Exergetic, Exergoeconomic and Exergoenvironmental Multi-Objective Genetic Algorithm Optimization of Qeshm Power and Water Cogeneration Plant

Authors

1 Division of Thermal Science & Energy Systems, Department of Mechanical Engineering, Faculty of Technology & Engineering, University of Qom, Qom, Iran 2 Center of Environmental Research, University of Qom, Qom, Iran

Abstract

In this study, optimization of Qeshm power and water desalting cogeneration plant has been investigated. The objective functions are related to maximizing exergetic efficiency and minimization of exergoeconomic and exergoenvironmental parameters. Also, the integration of RO desalination with the existing plant has been evaluated based on these analyses. This plant includes two MAPNA 25 MW gas turbines, two heat recovery steam generators, and two MEDTVC desalination units. Thermodynamic modeling and simulation of the plant have been performed in MATLAB software. The thermodynamic simulation verified by Thermoflex software and plant data with high accuracy. Also, the computer code has been developed to perform exergetic, exergoeconomic and exergoenvironmental analysis. Multi-Objective Genetic Algorithm (MOGA) has been applied to find optimum objective functions and decision variables based on exergetic, exergoeconomic and exergoenvironmental parameters. Results show that in the optimum plant overall exergetic efficiency of the plant has been increased by 27.78%, and total exergetic cost and total exergoenvironmental impacts have been decreased by 0.93% and by 0.89%.

Keywords


Nomenclature

 

RR

Recovery Ratio

A

Area

 

s

entropy

AC

Air Compressor

 

SA

specific area

B

Brine

 

T

Temperature

b

environmental impact per exergy unit

 

TIT

Turbine Inlet Temperature

 

environmental impact rate

 

TVC

thermal vapor compressor

bm

environmental impact per mass unit

 

U

overall heat transfer coefficient

c

cost per exergy unit

 

W

work

CC

Combustion Chamber

 

w

weight

 

cost rate

 

x

mole fraction

COND

Condenser

 

X

salinity

cp

specific heat at constant pressure

 

y

environmental impact of the component

Cr

compression ratio

   

environmental impact rate of the equipment

CRF

Capital Recovery Factor

   

Cost rate of the equipment

D

Distillate

     

DE

Deaerator

 

Greek letters

 

DPEV

Deaerator Pressure Evaporator

 

γ

ratio of the specific heats

 

Energy rate

 

Δ

Difference

Er

expansion ratio

 

ε

exergetic efficiency

ex

specific exergy

 

η

efficiency

 

Exergy rate

 

φ

maintenance factor

f

exergoeconomic factor

 

ρ

density

F

Feed

 

п

osmotic pressure

fb

exergo-environmental factor

     

FWPH

Feed Water Preheater

 

subscripts

 

GOR

gained output ratio

 

0

ambient condition

GT

Gas Turbine

 

fg

Flue gas

h

enthalpy

 

c

condenser

HPEC

High-pressureEconomizer

 

cwd

cooling water discharge

HPEV

High-pressure Evaporator

 

D

Destruction

HPP

High-pressure Pump

 

e

effect

HPSH

High-pressure Super Heater

 

F

Fuel

HRSG

Heat Recovery Steam Generator

 

fb

flash box

J

specific mass flow rate

 

fh

feed heater

LHV

Lower Heating Value

 

fw

feed water

 

Mass Flow Rate

 

gc

gas cycle

MED

multiple effect desalination

 

hp

high-pressure

Mr

Mixing Ratio

 

i

counter of streams

MW

molecular weight

 

k

counter of components

n

number of effects

 

P

Product

N

annual operating hours of the system

 

s

steam

NS

Nominal Size of TVC

 

sat

saturated

ny

operating years of the system

 

sub

sub cooled

P

Pressure

 

sup

superheated

PEC

Purchase Equipment Cost

 

sw

seawater

PR

Performance Ratio

     

Q

Heat Duty

 

superscripts

 

r

relative cost difference

 

*

restricted dead state

rb

relative environmental impact difference

 

0

global dead state

 

Universal Gas Constant

 

CH

Chemical

RO

reverse osmosis

 

PH

Physical

rp

Pressure ratio

     

 

  1. Introduction

Freshwater means water that contains less than 1000 milligrams of salinity per liter of water [1]. However, most of the water present on the surface of the earth has a salinity of up to 10,000 ppm, and the free water is usually salinity in the range of 35,000 ppm to 45,000 ppm in the form of salts dissolved in water [2]. Our country is no exception. On the other hand, the shortage of Freshwater resources in Iran and, on the other hand, access to saltwater resources of the Persian Gulf in the south, and the Caspian Sea in the north, necessitate the need for Freshwater supply from these resources for industrial, and domestic uses.The issue of Desalination has attracted attention in most countries of the world in recent years. Today, over 15,000 units of desalinating water units are operating around the world. The Middle East accounts for roughly 50% of the world's total freshwater production. Saudi Arabia, with about 26% of world freshwater production capacity, is the largest producer in the industry, and the United States with 17% is in the next category. In Saudi Arabia, thermal water desalination is most used[1]. The process of separating salt from saline water, like any other process, requires energy, and the amount of this energy is different for different methods of desalination. In a particular process, the amount of energy per unit volume of Freshwater produced depends on the chemical composition and degree of impurities of saline water and its thermodynamic characteristics[3].

Lack of energy and high and continuous costs of energy supply increased energy consumption, environmental pollution due to the consumption of fossil fuels and the deterioration of fossil fuels has led to issues of energy recovery in industrial and process units in recent years[4-8].There is some investigation related to energy reduction in the process industries in Iran by exergy analysis.

The identification of the sources of energy losses by the exergy method for the Marun Mega-Olefin petrochemical complex has been done by Paashang et al [9]. Ghorbani et al investigated an integrated nitrogen rejection unit with LNG and NGL co-production processes based on the MFC and refrigeration systems through exergoeconomic analysis [10]. Ghazizadeh et al studied C3MR, MFC and DMR refrigeration cycles in an integrated cryogenic process with advanced exergoeconomic analysis [11]. An advanced exergetic analysis of the integrated separation process with considering optimization refrigeration system has been investigated by Hamedi et al [12].Sheikhi et al applied pinch and exergy analysis for optimization of the refrigeration cycle in the petrochemical complex [13]. Optimization of an integrated process configuration for IGCC with a Fischer-Tropsch has been evaluated with coal and biomass fuels by Shariati Niassar[14]. In the other research, Hadadi et al performed and evaluated optimization of water and wastewater network related to a gas refinery with considering pressure drop and pumping cost using conceptual, mathematical and evolutionary methods[15]. Exergoeconomic and environmental optimization of a 160 MW combined cycle power plant through MOEA has been done by KhoshgoftarManesh and Babaelahi[16].

Over the years, extensive research has been done on power generation systems anddesalination systems. Tadros assessed the combination of a multi-stage flash (MSF) desalination unit with a variety of steam turbines, as well as a gas turbine, and boiler, in 1979, due to the extensive use of multi-effectdesalination. In the study, the economics of these systems, and thermodynamic characteristics were studied and optimization studies were carried out. The results have shown that a single unit of MSF can produce up to 1400 m3/h freshwater[17]. In 1997, Darwish et al. Used Exergy analysis to determine the cost and amount of energy consumed in the cogeneration system for the production of freshwater and power. To compare the energy consumption and cost, a variety of desalination methods as; multi-effect desalination (MED), Thermovapor compression (TVC), mechanical vapor compression (MVC), and reverse osmosis (RO) has been investigated[18]. Also, Wade in 1999, the cogeneration systems consisting of desalinating units and power generation units have been analyzed in terms of economics and energy. In his research, gas turbine power plants, combined cycle, and steam cycles, and their interconnections with MSF desalination, and reverse osmosis (RO) have been investigated using the reference cycle method, namely, the use of a single cycle for the production of electrical energy. The amount of energy allocated to produce Freshwater has been studied, and desalinating water was used as a MSF type. The results show that the MSF with a combined cycle power plant has the minimum cost allocation in all of the cases[19]. Then Dervish et al. (2004) suggested the use of gas turbines for Freshwater in Kuwait, due to the lack of freshwater in the country. They investigated several different combinations of gas turbine cycle and multi-stage flash desalinating water with a sudden drop in pressure, and oscilloscope [20]. In 2004, Cardona and Piacentino provided research to provide the optimal design of water, and energy generation units simultaneously. They investigated reverse osmosis and thermal desalination system with a sudden drop in pressure to improve system performance. They emphasized that the produced electrical energy could also be used to set up reverse osmosis, and auxiliary equipment, and tried to provide a measure based on exergy-economics and profit history for optimal design of such units. A thermoeconomic algorithm has been presented with an optimization method that has an objective function to allocate minimum cost to each component.[21]. In 2006, Wang et al. began his work on the integration of the MED-desalination system, and gas plant to have a cogeneration plant. With the integration of these two systems, the heating system required for the operation of the desalinationunit was supplied through the waste heat of the gas plant. In that same year, he examined the gas turbine cycle by injecting steam and connecting it with thermal water desalination. Using a recovery boiler, the steam needed to be injected into the combustion chamber, and the desalination plant was produced. They concluded that the injection rate of steam injected into the combustion chamber would have a profound effect on water, and power production; this increase would increase the production of power but reduced the production of freshwater, and, on the other hand, increasing the input temperature to the turbine would increase the power and water production [22]. In 2007, they carried out another study on the gas turbine plant by injecting steam into the desalination unit and to design another cogeneration system. From the analysis of the two different cycles in the previous and current research, they concluded that the fuel consumption for the production of freshwater during the steam injection process is 45% of total fuel, and in the wet air injection cycle, that is 31% to 54% of total fuel consumption in MEDTVC unit[23]. In 2009, KhoshgoftarManesh et al. Also performed a thermodynamic analysis, and multi-objective optimization of the combined heat, and power system with a thermal desalination unit, and nuclear reactor [24] while conducting research on dehumidification of water desalination process[25, 26].In 2012, Amidpour et al. Reviewed, and optimized the integration of multi-effect evaporation thermal vapor compressionwater desalination (MED-TVC) to the gas plant. The results show that the evaporator has the maximum exergy destruction in the plant. In the very high-pressure steam injection with pressure about 30 bar the minimum cost of desalinated water has been occurred in the MED-TVC unit[27].

In 2014, Alzahrani et al. has been investigated a gas turbine cycle integrated with MEDTVC desalination and RO units. An energy recovery device related the thermal desalination unit to the gas turbine cycle. An exergy analysis has been performed to show the destruction of each component. Effect No.4 of the MED thermal desalination unit has 45% of the total exergy destruction [28]. In 2015, Eshoul et al. has considered a combined cycle power plant standalone and integrated with a MEDTVC desalination unit. They performed thermodynamic and exergy analyses on the case study. Also, the amount of the environmental impact as carbon dioxide has been obtained and the results show that the emission rises by increasing the ambient air temperature. Every 10°C increase in the ambient air temperature rises the plant efficiency by about 0.42% and decreases the output power about 5.3%[29]. In 2018, Eshoul et al. has considered a MEDTVC desalination unit lonely and done energetic, exergetic, and economic analysis on it. The results show that thermocompressor is the main source of the exergy destruction in this system. By using a preheater in this system, the cost of the desalinated water has been decreased [30].

In four papers, Kamali et al. [31-34] developed and then optimized a model for thermodynamic simulation of a multi-stage desalination. The developed model is then compared and validated with the experimental data of one of the Kish Island desalination currently in operation. The developed model is based on the basics of the design of the cell shell transducers, although there is no discussion of the economic aspects of the system under study in their research. Also, the impact of the required vapor suction site from the country on the optimal desalination performance is evaluated.

Several researchers have also focused more on MED, MSF, and RO desalination systems, as well as the combined use of these desalination.

Muginstein et al. [35] are evaluated the performance of two reverse osmosis desalination and multi-stage desalination steam desalination both desalination was connected to a combined cycle power plant. Darwish et al. [36] Given the shortage of freshwater in Kuwait, they suggested the use of gas turbines to produce freshwater, all of the researchers' multi-stage desalination plants were of a multi-stage type with a sudden pressure drop to the turbine. In this paper, the researchers investigated many combinations between thermal desalination and reverse osmosis with thermal power plants. Most of this paper focuses on general engineering calculations and does not include process modeling and simulation.Messineo et al. [37] also conducted a study similar to the work of Cardona et al. [38] except that there was no thermal coupling between multistage distillate desalination, reverse osmosis desalination, and only freshwater of these two freshwaters mix to get the desired quality of acid.Rensonnet et al. [39] also studied the various configurations of the combination of multistage distillation desalination and reverse osmosis desalination and thermoeconomic power plants.

Mokhtari et al evaluated (GT + MED + RO) hybrid system for desalination in the Persian Gulf.     Promotion in performance of a GT + MED + RO to achieve more water production capacity

Talebbeydokhti etal performed evaluation and optimization low-temperature MED system powered by CSP. The selection of integrated LT-MED with CSP-DEC is investigated [40].

Dynamic simulation of MED-TVC desalination integrated with nuclear reactor with high modeling accuracy has been performed by Dong et al. A lumped-parameter for nuclear desalination plant has been considered [41].

Performance evaluation of an auto-tuning area ratio ejector for the MED-TVC desalination process has been proposed by Gu et al [42]. Evaluation of varying motive steam to performance are considered.

Elsayed et al investigated a transient simulation of MED desalination with different feed configurations[43]. Backward feed, forward feed, parallel feed and parallel/crossfeed are considered. MED-TVC with parallel/crossfeed has the best response.

The integration of MED with the solar Rankin cycle powered by the linear fresnel solar field has been proposed by Askari and Ameri. In this regard, fuel consumption is reduced significantly by using the solar energy[44].

Dynamic modeling of a MED-TVC plant has been proposed by Guimard et al [45].  A dynamic model based on mathematical equations has been implemented. Also, transient operations related to disturbances are considered. Based on the brine levels in the effects Strategies for process control under modification of regimes have been developed. 

Shayesteh et al investigated to find 4E optimum the ORC-RO system parameters for Water–Energy-Environment nexus. In this regard, the environmental impacts index has been defined for the RO system [46].Palenzuela et al evaluated based on Techno-Economic analysis between CSP+MED and CSP+RO in MENA Region[47].

As mentioned before, there is no study about simultaneous exergetic, exergeoconomic and exergoenvironmentthree-objectives optimization for power and desalination plants.

In this study, the Qeshm cogeneration plant with the gas turbine, HRSG and MEDTVC has been selected as a real case study to find optimum conditions based on Multi-Objective Genetic Algorithm (MOGA). In this regard, exergetic, exergoeconomic and exergoenvironmental optimization of Qeshm power and desalting plant have been investigated

 

  1. Case study

The Qeshmpower/water cogeneration plant includes two MAPNA 25 MW gasturbines, two Heat Recovery Steam Generators (HRSG) and two MED-TVC desalination units. The technical characteristics of the Qeshm power/water cogeneration plant is indicated in table 1. As shown in Figure 1, the integrated RO with existing MED-TVC plant are investigated.

 

Table 1. Technical characteristics of the multi-generation combined cycle power plant

Parameter                                          Unit           Value

SiteLevel

m

302.0

AirCompressionratio

-

19.23

Ambient Temperature

C

35.00

Net Power Output of Gas Cycle

MW

25.67

Isentropic Efficiency of AC

%

90.00

Isentropic Efficiency of GT

%

93.00

Efficiency of CC

%

99.00

Turbine Inlet Temperature

C

1232

Fuel Type

-

NG

MED NO. of Effects

-

5

MED Distillate Flow Rate

ton/h

186.2

Salinity of Seawater

g/Kg

38.7

MED Recovery Ratio

-

0.2957

HRSG High-pressure

bar

53.3

 

 

 

Figure 1. Schematic of the multi-generation combined cycle power plant

 

 

Table 2. Equations, inputs and outputs of the equipment of gas cycle

Component                              Equations                                                      inputs           outputs

Air Compressor

[55]

   

Combustion Chamber

[55]

   

Gas Turbine

[48, 55]

   

 

Table 3. Equations, inputs and outputs of the equipment of steam side

Component                                          Equations                                                                     inputs                     outputs

High-Pressure Super heater

[48, 49]

   

High-Pressure Evaporator

[48, 49]

   

High-Pressure Economizer 2

[48, 49]

   

High-Pressure Economizer 1

[48, 49]

   

Deaerator Pressure Evaporator

[48, 49]

   

Feed Water Preheater

[48, 49]

   

Deaerator

[48, 49]

   

High-Pressure Pump

[48, 49]

   

Desuperheater

[48, 49]

   

Valve 1

[48, 49]

   

Valve 2

[48, 49]

   

 


 

Table 4. Equations, inputs and outputs of the equipment of MED-TVC

Component   Equations                                                                                inputs           outputs  

MED-TVC

[56]

   

 

 

 

 

 

[57]

 

 

 

 


 

 

Table 5. Equations, inputs and outputs of the equipment of RO

            Component              Equations                                                                                                inputs                                 outputs

RO

[58]

   

 

 

 

 

 

 

  1. Methodology

3.1.  Thermodynamic analysis

Thermodynamics means studying energy, turning energy into different modes, and the ability to work energy. At first, three thermodynamic laws were drafted, but according to the fourth law, the so-called zeroth law was called, because the law had one, two, three, and it was not a fundamental principle.

Many power plants and heat engines generate useful work by converting energy. In all of them, energy translates into a mechanical component and leads to the production of work. This energy conversion is based on the first law of thermodynamics.

Mass and energy balances for each component are given as equation (1) & (2)[48, 49]:

 

(1)

 

(2)

 

The base thermodynamic equations of each component are expressed in Table 2, 3, 4, and5 respectively as follow.

The number of water desalination unit equations that must be solved simultaneously is relatively high because all of these equations must be solved in the number n of the simultaneous operation that increases the number of involved equations. On the other hand, the user in the analysis input, which makes the coding more complex, and requires more flexibility, can change the number of effects. Therefore, to provide this flexibility, MED coding modeling is used in the EES software environment. Nevertheless, the rest is coding in the MATLAB software environment. This decision is causing a disruption in the simultaneous implementation of the code developed in both software, which is not desirable; because we intend to analyze all parts of the system simultaneously with the implementation of the model, and the results of one section, on effect other sectors.

To solve this problem, the MACRO coding environment of the EES software utilizes the interfacing between the two software. In this way, when the developed model is implemented in MATLAB software, the instruction to run EES software, which includes the water desalination model MED, is issued by the MACRO environment. By doing this, by running the MATLAB software, the EES application is executed, and the problem described is resolved.

 

3.2.       Exergy analysis

To overcome the flaw in the separation of the first, and second laws of thermodynamics, we first obtain the general rule of the lost labor in general. In this section, the overall results will be simple. The potential of a system that only has a heat exchange environment is called its exergy state or thermodynamic access to its dead state. Exergy is the maximum useful work that can be obtained from a material stream or energy: as stated, useful work will be maximized if the process is reversible. Therefore, reversible work with exergy has a relationship.

The physical and chemical exergy values form the exergy of material streams can be calculated by equation (3) & (4).

The specific chemical exergy for methane can be obtained as equation (5)[48].

 

(3)

 

(4)

 

(5)

 

The chemical exergy of seawater streams (molar basis) in kJ/kmol is given as follow[50, 51]:

 

(6)

 

Which  is moles number of salt in seawater, and  is that of water.

Moreover,  is the molar chemical potential of salt in seawater in kJ/kmol, and  is that of water.

The superscript zero indicates the global dead state so that , and .   

The chemical exergy of seawater streams (mass basis) can be obtained in kJ/kg[50, 51]:

 

(7)

 

Which  is a mass fraction of salt in seawater, and  is that of water.

Moreover,  is a chemical potential of salt in seawater in kJ/kg at restricted dead state condition, and  is that of water.

The superscript * indicates the restricted dead state so that .

The total exergy of a material stream is given as follow[48, 49]:

 

(8)

 

The exergy rate of the material streams can be determined as follow[48, 49]

 

(9)

 

The exergy destruction rate and exergetic efficiency of each component can be calculated by equations 10 and 11[48, 49].

 

(10)

 

(11)

 

The fuel and product exergy rate are two major values that can be defined in each component of the cycle. Table 6 shows the exergy rate of the fuel and product streams in equipment.


 

Table 6. Fuel and Product exergy streams of the equipment

Component                                                    

Air Compressor

   

Combustion Chamber

   

Gas Turbine

   

High-Pressure Super heater

   

High-Pressure Evaporator

   

High-Pressure Economizer 2

   

High-Pressure Economizer 1

   

Deaerator Pressure Evaporator

   

Feed Water Preheater

   

Deaerator

   

HRSG Pack

   

High-Pressure Pump

   

De-super heater

   

Process Heat Exchanger

   

Valve 1

   

Valve 2

   

MED-TVC

   

RO

   

 

 

3.3.            Exergoeconomic analysis

Exergoeconomic or thermoeconomic is a branch of engineering that combines exergy analysis with economic principles, and thus provides designers of a system with information that is not available through routine analysis of energy and economic research, but for the design, and operation of an optimally priced system is critical. Therefore, the objectives of exergy control analysis include the separate calculation of the costs of each product produced by the multi-product system, the perception of the process of cost formation, and system flow, the optimization of specific variables in a single component, and the overall optimization of the system.

Different methods have been proposed for exergy-cosmetic analysis. In this research, a special cost method for exergy has been used. This cost-based approach to exergy units, exergy efficiency, and auxiliary equations for different components of the thermal system is based. This method involves the identification of exergy flows, the identification of fuel and product for each component of the thermal system, and the use of cost relationships.

In Exergy pricing, an expense is assigned to each exergy stream. These exergy currents include the exergy transmitted by the inlets and outlets, by work and by heat. Table 7 shows the purchased cost of equipment.

 

 

 

 

 

 

 

Table 7. Purchase Equipment Cost of the equipment in [$]

                      Component                                                                                            equation

Air Compressor

[49]

Combustion Chamber

[49]

Gas Turbine

[59]

HRSG

[59]

Deaerator

[52]

Pump

[60]

Valve

[61]

Desuperheater

[61]

MED

[52]

TVC

[61]

RO

[61, 62]

 

 

 

 

 

 

The cost rate of the equipment can be obtained as equation 9[49].

 

(9)

 

: the maintenance factor: 1.06[48, 49].

N: the annual operating hours of the system:   hours[48, 49].

 CRF: the capital recovery factor[48]:

 

(10)

 

i: interest rate

ny: the working years of the system that considered 25 years[48, 49].

the exergoeconomic balance equation for each component in the cycle can be written as follow[48].

 

(11)

 

(12)

 

(13)

 

The exergy destruction’s cost rate of the equipment is given as follows [48].

 

(14)

 

The exergoeconomic factor for each component can be calculated as follow[48].

 

(15)

 

The relative cost difference of the equipment is another parameter that can be obtained as equation 16 [48]:

 

(16)

 

3.4.  Exergoenvironmental analysis

The exergoenvironmental analysis includes three steps. First, an exergy analysis has been determined for each stream of the cycle, and in the second step the environmental impacts of each component in the process of the manufacturing has been calculated, and then in the third step the exergoenvironmental balance equation has been developed to calculate the environmental impact of each stream in the cycle.

The exergoenvironmental balance equation for each component can be written as follow[52].

 

(11)

 

(12)

 

(13)

 

The exergy destruction’s environmental impact rate of the equipment can be found in equation 14[52].

 

(14)

 

The exergoenvironmental factor for each component can be obtained as equation 15[52].

 

(15)

 

The relative environmental impact difference of the equipment are given as follow :[52]

 

(16)

 

Environmental impact of the equipment multiplying weight, and environmental impact per mass unit of the components:[52]

 

(17)

 

Which  is the environmental impact of the component in pts, and  is the weight of the component in tons.

 is environmental impact per mass unit of the component in pts/ton which is a function of the component’s material, and it can be derived from Eco-indicator 99 knowing the material composition of each component[53].

The weight function of each component is given in table 8.


 

Table 8. Weight function of the equipment in tons

Component                                                equation

Air Compressor

[52]

Combustion Chamber

[52]

Gas Turbine

[52]

Deaerator

[52]

Pump

[52]

Super heater

[52]

Evaporator

[52]

Economizer

[52]

MED

Environmental Impact of MED can be calculated directly

and independent of its weight

TVC

 

RO

Environmental Impact of RO can be calculated directly

and independent of its weight

 

 

The weight function of TVC is derived and proposed in this paper using technical data of TVCs in different nominal sizes manufactured by KADANT incorporation.

Environmental impact rate of RO in distillate using interpolation data gathered, and can be calculated by equation [54]:

 

(18)

 

The environmental impact rate of MED in distillate is equal to [54].

 

3.5.            Optimization

In short, the Genetic Algorithm (GA) is a programming technique that uses genetic evolution as a problem-solving paradigm. The problem to be addressed is input, and the solutions are coded according to a pattern called fitness function, and each path Evaluates the candidate solution, most of which are selected at random.Genetic Algorithm (GA) is a computer science search technique for finding optimal solutions and search problems. Genetic algorithms are one of a variety of evolutionary algorithms that are inspired by the science of biologics such as inheritance, mutation, natural selection, and natural selection.

The optimization procedure in the genetic algorithm is based on a random-directed procedure. This method is based on the theory of gradual evolution and Darwin's fundamental ideas. In this method, a set of random parameters is randomly generated for several constants called populations, after executing a numerical simulator that represents the standard deviation and Or we fit that set of information to that member of that population. We repeat this procedure for each of the created members, and then call upon the genetic algorithm operators, including fertilization, mutation, and next-generation selection, and this process will continue until the convergence criterion is satisfied.

Commonly, three criteria are considered as a stop criterion:

  • Algorithm execution time
  • The number of generations created
  • The convergence of error criteria

 


 

Table 9. Objective Functions of the system

Objective Function                                                                       Symbol                                        Unit

Total Exergetic Efficiency of the System

   

Total ExergeticCost Rate of the System

   

Total Exergoenvironmental Impact Rate of the System

   

 

 

 

 

Table 10. Decision Variables of the base case system

                           No.     Decision Variables                                                     Symbol               Unit           Constraint

1

Number of MED’s Effects

 

---

3-12

2

Recovery Ratio of MED

 

---

0.1-0.6

3

Evaporator Pinch Temperature Difference

   

5-75

4

Feed Water Preheater Approach Temperature

   

0-15

5

Economizer Approach Temperature

   

0-10

6

Superheater Superheated Temperature

   

5-80

7

Compressor Ratio of TVC

 

---

1.5-5

8

Feed Pressure of RO

 

bar

10-60

 

 

  1. Results and discussion

As stated, the studied cycle included the steam cycle of the Qeshm combined cycle power plant and one water desalination unit. To start the thermal water desalination, a discharge from the line of the LP steam cycle of the power plant has been used. In the following, the reason for using this section is the combined cycle, and then the results of the exergy analysis of this cycle and the effect of discharge on the operation of the power plant are explained. In a power plant, there is a combination of points that can be used as a source of energy in other heating systems, such as hot water sprinklers. These points include the heat dissipated by the outlet of the power plant chimney, the steam outlet from the LP line, and the entrance to the condenser, the discharge line of the LP and HP. Regarding the use of waste heat from the chimney, which is done by adding an auxiliary cycle to the end of the boiler, it should be noted that this mode cannot supply the pressure required for the commissioning of the thermocouple. However, it is suitable for use in other types of water Thermal desalination unit without Thermo compressor. In the case of the steam outlet from the LP line, and the use of the first stage of the desalination system instead of the condenser, it should be noted that this steam not only does not have the ability to supply the pressure required for the commissioning of the thermocouple compressor, but because of its low temperature, It is also not used in other types of thermal desalination. Concerning the withdrawal of the HP line due to the high steam pressure at this stage and that this pressure is outside the pressure range of the thermocouple compressors, the idea of  using high-pressure turbine steam line steam for use in MED-TVC

 It is also excluded. Here, the idea of using an auxiliary burner in a power plant boiler and supplying a desirable water supply can be made into mind. Nevertheless, because of the increased energy consumption in this case and the goal of recycling and reducing energy consumption in the survey.

The project for the production of electricity, and water Qeshm, to save fossil fuels and increase the efficiency of gas power plants, was exploited with a capacity of 50 megawatts of electricity and 18 thousand cubic meters of Freshwater.

The thermodynamic properties of the cycle include: mass flow, temperature, and pressure are presented in table 11. The exergy rate of each stream is indicated in this table, and the cost rate and environmental are determined.

 

 

Table 11. Thermodynamic, exergoeconomic and exergoenvironmental data of all material streams

                                                                         

 

1

83.58

35.00

1.0032

0.174

0.000

0.000

0.000

0.000

2

83.58

489.46

19.2923

36.73

13.93

1842.4

5.423

717.01

3

1.37

35.00

30.6400

72.79

6.50

1703.2

2.875

753.42

4

84.95

1232.2

18.5206

90.06

10.94

3548.5

4.536

1470.7

5

84.95

515.81

1.0302

19.31

10.94

760.82

4.536

315.32

6

84.95

491.33

1.0294

17.91

10.94

705.67

4.536

292.47

7

84.95

276.51

1.0200

7.161

10.94

282.14

4.536

116.94

8

84.95

228.41

1.0155

5.251

10.94

206.89

4.536

85.75

9

84.95

183.03

1.0138

3.713

10.94

146.31

4.536

60.64

10

84.95

177.36

1.0136

3.541

10.94

139.51

4.536

57.82

11

84.95

166.42

1.0132

3.220

10.94

126.88

4.536

52.58

12

12.71

75.46

1.2360

0.162

17.54

10.233

6.639

3.872

13

12.71

94.81

1.200

0.306

25.86

28.511

8.261

9.107

14

12.94

104.78

1.200

0.399

27.23

39.115

8.462

12.16

15

0.24

104.78

1.200

0.0075

27.23

0.7331

8.462

0.228

16

0.24

104.78

1.200

0.1061

27.45

10.488

7.978

3.048

17

12.71

104.78

1.200

0.399

27.23

39.115

8.462

12.16

18

12.71

106.03

53.30

0.477

25.61

43.981

8.231

14.14

19

12.41

106.03

53.30

0.466

25.61

42.964

8.231

13.81

20

12.41

186.40

52.55

1.594

19.73

113.22

6.783

38.92

21

12.41

264.90

51.75

3.327

17.05

204.19

5.853

70.11

22

12.41

266.10

51.75

12.02

14.97

647.49

5.679

245.66

23

12.41

317.50

50.00

13.01

15.12

708.15

5.735

268.59

24

5.66

317.50

50.00

5.934

15.12

323.01

5.735

122.51

25

0.29

106.03

53.30

0.011

25.61

1.017

8.231

0.327

26

5.95

278.90

50.00

5.900

15.31

325.17

5.783

122.84

27

5.95

217.11

12.90

4.882

18.50

325.17

6.989

122.84

28

6.75

315.00

50.00

7.052

15.12

383.85

5.735

145.59

29

6.75

82.22

1.236

0.120

15.12

5.981

5.735

2.269

30

5.95

67.50

16.00

0.064

18.50

4.244

6.989

1.603

31

5.95

67.79

1.236

0.056

21.25

4.251

8.014

1.603

32

316.42

35.00

1.0132

0.000

0.000

0.000

0.000

0.000

33

123.19

48.71

1.0132

1.498

0.000

0.000

0.000

0.000

34

51.72

48.15

4.500

0.206

471.2

349.83

487.1

361.64

35

141.48

35.00

2.000

0.367

0.000

0.000

0.000

0.000

36

141.48

65.00

1.400

1.170

89.75

378.18

34.01

143.32

37

141.51

45.00

1.0132

0.089

0.000

0.000

0.000

0.000

38

70.87

45.00

1.0132

0.224

161.12

129.94

38.98

31.437

39

70.65

46.45

1.0132

2.774

0.000

0.000

0.000

0.000

 

Table 12. Comparison of main parameters of thermodynamic modeling in Thermoflex with those of first law analysis programed in MATLAB concerned with the streams

                                                                                                                                              

              MATLAB      Thermoflex   Error [%]                MATLAB        Thermoflex       Error [%]                MATLAB       Thermoflex      Error [%]

1

83.58

83.53

0.06

 

35.00

35.00

0.00

 

1.0032

1.003

0.02

2

83.58

83.53

0.06

 

489.46

488.6

0.18

 

19.2923

19.5

1.07

3

1.37

1.419

3.45

 

35.00

35.00

0.00

 

30.6400

30.64

0.00

4

84.95

84.94

0.01

 

1232.2

1232.2

0.00

 

18.5206

18.72

1.07

5

84.95

84.94

0.01

 

515.81

515

0.16

 

1.0302

1.0302

0.00

6

84.95

84.94

0.01

 

491.33

491.4

0.01

 

1.0294

1.0294

0.00

7

84.95

84.94

0.01

 

276.51

279.1

0.93

 

1.0200

1.02

0.00

8

84.95

84.94

0.01

 

228.41

230

0.69

 

1.0155

1.0155

0.00

9

84.95

84.94

0.01

 

183.03

183.3

0.15

 

1.0138

1.0138

0.00

10

84.95

84.94

0.01

 

177.36

177.4

0.02

 

1.0136

1.0136

0.00

11

84.95

84.94

0.01

 

166.42

166.1

0.19

 

1.0132

1.0132

0.00

12

12.71

12.76

0.39

 

75.46

75.59

0.17

 

1.2360

1.236

0.00

13

12.71

12.76

0.39

 

94.81

94.81

0.00

 

1.200

1.2

0.00

14

12.94

13.00

0.46

 

104.78

104.8

0.02

 

1.200

1.2

0.00

15

0.24

0.24

0.00

 

104.78

104.8

0.02

 

1.200

1.2

0.00

16

0.24

0.24

0.00

 

104.78

104.8

0.02

 

1.200

1.2

0.00

17

12.71

12.76

0.39

 

104.78

104.8

0.02

 

1.200

1.2

0.00

18

12.71

12.76

0.39

 

106.03

106.1

0.07

 

53.30

53.3

0.00

19

12.41

12.46

0.40

 

106.03

106.1

0.07

 

53.30

53.3

0.00

20

12.41

12.46

0.40

 

186.40

186.4

0.00

 

52.55

52.55

0.00

21

12.41

12.46

0.40

 

264.90

264.9

0.00

 

51.75

51.75

0.00

22

12.41

12.46

0.40

 

266.10

266.1

0.00

 

51.75

51.75

0.00

23

12.41

12.46

0.40

 

317.50

317.5

0.00

 

50.00

50.00

0.00

24

5.66

5.668

0.14

 

317.50

317.5

0.00

 

50.00

50.00

0.00

25

0.29

0.299

3.01

 

106.03

106.1

0.07

 

53.30

53.3

0.00

26

5.95

5.967

0.28

 

278.90

278.9

0.00

 

50.00

50.00

0.00

27

5.95

5.967

0.28

 

217.11

217.2

0.04

 

12.90

12.9

0.00

28

6.75

6.788

0.56

 

315.00

317.5

0.79

 

50.00

50.00

0.00

29

6.75

6.788

0.56

 

82.22

82.22

0.00

 

1.236

1.236

0.00

30

5.95

5.967

0.28

 

67.50

67.74

0.35

 

16.00

16.00

0.00

31

5.95

5.967

0.28

 

67.79

68.02

0.34

 

1.236

1.236

0.00

32

316.42

343.4

7.86

 

35.00

35.00

0.00

 

1.0132

1.0132

0.00

33

123.19

123.1

0.07

 

48.71

47.68

2.16

 

1.0132

1.014

0.08

34

51.72

51.72

0.00

 

48.15

47.14

2.14

 

4.500

4.50

0.00

35

141.48

141.61

0.09

 

35.00

35.00

0.00

 

2.000

2.00

0.00

36

141.48

141.61

0.09

 

65.00

65.00

0.00

 

1.400

1.40

0.00

37

141.51

141.51

0.00

 

45.00

45.06

0.13

 

1.0132

1.014

0.08

38

70.87

70.64

0.33

 

45.00

48.74

7.67

 

1.0132

1.013

0.02

39

70.65

70.87

0.31

 

46.45

46.91

0.98

 

1.0132

1.013

0.02

 

 

The stream No.4, which is the output stream of the combustion chamber, has the highest exergy rate among all cyclic flows. This flow is about 90 megawatts of exergy. In addition, the flow of the outlet from the combustion chamber has the highest cost rate in the cyclic flows. This stream costs around $ 3,548.5 per hour per cycle. It also has the highest altitudinal rate throughout the entire cycle. In this process, the rate of annoyance is about 1471 mpts per second. The reason for the high rate of exergy in this flow is the high temperature, and pressure of the exhaust stream from the combustion chamber. Also due to the use of fossil fuels in the combustion chamber, the cost, and degree of contamination of this stream is high. Nevertheless, after the flow of the outlet from the combustion chamber, the fuel flow into it has the highest exergy rate. It has an exergy content of about 73 megawatts. The cost of the fuel flow is about $ 1703 per hour, and its pollution is about 753 mpts per hour.

The compressor consumes 62% of the power output by the gas turbine. The amount of heat exchanged in each of the heat exchangers through MATLAB coding, and thermoflow simulation is shown in Table 12. Also, the amount of error between computer code, and Thermoflex simulation has been reported. The performance ratio of each sweetener is another important parameter. This amount for MEDTVC is about 8.7. This value represents the proportion of sweet water produced to steam consumption. This amount for RO is 0.5.

The exergy destruction rate in each component has been presented in the figure 2.

 

Figure 2. Exergy destruction distribution of the equipment in MW and percent

 

According to Fig. 2, the highest rate of exergy degradation is related to the combustion chamber, which accounts for about 45.5% of the total exergy destruction of the cycle. The combustion chamber has about 19.5 megawatts of exergy destruction. The process heat exchanger is the next device that has the highest rate of exergy destruction. This equipment has about 6.14 megawatts of exergy destruction, which is about 15% of the total exergy destruction of the cycle. The exergy destruction of the air compressor is 13%, and the gas turbine has 8% of total exergy destruction. The MED unit also has a 7% destruction of the exergy cycle.

The ever-increasing demand for water, and services resulting from population growth, and rising standards of living, and health, on the one hand, and the limitation of water resources and droughts and climate change, on the other hand, is the view of planners and water experts from unconventional waters (sewage, wastewater, and saline water). Also, the disposal of industrial, and urban wastewater, and the penetration of existing contaminations into surface water, and groundwater resources is a major concern in many countries, including Iran. Sewage treatment and its application in various uses negatively affects the release of wastewater to the environment, and the health of human societies. Based on this, in this paper, the methodology of economic and environmental assessment of sweet water production from Persian Gulf and the economic and environmental assessment of this project has been addressed. The exergoenvironmental and exergoeconomic analysis results are presented in table 13.


 

Table 13. Investment cost rate, exergoeconomic factor and relative cost difference, environmental impact rate, exergoenvironmental factor, relative environmental impact difference and exergetic efficiency of the equipment

                     Component                                                

Air Compressor

109.41

32.96

21.98

222.58

5.2704

0.572

14.74

92.08

87.16

Combustion Chamber

28.4

4.31

21.70

629.78

25.856

0.989

21.62

261.18

82.24

Gas Turbine

58.4

32.04

4.87

123.84

32.297

6.253

4.68

51.33

95.56

High-Pressure Super heater

5.51

25.66

54.83

15.97

7.4787

11.17

41.22

6.62

71.04

High-Pressure Evaporator

19.78

19.58

29.50

81.20

2.0242

0.601

23.73

33.66

80.83

High-Pressure Economizer 2

15.71

69.32

33.20

6.96

0.2438

0.845

10.19

2.88

90.76

High-Pressure Economizer 1

9.67

37.45

58.11

16.15

0.2304

0.344

36.36

6.69

73.34

Deaerator Pressure Evaporator

2.95

98.44

77.45

0.05

0.1708

18.93

23.80

0.02

57.15

Feed Water Preheater

5.65

4.05

221.6

133.83

0.0580

0.010

122.3

55.46

44.99

Deaerator

0.11

8.40

3.65

1.26

0.0171

0.436

3.34

0.39

96.77

HRSG Pack

59.27

18.71

37.52

257.54

10.206

0.955

25.86

106.74

79.85

High-Pressure Pump

0.08

5.08

51.05

1.56

0.0497

0.768

48.49

0.65

67.36

Process Heat Exchanger

0.3

0.09

765.2

334.16

0.0237

0.001

764.4

126.74

11.57

MED-TVC

28.91

8.75

186.2

301.27

24041

67.87

677.3

113.81

38.04

RO

96.17

42.75

728.3

128.77

1746.8

24.69

810.4

53.27

24.72

 

 

The highest rate of purchase is related to the air compressor, and then desalination unit also have a high cost rate. The cost of exergy destruction in the combustion chamber has the highest rate, and it costs $ 630 per hour. The cost of exergy destruction in the combustion chamber is approximately 3 times the air compressor, and 5 times the gas turbine. Similarly, the rate of emissions associated with exergy destruction in the combustion chamber has the highest rate.

Genetic algorithms are better because of their strength and durability than other methods based on artificial intelligence. Unlike older artificial intelligence systems, the genetic algorithm is not quickly interrupted by slight changes in input values or by significant amounts of noise in the system. Also, in the search for a large state space, a multimodal state space, or a multidimensional procedure, the use of genetic algorithms has many advantages over conventional search techniques in other optimization techniques such as linear programming, random search, or the first search methods have depth, first level or praxis. The objective functions are produced by Genetic programming and the functions are presented in Table 13.

Figure 3shows the optimal pareto front solutions for twoObjective Functions (OFs) (exergetic efficiency and total exrgetic costs). In addition, Figure 4 and 5 determine pareto front optimal solutions for total exergeticcost vs exergeticenvironmental impacts OFs and exergeticefficiency vs exergeticenvironmental impactsOFs respectively.

 

 

 

Figure 3.Optimal Pareto Front Solutions for Two-Objective Functions -Base Case (Total Costs vas Exergetic Efficiency)

 

 

Figure 4. Optimal Pareto Front Solutions for Two-Objective Functions -Base Case (Total Exergetic Cost vsExergetic Environmental Impacts)

 

Figure 5. Optimal Pareto Front Solutions for Two-Objective Functions -Base Case (Exergetic Efficiency vs Exergetic Environmental Impacts)

 

 

Table 14 and 15 are indicated the optimized variables of the objective functions and decision variables.

As shown in the results, the overall exergetic efficiency of plant has been improved by 27.74%. Also, the exergeticcost of plant has been reduced by 0.93 $/min and exergoenvironmental impacts has been decreased by 0.85 pts/min.

Sensitivity analysis for objective functions has been performed based on variation of fuel LHV, ambient temperature, interest rate and exergy cost of fuel. The results of sensitivity analysis related to variation of fuel LHV, ambient temperature, interest rate, exergy cost of fuel have been demonstrated in Figure Fig.6-Fig.9 respectively.

 

 

Table 14. Selected Optimized solution for objective functions using MOGA

cases

     

Optimized (MOGA)

59.88

63.08

28.64

Base Case

46.86

64.01

29.49

 

Table 15. Selected Optimized solution for objective functions using MOGA

 

             

MOGA

8

0.60

6.43

2.70

7.45

74.12

4.98

Base Case

5

0.30

72.58

9.97

1.20

53.56

2.08

 

 

Figure 6. Sensitivity analyses based on variation of Fuel LHV

 

Figure 7. Sensitivity analysis of OFs based on variation of ambient temperature

 

Figure 8. Sensitivity analysis of OFs based on variation of interest rate

 

Figure 9. Sensitivity analysis of OFs based on cost of fuel

 

 

  1. Conclusion

In this study, energetic, exergetic, exergeoeconomic and exergoenvironmental analysis and optimization of  Qeshm MED-TVC cogeneration plant based on 25 MW, MAPNA Gas Turbine prime mover have been considered. In this regard, the computer program has been developed. Also, the integration of the RO desalination system has been investigated. MOGA optimization of existing plant-based on overall exergetic efficiency, total exergetic costs and total exergoenvironmental impacts have been done. The results indicate the optimum scenario has a good performance in view of exergetic, exergoeconomic and exergoenvironment.

The optimum plant overall exergeticefficiency hasbeen increased by 27.78%, and total exergetic cost and total exergoenvironmental impacts have been decreased by 0.93% and by 0.89%.

In the future research, the advanced exergetic, exergoeconomic and exergoenvironmental analysis can be done to better show the performance of system in the base and optimum cases precisely.

In addition, other recently optimization algorithms can be examined and evaluated. Finally, the use of renewable energy to improve the plant performance can be studied. 

 

L. F. Greenlee, D. F. Lawler, B. D. Freeman, B. Marrot, and P. Moulin, "Reverse osmosis desalination: water sources, technology, and today's challenges," Water research, vol. 43, no. 9, pp. 2317-2348, 2009.

F. Banat, "Economic and technical assessment of desalination technologies," in IWA Conference-New Technologies for Water and Wastewater Treatment in the 21st Century, Geneva, Switzerland, June, 2007, pp. 6-8.

D. Conti, "Thermodynamic and Economic Evaluation of Co-Production Plants for Electricity and Potable Water," Project paper, Polytechnic of Milan, 2003.

L. Garcia-Rodriguez, "Seawater desalination driven by renewable energies: a review," Desalination, vol. 143, no. 2, pp. 103-113, 2002.

A. Subramani and J. G. Jacangelo, "Emerging desalination technologies for water treatment: a critical review," Water research, vol. 75, pp. 164-187, 2015.

T. Matsuura, "Progress in membrane science and technology for seawater desalination—a review," Desalination, vol. 134, no. 1-3, pp. 47-54, 2001.

D. M. Warsinger, J. Swaminathan, E. Guillen-Burrieza, and H. A. Arafat, "Scaling and fouling in membrane distillation for desalination applications: a review," Desalination, vol. 356, pp. 294-313, 2015.

H. Sharon and K. Reddy, "A review of solar energy driven desalination technologies," Renewable and Sustainable Energy Reviews, vol. 41, pp. 1080-1118, 2015.

M. Paashang, S. M. Sharifi, and G. Salehi, "Identification of the Sources of Energy Loss through Exergy Analysis: Case Study of Marun Mega-Olefin Plant," Gas Processing, vol. 6, no. 2, pp. 37-48, 2018, doi: 10.22108/gpj.2018.108844.1020.

B. Ghorbani, M.-H. Hamedi, and M. Amidpour, "Exergoeconomic Evaluation of an Integrated Nitrogen Rejection Unit with LNG and NGL Co-Production Processes Based on the MFC and Absorbtion Refrigeration Systems," Gas Processing, vol. 4, no. 1, pp. 1-28, 2016, doi: 10.22108/gpj.2016.20410.

V. Ghazizadeh, B. Ghorbani, R. Shirmohammadi, M. Mehrpooya, and M. H. Hamedi, "Advanced Exergoeconomic Analysis of C3MR, MFC and DMR ‎Refrigeration Cycles in an Integrated Cryogenic Process," Gas Processing, vol. 6, no. 1, pp. 41-71, 2018, doi: 10.22108/gpj.2018.111251.1032.

M.-H. Hamedi, R. Shirmohammadi, B. Ghorbani, and S. Sheikhi, "Advanced Exergy Evaluation of an Integrated Separation Process with Optimized Refrigeration System," Gas Processing, vol. 3, no. 1, pp. 1-10, 2015, doi: 10.22108/gpj.2015.20181.

S. Sheikhi, B. Ghorbani, R. Shirmohammadi, and M.-H. Hamedi, "Thermodynamic and EconomicOptimization of a Refrigeration Cycle for Separation Units in the Petrochemical Plants Using Pinch Technology and Exergy Syntheses Analysis," Gas Processing, vol. 2, no. 2, pp. 39-51, 2014, doi: 10.22108/gpj.2014.20422.

M. Shariati Niassar, "Development and Optimization of an Integrated Process Configuration for IGCC Power Generation Technology with a Fischer-Tropsch Fuels from Coal and Biomass," Gas Processing, vol. 6, no. 1, pp. 85-108, 2018, doi: 10.22108/gpj.2018.112760.1038.

P. Haddadi, M. H. Khoshgoftar Manesh, and M. Sedighi, "Conceptual Optimization of Water and Wastewater Network of a Gas Refinery with Considering Pressure Drop and Pumping Cost," Gas Processing, vol. 7, no. 1, pp. 15-28, 2019, doi: 10.22108/gpj.2019.116559.1054.

M. H. Khoshgoftar Manesh and M. Babaelahi, "Thermoeconomic and environmental Optimization of a 160 MW ‎Combined Cycle Power Plant by MOEA," Gas Processing, vol. 5, no. 2, pp. 79-96, 2017, doi: 10.22108/gpj.2018.110226.1024.

S. Tadros, "A new look at dual purpose, water and power, plants-economy and design features," Desalination, vol. 30, no. 1, p. 613, 1979.

M. Darwish, F. A. Yousef, and N. Al-Najem, "Energy consumption and costs with a multi-stage flashing (MSF) desalting system," Desalination, vol. 109, no. 3, pp. 285-302, 1997.

N. M. Wade, "Energy and cost allocation in dual-purpose power and desalination plants," Desalination, vol. 123, no. 2-3, pp. 115-125, 1999.

M. Darwish, "Co-generation power desalting plants: new outlook with gas turbines," Desalination, vol. 161, no. 1, pp. 1-12, 2004.

E. Cardona and A. Piacentino, "Optimal design of cogeneration plants for seawater desalination," Desalination, vol. 166, pp. 411-426, 2004.

Y. Wang and N. Lior, "Performance analysis ofcombined humidified gas turbine power generation and multi-effect thermal vapor compression desalination systems—Part 1: The desalination unit and its combination with a steam-injected gas turbine power system," Desalination, vol. 196, no. 1-3, pp. 84-104, 2006.

Y. Wang and N. Lior, "Performance analysis of combined humidified gas turbine power generation and multi-effect thermal vapor compression desalination systems: part 2: the evaporative gas turbine based system and some discussions," Desalination, vol. 207, no. 1-3, pp. 243-256, 2007.

M. Khoshgoftar Manesh, M. Amidpour, and M. Hamedi, "Optimization of the coupling of pressurized water nuclear reactors and multistage flash desalination plant by evolutionary algorithms and thermoeconomic method," International Journal of Energy Research, vol. 33, no. 1, pp. 77-99, 2009.

S. Soufari, M. Zamen, and M. Amidpour, "Performance optimization of the humidification–dehumidification desalination process using mathematical programming," Desalination, vol. 237, no. 1-3, pp. 305-317, 2009.

M. Zamen, M. Amidpour, and S. M. Soufari, "Cost optimization of a solar humidification–dehumidification desalination unit using mathematical programming," Desalination, vol. 239, no. 1-3, pp. 92-99, 2009.

S. E. Shakib, M. Amidpour, and C. Aghanajafi, "Simulation and optimization of multi effect desalination coupled to a gas turbine plant with HRSG consideration," Desalination, vol. 285, pp. 366-376, 2012.

A. Alzahrani, J. Orfi, and Z. Alsuhaibani, "Performance analysis of a gas turbine unit combined with MED-TVC and RO desalination systems," Desalination and Water Treatment, vol. 55, no. 12, pp. 3350-3357, 2015.

N. M. Eshoul, B. Agnew, and R. Z. Mathkor, "Thermodynamic analysis of combined cycle power plant standalone and coupled with multi effect desalination with thermal vapor compression," in IREC2015 The Sixth International Renewable Energy Congress, 2015: IEEE, pp. 1-6.

N. Eshoul, A. Almutairi, R. Lamidi, H. Alhajeri, and A. Alenezi, "Energetic, Exergetic, and Economic Analysis of MED-TVC Water Desalination Plant with and without Preheating," Water, vol. 10, no. 3, p. 305, 2018.

R. Kamali, A. Abbassi, S. S. Vanini, and M. S. Avval, "Thermodynamic design and parametric study ofMED-TVC," Desalination, vol. 222, no. 1-3, pp. 596-604, 2008.

R. Kamali and S. Mohebinia, "Experience of design and optimization of multi-effects desalination systems in Iran," Desalination, vol. 222, no. 1-3, pp. 639-645, 2008.

R. Kouhikamali, M. Sanaei, and M. Mehdizadeh, "Process investigation of different locations of thermo-compressor suction in MED–TVC plants," Desalination, vol. 280, no. 1-3, pp. 134-138, 2011.

R. Kamali, A. Abbassi, and S. S. Vanini, "A simulation model and parametric study of MED–TVC process," Desalination, vol. 235, no. 1-3, pp. 340-351, 2009.

A. Muginstein, Y. Cohen, L. Levin, and S. Frant, "Production of desalinated water and electricity in a dual-purpose plant operating in a dispatchable electricity system—techno-economical analysis," Desalination, vol. 156, no. 1-3, pp. 361-366, 2003.

M. Darwish, S. Al Otaibi, and K. Al Shayji, "Suggested modifications of power-desalting plants in Kuwait," Desalination, vol. 216, no. 1-3, pp. 222-231, 2007.

A. Messineo and F. Marchese, "Performance evaluation of hybrid RO/MEE systems powered by a WTE plant," Desalination, vol. 229, no. 1-3, pp. 82-93, 2008.

E. Cardona, A. Piacentino, and F. Marchese, "Performance evaluation of CHP hybrid seawater desalination plants," Desalination, vol. 205, no. 1-3, pp. 1-14, 2007.

T. Rensonnet, J. Uche, and L. Serra, "Simulation and thermoeconomic analysis of different configurations of gas turbine (GT)-based dual-purpose power and desalination plants (DPPDP) and hybrid plants (HP)," Energy, vol. 32, no. 6, pp. 1012-1023, 2007.

P. Talebbeydokhti, A. Cinocca, R. Cipollone, and B. Morico, "Analysis and optimization of LT-MED system powered by an innovative CSP plant," Desalination, vol. 413, pp. 223-233, 2017/07/01/ 2017, doi: https://doi.org/10.1016/j.desal.2017.03.019.

Z. Dong, M. Liu, X. Huang, Y. Zhang, Z. Zhang, and Y. Dong, "Dynamical modeling and simulation analysis of a nuclear desalination plant based on the MED-TVC process," Desalination, vol. 456, pp. 121-135, 2019/04/15/ 2019, doi: https://doi.org/10.1016/j.desal.2019.01.020.

W. Gu, X. Wang, L. Wang, X. Yin, and H. Liu, "Performance investigation of an auto-tuning area ratio ejector for MED-TVC desalination system," Applied Thermal Engineering, vol. 155, pp. 470-479, 2019/06/05/ 2019, doi: https://doi.org/10.1016/j.applthermaleng.2019.04.018.

M. L. Elsayed, O. Mesalhy, R. H. Mohammed, and L. C. Chow, "Transient performance of MED processes with different feed configurations," Desalination, vol. 438, pp. 37-53, 2018/07/15/ 2018, doi: https://doi.org/10.1016/j.desal.2018.03.016.

I. B. Askari and M. Ameri, "Solar Rankine Cycle (SRC) powered by Linear Fresnel solar field and integrated with Multi Effect Desalination (MED) system," Renewable Energy, vol. 117, pp. 52-70, 2018/03/01/ 2018, doi: https://doi.org/10.1016/j.renene.2017.10.033.

L. Guimard et al., "New considerations for modelling a MED-TVC plant under dynamic conditions," Desalination, vol. 452, pp. 94-113, 2019/02/15/ 2019, doi: https://doi.org/10.1016/j.desal.2018.10.026.

A. A. Shayesteh, O. Koohshekan, A. Ghasemi, M. Nemati, and H. Mokhtari, "Determination of the ORC-RO system optimum parameters based on 4E analysis; Water–Energy-Environment nexus," Energy Conversion and Management, vol. 183, pp. 772-790, 2019/03/01/ 2019, doi: https://doi.org/10.1016/j.enconman.2018.12.119.

P. Palenzuela, D. C. Alarcón-Padilla, G. Zaragoza, and J. Blanco, "Comparison between CSP+MED and CSP+RO in Mediterranean Area and MENA Region: Techno-economic Analysis," Energy Procedia, vol. 69, pp. 1938-1947, 2015/05/01/ 2015, doi: https://doi.org/10.1016/j.egypro.2015.03.192.

A. Bejan, G. Tsatsaronis, M. Moran, and M. J. Moran, Thermal Design and Optimization. Wiley, 1996.

I. Dincer, M. A. Rosen, and P. Ahmadi, Optimization of Energy Systems. Wiley, 2017.

I. Dincer and M. A. Rosen, "Chapter 3 - Chemical Exergy," in Exergy (Second Edition), I. Dincer and M. A. Rosen Eds.: Elsevier, 2013, pp. 31-49.

M. H. Sharqawy, S. M. Zubair, and J. H. Lienhard, "Second law analysis of reverse osmosis desalination plants: An alternative design using pressure retarded osmosis," Energy, vol. 36, no. 11, pp. 6617-6626, 2011/11/01/ 2011, doi: https://doi.org/10.1016/j.energy.2011.08.056.

E. J. C. Cavalcanti, "Exergoeconomic and exergoenvironmental analyses of an integrated solar combined cycle system," Renewable and Sustainable Energy Reviews, vol. 67, pp. 507-519, 2017/01/01/ 2017, doi: https://doi.org/10.1016/j.rser.2016.09.017.

M. Goedkoop, R. Spriensma, S. Effting, and M. Collignon, The Eco-indicator 99: A Damage Oriented Method for Life-cycle Impact Assessment : Manual for Designers. PRé, Product Ecology consultants, 2000.

G. Raluy, L. Serra, and J. Uche, "Life cycle assessment of MSF, MED and RO desalination technologies," Energy, vol. 31, no. 13, pp. 2361-2372, 2006/10/01/ 2006, doi: https://doi.org/10.1016/j.energy.2006.02.005.

A. Valero et al., "CGAM problem: Definition and conventional solution," Energy, vol. 19, no. 3, pp. 279-286, 1994/03/01/ 1994, doi: https://doi.org/10.1016/0360-5442(94)90112-0.

K. H. Mistry, M. Antar, and J. H. Lienhard V, An improved model for multiple effect distillation. 2012, pp. 1-15.

A. S. Hassan and M. A. Darwish, "Performance of thermal vapor compression," Desalination, vol. 335, no. 1, pp. 41-46, 2014/02/17/ 2014, doi: https://doi.org/10.1016/j.desal.2013.12.004.

A. Al-Zahrani, J. Orfi, Z. Al-Suhaibani, B. Salim, and H. Al-Ansary, "Thermodynamic Analysis of aReverse Osmosis Desalination Unit with Energy Recovery System," Procedia Engineering, vol. 33, pp. 404-414, 2012/01/01/ 2012, doi: https://doi.org/10.1016/j.proeng.2012.01.1220.

H. Ghaebi, M. H. Saidi, and P. Ahmadi, "Exergoeconomic optimization of atrigeneration system for heating, cooling and power production purpose based on TRR method and using evolutionary algorithm," Applied Thermal Engineering, vol. 36, pp. 113-125, 2012/04/01/ 2012, doi: https://doi.org/10.1016/j.applthermaleng.2011.11.069.

F. A. Boyaghchi and P. Heidarnejad, "Thermoeconomic assessment and multi objective optimization of a solar micro CCHP based on Organic Rankine Cycle for domestic application," Energy Conversion and Management, vol. 97, pp. 224-234, 2015/06/01/ 2015, doi: https://doi.org/10.1016/j.enconman.2015.03.036.

Y. M. El-Sayed, The Thermoeconomics of Energy Conversions. Elsevier Science, 2013.

C. Park et al., "Stochastic cost estimation approach for full-scale reverse osmosis desalination plants," Journal of Membrane Science, vol. 364, no. 1, pp. 52-64, 2010/11/15/ 2010, doi: https://doi.org/10.1016/j.memsci.2010.07.055.