Performance enhancement of two-stage condensation combined cycle for LNG cold energy recovery using zeotropic mixtures

Accepted Manuscript Performance enhancement of two-stage condensation combined cycle for LNG cold energy recovery using zeotropic mixtures

Junjiang Bao, Yan Lin, Ruixiang Zhang, Xiaopeng Zhang, Ning Zhang, Gaohong He PII:

S0360-5442(18)31030-2

DOI:

10.1016/j.energy.2018.05.187

Reference:

EGY 13022

To appear in:

Energy

Received Date:

24 January 2018

Accepted Date:

28 May 2018

Please cite this article as: Junjiang Bao, Yan Lin, Ruixiang Zhang, Xiaopeng Zhang, Ning Zhang, Gaohong He, Performance enhancement of two-stage condensation combined cycle for LNG cold energy recovery using zeotropic mixtures, Energy (2018), doi: 10.1016/j.energy.2018.05.187

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ACCEPTED MANUSCRIPT 1

Performance enhancement of two-stage condensation combined

2

cycle for LNG cold energy recovery using zeotropic mixtures

3

Junjiang Bao, Yan Lin, Ruixiang Zhang, Xiaopeng Zhang, Ning Zhang, Gaohong He*

4

State Key Laboratory of Fine Chemicals, School of Petroleum and Chemical

5

Engineering, Dalian University of Technology, Panjin 124221, China

6

*Corresponding author. Tel.: +86 427 2631518

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Email address: [email protected] and [email protected] (Gaohong He)

8

Abstract

9

The isothermal phase transition process of pure working fluids cannot effectively match

10

the liquefied natural gas (LNG) gasification process, resulting in low efficiency of LNG cold

11

energy power generation systems. In order to improve the temperature matching

12

characteristics, mixed working fluids with a temperature glide during the phase change

13

process can be adopted. In our previous study, the two-stage condensing process also

14

effectively improved the temperature matching characteristics; thus, this paper presents a two-

15

stage condensation combined cycle using zeotropic mixtures, and the effects of the type and

16

number of components for mixed refrigerants on the two-stage condensing combined cycle

17

system performance are studied. With the net power output as the objective function, the

18

evaporation temperature, condensing temperatures, LNG expander inlet temperature, and

19

working fluid mole fractions are optimised by the genetic algorithm. The results demonstrate

20

that the net power output of n-C5H12 is the largest among the studied pure fluids. The net

21

power output of the binary mixed working fluid at the optimum mole fractions is obviously

22

superior to that of pure working fluids. The system performance is improved when the

23

hydrocarbon mixture is selected as a working fluid, and the optimum number of components

24

is three.

25 26

Keywords: two-stage condensation combined cycle; LNG cold energy; zeotropic mixtures

27

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ACCEPTED MANUSCRIPT 28

1. Introduction

29

With rapid economic development, environmental pollution is becoming increasingly

30

serious. Natural gas offers the characteristics of high calorific value and low pollution, and is

31

therefore extensively used [1, 2]. In order to facilitate transportation and storage, natural gas

32

is usually liquefied into liquefied natural gas (LNG) at a temperature of approximately -162

33

℃ and atmospheric pressure [3, 4]. However, LNG must be vaporised prior to use. Owing to

34

the large temperature difference between LNG and the environment, a significant amount of

35

cold energy will be released [5]. In theory, the cold energy released by 1 t of LNG is

36

equivalent to 240 kWh of electricity [6]. Therefore, research on the utilisation of LNG cold

37

energy is of great significance.

38

Power generation by LNG cold energy has been widely adopted in recent years for

39

numerous utilisation methods [7], because the industrial chain of the LNG cold energy power

40

generation system is short, is not disturbed by external factors and can recover cold energy in

41

a large temperature range. The Rankine cycle is one of the most common means of utilising

42

LNG cold energy for power generation [8, 9]. Because the traditional Rankine cycle uses a

43

pure refrigerant, the heat transfer characteristics between its isothermal phase transition and

44

LNG gasification processes are not effectively matched, resulting in significant room for

45

improvement in the Rankine cycle power generation efficiency.

46

In order to improve the efficiency of the LNG cold energy power generation system,

47

certain researchers have made improvements in terms of the cycle configuration and working

48

fluid. Zhang et al. [10] used 16 different pure working fluids as an example to optimise the

49

three systems using LNG cold energy. The results demonstrated that propane was the optimal

50

working fluid for the single system, while R245fa was the best for the tripartite system. Li et

51

al. [11] proposed a cascade organic Rankine cycle that utilised solar energy and LNG cold

52

energy. It was found that the system performance was optimal when using isopentane/R125 as

53

the working fluid. Lee and Kim [12] analysed a combined cycle for the recovery of low-grade

54

thermal energy and LNG cold energy, and considered eight different substances as working

55

fluids. The maximum exergy efficiency of the system was 33.7% when isopentane was

56

selected as the working fluid. Zhang et al. [13] optimised the combined cycle using LNG cold

57

energy and low-grade waste heat, and found that n-pentane was the most suitable working

58

fluid. Ferreira et al. [14] used the genetic algorithm to optimise the LNG cold energy

59

generation system with seven different working fluids. The net power output was highest

60

when C3H6 was selected as the system working fluid. Lee [15] optimised the multi-stage

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ACCEPTED MANUSCRIPT 61

Rankine cycle using LNG cold energy by means of the genetic algorithm. Ethane and propane

62

were used as circulating fluids, the net power output of propane was found to be higher than

63

that of ethane following optimisation.

64

The majority of previous scholars have mainly studied pure working fluids. In order to

65

improve the efficiency of LNG cold power generation and the temperature matching

66

characteristics between the working fluid phase transformation and LNG gasification process,

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certain scholars have focused on mixed working fluids. Kim et al. [16] proposed a multi-stage

68

power generation system using LNG cold energy. In order to reduce the irreversible losses in

69

each condenser stage, mixed working fluids were used. It was found that when the first stage

70

used R14-propane, and the second and third stages used ethane/n-pentane as the working

71

fluid, the exergy efficiency of the system could reach 27.11%. Sun et al. [17] optimised a new

72

Rankine cycle that used LNG cold energy. Using a mixture of three hydrocarbons as the

73

working fluid, the results demonstrated that higher efficiency could be achieved. Liu and Guo

74

[18] proposed a new low-temperature cycle using LNG cold energy, with CF4 and C3H8

75

mixtures as working fluids. The results demonstrated that the system net power output was

76

significantly higher than that of the Rankine cycle with C3H8 as the working fluid. Lee and

77

Mitsos [19] optimised multicomponent working fluid of the organic Rankine cycle (ORC) by

78

means of LNG cold energy based on the genetic algorithm, and found that the system

79

performance was optimal when the working fluid was n-C5H12/CF4/CHF3 (15.45%/11.8

80

%/72.8%). Xue et al. [20] studied a Rankine cycle power generation system based on LNG

81

cold energy. The working fluid composition optimisation was performed by means of the

82

genetic algorithm, and the thermal and exergy efficiencies of the system increased from 3.5%

83

and 7.6% to 17.3% and 25.7%, respectively.

84

According to the literature review, most scholars have mainly focused on the

85

optimisation of the cycle structure and pure working fluids to improve the LNG cold energy

86

generation efficiency. However, few studies have been conducted on the effects of the

87

component types and numbers for mixed working fluids. For the purpose of increasing the

88

LNG cold energy generation efficiency, the authors proposed a multi-stage condensation

89

cycle in previous work [21]. By means of optimisation and analysis, it was found that the two-

90

stage condensation combined cycle exhibited optimal performance. Therefore, this paper

91

presents a two-stage condensation combined cycle using zeotropic mixtures. The effects of

92

the type and number of components for mixed refrigerants on the two-stage condensing

93

combined cycle system performance are studied. Using the net power generation as the

94

objective function, the evaporation temperature, condensation temperatures and LNG

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ACCEPTED MANUSCRIPT 95

expander inlet pressure of the two-stage condensation combined cycle, as well as the working

96

fluid molar fractions, are optimised based on the genetic algorithm. Firstly, 11 working fluid

97

types that are suitable for LNG cold power generation are selected, including hydrocarbons

98

(HCs) and hydroflurocarbons (HFCs), and their system performances are investigated.

99

Thereafter, binary mixtures formed by the 11 working fluids are studied, and the mixture

100

compositions are optimised in order to determine the best component type. Finally, the effect

101

of the number of components on the net power output of the two-stage condensation

102

combined cycle is studied.

103

2. System description Seawater

S1

W10 Pump 3 W6

Seawater

S2 Pump 4 W1 Mixer

W2 Evaporator

W5 L1

104 105

106 107

Pump 1

S4

S7

Pump 5

Pump 6

Splitter W7

W3

S3

G Turbine 1

Pump 2 W9

Seawater

G Turbine 2

G S5

Turbine 3 S8

W8

W4 L2 Condenser 1

L3 L4 Condenser 21 Heater S6

L5

NG Reheater S9

Fig. 1. Schematic of two-stage condensation combined cycle.

Fig. 2. T-s diagram of two-stage condensation combined cycle.

108 109 110

A schematic of the two-stage condensation combined cycle is presented in Fig. 1, and the T-s diagram is provided in Fig. 2. The principle of the proposed two-stage condensation

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combined cycle is as follows: the working fluids are evaporated into saturated vapour through the evaporator and enter the splitter, where they are liquefied to low-pressure saturated liquids. The two streams flow into turbines 1 and 2 for expansion, and expand to different pressures, as illustrated in the T-s diagram of the two-stage condensation combined cycle. Following expansion, the low-pressure streams move into condensers 1 and 2 and transfer heat with LNG at different temperatures, where they are cooled to low-pressure liquids. Thereafter, the low-pressure liquids are pressurised by the respective pumps and mixed in the mixer. Finally, the high-pressure liquid moves back to the evaporator to restart a new cycle. The pressurised LNG is heated to vapour by condenser 1, condenser 2 and the heater, respectively, and then flows into turbine 3 to expand to the pipe network delivery pressure. Finally, the NG is heated to the pipe network delivery temperature by the reheater.

122

3. Mathematical modelling

123

3.1 Assumptions

124

In order to simplify the calculations, this study adopts the following assumptions: only

125

the pressure changes of pumps and turbines are considered, and the pressure drops of other

126

components are negligible; the heat loss and friction of the entire system are ignored; the

127

system simulation is carried out under steady-state conditions; and the working fluid is

128

saturated vapour at the evaporator outlet, and saturated liquid at the condenser outlet.

129

3.2 Energy analysis

130

The two-stage condensation combined cycle is analysed based on the conservation of

131

mass and energy. The calculation formulas for the two-stage condensation combined cycle

132

can be expressed as follows.

133 134 135

Turbine power of two-stage condensation combined cycle:

Wtur ,i  mtur ,i (htur ,i ,in  htur ,i ,out )

(1)

W pump , j  m pump , j (h pump , j ,out  h pump , j ,in )

(2)

Wnet   Wtur ,i   W pump , j

(3)

. Pump power of two-stage condensation combined cycle: . Net power output of two-stage condensation combined cycle: ,

136

where i=1, 2, 3; j=1, 2, 3, 4, 5, 6, corresponding to Fig. 1.

137

Total heat absorption for two-stage condensation combined cycle:

Qtot  Qeva  Qheater  Qreheater . 138

(4)

Thermal efficiency for two-stage condensation combined cycle:

th  Wnet / Qtot . 139

(5)

Total exergy of two-stage condensation combined cycle:

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140

141

Extot  ExLNG  ExSW

(6)

 Ex  Wnet / Extot .

(7)

. Exergy efficiency of two-stage condensation combined cycle:

3.3 System parameters and optimisation methods

142

The two-stage condensation combined cycle uses seawater as the heat source and LNG

143

as the cold source. The mole fractions of LNG are 91.33% for CH4, 5.36% for C2H6, 2.14%

144

for C3H8, 0.47% for i-C4H10, 0.46% for n-C4H10, 0.01% for i-C5H12, 0.01% for n-C5H12 and

145

0.22% for N2 [22].The system parameters are displayed in Table 1.

146

Table 1 Thermodynamic conditions considered in modelling process. Parameters LNG mass flow rate LNG temperature LNG pressure NG pressure NG temperature Heat source inlet temperature Heat source outlet temperature Minimum approach temperature in condenser, heater and reheater

Value 30 kgs-1 -162 ℃ 100 kPa 70 bar 10℃ 15℃ 10℃ 5℃

Minimum approach temperature in evaporator

≥3℃

Discharged pressure of seawater pump Adiabatic efficiency of pump Adiabatic efficiency of turbine

300 kPa 80% 80%

147 148

In this study, the Aspen Hysys software is used to construct the two-stage condensation

149

combined cycle model. The PR (Peng-Robinson) equation is used in the simulation process.

150

This equation can be accurately calculated for numerous systems under a wide range of

151

operating conditions. It is known to provide relatively accurate analysis of the thermodynamic

152

properties of hydrocarbons, including LNG and refrigerants, and was applied in the

153

thermodynamic analysis of the process. The net power output is selected as the objective

154

function, and the system parameters of the two-stage condensation combined cycle

155

(evaporation temperature, condensation temperatures and LNG expander inlet pressure) and

156

working fluid compositions are optimised with the genetic algorithm. The basic principle of

157

the genetic algorithm is the evolutionary law of “natural selection and survival of the fittest”

158

in the biological world, and the algorithm can search for the optimal solution by simulating

159

the natural evolution process [23].

160 161

The detailed optimisation process of the genetic algorithm is illustrated in Fig. 3. When using the genetic algorithm for optimisation in MATLAB, the optimisation variables

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ACCEPTED MANUSCRIPT 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178

(evaporation temperature, condensation temperatures, NG turbine inlet pressure and mixed working fluid molar fractions) of the two-stage condensation combined cycle are firstly input. The range of values for the optimisation variables is illustrated in Fig. 3. According to the range of the optimisation variables, the MATLAB creation function generates the initial population evenly, based on the population number. Thereafter, population performance assessment is conducted (Hysys simulation calculation), and the objective function (net power output) of the two-stage condensation combined cycle is calculated and mapped to the fitness value, followed by adjustment of the fitness value (selection of regenerated individuals based on fitness, high probability of selection of individuals with high fitness and elimination of individuals with low fitness). Thereafter, the termination condition is evaluated. When the fitness function value for the optimal point in the current population is less than or equal to 10-6 and the number of generations reaches 200, the optimal objective function value and corresponding optimisation variables are output. If the conditions are not satisfied, a new population is produced by selection of the parents, reproduction of the offspring, and crossover and migration. Finally, performance evaluation is carried out. Specific parameters of genetic algorithm are shown in Table 2. Start Input optimization variables and range Population generation

Teva：7~13 ℃， Tco1：-140~-90 ℃ Tcon2：-90~-25 ℃ PNG：7500~15000 kPa Mole fraction：0~1

Population size 500

Process simulation

Yes

Calculate fitness function

Connect Matlab and Hysys

Terminate

Hysys calculation of fitness function (net power output)

No

Output: optimal solution, variable parameters

Parents selection

Pass fitness function value to Matlab

Offspring

Adjust fitness value Stopping criteria: ①Fitness limit: The algorithm stops when the value of the fitness function for the best point in the current population is less than or equal to 10-6 ②Generations: The algorithm stops when the number of generations reaches the value of 200

Crossover

End

179 180

Migration

New population

Fig. 3. Schematic of optimisation process by genetic algorithm.

181 182 183

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Table 2 Specific parameters of genetic algorithm. Parameter Population size Migration interval Crossover fraction

Value 500 20 0.8

Parameter Generation Elite count Migration fraction

Value 200 2 0.2

185 186

3.4 Working fluid selection

187

For power generation systems using LNG cold energy, the working fluid selection has a

188

significant influence on the system performance. Owing to the low temperature of LNG, it is

189

necessary to consider several aspects when selecting working fluids. Based on a previous

190

study [24], in this study, 11 of working fluid types are selected, HCs and HFCs, the physical

191

properties of which are displayed in Table 3.

192 193 Working fluid R170 R1270 R290 R600 R601 R23 R134a R125 R116 R218

Table 3 Physical properties of selected pure working fluids. Critical Normal Chemical Critical formula temperature (℃) pressure (bar) boiling point (℃) C2H6 32.17 48.72 -88.82 C3H6 91.06 45.55 -47.62 C3H8 96.74 42.51 -42.11 i-C4H8 144.94 40.09 -7.00 n-C4H10 151.98 37.96 -0.49 n-C5H12 196.55 33.70 36.06 CHF3 26.14 48.32 -82.09 C2H2F4 101.06 40.59 -26.07 C2HF5 66.02 36.18 -48.09 C2F6 19.88 30.48 -78.09 C3F8 71.87 26.40 -36.79

Triple point temperature (℃) -182.78 -185.20 -187.62 -185.35 -138.25 -129.68 -155.13 -103.30 -100.63 -100.05 -147.70

194 195

4. Results and discussion

196

4.1 Optimisation of pure working fluid

197

In order to determine whether the mixed working fluid performance is superior to that of

198

the pure working fluid, pure working fluids are firstly studied in this section. The evaporation

199

temperature, condensation temperatures and NG turbine inlet pressure of the two-stage

200

condensation combined cycle are optimised by the genetic algorithm with the net power

201

output as the objective function, as discussed in section 3.3. The optimisation results are listed

202

in Table 4. The maximum net power outputs and critical temperatures of the 11 pure working

203

fluids are illustrated in Fig. 4.

204 205

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Table 4 Optimised system parameters and maximum net power output for pure working fluids. Working fluid n-C5H12 n-C4H10 i-C4H8 C3H6 C2H2F4 C3F8 C3H8 C2HF5 C2H6 CHF3 C2F6

Teva

Tcon1

(℃)

(℃)

Pcon1 (kPa)

8.34 8.37 8.36 8.45 8.55 9.28 8.54 8.91 9.32 9.52 10.31

-100.14 -99.46 -98.96 -99.04 -99.31 -102.18 -98.91 -97.07 -99.41 -98.93 -100.61

0.01 0.19 0.33 4.51 0.95 1.33 3.36 4.26 54.96 34.05 26.77

Tcon2 (℃)

Pcon2 (kPa)

P (kPa)

Wnet (kW)

ηth (%)

ηEx (%)

-41.45 -41.05 -40.78 -41.51 -41.36 -41.57 -41.54 -39.85 -41.83 -41.39 -43.60

2.55 16.02 22.46 131.3 45.68 80.48 104.20 149.10 733.00 673.00 481.80

10930.71 10885.54 10983.42 11011.22 11039.61 11176.81 11536.34 11059.85 11581.25 11643.27 12062.36

2712.41 2688.17 2676.07 2637.33 2634.03 2625.99 2610.17 2504.35 2366.79 2345.28 2158.49

10.54 10.45 10.41 10.28 10.27 10.24 10.18 8.81 9.32 9.25 8.57

25.91 25.68 25.56 25.19 25.16 25.08 24.93 23.92 22.59 22.39 20.60

207

208 209

Fig. 4. Maximum net power outputs and critical temperatures for different working fluids.

210 211

It can be observed from Fig. 4 that the net power output of the two-stage condensation

212

combined cycle is highest when n-C5H12 is selected as the working fluid, and the C2F6 net

213

power output is the lowest. It can also be found that the net power output variation trend is

214

approximately the same as that of the critical temperature of the working fluids. With an

215

increase in critical temperature, the system net power output roughly increases.

216

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217 218 219

Fig. 5 Heat transfer curves between: a) seawater and working fluids, and b) working fluids and LNG, for

220

different pure working fluids.

221 222

In order to reveal the reasons for the large gap in the net power outputs of the two-stage

223

condensation combined cycle for different working fluids, taking n-C5H12, C3H8 and C2F6 as

224

examples, the heat transfer curves of the evaporation and condensation processes are

225

analysed, as illustrated in Fig. 5. It can be seen that, when different types of pure working

226

fluids are selected, the temperature glide match degree of the heat transfer curve between the

227

working fluid and LNG in the condenser is basically the same. However, the temperature

228

glide match degree between the seawater and working fluid in the evaporator is quite

229

different. This implies that, when pure working fluids are selected, the system net power

230

output is significantly affected by the evaporation side, and less affected by the condensation

231

side. A good temperature glide match means the heat transfer temperature difference between

232

heat and cold fluid in heat exchanger is almost the same everywhere. On the contrary, if this

233

condition is not satisfied, the degree of temperature glide match is bad. According to Fig. 5a),

234

because the temperature glide match degree of the heat transfer curve between n-C5H12 and

235

seawater in the evaporator is superior to other working fluids, and the temperature glide

236

match degree of C2F6 is the worst in the evaporator, the net power output of the n-C5H12 is the

237

highest, while that of the C2F6 is the lowest.

238

4.2 Effects of compositions of mixed working fluids

239

Section 4.1 mainly analyses the change in the net power output of the two-stage

240

condensation combined cycle for pure working fluids. When a pure substance is selected as

241

the working fluid, an isothermal phase transition occurs during the condensation process and

242

the heat transfer temperature difference between the LNG and pure working fluid is large in

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ACCEPTED MANUSCRIPT 243

the condenser. The mixture compositions will affect the temperature glide, and the

244

improvement in the zeotropic mixture temperature glide match degree depends on its

245

temperature glide. Therefore, the effects of compositions of mixed working fluids on the two-

246

stage condensation combined cycle net power output are discussed. It can be demonstrated

247

that the system exhibits the highest net power output when selecting n-C5H12 as the working

248

fluid. In this section, n-C5H12 is used as a reference substance and mixed with other working

249

fluids. The effects of the compositions on the maximum net power output of the two-stage

250

condensation combined cycle are illustrated in Fig. 6. The maximum net power output means

251

that the net power output is obtained by optimising the parameters at the specified

252

composition. It can be observed from Fig. 6 that an optimal composition exists for mixed

253

working fluids, at which the system net power output is maximized. When n-C5H12 is mixed

254

with C3H6, C3H8, C2H2F4, C2HF5 and C3F8, respectively, a composition exists at which the net

255

power output of system using mixed working fluids is no better than that of the pure working

256

fluid. 3200

n-C5H12-C3H6 n-C5H12-C3H8

Wnet (kW)

3100

n-C5H12-i-C4H8

3000

n-C5H12-n-C4H10

2900

n-C5H12-C2HF5

n-C5H12-C2H2F4 n-C5H12-C3F8

2800 2700 2600 2500

0.0

257 258

0.2

0.4 0.6 0.8 Mole fraction of n-C5H12

1.0

Fig. 6 Variation of system net power output with n-C5H12 mole fraction.

259 260

In order to explain the reason that the system net power output firstly increases and then

261

decreases for binary mixtures, n-C5H12/C3H6 is taken as an example. Fig. 7 illustrates the heat

262

transfer curves between seawater and n-C5H12/C3H6, as well as n-C5H12/C3H6 and LNG, at

263

different mole concentrations. From Fig. 7, it can be found that the temperature glide match

264

degree of the heat transfer curve between the pure working fluid and seawater in the

265

evaporator is superior to that of the binary mixtures, because the seawater undergoes a small

266

temperature change. While the temperature glide match degree of the heat transfer between

267

the mixed working fluids and LNG is obviously superior to that of pure working fluids, when

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ACCEPTED MANUSCRIPT 268

the n-C5H12 mole fraction is 0.4, the working fluid exhibits the best temperature glide match

269

with LNG in the condenser, and the system net power output is highest.

270 271

Fig. 7. Heat transfer curves between: a) seawater and working fluid, and b) working fluid and LNG, at

272

different compositions.

273 274

With the purpose of revealing the reason for the mixture net power output at a certain

275

concentration being lower than that of pure working fluids, the heat transfer curves in the

276

evaporator and condenser when n-C5H12 is mixed with C3H6, C3H8, C2H2F4, C2HF5 and C3F8,

277

respectively, are illustrated in Fig. 8. It can be observed that the temperature glide match

278

degree of the heat transfer curve between the pure working fluids and seawater in the

279

evaporator is superior to that of the binary working fluids, and the temperature glide match

280

degree of the heat transfer curve between the binary mixture working fluids and LNG in the

281

condenser is not obviously improved compared to the pure working fluids. Therefore, when

282

the n-C5H12 concentration is 0.9, the binary mixture working fluid net power output is lower

283

than that of pure working fluid.

284 285

Fig. 8. Heat transfer curves between: a) seawater and working fluid, and b) working fluid and LNG, when

286

n-C5H12 mole fraction is 0.9.

287

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ACCEPTED MANUSCRIPT 288

4.3 Optimisation of compositions for binary mixtures

289

According to the analysis in section 4.2, an optimal composition exists for binary

290

working fluids, at which the system net power output is highest; therefore, the binary working

291

fluid compositions need to be optimised. In this section, 11 pure working fluids are combined

292

to form binary mixtures. Using the net power output as the objective function, the evaporation

293

temperature (in this study, this refers to the bubble point temperature), condensation

294

temperatures (this refers to the dew point temperature), NG turbine inlet pressure and molar

295

fraction of the binary working fluids are optimised. When the system net power output is

296

maximum, the corresponding system parameters and optimised results of the different binary

297

mixtures are displayed in Tables 5 to 7, according to the mixture component types.

298 299

300 301

Table 5 System parameters and optimisation results of different binary mixtures (HCs+HCs). Working fluid

Mole fraction

C3H8/n-C5H12 C3H6/n-C5H12 C2H6/i-C4H8 i-C4H8/n-C5H12 C3H6/n-C4H10 n-C4H10/n-C5H12 C3H8/n-C4H10 C3H6/i-C4H8 C2H6/n-C4H10 C3H8/i-C4H8 C2H6/C3H8 C2H6/C3H6 i-C4H8/n-C4H10 C3H6/C3H8

0.6366/0.3634 0.5972/0.4028 0.6302/0.3698 0.7865/0.2135 0.7580/0.2420 0.7725/0.2275 0.7599/0.2401 0.7357/0.2643 0.5463/0.4537 0.7322/0.2678 0.6783/0.3217 0.6205/0.3795 0.4188/0.5812 0.9426/0.0574

Teva

Tcon1

Tcon2

(℃)

(℃)

(℃)

11.98 12.00 11.96 12.00 12.00 12.00 12.00 12.00 11.99 11.98 12.00 12.00 8.82 8.50

-131.57 -133.43 -132.39 -111.65 -113.06 -108.66 -111.33 -110.38 -138.29 -108.45 -117.28 -115.07 -99.74 -99.64

-77.48 -77.62 -73.95 -51.42 -54.60 -48.74 -52.02 -51.00 -79.28 -48.60 -57.72 -55.10 -40.98 -42.09

P (kPa) 10171.30 10255.04 10632.73 10097.76 10191.00 10598.42 10441.88 10412.08 10619.66 10562.35 10701.44 10853.19 10895.83 11259.98

ηth (%)

ηEx (%)

11.89 11.79 11.48 11.44 11.41 11.30 11.19 11.20 11.09 11.02 10.86 10.75 10.49 10.27

29.71 29.43 28.53 28.43 28.34 28.04 27.72 27.75 27.44 27.26 26.81 26.50 25.77 25.18

Table 6 System parameters and optimisation results of different binary mixtures (HFCs+HFCs). Working fluid

Mole fraction

CHF3/C2H2F4 C2H2F4/C2F6 C2H2F4/C3F8 C2H2F4/C2HF5 CHF3/C3F8 C2HF5/C3F8 CHF3/C2HF5 C2HF5/C2F6 CHF3/C2F6 C2F6/C3F8

0.7387/0.2613 0.2381/0.7619 0.5575/0.4425 0.7808/0.2192 0.7378/0.2622 0.2995/0.7005 0.6359/0.3641 0.5486/0.4514 0.9771/0.0229 0/1

Teva

Tcon1

Tcon2

(℃)

(℃)

(℃)

11.99 12.00 10.94 11.97 11.97 10.46 12.00 12.00 9.50 9.28

-118.53 -120.99 -99.10 -98.97 -115.18 -100.57 -109.45 -110.20 -99.01 -102.18

302 303 304

- 13 -

-64.79 -61.98 -40.20 -42.20 -56.73 -40.00 -49.99 -48.41 -40.41 -41.57

P (kPa)

ηth (%)

ηEx (%)

10282.57 10901.19 10680.22 10616.44 10799.14 10723.69 10896.46 11052.13 11300.47 11176.81

10.95 10.77 10.50 10.48 10.41 10.26 10.18 9.9 9.28 10.24

27.06 26.54 25.81 25.75 25.54 25.15 24.94 24.18 22.48 25.08

ACCEPTED MANUSCRIPT 305

Table 7 System parameters and optimisation results of different binary mixtures (HCs+HFCs). Working fluid

Mole fraction

n-C5H12/C2H2F4 C2H6/C2H2F4 i-C4H8/CHF3 n-C5H12/C3F8 n-C4H10/CHF3 n-C5H12/C2HF5 i-C4H8/C2F6 n-C4H10/C2F6 C2H6/C3F8 n-C4H10/C2H2F4 n-C4H10/C2HF5 n-C4H10/C3F8 C3H6/C2H2F4 i-C4H8/C3F8 i-C4H8/C2HF5 i-C4H8/C2H2F4 C3H8/C2H2F4 C2H6/C2HF5 C3H6/CHF3 C3H8/CHF3 C3H6/C2F6 C3H8/C3F8 C3H6/C3F8 C3H8/C2F6 C3H8/C2HF5 C2H6/C2F6 C2H6/CHF3 n-C5H12/CHF3 n-C5H12/C2F6 C3H6/C2HF5

0.1672/0.8328 0.7487/0.2513 0.3433/0.6567 0.2734/0.7266 0.4219/0.5781 0.3750/0.6250 0.2864/0.7136 0.3992/0.6008 0.7800/0.2200 0.3719/0.6281 0.2203/0.7797 0.2546/0.7454 0.7415/0.2585 0.3282/0.6718 0.2810/0.7190 0.5344/0.4656 0.5706/0.4294 0.7067/0.2933 0.8932/0.1068 0.4093/0.5907 0.9329/0.0671 0.0705/0.9295 0.0672/0.9328 0.8839/0.1161 0.9115/0.0885 0.9571/0.0429 0.8453/0.1547 1/0 1/0 1/0

Teva

Tcon1

Tcon2

(℃)

(℃)

(℃)

12.00 11.96 12.00 12.00 12.00 11.93 11.92 12.00 12.00 11.97 12.00 11.99 11.99 11.99 12.00 10.69 11.57 11.99 11.82 12.00 11.30 9.25 9.21 11.96 8.93 9.27 9.38 8.34 8.34 8.45

-118.13 -126.67 -130.69 -127.41 -136.15 -133.69 -127.91 -136.62 -122.57 -103.67 -111.11 -106.90 -102.17 -104.07 -108.43 -101.21 -101.86 -116.88 -103.38 -110.37 -103.46 -101.02 -101.53 -107.41 -98.73 -97.25 -100.12 -100.14 -100.14 -99.04

306

- 14 -

-59.00 -69.66 -76.98 -69.35 -84.20 -78.72 -70.05 -81.26 -59.37 -43.32 -53.97 -46.75 -43.03 -44.34 -51.45 -41.67 -42.67 -52.97 -45.03 -55.25 -44.07 -39.00 -39.38 -47.98 -41.28 -36.91 -42.32 -41.45 -41.45 -41.51

P (kPa)

ηth (%)

ηEx (%)

9667.43 10051.73 10143.44 10468.13 10896.56 10252.94 11063.03 10613.90 10698.56 10470.97 10444.78 10462.53 10553.57 10545.93 10612.32 10661.73 10747.57 10857.95 10745.86 10829.57 11062.24 10995.13 10772.23 11045.80 11318.55 10970.36 11558.12 10930.71 10930.71 11011.22

11.84 11.62 11.34 11.33 11.24 11.21 11.17 10.96 10.91 10.85 10.82 10.82 10.76 10.72 10.65 10.64 10.62 10.57 10.47 10.42 10.39 10.33 10.29 10.27 10.18 9.88 9.33 10.54 10.54 10.28

29.56 28.94 28.16 28.11 27.88 7.77 27.66 27.08 26.95 26.78 26.69 26.68 26.53 26.42 26.23 26.18 26.14 25.99 25.73 25.59 25.49 25.33 25.21 25.16 24.92 24.11 22.61 25.91 25.91 25.19

ACCEPTED MANUSCRIPT

ut (kW) Net power outp Net power output (kW)

nt

ne

m

po

1

3

4

5

co

12

6

5

nC

H

10

8

H

6

4

nC

2

6

H 4

i-C

3

C

2

C

H

comp onen t

H

4

F

8

nd

8

F 3

C

3

H

5

2

C

2

C

307

3100 3000 2900 2800 2700 2600 2500 2400 2300 2200 2100

co

6

F H

2

C

H

3

F

6

F 2

C

C H

First

b) 3200

n-C H 5 12 n-C H i-C H4 10 C H4 8 C H3 6 C F2 2 F4 C H3 8 C H3 8 2 F CH 5 2 CH 6 C F F3 2

Se

a)

0 320 0 310 0 300 0 290 0 280 0 270 0 260 0 250 0 240 0 230 0 220 0 210

7

8

9

10

11

6: n-C5H12/C3F8 1: C2H2F4/C2F6

7: n-C5H12/C2H2F4

2: i-C4H8/CHF3

8: n-C5H12/C3H6

3: C2H6/C2H2F4

9: C2H6/i-C4H8

4: n-C5H12/C2HF5 10: C3H6/n-C4H10 5: C3H8/n-C5H12 11: C3H8/n-C5H12 C2F6 CHF3 C2H6 C2HF5 C3H8 C3F8 C2H2F4 C3H6 i-C4H8 n-C4H10 n-C5H12

Second component

308 309 310 311

Fig. 9. Maximum net power outputs of different pure working fluids and binary mixtures: a) 3D histogram, and b) 2D diagram.

312

According to the optimised results in Tables 5 to 7, it can be demonstrated that, during

313

the optimisation process, the binary mixtures of C2H6/n-C5H12, C2F6/C3F8, n-C5H12/CHF3, n-

314

C5H12/C2F6 and C3H6/C2HF5 become pure working fluids. Furthermore, it can be observed

- 15 -

ACCEPTED MANUSCRIPT 315

that the exergy and thermal efficiencies of the system are maximum when selecting C3H8/n-

316

C5H12 as the working fluid among all binary mixtures.

317 318 319 320 321 322 323 324 325 326 327

In order to observe the two-stage condensation combined cycle net power output with pure working fluids and binary mixtures, the results in Tables 5 to 7 are illustrated in Fig. 9. The two horizontal coordinates in Fig. 9a) represent the first and second binary mixture components, respectively. The grey dotted line in Fig. 9b) represents the trend line of the net power output of the 11 pure working fluids, while the black dotted line represents the trend line of the maximum net power output in each column. From Fig. 9b), it can be observed that the optimal net power output for the pure working fluids changes from 2158.49 kW to 2712.41 kW, while the optimal net power output for the mixtures is distributed between 2894.47 kW and 3107.91 kW, which indicates an obvious increase compared to that for the pure fluids, and that the variation range for the mixtures is significantly smaller than that of the pure fluids.

328 329 330

Fig. 10. Heat transfer curves between: a) seawater and working fluid, and b) working fluid and LNG, for

331

several mixtures under optimal conditions.

332 333

For the purpose of explaining why the different binary mixture working fluids exhibit

334

large variations in net power output, the binary working fluids formed by C3H8 and others are

335

taken as an example. Fig. 10 illustrates the heat transfer curves in the evaporator and

336

condenser when C3H8/n-C5H12, C3H8/n-C4H10, C3H8/i-C4H8, C3H8/C2H2F4, C3H8/C2HF5 and

337

C3H8, respectively, are selected as working fluids. It can be observed from Fig. 10 that the

338

temperature glide match degree between the seawater and C3H8/n-C5H12 in the evaporator is

339

lowest, while C3H8/n-C5H12 exhibits the best match with LNG in the condenser, owing to the

340

largest temperature glide among all of the studied mixtures, followed by C3H8/n-C4H10,

341

C3H8/i-C4H8, C3H8/C2H2F4, C3H8/C2HF5 and C3H8. Moreover, it can be observed from Tables

342

5 and 7 that C3H8/n-C5H12 exhibits the largest net power output, followed by C3H8/n-C4H10,

- 16 -

ACCEPTED MANUSCRIPT 343

C3H8/i-C4H8, C3H8/C2H2F4, C3H8/C2HF5 and C3H8. The net power output ranking is the same

344

as that of the match degree of the heat transfer curve in the condenser. 3200

Net power output (kW)

3100 3000 2900 2800 2700 2600 2500 2400 2300

345 346

HFCs+HFCs

HCs+HCs

HCs+HFCs

Type of binary mixture working fluid

Fig. 11. Influence of component types for mixed working fluids on net power output.

347 348

In order to observe more clearly the effects of the binary working fluid component types

349

on the two-stage condensation combined cycle net power output, the results in Tables 5 to 7

350

are plotted in Fig. 11, according to the mixture component types. It can be observed from Fig.

351

11 that, when hydrocarbon mixtures are selected as working fluids, the overall performance of

352

the two-stage condensation combined cycle is superior to that of the other two mixture types.

353

Therefore, hydrocarbon mixtures are taken as an example to study the influence of the

354

mixture component number on the system net power output in the following section.

355

4.4 Influence of component number of mixed working fluids

356

Based on the previous analysis, six hydrocarbons (C2H6, C3H6, C3H8, i-C4H8, n-C4H10

357

and n-C5H12) are the components studied in this section, and the influence of the component

358

number of the mixed working fluids on the two-stage condensation combined cycle net power

359

output is discussed. The optimisation methods and variables are the same as in section 4.3.

360

When the system net power output is maximum, the optimisation results and system

361

parameters of the ternary, quaternary and quinary mixtures are displayed in Tables 8 to 10,

362

respectively. Furthermore, it should be pointed out that the results of the system with pure

363

fluids and binary mixtures are listed in Tables 4 and 5. From Table 4 and 5, and 8 to10, it can

364

be observed that, among all of the calculated working fluids, when C3H6/i-C4H8/n-C4H10/n-

365

C5H12 is selected, the two-stage condensation combined cycle net power is maximum at

366

3190.89 kW, which is approximately 106.36 kW/(kg·s). During the optimisation process, the

367

component numbers of certain mixtures are reduced, as indicated by the shadowing in the

- 17 -

ACCEPTED MANUSCRIPT 368

tables. For example, following optimisation, the quinary C3H6/C3H8/i-C4H8/n-C4H10/n-C5H12

369

mixture is changed to the quaternary C3H6/i-C4H8/n-C4H10/n-C5H12, while the quaternary

370

C3H6/C3H8/n-C4H10/n-C5H12 mixture is reduced to the ternary C3H6/n-C4H10/n-C5H12.

371 372

Table 8 Optimised system parameters and results for ternary mixtures (HCs+HCs). Working fluid

Mole fraction

C3H6/n-C4H10/n-C5H12 C3H6/i-C4H8/n-C5H12 C3H8/n-C4H10/n-C5H12 C3H8/i-C4H8/n-C5H12 C2H6/C3H6/n-C4H10 C2H6/C3H6/i-C4H8 C2H6/C3H8/n-C4H10 C3H6/C3H8/n-C5H12 C2H6/C3H8/i-C4H8 C2H6/i-C4H8/n-C4H10 C2H6/i-/C4H8/n-C5H12 i-C4H8/n-C4H10/n-C5H12 C3H6/C3H8/n-C4H10 C2H6/C3H8/n-C5H12 C2H6/C3H6/n-C5H12 C3H6/i-C4H8/n-C4H10 C2H6/n-C4H10/n-C5H12 C3H8/i-C4H8/n-C4H10 C3H6/C3H8/i-C4H8 C2H6/C3H6/C3H8

0.6260/0.1700/0.2040 0.5812/0.1986/0.2202 0.6577/0.1360/0.2063 0.6267/0.1245/0.2488 0.5048/0.2413/0.2539 0.6018/0.1813/0.2169 0.5495/0.2489/0.2016 0.1017/0.5443/0.3540 0.6036/0.1647/0.2317 0.5896/0.4025/0.0079 0.6096/0.3903/0.0001 0.7834/0.0034/0.2132 0.7498/0.0032/0.2470 0/0.6366/0.3634 0/0.5972/0.4028 0.7580/0/0.2420 0/0.7725/0.2275 0.7599/0/0.2401 0.7357/0/0.2643 0.6783/0/0.3217

373 374

Teva

Tcon1

Tcon2

(℃)

(℃)

(℃)

12.00 12.00 12.00 11.99 11.99 12.00 12.00 12.00 11.99 11.99 12.00 12.00 12.00 11.98 12.00 12.00 12.00 12.00 12.00 12.00

-130.14 -131.28 -128.17 -129.12 -139.39 -133.02 -137.81 -130.84 -135.00 -136.21 -134.35 -111.30 -113.62 -131.57 -133.43 -113.06 -108.66 -111.33 -110.38 -117.28

-74.57 -75.04 -71.77 -73.67 -83.34 -77.72 -81.32 -75.12 -80.10 -79.87 -76.63 -51.37 -55.25 -77.48 -77.62 -54.60 -48.74 -52.02 -51.00 -57.72

P (kPa) 10224.49 9722.89 9631.62 9898.73 9850.42 10389.00 9896.61 10149.80 9894.03 10606.14 10464.40 10201.50 10247.42 10171.30 10255.04 10191.00 10598.42 10441.88 10412.08 10701.44

ηth (%)

ηEx (%)

12.17 12.14 12.12 12.09 11.90 11.90 11.89 11.90 11.84 11.51 11.50 11.44 11.41 11.89 11.79 11.41 11.30 11.19 11.20 10.86

30.50 30.43 30.36 30.27 29.75 29.73 29.70 29.73 29.57 28.63 28.61 28.43 28.34 29.71 29.43 28.34 28.04 27.72 27.75 26.81

Table 9 Optimised system parameters and results for quaternary mixtures (HCs+HCs). Teva

Working fluid

Mole fraction

C3H6/i-C4H8/n-C4H10/n-C5H12 C3H8/i-C4H8/n-C4H10/n-C5H12 C2H6/C3H6/i-C4H8/n-C4H10 C2H6/C3H6/C3H8/n-C4H10 C2H6/C3H8/i-C4H8/n-C4H10 C2H6/C3H6/C3H8/i-C4H8 C3H6/C3H8/n-C4H10/n-C5H12 C2H6/C3H6/n-C4H10/n-C5H12 C2H6/C3H6/i-C4H8/n-C5H12 C3H6/C3H8/i-C4H8/n-C5H12 C2H6/C3H8/i-C4H8/n-C5H12 C2H6/C3H8/n-C4H10/n-C5H12 C2H6/C3H6/C3H8/n-C5H12 C3H6/C3H8/i-C4H8/n-C4H10 C2H6/i-C4H8/n-C4H10/n-C5H12

0.6297/0.0228/0.1664/0.1811 0.6625/0.0076/0.1285/0.2014 0.5346/0.2322/0.0372/0.1960 0.5181/0.1937/0.0480/0.2402 0.5852/0.2324/0.0137/0.1687 0.6085/0.1707/0.0014/0.2194 0.6260/0/0.1700/0.2040 0/0.6260/0.1700/0.2040 0/0.5812/0.1986/0.2202 0.5812/0/0.1986/0.2202 0/0.6267/0.1245/0.2488 0/0.6576/0.1361/0.2063 0/0.1017/0.5443/0.3540 0.7498/0.0032/0/0.2470 0.5896/0.4025/0.0079/0

375 376

Tcon1

Tcon2

(℃)

(℃)

(℃)

12.00 12.00 12.00 11.98 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 11.99

-129.60 -127.23 -136.57 -138.22 -134.29 -132.50 -130.14 -130.14 -131.28 -131.28 -129.12 -128.17 -130.84 -113.62 -136.21

-73.49 -70.84 -79.87 -82.56 -76.64 -75.93 -74.57 -74.57 -75.04 -75.04 -73.67 -71.77 -75.12 -55.25 -79.87

P (kPa)

ηth (%)

ηEx (%)

10150.06 9896.40 10311.13 10380.19 10560.70 10077.16 10224.49 10224.49 9722.89 9722.89 9898.73 9631.62 10149.80 10247.42 10606.14

12.17 12.12 11.93 11.91 11.90 11.91 12.17 12.17 12.14 12.14 12.09 12.12 11.90 11.41 11.51

30.50 30.37 29.81 29.77 29.73 29.75 30.50 30.50 30.43 30.43 30.27 30.36 29.73 28.34 28.63

Table 10 Optimised system parameters and results for quinary mixtures (HCs+HCs). Working fluid

Mole fraction

- 18 -

Teva

Tcon1

Tcon2

(℃)

(℃)

(℃)

P (kPa)

ηth (%)

ηEx (%)

ACCEPTED MANUSCRIPT C3H6/C3H8/i-C4H8/n-C4H10/n-C5H12 C2H6/C3H6/i-C4H8/n-C4H10/n-C5H12 C2H6/C3H6/C3H8/n-C4H10/n-C5H12 C2H6/C3H6/C3H8/i-C4H8/n-C5H12 C2H6/C3H8/i-C4H8/n-C4H10/n-C5H12 C2H6/C3H6/C3H8/i-C4H8/n-C4H10

0.6297/0/0.0228/0.1664/0.1811 0/0.6297/0.0228/0.1664/0.1811 0/0.626/0/0.1700/0.2040 0/0.5812/0/0.1986/0.2202 0/0.6625/0.0076/0.1285/0.2014 0.5346/0.2322/0/0.0372/0.1960

12.00 12.00 12.00 12.00 12.00 12.00

-129.60 -129.60 -130.14 -131.28 -127.23 -136.57

-73.49 -73.49 -74.57 -75.04 -70.84 -79.87

10150.06 10150.06 10224.49 9722.89 9896.40 10311.13

12.17 12.17 12.17 12.17 12.12 11.93

30.50 30.50 30.50 30.50 30.37 29.81

377 3300 3

Net power output (kW)

3200

4

5

2

3100 3000 2900 2800 2700

1: n-C5H12 1

3: C3H6/n-C4H10/n-C5H12

2600

4: C3H6/i-C4H8/n-C4H10/n-C5H12

2500

5: C3H6/i-C4H8/n-C4H10/n-C5H12

2400 2300

2: C3H8/n-C5H12

1

2 3 4 5 Component number of working fluid Fig. 12. System net power output corresponding to mixtures with different component numbers.

378 379 380 381

Fig. 12 illustrates the maximum net power output of the two-stage condensation

382

combined cycle when the component numbers of the working fluids change from one to five.

383

When the component number of the mixed working fluid is five, it is actually a quaternary

384

mixture owing to the optimisation results. In Fig. 12, the data in each column are obtained

385

from Tables 4, 5, 8 and 9, except for the data in shadow. As illustrated in Fig. 12, with the

386

increase in the component number of the mixed working fluid, the two-stage condensation

387

combined cycle net power output is increased, although the increasing rate is gradually

388

reduced. When the component numbers of the mixed working fluid are three and four, the

389

system net power output is almost the same. With an increase in the mixture component

390

number, the difficulty of charging the working fluids into the system becomes significant.

391

Therefore, considering the net power output increasing rate and the difficulty of charging

392

working fluids, the optimum component number of hydrocarbon mixtures for the two-stage

393

condensation combined cycle is three.

- 19 -

ACCEPTED MANUSCRIPT

394 395 396

Fig. 13. Heat transfer curves between: a) seawater and working fluid, and b) working fluid and LNG, for

397

mixtures with different component numbers.

398 399

In order to reveal the reason for the increasing rate of the system net power output being

400

gradually reduced with an increase in the mixture component number, the heat transfer curves

401

in the evaporator and condenser are analysed, as illustrated in Fig. 13. From Fig. 13b), it can

402

be observed that the temperature glide match degree of the heat transfer curve in the

403

condenser is obviously improved when the working fluid changes from pure to a binary

404

mixture, and this improvement becomes significantly slighter from the binary to ternary

405

mixture. The temperature glide match degree of the heat transfer curve remains almost

406

unchanged from the ternary to quaternary mixture. Therefore, with an increase in the mixture

407

component numbers, the system net power output increasing rate is gradually reduced.

408

5. Conclusions

409

In order to improve the efficiency of the LNG cold energy power generation system, the

410

effects of the component type and number of mixed working fluids on the two-stage

411

condensation combined cycle performance are studied. Using the net power output as the

412

objective function, the evaporation temperature, condensing temperatures, LNG expander

413

inlet pressure and working fluid mole fractions are optimised by means of the genetic

414

algorithm. Based on the research presented in this paper, the main conclusions are as follows.

415

1. For pure fluids, the two-stage condensation combined cycle net power output is

416

highest when n-C5H12 is selected as the working fluid, while the C2F6 net power output is the

417

lowest. With an increase in the critical temperature, the system net power output increases

418

roughly.

- 20 -

ACCEPTED MANUSCRIPT 419

2. When binary mixtures are selected as working fluids, an optimal composition exists

420

for the mixed working fluids at which the system net power output is maximised. The highest

421

net power output of each binary mixture is obviously superior to that of the corresponding

422

pure working fluid. When hydrocarbon mixtures are selected as working fluids, the overall

423

performance of the two-stage condensation combined cycle is superior to that of the two other

424

mixture types.

425 426

3. When C3H6/i-C4H8/n-C4H10/n-C5H12 is selected as a working fluid, the two-stage condensation combined cycle net power is maximum, at approximately 106.36 kW/(kg·s)

427

4. With an increase in the mixed working fluid component number, the two-stage

428

condensation combined cycle net power output is increased, but the increasing rate is

429

gradually reduced. The optimum component number of the hydrocarbon mixtures is three.

430

Acknowledgements

431

This research was financially supported by the National Natural Science Foundation of China

432

(No. 51606025), MOST innovation team in key area (No. 2016RA4053), the Fundamental

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Research Funds for the Central Universities (Grant No. DUT17RC (4)29), Liaoning Province

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S & T Department (Grant No. 201601034) and Education Department (LT2015007).

435 Nomenclature Q heat transfer rate (kJ/h) m mass flow rate (kg/s) h mass enthalpy (kJ/kg) W power (kW) Abbreviations LNG liquefied natural gas NG natural gas WF working fluid Subscripts eva evaporator tur turbine con1 condenser 1 con2 condenser 2 con condensation sw sea water

η Ex T P

efficiency (%) exergy (kW) temperature (℃) pressure (kPa)

HCs HFCs

hydrocarbons hydrofluorocarbons

tot th In out i j

total Thermal Inlet outlet The number of turbine The number of pump

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Reference

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[1] Liu M, Lior N, Zhang N, Han W. Thermoeconomic analysis of a novel zero-CO 2-emission highefficiency power cycle using LNG coldness. Energy convers Manage 2009; 50: 2768-81. [2] Fahmy M F M, Nabih H I. Impact of ambient air temperature and heat load variation on the performance of air-cooled heat exchangers in propane cycles in LNG plants – Analytical approach. Energy convers Manage 2016; 121: 22-35. [3] Lu T, Wang K S. Analysis and optimization of a cascading power cycle with liquefied natural gas (LNG) cold energy recovery. Appl Therm Eng 2009; 29: 1478-84. [4] Mehrpooya M, Esfilar R, Moosavian S A. Introducing a novel air separation process based on cold energy recovery of LNG integrated with coal gasification, transcritical carbon dioxide power cycle and cryogenic CO 2 capture. J Clean Prod 2016. [5] Miyazaki T, Kang Y T, Akisawa A, Kashiwagi T. A combined power cycle using refuse incineration and LNG cold energy. Energy 2000; 25: 639-55. [6] Bao J, Lin Y, Zhang R, Zhang N, He G. Strengthening power generation efficiency utilizing liquefied natural gas cold energy by a novel two-stage condensation Rankine cycle (TCRC) system. Energy convers Manage 2017; 143: 312-25. [7] Zhang G, Zheng J, Yang Y, Liu W. A novel LNG cryogenic energy utilization method for inlet air cooling to improve the performance of combined cycle. Appl Energy 2016; 179: 638-49. [8] Tomkow L, Cholewinski M. Improvement of the LNG (liquid natural gas) regasification efficiency by utilizing the cold exergy with a coupled absorption - ORC (organic Rankine cycle). Energy 2015; 87: 645-53. [9] Sun Z, Xu F, Wang S, Lai J, Lin K. Comparative study of Rankine cycle configurations utilizing LNG cold energy under different NG distribution pressures. Energy 2017; 139: 380-93. [10] Zhang M-G, Zhao L-J, Xiong Z. Performance evaluation of organic Rankine cycle systems utilizing low grade energy at different temperature. Energy 2017; 127: 397-407. [11] Li P C, Li J, Pei G, Munir A, Ji J. A cascade organic Rankine cycle power generation system using hybrid solar energy and liquefied natural gas. Sol Energy 2016; 127: 136-46. [12] Lee H Y, Kim K H. Energy and exergy analyses of a combined power cycle using the organic rankine cycle and the cold energy of liquefied natural gas. Entropy 2015; 17: 6412-32. [13] Zhang M-G, Zhao L-J, Liu C, Cai Y-L, Xie X-M. A combined system utilizing LNG and lowtemperature waste heat energy. Appl Therm Eng 2016. [14] Ferreira P A, Catarino I, Vaz D. Thermodynamic analysis for working fluids comparison in Rankinetype cycles exploiting the cryogenic exergy in Liquefied Natural Gas (LNG) regasification. Appl Therm Eng 2017; 121: 887-96. [15] Lee S. Multi-parameter optimization of cold energy recovery in cascade Rankine cycle for LNG regasification using genetic algorithm. Energy 2017; 118: 776-82. [16] Kim K, Lee U, Kim C, Han C. Design and optimization of cascade organic Rankine cycle for recovering cryogenic energy from liquefied natural gas using binary working fluid. Energy 2015; 88: 304-13. [17] Sun H, Zhu H M, Liu F, Ding H. Simulation and optimization of a novel Rankine power cycle for recovering cold energy from liquefied natural gas using a mixed working fluid. Energy 2014; 70: 31724. [18] Liu Y N, Guo K H. A novel cryogenic power cycle for LNG cold energy recovery. Energy 2011; 36: 2828-33.

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ACCEPTED MANUSCRIPT 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495

[19] Lee U, Mitsos A. Optimal multicomponent working fluid of organic Rankine cycle for exergy transfer from liquefied natural gas regasification. Energy 2017; 127: 489-501. [20] Xue F, Chen Y, Ju Y. Design and optimization of a novel cryogenic Rankine power generation system employing binary and ternary mixtures as working fluids based on the cold exergy utilization of liquefied natural gas (LNG). Energy 2017; 138: 706-20. [21] Bao J, Lin Y, Zhang R, Zhang N, He G. Effects of stage number of condensing process on the power generation systems for LNG cold energy recovery. Appl Therm Eng 2017; 126: 566-82. [22] Lee S, Choi B C. Thermodynamic assessment of integrated heat recovery system combining exhaustgas heat and cold energy for LNG regasification process in FSRU vessel. J Mech Sci Technol 2016; 30: 1389-98. [23] 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. [24] Gómez M R, Garcia R F, Gómez J R, Carril J C. Review of thermal cycles exploiting the exergy of liquefied natural gas in the regasification process. Renew Sust Energy Rev 2014; 38: 781-95.

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ACCEPTED MANUSCRIPT Research Highlights In this study:  The two-stage condensation combined cycle is enhanced by zeotropic mixtures  The performance increased by zeotropic mixture is more than 10%  Component type and number for mixtures are optimized by genetic algorithm  The best component type in this paper is HCs+HCs.  The optimum component number of mixture is three.