Exergoeconomic analysis of zeotropic mixture on the new proposed organic Rankine cycle for energy production from geothermal resources

Journal Pre-proof Exergoeconomic analysis of zeotropic mixture on the new proposed organic Rankine cycle for energy production from geothermal resources

Fereshteh Samadi, Neda Kazemi PII:

S0960-1481(20)30043-4

DOI:

https://doi.org/10.1016/j.renene.2020.01.038

Reference:

RENE 12900

To appear in:

Renewable Energy

Received Date:

09 July 2019

Accepted Date:

09 January 2020

Please cite this article as: Fereshteh Samadi, Neda Kazemi, Exergoeconomic analysis of zeotropic mixture on the new proposed organic Rankine cycle for energy production from geothermal resources, Renewable Energy (2020), https://doi.org/10.1016/j.renene.2020.01.038

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. © 2019 Published by Elsevier.

Journal Pre-proof

Exergoeconomic analysis of zeotropic mixture on the new proposed organic Rankine cycle for energy production from geothermal resources

Fereshteh Samadi a, Neda Kazemi a a

Department of Chemical Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran

Abstract

 Corresponding author at Department of Chemical Engineering, Faculty of Engineering. . E-mail address: [email protected] (Fereshteh Samadi).

1

Journal Pre-proof

In this study, the performance of Isobutane/Isopentane (zeotropic mixture) is studied in the new proposed Organic Rankine Cycle (NORC). Operating parameters included temperatures of two series evaporators and regenerative, degree of superheating, and pinch point temperature difference in the first evaporator are optimized with multi-objective functions i.e., exergy as thermodynamic, Specific Investment Cost (SIC) as economic and exergoeconomic as the third objective function that considered exergy and SIC simultaneously. Optimization results for Isobutane/Isopentane indicate that the value of exergy has a sharp decrease when the mole fraction of Isobutane increases to 0.5, and after that, the exergy amount rises significantly and reaches a peak at 54.584% for pure Isobutane. Also, return on investment (ROI) is employed as an economic indicator to get the center of attention in economic proficiency by utilizing Isobutane/Isopentane in NORC configuration according to the exergoeconomic results for fifteen countries as two groups of developed and developing in order to compare profitability all around the world. The results represent that, by the growing of Isobutane mole fraction in Isobutane- Isopentane mixture, ROI indicator is reduced. The maximum and the minimum value of ROI are related to Germany (57.64%) and India (4.09%), respectively.

Keywords: ORC; Zeotropic mixtures; Geothermal sources; Exergoeconomic analysis; Optimization; Environment.

1. Introduction: In recent decades, due to the awareness of the topics of energy shortage and many serious environmental issues likewise global warming, air purulence and destruction of the ozone layer which are caused by consumption of fossil fuels, exploiting clean and renewable energy plays a significant role for environmental protection and economic growth rate [1]. Among many kinds of renewable energy, geothermal as a low-grade heat source of energy with great potential of consistency and reliability to generate useful power is captured more attention. Organic Rankine cycle (ORC) is applied widely as an appropriate technology for utilizing low-temperature geothermal 2

Journal Pre-proof

source to produce electricity by using an organic working fluid instead of water in steam Rankine cycle with the advantage of lower boiling point temperature and higher vapor pressure that can enhance the cycle performance compared to water [2]. Therefore, recently, more and more attention has been captured to different configurations based on the basic ORC system and also many researchers focused on working fluid selection and parametric optimization of ORC not only to improve the energy efficiency of industrial processes but also to reduce the thermal pollution into the environment. Accordingly, Luo et al. [3] proposed a variable capacity power generation superstructure based on Flash and ORC for geothermal energy and worked experimentally. Their results indicated that the isotropic efficiency of the modified compressor obtained between 0.2 and 0.25 and by the heat source temperature of 120 ℃ and 130℃ the power output of the geofluid mass flow rate obtained 0.78 kWh.t

-1

and 1.31 kWh.t -1, respectively. In another investigation,

Manente et al., [4] worked on two different binary geothermal power plant layouts to result in better integration. The overall H2S and CO2 abatement efficiencies reached 99% and 90% in the integrated flash-binary plant design, respectively .a micro- geothermal ORC system by using R134a as working fluid was investigated by Bianchi et al. [5]. They obtained a value of 4.4 % for cycle net efficiency which was congruent with the state-of-the-art micro-scale ORCs. In another thermo-economic investigation, Mohammadzadeh Bina et al. [6] studied four different configurations of ORCs just for dry organic working fluids. Their optimizations were done through the variable metric searching method. Their results illustrated that maximum energy (20.57%) and exergy efficiency (63.72%) belonged to the IHE-ORC, where the lowest total energy cost was referred to basic ORC. Also, they ranked four considered cycles as the IHE-ORC, basic, regenerative, and dual fluid ORC from the first to the fourth, respectively. Bonalumi et al. [7] compared the performances of flash and ORC plants at 150oC, 175oC and 200oC of the geothermal heat source and analyzed the effect of outcomes on the

3

Journal Pre-proof

environment. They found that the effect of the CO2 content on plant performance is remarkable. And when the value of CO2 closed to the 5 % system efficiency could be affected greatly. Furthermore, the economic investigation of the optimization of geothermal ORC was taken into account by Yang and Yeh [1]. From their research, a significant result obtained. They understood that six considered working fluids included: R600, R600a, R1233zd, R1234yf, R1234ze, and R290 showed desirable efficiencies from high to low rates in economic viewpoint. Moreover, a comparative performance study in three considered organic Rankine configurations from thermodynamic and economic aspects was conducted by Zare [2]. The results indicated that in the thermodynamic aspect (energy and exergy) although, ORC with regenerative acted priority, basic organic Rankine cycle showed the best performance among all their considered from an economic viewpoint. Kazemi and Samadi [8] proposed a new ORC configuration included regenerative and two-stage evaporator for power generation from geothermal resources. The results showed that their new proposed cycle gave better performances both in thermodynamic and economic aspects when they investigated pure working fluids Dai et al. [9] investigated the impact of different pure organic fluids on ORC in comparison with water in converting low-grade heat to electricity. They illustrated that the cycle with organic fluids gave a much better performance than water and also R236EA had the highest exergy and thermal efficiency among all considered working fluids. Liu et al. [10] analyzed the efficiency of basic ORC in three different geothermal water inlet temperatures of 110 oC, 130 oC and 150 oC by utilizing R600a/R601a as zeotropic mixture. The results revealed that R600a/R601a mixture could produce more useful power than pure R600a in three mentioned geothermal water inlet temperatures. Parametric optimization and performance evaluation of zeotropic mixtures for ORC carried out by Kang et al. [11]. They found that R245fa/R600a (0.9/0.1) was the preferable zeotropic mixture among all considered working fluids in their thermodynamic research. Yildirim and Ozgener [12]

4

Journal Pre-proof

investigated power plants with thermodynamic (exergy) and exergoeconomic aspects, concurrently. They researched the trend of exergy efficiency changes based on the impacts of working fluids on ORC technologies. Toffolo et al. [13] designed parameters of ORC for the optimum choice of organic fluids and illustrated a multi-criteria approach in similar cycles. They found that a high value of electricity production from R134a in all taken into account temperatures was higher than isobutane. In another research, Shokati et al. [14] compared the performances of basic, dual-pressure, dual-fluid ORCs and Kalina in ORC systems. They optimized parameters of mentioned cycles through exergoeconomic objective function to maximize the energy production and minimize the cost of power generation. Their results indicated that dual-pressure ORC produced the most amount of electricity production, and also Kalina cycle had the best economic performance due to the least amount of cost in electricity production. In addition to the mentioned researches, many studies are carried out by numerous researchers about the results of ORCs performance with different optimization points of view, as can be seen in Table 1. Table 1 In this article, the performance of a new ORC proposed by Kazemi and Samadi in the previous work (Ref.8) is studied with the selection of Isobutane/Isopentane as a dry/dry category of the zeotropic mixture. Furthermore, the operating parameters of the mentioned cycle are contained heat exchangers temperatures (excluding condenser), pinch point temperature difference of the first evaporator, and degree of superheat are optimized with three various objective functions listed as exergy, economic and exergoeconomic. Additionally, from an economic viewpoint, the profitability estimation of the new proposed ORC is evaluated through ROI factor for fifteen countries in two groups of developed and developing. 5

Journal Pre-proof

Therefore, in this study, the most attention which is taken into consideration is the analysis of zeotropic mixtures performance in the NORC system by using geothermal energy as an alternative method for power generation. Besides, the most significant novelty of this research is focusing on an exergoeconomic point of view to investigate zeotropic mixtures performance. Therefore, innovations of this work are listed as follow: 

A novel ORC configuration.



Investigation of zeotropic mixtures performances.



Analysis of NORC efficiency with three various points of view.



Optimization of linear objective functions through thermodynamic and economic aspects, simultaneously.



And finally, profitability estimation of new proposed ORC by ROI factor for fifteen countries, two groups of developed and developing.

2. Methodology 2.1. System Configuration The schematic of the new ORC configuration (proposed by Kazemi and Samadi in the previous work in Ref. 8) is illustrated in Fig. 1(a). According to this figure, a new ORC consists of two series evaporators, a regenerative, a turbine, a condenser and three pumps. Furthermore, the T-S diagram of NORC is depicted in Fig. 1(b). As explained in the previous work in Ref. 8, in comparison with the basic ORC process, the outlet subcooled working fluid from pump 1 enters a regenerative to heat the working fluid by an amount of superheated working fluid taken from the turbine. Then, the saturated working fluid is entered into the evaporator 2 bypassing the second pump to obtain heat from the geothermal water. In this step, a part of the working fluid gets into the turbine, and the rest of it moves to the first evaporator. In the first evaporator, the working fluid is heated by absorbing the energy from geothermal water and then is expanded by the turbine and generates electricity. 6

Journal Pre-proof

Fig. 1(a,b)

2.2. Adoption of Working Fluid As well as cycle structure, attention to the type and the number of equipment, and also operating parameters optimization, selection of a suitable working fluid could affect the power plant performance due to enhance geothermal ORCs efficiency. Accordingly, in this article, some indicators and characteristics are gotten the center of attention to obtain a good candidate for working fluids as follow: 

Medium critical temperature and pressure.



Low viscosity and high thermal conductivity, which resulted in high heat transfer coefficients and low friction losses in the heat exchangers.



Environmentally friendly according to zero ODP and low GWP.



Safety and chemical stability [22].



determine the working fluids categories included: dry, isentropic, and wet [23,8]:

According to the mentioned characteristics, the selection of an appropriate working fluid with all considerations simultaneously can be caused by some limitations. One of the alternative methods to solve this problem is utilizing zeotropic mixtures instead of pure fluids as ORC working fluid. Zeotropic mixtures can be applied as appropriate candidates for ORC working fluids due to the better match between the working fluid and the heat source/sink temperatures according to the temperature glide in processes of heat exchangers (evaporation, condensation, and regeneration). Therefore, the temperature glide has a significant impact on cycle performance [24]. In this article, Isobutane/Isopentane as (dry/dry) zeotropic mixture is investigated to evaluate the newly proposed ORC performance. Table 2 shows the thermodynamic properties of considered zeotropic mixtures. Table 2 2.3. Hypothesizes In this study, considered assumptions for thermodynamic and economic modeling of new proposed cycles to simplify MATLAB calculation are as follow: 7

Journal Pre-proof



Each operation in the cycles is assumed at the steady-state condition.



In all heat exchangers, losses included heat and friction are not to be considered.



Kinetic and potential energy are neglected for water, which is selected as a heat source and sink media.



For calculation of heat transfer parameters, entirely developed flow is considered.



Heat exchangers contained evaporators, condenser, and regenerative are chosen as a kind of Shell and tube.



Isobutane/Isopentane is selected as an organic zeotropic mixture to investigate efficiency.



Peng Robinson equation of state (PR EOS) is utilized to calculate the thermodynamic properties of zeotropic mixtures.

2.4. Exergoeconomic Definition Exergy analysis and economic evaluation are two main terms of exergoeconomic knowledge as a common branch of chemical engineering. This knowledge is applied for preparing some properties cannot be obtained from energy investigation and cost approximation. Though, these properties are very considerable in designing of ORC configuration with reasonable cost parameters [25]. Consequently, this research focused on exergoeconomic modeling for new proposed ORC by the utilization of zeotropic mixture and explained part by part to investigate the performance of the ORCs as follows.

2.4.1. Exergy Analysis Thermal and exergy analysis based on the first and second laws of thermodynamics could be considered in a complementary procedure to evaluate the ORC performance. Generally, energy losses in a cycle or device caused by the conversion of thermal energy into electricity cannot be analyzed through the first law of thermodynamic due to the inability to the distinction of two properties of losses (quality and quantity). Consequently, recently, exergy evaluation is a practical approach to investigate energy efficiency. In this respect, the exergy of a thermodynamic system describes as the highest rate of useful work in a thermodynamic equilibrium operation can be achieved from power plants [26]. The exergy efficiency of a system is defined as in Ref [8]. 8

Journal Pre-proof

Concerning the first and the second laws of thermodynamics, relations of thermal and exergy efficiencies have been used as a model for each operation in organic Rankine cycles [27].

 wf , m  c and X are mass flow rate of zeotropic mixtures, cooling water, and also a Additionally, m fraction of mass flow rate of working fluids into the regenerative described in the previous work (Ref. 8).

According to the mentioned equations in (Ref. 8), numerous key thermodynamic

parameters like enthalpy, entropy, and vapor pressure are essential for the calculation of exergy efficiency. Therefore, in this research, the Peng Robinson (PR) equation of state is selected to obtain enthalpy and entropy as thermodynamic properties [28], and the modified Wagner equation is employed to obtain vapor pressures of zeotropic mixture [29]. In addition, constant design parameters of NORC are shown in Table 3. Table 3

2.4.2. Economic Evaluation 2.4.2.1. Calculating the area of heat exchangers Considering some parameters such as size and type of heat exchangers are crucial to evaluate the economic performance of a system since 80-90% of the total cost of a system is allocated to the heat exchangers (evaporators, regenerative, and condenser) [30]. In general, heat exchanger types will  ) as well as play a significant role in calculating heat exchanger area, productive capacity ( W net

operating conditions. It should be noted that the type of exchangers is determined according to the  [31]. Hence, the specification of shell and tube heat exchangers selected in this study range of W net

can be seen in Table 4. Table 4

In the present study, for the calculation of shell and tube heat exchangers surface area, Kern method is applied [32]. Relations of a heat transfer operation in heat exchangers are explained in Table 5. Table 5

9

Journal Pre-proof

In addition to obtaining a heat exchanger surface area, specific investment cost (SIC) should be taken into account as a determining factor to analyze the economic performance of a system in the exergoeconomic analysis. The SIC relation achieve from Table 5 [36]:

2.4.2.2. Equipment Cost In the present study, the bare module method [37] is employed for the calculation of each component cost in two basic ORC, and NORC configurations, as shown in Table 6 and Table 7. Besides, in all heat exchangers, carbon steel is selected as a material structure [40].

Table 6 Table 7

2.4.3. Optimization In the present article, optimization of operating parameters has been carried out as follow: In basic ORC structure, three parameters included a temperature of the evaporator, degree of superheating, and pinch point temperature difference in evaporator are optimized and also in NORC configuration, the temperature of regenerative as well as the second evaporator are added to the basic ORC operating parameters. Therefore, in this paper, optimization variables in NORC configuration determine as five operating parameters which optimized to analyze the cycle performance. These operating parameters included: 1) the temperature of the first evaporator 2) the temperature of the second evaporator 3) temperature of regenerative 4) degree of superheating 5) pinch point temperature of the first evaporator

10

Journal Pre-proof

In addition, three different aspects include exergy, economic and exergoeconomic, are considered for optimization of the mentioned parameters in basic ORC and NORC. Consequently, exergy efficiency is maximized as thermodynamic objective function as follow: F1 (x) = Maximize (ηex ) =

 -W  W t p

 h  h hsi - h hso - T0  s hsi - s hso   m

(1)

Furthermore, focus on the specific investment cost (explains in Table 5) as the second objective function, which should be minimized in optimization, could release the overall cost from an economical point of view. (2)

F2 (x) = Minimize (SIC)

Also, for exergoeconomic optimization, a linear weighted function is applied according to Ref 41:

F(x) = αF1 (x) + βF2 (x)

(3)

Consequently, the final objective function of exergoeconomic optimization contains thermodynamic and economic aspects simultaneously shown as follow: (4)

F(x) = α(ηex ) + β(SIC)

Where α and β are weight coefficients of linear weighted evaluation function obtained from:

F - F  α=  F - F  +  F - F    

(5)

β = 1- α

(6)

1 2

2 1

1 1

2

2

1 2

2

2

According to equations (18) and (19), F11 is the maximum value of F1 , F12 is the value of the function

F1 when F2 obtained a minimum value, F2 2 is the minimum value of F2 and F21 is the value of the function F2 when F1 obtained a maximum value [41]. As mentioned, one of the most important novelties of this research is the analysis of NORC efficiency with exergoeconomic viewpoint. Therefore, optimization used to find the best and

11

Journal Pre-proof

optimized amount of variables in NORC to achieve not only the maximum level of exergy but also to obtain the minimum amount of Specific Investment Cost. In the present work, two notes should be taken in to account. Firstly, the cost of geothermal water as a heat source is neglected, and secondly, the cost of the zeotropic mixture in NORC configuration is not considered. Moreover, the Genetic algorithm method is applied for the optimization of NORC modeling in Matlab software [15] and [17]. In addition, Table 8 illustrates the optimization variables, constraints, and bounds of Genetic Algorithm optimization, GA operating parameters and also its structures in the step of optimization are given in Table 9. Table 8 Table 9

2.5. Profitability In the present article, while the economic performance of NORC is analyzed through exergoeconomic optimization with the help of mentioned objective functions, the profitability estimation for considered configuration is also conducted. Consequently, a simple return on investment (ROI) is selected as an economic indicator to evaluate the profitability of NORC system and zeotropic mixture for fifteen countries.

2.5.1. Return on Investment (ROI) ROI indicator defines as follow [42]:

ROI =

(1- t corp )(Sannual - CTPC )

(7)

CTCI

Where, Sannual , CTPC , t corp and CTCI are the annual sales revenue, the total production cost for electricity production, the corporate tax rate and the cost of total capital investment, respectively. The corporate tax rates for fifteen considered countries are assumed from Table 10 [43,44 and 45]. 12

Journal Pre-proof

Table 10

Moreover, the relations for total production costs are illustrated in Table 11. Table 11

The annual sales revenue, as well as the total capital investment relations required for ROI calculation in equation (5), are obtained as follow: 

Annual sales revenue

Sannual = M el Cel

(8)

 elec M el = H annual W net

(9)

 elec = W  elec - W  elec W net t p

(10)

H annual = 0.9×365× 24

(11)

Where M el indicates the electricity produced annually with respect to the net output electricity. Furthermore, Cel reveals the price of electricity in the industry for the year 2018 and is listed for fifteen candidates as developed and developing countries in Table 10 [46- 49]. 

Total capital investment

CTCI  CTPI  C WC

(12)

Where CTPI is a total permanent investment and C WC is working capital that assumed to be zero in this work. Moreover, CTPI is obtained as follow: (13)

CTPI = CTDC + Cland + Croyal + Cstartup

13

Journal Pre-proof

Where CTDC is total depreciable capital, Cland and Croyal are costs of land and royalties which are assumed to be zero in this study and Cstartup is the cost of plant startup. Also, the cost of plant startup and total depreciable capital are obtained from equation (12) and (13): Cstartup = 0.1CTDC

(14)

CTDC = CDPI + Ccont

(15)

As it is shown in equation (13), total depreciable capital contains total direct permanent investment (CDPI), cost of contingencies, and also contractor's fee (Ccont), like:

CDPI = TCB + Csite + Cserv + Calloc

(16)

Ccont = 0.18CDPI

(17)

According to the equation (14), the total direct permanent investment includes the summation of four indicator costs which present total bare module cost (TCB), cost of site preparation (Csite), cost of service facilities (Cserv) and allocated costs for utility plants and related facilities (Calloc). Also, the following relations can be applied for the calculation of the mentioned parameters.

Csite = 0.05TCB

(18)

Cserv = 0.05TCB

(19)

c Calloc = 792 m

(20)

It should be mentioned that in this paper, required equations (Eqs.14-20), which must be taken into account for the evaluation of total capital investment adapted from Ref [48 and 27].

3. Results and discussion 3.2. Parametric analysis of system performance 3.2.1 Effect of mole fraction on system performance

14

Journal Pre-proof

In this part, the first and second evaporators, regenerative and condenser temperatures are assumed to be 400 K, 380K, 340K, and 308 K, respectively. Also, the pinch point temperature difference and degree of superheat in the first evaporator are 5K and 10K, respectively. The variations of exergy efficiencies and SIC for a mixture of Isopentane/Isobutane with the different mole fraction of Isobutane is illustrated in Fig. 2. Fig. 2

As can be observed in Fig. 2 , the variation of exergy efficiency for a mole fraction of Isobutane showed an initial decrease, and then it continued by an increase. Furthermore, the minimum amount of exergy efficiency for the mole fraction of Isobutane obtains at 0.5. In addition, the variation of SIC versus mole fraction of Isobutane is demonstrated in Fig. 2. Firstly, The SIC increases until the mole fraction of Isobutane reaches 0.5, and after that, it almost remains steady up to mole fraction 0.9, and for pure Isobutane, SIC decreases finally (by 54% exergy efficiency). In Fig. 2 it can also be found that the mixture of working fluids has worse thermodynamic performance than the pure ones. As can be observed in Fig. 2, the mixture doesn’t always present better economic and thermodynamic performances in accordance with the pure working fluids. While the thermodynamic and economic performance of mixtures and pure working fluids depend on the operation parameters and mole fraction of mixture.

3.2.2 Effect of operating Parameters on system performance In this study, exergoeconomic investigation has been carried out based on two generally accepts. At the first step, to optimize crucial parameters, thermodynamic operating parameters in NORC could be analyzed and optimized by utilizing three various objective functions contained exergy, economic, and exergoeconomic. At a second step, the value of exergy and SIC are required separately to reach optimal parameters from exergoeconomic optimization, as it is illustrated in equation (4). Hence, in this investigation, the effects of operating parameters on two final system performances based on exergy efficiency and SIC in NORC are investigated as follow: 15

Journal Pre-proof

3.2.2.1 Effect of DS and Pinch point temperature on NORC performance In Fig 3.(a,b), the effects of two thermodynamic operating parameters include DS and pinch point temperature, have been studied under these conditions: (Wt=0.2, Tevap1=400K, Tevap2=380K, Treg=340K). As can be seen in Fig 3.(a), DS, and pinch point temperature have a direct impact on the exergy efficiency of the NORC. In other words, by increasing these two operating parameters, exergy efficiency is also growing up, and by decreasing them, the performance of the NORC has descent. It should be noted that the system shows the best performance in the high amount of both DS and pinch point temperature. In Fig 3.(b), the variation of SIC could be seen by the changes of DS and pinch point temperature. Although the DS has a reverse impact on the specific investment cost, pinch point temperature has a direct relationship with SIC. Fig 3.(a,b)

3.2.2.2 Effect of DS and first evaporator temperature on NORC performance As can be seen in Fig. 4(a,b), effects of two other thermodynamic operating parameters include DS and first evaporator temperature has been studied under conditions of (Wt=0.2, Tevap2=380K, Treg=340K, Pinch=5K). Fig. 4(a), demonstrates that DS and first evaporator temperature have a reverse impact on the exergy efficiency of the NORC. In other words, by increasing DS as an operating parameter, exergy efficiency is also raised, and by decreasing of first evaporator temperature, the performance of the NORC has improved. It should be noted that the system shows the best performance in the high amount of both DS and low amount of the first evaporator temperature. In Fig. 4(b), the variation of SIC could be seen by the changes of DS and the first evaporator temperature. Although the DS has a reverse impact on the specific investment cost, the first

16

Journal Pre-proof

evaporator temperature has a direct relationship with SIC. It means, if the temperature of the first evaporator is increased, the SIC has an upward trend too. Fig. 4(a,b)

3.2.2.3 Effect of DS and secondly evaporator temperature on NORC performance

Fig. 5(a,b), indicates the effect of DS and secondly evaporator temperature under conditions of (Wt=0.2,Tevap1=400K, Treg=340K,pinch=5K) on the NORC. Fig. 5(a), shows that secondly, the evaporator temperature has a reverse impact on the exergy efficiency, but DS has a direct influence on the NORC performance. In Fig. 5(b), the variation of SIC could be seen by the changes of DS and the second evaporator temperature. Not only the DS has a direct impact on the specific investment cost, but also secondary evaporator temperature has a straight relationship with SIC. In other words, if the temperature of the second evaporator is increased, SIC also has been risen. Fig. 5(a,b)

3.2.2.4 Effect of DS and regenerative temperature on NORC performance The last but not the least thermodynamic operating parameters studied in this article is the effect of DS and regenerative temperature under conditions of (Wt=0.2, Tevap1=400K, Tevap2=380K, pinch=5K) on the NORC and shown in Fig. 6(a,b). As can be seen in this figure, although the regenerative temperature has a reverse impact on the exergy efficiency, DS has a direct influence on the thermodynamic performance. In Fig. 6 (b), the variation of SIC could be analyzed by the changes of DS and regenerative temperature. Investigations illustrate that both DS and regenerative temperature have a similar and direct impact on the specific investment cost. Fig. 6 (a,b) 17

Journal Pre-proof

3.3 Optimization results Table 12 demonstrates optimized parameters using exergoeconomic objective function in NORC for Isobutane/Isopentane as considered zeotropic mixture. As represented in Table 12, exergy efficiencies value decreases unremarkably as the mole fraction of Isobutane increases up to 0.5, and after that, the exergy efficiency rises slowly and reaches a peak at 54.584% for pure Isobutane. As can be seen in optimization results, from an economic viewpoint, the amounts of SIC increases when the mixture goes forward to the pure Isobutane. Furthermore, temperatures of the primary evaporator show the fluctuating behavior during the investigation of mixture performance. Indeed, the highest and the lowest amount of the primary evaporator temperature obtain 408.937 (in mass fraction 0.8) and 393.909(pure Isobutane), respectively. Also, the thermal efficiency is volatile. In addition, the trend of changes in other optimized parameters included DS, ΔTpp,evap , Tev2 and Trg certainly clarify and can be seen in this table. Another significant point is that whether DS and ΔTpp,evap can affect optimization results but does not play a crucial role in both exergy and SIC values

from the thermodynamic and economic aspects.

Table 12

3.4 Profitability results In the present study, in accordance with the investigation of profitability, ROI is selected as an economic indicator for industrial consumers. In this respect, Table 13 highlights the ROI results by using Isobutane/Isopentane for fifteen considered candidates as developed and developing countries. As can be seen in Table 13, by the growing of the Isobutane mole fraction from z=0 to 1, ROI indicator is reduced. The maximum and the minimum value of ROI are allocated to Germany (57.64%) and India (4.09%), respectively. Table 13

18

Journal Pre-proof

To clarify the results obtained from ROI indicator, which are shown in Table 13, the effect of mole fraction on the economic indicator as a profitability candidate for Isobutane/Isopentane is illustrated in Fig 7. As

can be seen in this figure, the ROI obtained for Germany stands at the first place in mole fraction of 0.0 for Isobutane. In other words, for more profitability as an economic point of view in NORC, pure Isopentane (in a mole fraction of 0.0 for Isobutane) acts better than the other mole fraction of Isobutane/Isopentane mixture. Furthermore, ROI for the Indian industry had almost steady changes and showed the worst profitability among all fifteen considered countries.

Fig 7

4. Conclusions In this research, the usage of renewable energy (geothermal) was investigated for generating electricity as useful power with the aim of two various points of view. In the first step, NORC configuration which was proposed in the previous work planed as a kind of transformation technology in order to adopt heat from a geothermal source into useful power like electricity by using zeotropic mixtures. In the next step, after thermodynamic and economic modeling, was carried out and also five specific operating parameters in NORC were selected for exergoeconomic optimization of cycle efficiency. The effect of Isobutane/Isopentane in different mole fractions studied to evaluate cycle performance. In the second step, the profitability estimation of NORC technology with a selection of ROI indicators was done based on the results obtained from exergoeconomic optimization. Furthermore, fifteen countries were listed as candidates to cover all around the world among five contents due to comparing their profitability. So, the following results were obtained: •

PR equation of state was used to calculate thermodynamic specifications of zeotropic mixture as working and water as geothermal fluids, respectively. It should be mentioned that thermodynamic specifications obtained in this study verified with the NIST databank.

•

In NORC, among all operating parameters, the temperature of regenerative had a significant effect on the cycle performance than other parameters.

19

Journal Pre-proof

•

The greatest and the lowest performance of NORC in exergoeconomic investigation calculated as 54.584 % in z=1 and 45.708 % in z=0.5 by usage Isopentane- Isobutane zeotropic mixture.

•

From the economic point of view, the maximum and the minimum value of SIC were calculated for pure Isobutane and pure Isopentane, respectively, in NORC.

•

According to the estimation of profitability results, Germany and India have the most and the least ROI value in z=0 and 1, respectively.

•

As can be seen in optimization results, from an economic aspect, the amounts of SIC has an upward trend by the increase of Isobutane mole fraction.

Acknowledgment The authors are grateful to the Islamic Azad University of Shiraz for supporting this research.

References: [1] Yang MH, Yeh RH. Economic performances optimization of an organic Rankine cycle system with lower global warming potential working fluids in geothermal application. Renewable Energy. 2016; 85: 1201-1213. [2] Zare V. A comparative exergoeconomic analysis of different ORC configurations for binary geothermal power plants. Energy Conversion and Management. 2015; 105: 127–138. [3] Luo Chao, Gong Yulie, Lu Zhenneng, Zhao Jun, Wang Yongzhen. The stability study of the flash-binary power system based on an experiment. Energy Procedia. 2019; 158: 6055-6060. [4] Giovanni Manentea, Alessio Bardib, Andrea Lazzarettoa, Marco Pacic. Low emission flashbinary and two-phase binary geothermal power plants with water absorption and reinjection of noncondensable gases. Geothermics. 2019; 80: 155-169. [5] Bianchi M, Branchini L, De Pascale A, Melino F, Ottaviano S, Peretto A, Torricelli N, Zampieri G. Performance and operation of micro-ORC energy system using a geothermal heat source. Energy Procedia. 2018; 148: 384-391.

20

Journal Pre-proof

[6] Mohammadzadeh Bina S, Jalilinasrabady S, Fujii H. Thermo-economic evaluation of various bottoming ORSs for geothermal power plant, determination of optimum cycle for Sabalan power plant exhaust. Geothermics. 2017; 70: 181-191. [7] Bonalumi D, Bombarda P, Invernizzi C. Potential performance of the environmental friendly application of ORC and flash technology in geothermal power plants. Energy Procedia. 2017; 129: 621-628. [8] Kazemi N, Samadi F. Thermodynamic, economic and thermo-economic optimization of a new proposed organic Rankine cycle for energy production from geothermal resources. Energy Conversion and Management. 2016; 121: 391–401. [9] Dai Y, Wang J, Gao L. Parametric optimization and comparative study of organic Rankine cycle (ORC) for low-grade waste heat recovery. Energy conversion and management. 2009; 50: 576-82. [10] Liu Q, Shen A, Duan Y. Parametric optimization and performance analyses of geothermal organic Rankine cycles using R600a/R601a mixtures as working fluids. Applied Energy. 2015; 148: 410–420. [11] Kang Z, Zhu J, Lu X, Li T, Wu X. Parametric optimization and performance analysis of zeotropic mixtures for an organic Rankine cycle driven by low-medium temperature geothermal fluids. Applied Thermal Engineering. 2015; 89: 323–331. [12] Yildirim D, Ozgener L. Thermodynamics and exergoeconomic analysis of geothermal power plants. Renewable and Sustainable Energy Reviews. 2012; 16: 6438–6454. [13] Toffolo A, Lazzaretto A, Manente G, Paci M. A multi-criteria approach for the optimal selection of working fluid and design parameters in Organic Rankine Cycle systems. Applied Energy. 2014; 121: 219–232. [14] Shokati N, Ranjbar F, Yari M. Exergoeconomic analysis and optimization of basic, dualpressure and dual-fluid ORCs and Kalina geothermal power plants: A comparative study. Renewable Energy. 2015; 83: 527-42. [15] Xi H, Li M-J, Xu C, He Y-L. Parametric optimization of regenerative organic Rankine cycle (ORC) for low-grade waste heat recovery using genetic algorithm. Energy. 2013; 58: 473-82. [16] Roy J, Misra A. Parametric optimization and performance analysis of a regenerative Organic Rankine Cycle using R-123 for waste heat recovery. Energy. 2012; 39: 227-35. [17] Imran M, Park BS, Kim HJ, Lee DH, Usman M, Heo M. Thermo-economic optimization of Regenerative Organic Rankine Cycle for waste heat recovery applications. Energy conversion and management. 2014; 87: 107-18. [18] Li T, Wang Q, Zhu J, Hu K, Fu W. Thermodynamic optimization of organic Rankine cycle using two-stage evaporation. Renewable Energy. 2015; 75: 654-64.

21

Journal Pre-proof

[19] Ghasemi H, Paci M, Tizzanini A, Mitsos A. Modeling and optimization of a binary geothermal power plant. Energy. 2013; 50: 412–28. [20] Heberle F, Bassermann P, Preißinger M, Brüggemann D. Exergoeconomic optimization of an Organic Rankine Cycle for low-temperature geothermal heat sources. International Journal of Thermodynamic. 2012; 15:119–26. [21] Kanoglu M, Bolatturk A. Performance and parametric investigation of a binary geothermal power plant by exergy. Renewable Energy. 2008; 33:2366–74. [22] Lemmon E, McLinden M, Friend D. Thermophysical Properties of Fluid Systems in NIST Chemistry WebBook, NIST Standard Reference Database Number 69, Eds. Linstrom, PJ, and Mallard, WG, National Institute of Standards and Technology, Gaithersburg MD, 20899. webbook nist gov/chemistry/fluids (accessed April 2015). 2011. [23] Nouman J. Comparative studies and analyses of working fluids for Organic Rankine Cycles – ORC. KTH School of Industrial Engineering and Management. Master of Science Thesis EGI-2012086MSC. [24] Quoilin S, Broek M, Declare S, Dewallef P, Lemort V, Techno-economic survey of Organic Rankine Cycle (ORC) systems, Renew. Sustainable Energy Rev. 2013; 22: 168 –186. [25] Ahmadi Boyaghchi F, Heidarnejad P. Thermoeconomic assessment and multi-objective optimization of a solar micro CCHP based on Organic Rankine Cycle for domestic application. Energy Conversion and Management. 2015; 97:224–234. [26] Tsatsaronis G. Definitions and nomenclature in exergy analysis and exergoeconomics. Energy. 2007; 32:249–253. [27] Le VL, Kheiri A, Feidt M, Pelloux-Prayer S. Thermodynamic and economic optimizations of waste heat to power plant driven by a subcritical ORC (Organic Rankine Cycle) using pure or zeotropic working fluid. Energy. 2014; 78:1-17. [28] Peng D-Y, Robinson DB. A new two-constant equation of state. Industrial & Engineering Chemistry Fundamentals. 1976; 15:59-64. [29] Perry Robert H, Green Don W, Maloney James O. Perry's chemical engineers' handbook. Mc Graw-Hills New York. 1997:56-64. [30] Xiao L, Wu S, Yi T, Liu C, Li Y. Multi-objective optimization of evaporation and condensation temperatures for subcritical organic Rankine cycle. Energy. 2015; 83: 723-733. [31] Li T, Wang Q, Zhu J, Hu K, Fu W. Thermodynamic optimization of organic Rankine cycle using two-stage evaporation. Renewable Energy. 2015; 75:654-64. [32] Kern DQ. Process Heat Transfer: International Student Edition: McGraw-Hill International Auckland Bogota; 1986. [33] TEMA. Standards of the tubular exchanger manufacturers association. 9th ed. 2007. New York, USA. 22

Journal Pre-proof

[34] Bowman RA, Mueller AC, Nagle WM. Mean temperature difference in design. Trans ASME. 1940;62(4):283-94. [35] Gnielinski V. New equations for heat and mass transfer in turbulent pipe and channel flow. International chemical engineering. 1976; 16:359-68. [36] El-Emam RS, Dincer I. Exergy and exergoeconomic analyses and optimization of geothermal organic Rankine cycle. Applied Thermal Engineering. 2013; 59:435–44. [37] Turton R, Bailie RC, Whiting WB, Shaeiwitz JA. Analysis, synthesis, and design of chemical processes: Pearson Education; 2008. [38] Peters MS, Timmerhaus KD, West RE, Timmerhaus K, West R. Plant design and economics for chemical engineers: McGraw-Hill New York; 1968. [39] Chemical engineering plant cost indexes for the year 2018 on www.chemengonline.com [40] Calise F, Capuozzo C, Carotenuto A, Vanoli L. Thermoeconomic analysis and off-design performance of an organic Rankine cycle powered by medium temperature heat sources. Solar Energy. 2014; 103:595–609. [41] Xiao L, Wu S-Y, Yi T-T, Liu C, Li Y-R. Multi-objective optimization of evaporation and condensation temperatures for subcritical organic Rankine cycle. Energy. 2015; 83:723-33 [42] Peters MS, Timmerhaus KD, West RE, Timmerhaus K, West R. Plant design and economics for chemical engineers: McGraw-Hill New York; 1968. [43] KPMG. 2018. Corporate tax rates table. [44] Ministry of Energy. Renewable Energy Organization of Iran (SUNA). http://www.suna.org.ir/; 2018; [in Persian]. [45] Karimi Sh, Mansouri S. A comparative profitability study of geothermal electricity production in developed and developing countries: Exergoeconomic analysis and optimization of different ORC configurations. Renewable Energy. 2018; 115:600-619. [46] www.cbi.ir/ 2018; [in Persian]. [47] EIA US. 2018. Electric power monthly. [48] Seider WD, Seader JD, Lewin DR. Product, and process design principles: synthesis, analysis, and evaluation. John Wiley; 2003 [49] Jim H. Standards of the Tubular Exchanger Manufacturers Association.8th ed. Tubular Exchanger Manufacturers Association, Inc; 1999.

23

Journal Pre-proof

Nomenclature A

heat exchanger surface area

Ex

exergy flow rate (kW)

h

specific enthalpy (kJ/kg)

 m

mass flow rate (kg/s)

P

Pressure (bar)

 Q

heat transfer flow rate (kW)

S

specific entropy (kJ kg-1 K-1)

T

temperature ( 0C)

U

overall heat transfer coefficient (W m-2 K-1)

 W

power output/input (kW)

X

vapor quality

SIC

specific investment cost ($/kW)

ΔTpp

pinch point temperature difference (0C)

ROI

return on investment (%)

ORC

organic Rankine cycle

NORC

new proposed organic Rankine cycle

Greek letters α

coefficient of a linear weighted evaluation function

β

coefficient of a linear weighted evaluation function

η

efficiency (%)

Subscripts/superscripts cond

condenser/condensation

eva

evaporator/evaporation

t

turbine

p

pump

in/out

inlet/outlet

i/o

inside/outside

is

isentropic

24

Journal Pre-proof

hsi/hso

heat source (or geothermal water) inlet/outlet

csi/cso

heat sink (or cooling water) inlet/outlet

h/c

heat source/sink or hot/cold

wf

Working fluid

elec

electrical

gen

generator

rg/reg

regenerative

Figure captions Fig. 1: NORC (a) schematic of the New proposed cycle and (b) T-S diagram. Fig. 2: Investigation of the effect of different mole fraction of Isobutane on exergy efficiency and SIC in NORC. Fig. 3: Effect of DS and Pinch point temperature on NORC performances (a) exergy efficiency and (b) SIC. Fig. 4: Effect of DS and first evaporator temperature on NORC performances (a) exergy efficiency and (b) SIC. Fig. 5: Effect of DS and secondly evaporator temperature on NORC performances (a) exergy efficiency and (b) SIC Fig. 6: Effect of DS and regenerative temperature on NORC performances (a) exergy efficiency and (b) SIC Fig. 7: Effect of mole fraction on a total trend of ROI changes in considered zeotropic mixture.

25

Journal Pre-proof

Evaporator 1

7

Pump 3

6

Evaporator 2

5 5"

Pump 2

4

Turbine 3

Regenerative

3"

8

Pump 1

2 Condenser 1

Fig. 1(a)

440

hsi

420

Th1 Th2

400

Temperature (T) [K]

hso Pinch h1

380

360

Pinch h2 340

Pinch c 320

Cso 300

280 0.5

Csi

Tc 1

1.5 -

-

Entropy (S) [J.kg 1.K 1]

Fig. 1(b)

26

2

4

50

3.5

45

3

SIC ($/kW)

55

40

0

0.1

0.2

0.3

0.4 0.5 0.6 0.7 Mole fraction of Isobutane

0.8

2.5 1

0.9

Fig. 2

-39

%Exergy efficiency

Exergy efficiency (%)

Journal Pre-proof

-38

-40

-40

-41

-42

-42

-44

-43

-46

-44

-48 20

-45 15

20 10

15 5

D.S, [K]

10 0

5 Pinchh,[K]

27

-46

Journal Pre-proof

Fig. 3(a)

3.8

SIC, [$/W]

4

3.6

3.5

3.4

3

3.2

3

2.5 20 15

20 10

15 5

2.8

10 0

D.S, [K]

5

Pinchh,[K]

%Exergy efficiency

Fig. 3(b)

-45

-45.5

-46 -46

-47 -48 20

-46.5 10 D.S, [K]

0

390

392

394

396

Tevap1, [K]

28

398

400 -47

Journal Pre-proof

Fig. 4(a)

2.95 3 2.9

2.95 2.9

2.85

2.85

SIC, [$/W]

2.8 2.8

2.75 2.7

2.75

2.65 2.6

2.7

2.55 2.5 340

2.65 335 2.6 330

325 Tregen, [K]

20

18

16

12

14

D.S, [K]

Fig. 4(b)

29

10

8

6

4

2

0 2.55

%Exergy efficiency

Journal Pre-proof

-36

-37

-38

-38 -39

-40

-40 -42 -41 -44 -42 -46 20

-43 15

370

-44

365

10 360

5

355 0

D.S, [K]

350 Tevap2, [K]

Fig. 5(a)

3.3

3.5

3.2

3.4 3.3

3.1

SIC, [$/W]

3.2 3.1

3

3 2.9

2.9

2.8 2.7 2.6 350

0

2.8

5 352

354

356

10 358

360

362

364

15 366

368

370

20 D.S, [K]

Tevap2, [K]

Fig. 5(b)

30

2.7

Journal Pre-proof

-45 -45.2

-44.5

%Exergy efficiency

-45.4 -45 -45.6 -45.5 -45.8 -46

-46

-46.5

-46.2

-47 20

-46.4 15

340 10

-46.6

335 5

D.S, [K]

-46.8

330 0

325

Tregen, [K]

Fig. 6(a)

2.95 3 2.9

2.95 2.9

2.85

2.85

SIC, [$/W]

2.8 2.8

2.75 2.7

2.75

2.65 2.6

2.7

2.55 2.5 340

2.65 335 2.6 330

325 Tregen, [K]

20

18

16

12

14

D.S, [K]

31

10

8

6

4

2

0 2.55

Journal Pre-proof

Fig. 6(b)

60 India Germany

Total trend of ROI changes

50

40

30

20

10

0

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Z (Mole fraction of Isobutane)

Fig. 7

Table captions

Table 1: Summary of the relevant literature reviews results. Researcher

Xi et al.

Roy and

Viewpoint of investigation

Results

Ref.

Thermodynamic

ORC with two-stage regenerative had higher exergy

[15]

optimization

efficiency than basic ORC

Thermodynamic

The performance of R123 as a working fluid was better

optimization

than R134a for the regenerative ORC system at a constant

Misra Imran et al.

[16]

pressure of 2.5 MPa. Thermo-economic

R245fa was the best possible working fluid under

evaluation

regenerative ORC conditions, and the thermal efficiency of

[17]

ORC was improved by adding a regenerative to the cycle Li et al.

Thermodynamic

system performance would increase with different range of

optimization

evaporator temperature in two-stage evaporator ORC

[18]

according to the reduction of irreversible losses Ghasemi et

Thermodynamic

Enhanced thermodynamic model when isobutane was

32

[19]

Journal Pre-proof

al.

Heberle et al.

optimization

utilized as a working fluid.

Thermo-economic

The lowest temperature difference for isobutane obtained 3

evaluation

K and 7 K in evaporator and condenser in exergoeconomic

[20]

optimization Kanoglu et

Thermodynamic

10.2% of energy and 33.5% of exergy efficiencies were

al.

optimization

obtained.

Table 2: Thermodynamic properties of zeotropic mixtures [17]. Substances

Tc (K)

Pc (bar)



Atmospheric Lifetime (years)

isobutane

407.85

36.40

0.19

12 ± 3

0

isopentane

460.35

33.95

0.23

12 ± 3

0

Semi Empirical ODP

Table 3: Constant design parameters for new considered ORC modeling [8]. Parameters

Values 80

p is

Isentropic efficiency of pump, η (%) Isentropic efficiency of turbine, ηis (%)

76

t ηgen (%)

95

t

Electrical generator efficiency, Heat source and sink media

water

Heat source inlet temperature, Thsi (k)

423.15

Heat sink inlet temperature, Tcsi (k)

293.15

Heat source inlet pressure, Phsi (bar)

5

Heat sink inlet pressure, Pcsi (bar)

2

temperature of condenser,

308

Tc (k)

Pinch point temperature in condenser, Heat source mass flow rate,

c Tpinch (k)

 h (kg s) m

5 50

Temperature of the environment taken as standard-state value,

298.15

T0 (k)

33

[21]

Journal Pre-proof

Table 4: heat exchanger specifications [8]. Inner tube diameter, d i (mm)

10.92

Outer tube diameter, do (mm)

12.7

Tube pinch, PT (mm)

19.05

Fouling factor (m2 oC/W) [48] Hot water Cold water Refrigerant (vapor) Refrigerant (liquid) Total fouling resistance with two-phase

0.0001761 0.0001761 0.0001761 0.0003522 0.00067

Table 5: relations of a heat exchanger and specific investment cost Components

Equations

Definitions

Q hx : heat transfer in heat exchangers (kW)

U : Overall heat transfer coefficient 2

A hx = Heat exchanger surface area

( w / m k ) Ref [33].

Q hx UF(ΔTLm )

F: LMTD (logarithmic mean temperature difference) correction factor Ref [34].

R d  d d ln(d o / d i ) 1 U =  o + f,i o + o + R f,o +  di 2k ho   h idi

h i and h o : heat transfer coefficient inside and outside tubes from Gnielinski

equation [35] and Kern method [32].

And Heat transfer

-1

ΔTLm =

(Th,out - Tc,in ) - (Th,in - Tc,out ) ln (Th,out - Tc,in ) / (Th,in - Tc,out ) 

process

R f,i and R f,o : fouling factors inside and outside tubes

d o and d i : outer and inner diameters of tubes

Th,in

k: fluid thermal conductivity and Th,out : the inlet and outlet of hot

Tc,in and Tc,out

fluid : the inlet and outlet of

cold fluid

34

Journal Pre-proof

SIC = FS ×

Specific investment

TCB  W net

FS : Correction factor for overhead cost from Table 6.

n

TCB =  Ci

cost (SIC)

TCB : the total cycle cost

i=1

Ci : Each component cost from Table 7.

Table 6: Constants of bare module equation [37]. Constants

K1

Equipment Heat exchanger 4.3247

Pump 3.3892

Turbine 2.2476

K2

-0.3030

0.0536

1.4965

K3

0.1634

0.1538

-0.1618

C1

0.03881

-0.3935

-

C2

-0.1127

0.3957

-

C3

0.0818

-0.0023

-

B1

1.6300

1.8900

-

B2

1.6600

1.3500

-

FM

1.0000

1.6000

3.5000

FS

1.7000

1.7000

1.7000

Table 7: Equipment cost equations Components

Equations

CHX = Cost of heat Exchangers ( C HX )

Definitions

567.5 × C0,HX ×  B1,HX +  B2,HX × FM,HX × FP,HX   397

logC0,HX

2 =  K1,HX + K 2,HX  logA HX  + K 3,HX  logA HX    

2 logFP,HX = C1,HX + C2,HX  logPHX  + C3,HX  logPHX    

567.5 × C0,p ×  B1,p +  B2,p × FM,p × FP,p   397  + K logW  2 logC0,p =  K1,p + K 2,p logW p 3,p p   Cp =

Cost of pump







35



CHX : the cost of a heat exchanger FM,HX : the material factor of a heat exchanger

FP,HX : pressure factor

PHX : pressure C0,p : the initial cost of the pump FP,p : pressure factor

Journal Pre-proof

2 logFP,p = C1,p + C2,p  logPp  + C3,P  logPp    

( CP )

Ct =

Cost of turbine ( Ct )

567.5 × C0,t × FM,t 397  + K logW  =  K1,t + K 2,t logW t 3,t t 

Pp : pump pressure

logC0,t







C0,t : the initial cost of the pump

  2

FM,t : the material factor of the turbine

*B1, B2, K1, K2, K3, C1, C2 and C3 are constants of the material used in the structure of equipment (Table 6) *567.5 and 397: chemical engineering plant cost indexes for the years 2018 and 2001, respectively [38] and [39].

Table 8: Constraints and bounds of Genetic Algorithm optimization

Parameters (constraints)

Lower bound

Upper bound

Temperature of evaporator 1

335 (K)

465(K)

335 (K)

410 (K)

335 (K)

465 (K)/ 410(K)

335 (K)

465 (K)/ 410(K)

Degree of superheat

0 (K)

20 (K)

PPTD in evaporator 1

5(K)

20 (K)

Pressure of evaporator 1

5(bar)

30 (bar)

(pure Isopentane) Temperature of evaporator 1 (pure Isobutane ) Temperature of evaporator 2 (pure Isopentane / pure Isobutane) Temperature of regenerative (pure Isopentane / pure Isobutane)

*It should be mentioned that for other mole fraction of Isobutane in a mixture between 0 and 1 the lower and upper bounds vary due to the critical temperature of the mixture. So lower and upper bounds are determined between the values reported in this table.

Table 9: Genetic algorithm operating parameters and structures for optimization of the considered system [45] GA operating parameters

Type and range

Population type

Double vector

36

Journal Pre-proof

Population size

20.0

Creation function

Constraint dependent

Scaling function

Rank

Selection function

Stochastic uniform

Reproduction (Elite count)

2.0

Reproduction (crossover function)

0.8

Mutation function

Constraint dependent

Crossover function

scattered

Migration (fraction)

0.2

Migration (interval)

20.0

Table 10: Rates of Corporate tax and mediocre of electricity prices for fifteen countries [43 and 46] Number

country

Corporate tax rates in 2018 (% tcorp)

Average electricity prices in 2018 (Cel) [dollars per kilowatt hour]

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Australia Brazil Germany Italy Japan France United Kingdom United States Iran Canada India Spain Thailand China Netherland

30.00 34.00 30.00 24.00 30.86 33.00 19.00 27.00 21.00 26.50 35.00 25.00 20.00 25.00 25.00

0.23 0.13 0.33 0.27 0.22 0.19 0.22 0.13 0.14 0.11 0.08 0.24 0.11 0.08 0.10

Table 11: Relations for the cost of total production [Ref 48 and 27]. Cost of wages and benefits,

C WB

Cost of salaries and benefits,

C WB = 0.035CTDC

CSB

CSB = 0.25C WB

Cost of materials and services,

CMS

CMS = C WB

Cost of maintenance overhead,

CMO

CMO = 0.05C WB

Direct manufacturing costs,

CDMC

CDMC = C WB + CSB + CMS + CMO

Cost of property taxes and liability insurance,

CPI = 0.02CTDC

CPI 37

Journal Pre-proof

Fixed manufacturing costs,

CFIX

CFIX = CPI

Total annual cost of manufacture, General expenses,

CCOM

CCOM = CDMC + CFIX CGE  0

CGE

Total production cost,

CTPC

CTPC = CCOM + CGE

Table 12: Exergoeconomic optimization results in the new proposed cycle for a mixture of Isobutane- Isopentane.

ΔTpp,ev

% ηex

% ηth

α

0.129

(K) 5.000

SIC ($/W)

53.029

13.784

3.191

0.9439

328.598

0.733

5.000

49.746

13.027

3.310

0.9440

385.729

333.697

0.082

5.102

47.938

12.777

3.495

0.9606

398.314

383.180

332.477

0.052

5.008

46.369

12.258

3.605

0.9454

0.4

400.780

385.780

333.539

0.188

5.000

45.951

12.260

3.689

0.9579

0.5

399.139

383.866

333.735

0.050

5.041

45.708

12.160

3.777

0.9589

0.6

400.620

385.561

333.133

0.143

5.000

46.320

12.266

3.819

0.9653

0.7

401.386

386.346

335.851

0.323

5.066

47.400

12.567

3.901

0.9514

0.8

408.937

392.090

341.266

0.048

5.102

51.100

13.745

4.084

0.9821

0.9

400.019

382.372

330.683

0.081

5.000

52.070

13.174

4.226

0.9693

1 ( Isobutane )

393.909

375.064

328.766

0.018

5.000

54.584

13.151

5.041

0.9796

Isobutane Mass fraction

Tev1 (K)

Tev2 (K)

Trg (K)

DS (K)

0 ( Isopentane )

397.662

382.640

328.530

0.1

398.258

383.237

0.2

400.855

0.3

Table 13: ROI results of Isobutane/ Isopentane in NORC for fifteen considered countries based on exergoeconomic results.

Number 1 2 3 4 5 6 7 8 9 10 11

Country Australia Brazil Germany Italy Japan France United Kingdom United States Iran Canada India

0 38.24 17.76 57.64 49.94 36.30 29.17 42.00

0.1 37.02 17.11 55.90 48.39 35.13 28.20 40.66

ROI results based on Mole fraction of Isobutane 0.2 0.3 0.4 0.5 0.6 0.7 0.8 35.27 34.23 33.76 33.13 33.04 32.56 31.71 16.18 15.62 15.37 15.03 14.98 14.73 14.28 53.40 51.89 51.22 50.31 50.18 49.49 48.28 46.17 44.83 44.23 43.43 43.32 42.70 41.63 33.47 32.46 32.01 31.40 31.33 30.87 30.06 26.83 25.99 25.63 25.13 25.06 24.68 24.01 38.72 37.56 37.04 36.34 36.25 35.72 34.78

19.64 23.44 15.81 8.48

18.92 22.60 15.19 8.08

17.93 21.41 14.31 7.52

17.27 20.69 13.77 7.18

38

17.00 20.37 13.54 7.03

16.63 19.93 13.22 6.83

16.57 19.87 13.17 6.79

16.29 19.54 12.93 6.64

15.79 18.96 12.50 6.37

0.9 30.36 13.56 46.34 39.90 28.76 22.94 33.28

1 24.68 10.53 38.19 32.66 23.32 18.44 26.99

14.99 18.03 11.83 5.93

11.64 14.12 8.95 4.09

Journal Pre-proof

12 13 14 15

Spain Thailand China Netherland

43.05 17.09 9.78 13.94

41.70 16.43 9.33 13.37

39.74 15.47 8.68 12.56

38.56 14.89 8.29 12.07

39

38.04 14.64 8.11 11.85

37.33 14.29 7.78 11.56

37.24 14.24 7.84 11.52

36.70 13.98 7.66 11.29

35.75 13.52 7.35 10.90

34.24 12.78 6.84 10.27

27.89 9.67 4.72 7.62

Journal Pre-proof

Author Contribution Statement Fereshteh Samadi: Conceptualization, Methodology, Software, Writing- Reviewing and Editing.

Neda Kazemi: Methodology, Software, Writing- Original draft preparation, Writing- Reviewing and Editing.

Journal Pre-proof

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:

Journal Pre-proof Highlights 

A new cycle was designed to improve basic organic Rankine cycle performance.



Peng Robinson equation of state was used to obtain properties of organic fluids.



Operating parameters were optimized with two objective functions simultaneously.



The new cycle performance was investigated using zeotropic mixture.



Return on investment of 15 countries was estimated as economic viewpoint.