m-./0

2 3 4

x(3) = x ' x(4) = T ' x(1) = T ( x(6) = P'

Unit

A

B

C

% $ kWh kg s kg s ℃ ℃ kPa

4.9 0.0029

8.1 0.006

14.9 0.014

2.4

2.93

3

1

0.61

0.2

42.3 41.9 78.7 90

49.1 48.8 80.5 90

50 49.7 79.2 90

To provide a better evaluation, we define a new criterion, as follows:

žŸž ›œ•

5 6

This criterion is a ratio of electricity and cooling cost to exergy efficiency.

7

In order to minimize this ratio, the point with the lowest ratio of electricity and cooling cost

8

to exergy efficiency is selected. In general, the desired point on the Pareto graph is a point

9

that has the maximum exergy efficiency and minimum electricity cost. f¤

¥o¦

10

Point D in Figure 11 has the lowest value of

11

Table 8 shows the target functions and decision variables values for point D.

12 13 14 15 16 17

29

.

1

Table 8. Values of the target functions and decision variables for point D in Figure 13 Variable η C [

x(1) x(2) x(3) x(4) x(5) x(6)

2

Unit % $ kWh kg s kg s _ ℃ ℃ kPa

Value 6.8 0.0033 2.47 0.98 45.5 47.8 80.1 0.91

3

5.5. Sensitivity analysis

4

Figure 12 shows the exergy efficiency and the cost variations of the produced electricity

5

and cooling of the cycle in terms of the mass flow rate of the working fluid (R134a) in the

6

ORC. The figure shows that increasing the working fluid mass flow rate of the ORC results

7

in an increase in the exergy efficiency of the cycle. Additionally, the cost of electricity and

8

cooling is seen to increase with the mass flow rate of the working fluid (R134a) of the

9

ORC. Since, by increasing the mass flow rate of the working fluid (R134a), the installation

10

and purchase cost is increased (according to Tables 3 and 4), the cooling and electricity

11

cost increases according to Equation 15. The increase in the exergy efficiency is caused by

12

two opposing effects: 1) increasing the power production of the ORC (the numerator of

13

Equation 13); 2) rising the difference between inlet and outlet exergies (denominator of

14

Equation 13). But the first effect is dominant, so the exergy efficiency is increased.

15

Figure 13 displays the energy and exergy efficiency variations of the cycle in terms of x

16

(concentration of LiBr in generator). As is shown, the energy efficiency declines with

17

increasing concentration. But the exergy efficiency variation exhibits the opposite trend.

18

In Figure 13, increasing §

19 20 21

'

'

decreases the energy efficiency but it increases the exergy

efficiency. However, it is notable that the increase is not considerable. Increasing §

'

a decrease in energy efficiency. In case of exergy efficiency, however, increasing x

'

causes the increase of the differential temperature between points 20 and 21, which causes

30

1

decreases the difference between inlet and outlet exergy rate in the system which, in turn,

2

leads to a decrease in exergy efficiency.

3

Figure 14 reveals the exergy efficiency and COP variations of the combined power and

5 6 7 8 9 10

cooling cycle with respect to the output temperature of absorber T ' . As is shown, the increase in temperature T

'

causes an increase in exergy efficiency, but this increase is not

significant. The increase in the outlet temperature of the absorber from 40° C to 50° C, for example, results in an increase in the exergy efficiency from 4.6% to 5.8%. Similarly, the COP increases with T ' , however, this increase is not significant as well. For instance, when the temperature T

'

increases from 0.82 to 0.84.

changes from 40° C to 50 C, the absorption chiller COP

0.00331

6.3

0.00330

6.2 6.1

CEC ($/kWh)

0.00329

6.0

0.00328

5.9

0.00327

5.8

0.00326

5.7 5.6

0.00325

Electricity and cooling cost

0.00324

Exergy efficiency

5.5 5.4 5.3

0.00323 2.0 11 12 13

ƞex (%)

4

2.2

2.4 2.6 ṁORC (kg/s)

2.8

3.0

Figure 12. The exergy efficiency and the cost variations of the produced electricity and cooling of the cycle with mass flow rate of R134a in the ORC

31

9.60

5.8

9.55 9.50

Energy efficiency Exergy efficiency

9.45

5.6

ƞex (%)

ƞen (%)

5.7

9.40 5.5 9.35 9.30

5.4 45

46

47

48

50

x15 (%)

1

Figure 13. The energy and exergy efficiencies variations of combined power and cooling cycle with concentration of LiBr in generator x ' 0.850

7.0

0.845

6.0 5.0

COP

0.840

4.0 0.835 3.0 0.830

2.0 COP

0.825

1.0

Exergy efficiency

0.0

0.820 45 4 5 6 7 8

ƞex (%)

2 3

49

46

47

48

49

50

T15 (oC)

Figure 14. The exergy efficiency and COP variations of the combined power and cooling cycle with outlet temperature of absorber T ' Figure 15 presents the exergy efficiency variation of the combined power and cooling

cycle in terms of the temperature of the generator T ( . As the figure shows, changing the 32

1

generator temperature from 70 °C to 90 °C causes the exergy efficiency to decline from

2

5.7% to 5.5%. 6.0 5.9 5.8 ƞex (%)

5.7 5.6 5.5 5.4 5.3 5.2 5.1 5.0 70

75

80 T16 (oC)

3 4 5 6

85

Figure 15. The exergy efficiency Variation of combined power and cooling cycle with temperature of a generator of the absorption chiller T (

7

6. Conclusions

8

The application of a hybrid system containing an ORC and absorption chiller, both driven

9

by geothermal energy, is examined for the city of Bandar Abbas in Iran. The system is

10

modeled and evaluated based on mass, energy and exergy balances, and economic

11

equations. The source of geothermal energy is considered to be hot water at a pressure of 2

12

bar and a temperature of 120°C, with a mass flow rate varying between 3 and 5 kg/s. Rates

13

of produced and consumed energy and heat transfer between components are calculated for

14

the cycle along with exergy destruction rates. Optimization is carried out, considering two

15 16 17

objective functions: cost of electricity and cooling CEC ($/kWh) and exergy efficiency η

(%). The decision variables considered are mass flow rates of the working fluid of the ORC cycle (m*+ ) and of the absorption chiller (m¨“" ), mass ratio of lithium bromide to 33

90

1 2 3

total mass flow in the output of absorber (x ' ), temperature of the absorber (T ' ),

temperature of generator of the absorption chiller (T ( ) and pressure of the regenerator of the ORC cycle (P' ).

4

The optimization is carried out based on MOPSO, along with Pareto diagrams. Several

5

important points are noted on the Pareto diagrams. Point A has the lowest exergy

6

efficiency and cost while point B has a medium exergy efficiency and cost of produced

7

electricity and cooling. Finally, point C has the highest exergy efficiency and cost of

8

produced electricity and cooling. Using the definition of the term

9

point (Point D) on the Pareto diagram.

f¤

¥o¦

, leads to an optimal

10

Based on the results of the mathematical model presented for the proposed system in this

11

work, the following main conclusions can be drawn:

12

1. Adding an absorption chiller to the ORC cycle increases the energy efficiency

13

(desirable), reduces the cost of electricity and cooling (desirable), but reduces the exergy

14

efficiency (undesirable)

15

2. In competition with gas power plants, geothermal power plants (based only on an ORC

16

cycle) are not economically justified in Iran due to the large and inexpensive resources of

17

gas. Of course, the social and environmental costs need to be considered for the gas power

18

plants. However, combined cooling and power generation generated by geothermal energy

19

is economically justified.

20

3. Several important sensitivities are identified. Increasing the outlet temperature of the

21 22

absorber from 45° C to 50° C, causes the COP and exergy efficiency to increase from 0.82

and 6.38% to 0.84 and 6.89%, respectively. Increasing the concentration of LiBr in the

23

generator from 45% to 50%, causes a decrease in the energy efficiency from 10.0% to

24

9.8%, but the exergy efficiency increases from 6.7% to 7.1%. Increasing the mass flow rate

25

of R134a in the ORC from 2

[email protected] kg s to 6 @s, causes an increase in the cost of electricity and

34

$

cooling from 0.0031

2

6.9%.

3

The present research indicates that the use of geothermal energy is beneficial in the

4

cogeneration plant. It could be a useful source of energy when adding an electrolyzer (to

5

produce hydrogen), or a methanation unit (to produce methane gas), or a heat pump (to

6

produce heat and cooling) to the proposed system in future work.

7

Nomenclature:

R©

to 0.0033

$

1

R©

and the exergy efficiency increases from 4.7% to

C

Cost ($)

cp

Specific heat at constant pressure (kJ/kgK) Combined cooling and power Coefficient of performance Specific exergy (kJ/kg) Rate of exergy destruction (kW)

CCP COP e ĖS ℎ Ž L ṁ P

Specific enthalpy (kJ/kg) Interest rate Life time of system (year) Mass flow rate (kg/s) Pressure (kPa) Heat transfer rate (kW)

Q s

T W § Z η

Specific entropy (

`

!a

)

o

Temperature ( C) Work rate (kW) Concentration of LiBr in generator Depth of well (m) Greek Symbols Efficiency

Subscripts A Abs C E e EC en

Absorber Absorption chiller Condenser Evaporator; electricity Exit Electricity and cooling Energy 35

ex G geo HX in LiBr OM ORC P RG T 0

Exergy Generator Geothermal Heat exchanger Input Lithium bromide Operation and maintenance Organic Rankine Cycle Pump Regenerator Turbine Reference state

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34

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Research highlights • A novel combined power and cooling cycle using geothermal energy is proposed; • Energy, exergy and economic assessments are performed of the cycle • The cycle is optimized with the MOPSO algorithm;

Author contribution section The contribution of all authors for the paper entitled” Investigation of an integrated system combining an Organic Rankine Cycle and absorption chiller driven by geothermal energy: Energy, exergy, and economic analyses and optimization”

are the same.

Corresponding author M.A.Ehyaei

Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

M.A.Ehyaei Corresponding author