Performance and economic evaluation of linear Fresnel reflector plant integrated direct contact membrane distillation system

Accepted Manuscript Performance and economic evaluation of linear Fresnel reflector plant integrated direct contact membrane distillation system

Mujeeb Iqbal Soomro, Woo-Seung Kim PII:

S0960-1481(18)30640-2

DOI:

10.1016/j.renene.2018.06.010

Reference:

RENE 10166

To appear in:

Renewable Energy

Received Date:

11 January 2018

Accepted Date:

03 June 2018

Please cite this article as: Mujeeb Iqbal Soomro, Woo-Seung Kim, Performance and economic evaluation of linear Fresnel reflector plant integrated direct contact membrane distillation system, Renewable Energy (2018), doi: 10.1016/j.renene.2018.06.010

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ACCEPTED MANUSCRIPT 1

Performance and economic evaluation of linear Fresnel reflector plant

2

integrated direct contact membrane distillation system

3

Mujeeb Iqbal Soomroa,b and Woo-Seung Kimc,*

4

a Department

5

ro, Sangnok-gu, Ansan, Gyeonggi-do 15588, Republic of Korea

6

([email protected])

7

b Department

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SZAB Campus, Khairpur Mir’s 66020, Sindh, Pakistan

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c Department

of Mechanical Design Engineering, Hanyang University, 55 Hanyangdaehak-

of Mechanical Engineering, Mehran University of Engineering & Technology,

of Mechanical Engineering, Hanyang University, 55 Hanyangdaehak-ro,

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Sangnok-gu, Ansan, Gyeonggi-do 15588, Republic of Korea

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*Corresponding author: Tel. +82-31-400-5248, Fax. +82-31-418-0153

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E-mail address: [email protected]

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ABSTRACT

14

This paper presents an investigation of a 111 MWe linear Fresnel reflector (LFR) plant

15

integrated direct contact membrane distillation (DCMD) system. Both the technologies are

16

synergized by using seawater as cooling fluid in the condenser, and then utilizing heated

17

seawater from the condenser into the DCMD unit. The performance analysis of the LFR plant

18

and DCMD unit has been conducted mainly in regard to direct normal irradiance (DNI) and

19

feed water temperature, respectively. For the LFR plant, electricity generation increased with

20

increasing DNI. The highest and the lowest energy production was 38.33 GWh and 14.08 GWh

21

in June and December, respectively. The real levelized cost of energy was found to be

22

0.34 ¢/kWh. For DCMD unit, the evaporation efficiency increased from 39.13% to 50.01%

23

corresponding to a feed temperature increase from 30 °C to 45 °C. The average freshwater

24

production capacity of the DCMD unit was found to be 31,844.6 L/day with a water production

25

cost $0.425/m3. The investigations revealed that the performance of the proposed system is 1

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quite satisfactory, and the low unit production cost of electricity and freshwater make it

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competitive to eradicate energy and freshwater crises.

28 29

Keywords: Solar energy; concentrated solar power; renewable energy; seawater desalination;

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membrane distillation; Abu Dhabi.

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1. Introduction

33

Freshwater is essential to life, and energy is a crucial part of modern society. Demand for

34

both (freshwater and energy) is increasing with increasing population, urbanization, and

35

industrialization [1]. Therefore, simultaneous production of energy and freshwater could be a

36

suitable way to address the growing demand. This involves a combination of a power plant for

37

energy production and a waste heat and/or electricity-driven desalination technology for

38

freshwater production. Energy production could be achieved either using a fossil fuel-fired

39

power plant or a renewable energy-driven power plant. Since fossil fuels are major sources of

40

environmental pollution and greenhouse gases (GHG), renewable energy-driven power plants

41

are environmentally friendly and could be a better option to meet energy demand [1]. Large

42

scale renewable energy production technologies include wind, hydropower, and solar energy.

43

Solar energy is an abundant resource of energy; solar energy incident on the earth is 10,000

44

times the annual energy demand. Solar energy can be converted into electricity either using

45

solar photovoltaic (SPV) or concentrated solar power (CSP) [1]. However, CSP is a leading

46

and mature technology for large scale production. Over the last decade, commercialization of

47

CSP increased significantly [2]. Spain has invested heavily in CSP and has a production of

48

2300 MW, and the United States produced 1738 MW out of the world’s total CSP capacity of

49

4755 MW as of the end of 2015 [2]. At present, there are four types of CSPs: parabolic trough

50

collectors (PTCs), solar power towers (SPTs), linear Fresnel reflectors (LFRs), and parabolic

51

dish systems (PDSs) [3, 4]. A comparison of CSP technologies is summarized in Table 1. For

52

sustainable development, combining CSP and desalination (CSP+D) for electrical energy and

53

freshwater production, respectively, can mitigate energy and freshwater crises worldwide.

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Table 1 Comparison of CSP technologies. Adapted from [5, 6]. CSP Technology

Capital cost $/m2

Land occupancy km2/MW

Cooling water (L/MWh)

Operating temperature range (°C) 20-400

Solar concentration ratio 15-45

Outlook for improvements

3000 or dry

Annual solar/electric efficiency (%) 15

PTC

424

0.025

LFR

234

0.008

3000 or dry

8-10

50-300

10-40

Significant

SPT

476

0.036

1500 or dry

20-35

300-565

150-1500

-

0.011

None

25-30

120-1500

100-1000

Very significant High potential through mass production

PDC

Limited

58 59

Few attempts have been made to integrate power and freshwater production using CSP

60

technology [7-9]. In an attempt, Trieb and Steinhagen [10] studied few configurations of CSP

61

and desalination technologies, which include multi-effect distillation (MED) and reverse

62

osmosis (RO), for the Middle East and North Africa (MENA) region. The results showed that

63

integration of CSP with desalination can be used to manage the increasing demand of energy

64

and freshwater in the MENA region. Gastli et al. [11] presented an investigation of CSP

65

integrated desalination for Wilayat Duqum–Oman. Two approaches were adopted to integrate

66

CSP plant and desalination technologies. MED was integrated with the exhaust heat from the

67

steam cycle of the CSP plant, and RO was integrated with electricity produced by the CSP

68

plant. The CSP/MED was found to have a low primary energy consumption, less environmental

69

effect, and higher performance. However, the initial investment of MED was 50% higher than

70

that of RO. Due to better technical performance, water cost of CSP/MED was slightly lower

71

than the one of CSP/RO. A thermodynamic evaluation of PT-CSP plant integrated desalination

72

technologies (RO and MED) for Abu Dhabi, United Arab Emirates (UAE), was presented by

73

Palenzuela et al. [12]. The results showed that CSP integrated MED (which is a thermally-

74

driven process) was more efficient than combining CSP with RO (which is a membrane-based

75

process). An integration of CSP with hybrid MED-RO desalination was presented by

76

Iaquaniello et al. [13]. The study concluded that integrating CSP with hybrid desalination is

77

more effective way for continuous and economical freshwater production. In another study, an 4

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assessment of CSP+SPV+MED for cogeneration of electricity and freshwater, for Chile, was

79

presented by Valenzuela et al. [14]. The study revealed that the integrating CSP+SPV+MED

80

lead to lower the levelized cost of energy (LCOE) and water cost. Furthermore, Hoffmann et

81

al. [15] presented an integration of MED with 100 MWe SPT plant for Namibia. The

82

investigation revealed that SPT plant driven MED have the potential of fulfilling the freshwater

83

demand in the region.

84

All of the previously mentioned studies have attempted to incorporate CSP plants with MED

85

and/or RO. However, no evaluation of CSP plant integrated with membrane distillation (MD)

86

has been presented. MD has been known for decades as a suitable desalination technology, of

87

which direct contact membrane distillation (DCMD) is a commonly used configuration [16]. It

88

is worth mentioning that MD can be operated with low-grade/waste heat which makes it very

89

attractive [17]. Therefore, this study presents an investigation of LFR plant integrated DCMD

90

system for Abu Dhabi, UAE. The LFR has been selected for the proposed study because it has

91

lowest land occupancy and capital cost, and the potential for technology improvements are

92

significant (as listed in Table 1).

93

The ultimate objective of this work is to investigate the performance and economic

94

evaluation of LFR plant integrated DCMD system. The simulations of LFR plant are first

95

carried out to examine the performance in terms of electricity production, gross-to-net

96

conversion factor, and capacity factor. The economic evaluation of the LFR plant has been

97

reported in terms of LCOE. In addition, sensitivity analysis of the plant has been presented to

98

examine the impact of the uncertainties on performance and economic outcomes. The

99

simulations of DCMD system are then carried out to investigate the performance in terms of

100

evaporation efficiency (EE), specific thermal energy consumption (STEC), and freshwater

101

production followed by economic evaluation in terms of water production cost (WPC).

102

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2. Methodology

104

2.1. System description

105

A schematic diagram of the proposed LFR plant integrated DCMD system is presented in

106

Fig. 1. As shown, the proposed system is comprised of two subsystems: the LFR plant and

107

DCMD unit. The LFR plant has been primarily used for electrical energy production. The solar

108

field of the LFR plant consists of long, thin segments of curved mirrors and absorber tubes.

109

Mirrors focus the solar radiation onto the absorber tube, and a heat transfer fluid (HTF) is used

110

to absorb thermal energy from absorber tube. Then, the HTF is directed to the thermal energy

111

storage (TES) system where (i) heat is supplied to the steam generator, and (ii) stored additional

112

thermal energy could provide a heat source when solar energy is not available. After supplying

113

heat to the steam generator, the HTF is directed to an absorber tube again via a cold storage

114

tank to repeat the cycle. Steam is used to run the turbine, which is coupled to the generator to

115

produce electricity. Subsequently, steam is extracted from the turbine in the evaporative

116

condenser. Since warm water from the condenser has to be used in a desalination unit, a

117

wet/evaporative cooling system has been proposed, and seawater was used as a cooling agent

118

in the condenser of the power plant. Condensed steam from the condenser is directed to the

119

steam generator to repeat the cycle, whereas warm seawater from the condenser was directed

120

to the second subsystem, the DCMD unit. The DCMD unit consists of two flow compartments

121

and a hydrophobic membrane. Warm seawater is directed into one compartment, and cold

122

water (permeate) flow into another compartment. A pressure difference (due to temperature

123

difference) is created between the two streams and allows water vapor to cross through the

124

membrane. Finally, freshwater is collected from the permeate side into the permeate tank, and

125

brine collected from the DCMD unit is discharged into the sea.

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Fig. 1. A schematic diagram of an LFR plant integrated DCMD system.

128 129

2.2. System analysis

130

2.2.1. LFR plant analysis

131

The performance and economic analysis of the first subsystem i.e., the LFR plant, was

132

carried out for Abu Dhabi, UAE. Abu Dhabi (longitude: 54.65 °East; latitude: 24.43 °North) is

133

located in the MENA region. Abu Dhabi was selected for this study because it has good

134

potential to produce of solar energy, and desalination is a major source of freshwater in the

135

region. The average direct normal irradiance (DNI) for the proposed location is presented in

136

Fig. 2. The analysis for the LFR plant has been carried out using the United States’ National

137

Renewable Energy Laboratory’s (NREL’s) System Advisor Model (SAM) software [18]. SAM

138

is a widely used, open access model based on collaboration between NREL and the CSP

139

industry. SAM software have been adopted by several studies, available in the literature, for

140

the assessment of CSP technology [6, 19-22]. Design characteristics and specifications used 7

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for the simulations of the proposed LFR plant are summarized in Table 2. The expected useful

142

life of the plant is 30 years. The average monthly seawater temperature supplied to the

143

condenser in Abu Dhabi is shown in Fig. 3 [23]. As shown, the lowest seawater temperature is

144

20.6 °C in February, and the highest seawater temperature is 33.8 °C in August.

145

Fig. 2. Direct normal irradiance in Abu Dhabi, UAE.

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Table 2 Design characteristics and specifications of the LFR plant. Solar field Solar field parameters

Heat transfer fluid

Design point

Land area

Solar multiple

2.3

Field aperture

850000 m2

Number of collector modules in a loop

16

Number of subfield headers

2

Field HTF

Hitec solar salt

Field HTF min: operating temperature

238 °C

Field HTF max: operating temperature

593 °C

Single loop aperture

7524.8 m2

Number of loops

68

Solar field area

291.929 acres

Reflective aperture area of the collector

470.3 m2

Length of the collector module

44.8 m

Length of crossover pipping in a loop

15 m

Design gross output

111 MWe

Estimated gross-to-net conversion factor

0.9

Estimated net output at design

100 MWe

Rated cycle conversion efficiency

0.38

Reference HTF outlet temperature at design

525 °C

Reference HTF inlet temperature at design

293 °C

Boiler operating pressure

100 bar

Condenser type

Evaporative

Reference condenser water dT

10 °C

Equivalent full-load thermal storage hours

12 hours

Total tank volume

19641.5 m3

Storage HTF fluid

Hitec Solar salt

Tank diameter

35.36 m

Loss coefficient from the tank

0.4 W/m2.K

Collector and Receiver

Power cycle Plant capacity

Power block design point

Rankine cycle parameters

Thermal storage

149

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150

Fig. 3. Average monthly seawater temperatures in Abu Dhabi.

151 152

2.2.2. DCMD unit analysis

153

The proposed DCMD unit consists of a flat-sheet membrane and two flow segments (feed

154

and permeate side). The specifications of the DCMD module are as follows: (i) channel width

155

0.2 m, (ii) channel length 0.7 m, and (iii) channel thickness 0.001 m. The effective membrane

156

area of each module is 0.56 m2, which is achieved was assembled from four layers of membrane.

157

The number of DCMD units were calculated based on cooling water requirements for the

158

condenser of the LFR plant and feed flow rate in the DCMD module. The temperature of the

159

feed water in the DCMD module was varied based on the average monthly seawater

160

temperature (Fig. 3) and reference condenser cooling water temperature difference 10 °C

161

(Table 2). Membrane characteristics and operating parameters of the DCMD unit are listed in

162

Table 3. The performance of the DCMD unit was evaluated by solving DCMD mathematical

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model using MATLAB® software. The detailed DCMD mathematical model used for the

164

analysis can be found elsewhere [24, 25].

165 166 167 168 169 170 171 172

Table 3 Membrane characteristics and operating parameters of the DCMD unit. Membrane characteristics Pore size (µm)

0.22

Membrane thickness (µm)

120

Porosity (%)

91

Tortuosity

1.098

Thermal conductivity of membrane (W/m- ºC)

0.20

Operating parameters Concentration of hot feed (g/L)

35

Feed flow rate (l/min)

20

Feed velocity (m/s)

1.666

174

Concentration of permeate (g/L)

0

175

Permeate inlet temperature (°C)

20

Permeate flow rate (l/min)

20

Permeate velocity (m/s)

1.666

Seawater thermal conductivity

0.596 W/m-ºC

Freshwater thermal conductivity

0.607 W/m-ºC

Seawater density

1035 kg/m3

179

Freshwater density

998.2 kg/m3

180

Specific heat of seawater

3580 J/kg-ºC

Specific heat of freshwater

4180 J/kg-ºC

Molecular weight of water

18.07489 kg/mole

173

176 177 178

181 182

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3. Results and discussion

184

The objective of this study is to provide a concept description, performance, and economic

185

analyses of an LFR plant integrated DCMD system. Since the proposed system consists of two

186

subsystems (LFR plant and DCMD unit), an analysis of an LFR plant was carried out, followed

187

by DCMD unit analysis.

188 189

3.1. LFR plant analysis

190

3.1.1. Performance analysis

191

It is well known that the DNI is the primarily parameter for the operation of the CSP plant.

192

Hence, performance of the CSP plant is dependent on the DNI. It has been observed in Fig. 2

193

that the maximum DNI is 867.5 W/m2 in September and minimum value is 670.64 W/m2 in

194

December, respectively. Therefore, with an increase or decrease in the DNI, the energy

195

production is expected to increase/decrease. On the basis of the DNI, the average hourly

196

thermal power incident on the solar field and the average hourly thermal power produced by

197

the LFR plant are depicted in Fig. 4. As shown, the hourly power incident on the field varied

198

throughout the year. However, the lowest hourly power incident on the solar field was observed

199

in March (691 MWt), and the highest hourly power incident on the field was found to be 1024

200

MWt in September. Moreover, the monthly power incident on the field and the monthly

201

thermal power produced are shown in Fig. 5. As shown, the monthly power incident on the

202

field was maximum in May i.e., 267.1 GWt, whereas it was lowest in December i.e., 190.12

203

GWt. However, the thermal power produced was found to be maximum in June, i.e., 118.21

204

GWt, and minimal in December, i.e., 45.47 GWt. Although power incident on the solar field

205

was a bit lower for June compared to May, the thermal power produced in June was higher

206

than that output in May. It is attributed to the higher ambient temperature in June.

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207

208 209

Fig. 4. Hourly power incident on the solar field and thermal power produced by the plant.

Fig. 5. Monthly power incident on the solar field and thermal power produced.

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Moreover, the electricity production depends on the power incident on the solar field and

211

thermal power produced. The simulation results for the monthly electricity production by the

212

LFR plant are presented in Fig. 6. As shown, the maximum and minimum electricity production

213

was 38.33 GWh and 14.08 GWh, respectively, in June and December, respectively. It is

214

observed that highest electricity production occurred in the summer whereas lowest electricity

215

production corresponds to winter. The reason behind the phenomenon is the higher solar

216

irradiance in summers (Fig. 2) which increases the power incident and field thermal power

217

produced (Fig.4 and Fig. 5), and consequently resulted in increased electricity production (Fig.

218

6). Finally, the cooling water requirements for the proposed LFR plant have been analyzed, as

219

shown in Fig. 7. It is found that the cooling water requirements for the proposed plant increased

220

with increasing electricity production. For instance, the maximum and minimum cooling water

221

requirement was 120,983 m3 and 48,048 m3, respectively, in June and December, respectively.

222

Moreover, the simulations results revealed that the gross electric output of the proposed LFR

223

plant was 325.8 GWh/year. Whereas, the capacity factor and gross-to-net conversion factor

224

was 37.2% and 96.1%, respectively. In summary, adequate electricity production, high capacity

225

and gross-to-net conversion factor revealed that the overall performance of the proposed LFR

226

plant is quite satisfactory for the weather conditions of Abu Dhabi.

227

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228 229

Fig. 6. Monthly electricity production.

230 231

Fig. 7. Monthly cooling water requirements.

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3.1.2. Economic analysis To evaluate the economics of the LFR plant, the LCOE of the proposed plant has been calculated. The LCOE is calculated by the relation: N

Co  236

LCOE( r ) 

C n 1

n

(1  d n ) n

(1)

N

Q

n

n 1

(1  d r ) n 237 N

Co  238

LCOE( n ) 

C n 1

n

(1  d n ) n

(2)

N

 Qn n 1

(1  d n ) n 239

Where

240

LCOE(r) = real levelized cost of energy

241

LCOE(n) = nominal levelized cost of energy

242

Qn = electricity generated by the LFR plant in N years

243

N = analysis period

244

Co= project equity investment

245

Cn= project annual cost in number of years

246

dr = discount rate for real case

247

dn = discount rate for nominal case (with inflation)

248 249

Assumptions and data used for economic analysis of the LFR plant are summarized in Table

250

4. The LCOE(n) and LCOE(r) were found to be 13.52 ¢/kWh and 10.34 ¢/kWh, respectively.

251

The calculated LCOE is in good agreement with the LCOE available in the recent literature 16

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[22, 26]. Therefore, it can be concluded that the LFR plant is economically viable.

253 254

Table 4 Assumptions and data used for economic analysis of an LFR plant. Parameters Annual electricity generated (Qn) Net capital cost Life time Project equity investment Real discount rate Nominal discount rate Inflation rate

255 256

Unit

Values

kWh $ Years $ %/year %/year %/year

325,800,352 696,198,016 30 [18] 348,099,008 5.5 [18, 27] 8.14 [18, 27] 2.5[18, 27]

3.1.3. Sensitivity analysis

257

The influence of the deviation on performance and financial outcome of a project could be

258

incorporated in the sensitivity analysis. Therefore, sensitivity analysis was performed in this

259

study to examine the influence of uncertain inputs on performance and financial parameters.

260

The sensitivity analysis was conducted using parametric and macros tool in SAM. An

261

uncertainty of +10% was considered for both the performance and financial estimations. Firstly,

262

sensitivity analysis was performed to estimate the influence of different uncertainties in input

263

on the performance of the system. The performance parameters including energy production,

264

capacity factor, and gross-to-net conversion factor were observed. To study the influence of

265

weather sensitivity, monthly energy production with the DNI +10% was evaluated. The

266

monthly energy production of the system is presented in Fig. 8. It was found that the highest

267

electricity production occurred in the summers (May to September) when the DNI is high,

268

whereas lower electricity production occurred in the winters. However, in comparison, impact

269

of higher DNI is larger compared to that of lower DNI. For instance, the energy output

270

improvements with DNI +10% is larger than the energy output difference between the base

271

model and DNI -10%. In addition, influence of uncertainty in solar multiple on performance

272

parameters was assessed. Solar multiple is an important parameter which specifies the solar

273

field area as a multiple of power block’s capacity. Generally, increase in solar multiple 17

ACCEPTED MANUSCRIPT 274

increases the system’s output. However, too large solar multiple can reduce energy production

275

due to more thermal energy production than the capacity of the plant. Hence, sensitivity

276

analysis was performed to evaluate influence of solar multiple with +10% uncertainty on

277

performance parameters, as presented in Fig. 9 (a-c). As shown in Fig.9, an increase/decrease

278

in the solar multiple increases/decreases the annual energy production, capacity factor, and

279

gross-to-net conversion factor. The reason behind this is that the higher/lower solar field area

280

led to higher/lower solar multiple, which certainly affects the energy production. Therefore,

281

uncertainty in the solar multiple has a noticeable impact on the performance of an LFR plant.

282

Moreover, sensitivity analysis was also performed to estimate the influence of different

283

uncertainties in inputs on the financial outcomes of the plant. The sensitivity analysis of

284

financial estimation was conducted for both the nominal and real LCOE. The financial input

285

parameters include solar field cost, power plant cost, storage cost, and HTF cost. Fig. 10 (a-b)

286

depicts the tornado charts showing the effect of financial input parameters on the LCOE. As

287

shown in Fig.10, an increase in different costs increases the LCOE. However, the uncertainty

288

in solar field cost has the highest impact on the LCOE. For instance, increase in solar field cost

289

considerably increases the LCOE. It is due to the fact that the solar field is the most expensive

290

part of a CSP plant [22].

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291

Fig. 8. Monthly electricity production with base model, DNI +10%, and DNI -10%.

19

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292 293 294

Fig. 9. Influence of uncertainties in solar multiple on performance parameters: (a) annual energy (kWh), (b) capacity factor (%), and (c) gross-to-net conversion factor (%).

295

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296 297 298

Fig. 10. Influence of uncertainties in financial input parameter on LCOE: (a) real LCOE (cents/kWh), and (b) nominal LCOE (cents/kWh).

299

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3.2. DCMD unit analysis

302

3.2.1. Performance analysis

303

Temperature of feed water is an important parameter that affects the performance of a

304

DCMD unit. In our proposed LFR plant integrated DCMD system, seawater was used as a

305

cooling agent in the condenser, and warm seawater from the condenser was used as feed water

306

in the DCMD unit. Therefore, the effect of feed water temperature on the performance of the

307

DCMD unit have been investigated. As stated earlier, feed water temperature was based on the

308

monthly average seawater temperature (Fig. 3) and the reference condenser water dT (Table

309

2). The feed seawater temperatures for DCMD unit are listed in Table 5.

310 311

Table 5 Feed seawater temperature for DCMD unit. Month

1

2

3

4

5

6

7

8

9

10

11

12

Temperature (°C)

32.2

30.6

32.4

35

39

41.6

42.7

43.8

43.4

41.5

38.3

34.5

312 313

The performance analysis of DCMD unit has been presented in terms of EE, STEC, and

314

freshwater production. The EE is an important parameter to evaluate the performance of MD

315

systems. The EE is the ratio of heat transfer through evaporation to the total heat transfer

316

through the MD module (or membrane). The EE of the DCMD module increased from 39.13%

317

to 50.01%, corresponding to a feed water temperature increase from 30 °C to 45 °C, as shown

318

in Fig. 11. The reason for the increase in EE is the increase in permeate flux which increases

319

with the increase in feed temperature. Another important parameter in the performance analysis

320

of the DCMD unit is the STEC, which is the thermal energy required to produce a cubic meter

321

(m3) of freshwater. Fig. 11 also depicts the STEC of the DCMD unit. As shown, an increase in

322

feed water temperature from 30 °C to 45 °C reduced the STEC from 1854 kWh/m3 to 1436

323

kWh/m3. It is attributed to the fact that increase in feed temperature increases the permeate

324

flux, which lead to reduction in STEC. In summary, the increase in feed water temperature can

325

lead to increase system’s performance by increasing EE and reducing STEC. 22

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326 327 328

Fig. 11. Effect of feed temperature on EE and STEC (Operating conditions: permeate temperature 20 ºC, feed and permeate flow rate 20 L/min, and feed concentration 35 g/L).

329

Lastly, the amount of freshwater produced from a DCMD unit has been estimated. The

330

freshwater produced by a DCMD unit can be calculated relative to the total quantity of feed

331

water supplied and number of DCMD modules used. The monthly feed seawater supplied to

332

the DCMD unit and required number of modules are summarized in Table 6. Feed water

333

temperature quantity varied throughout the year relative to the electricity produced. For

334

instance, the minimum quantity of feed water was 1075 L/min in December, whereas the

335

maximum quantity of feed water was 2800 L/min in June. The feed flow rate was maintained

336

at 20 L/min in each module. Freshwater production from the DCMD unit is depicted in Fig.

337

12. It can be observed that the freshwater produced through the DCMD unit varied throughout

338

the year depending on feed water quantity and temperature. As shown, the maximum

339

freshwater production was 56,949 L/day in August, whereas the minimum freshwater

340

production 9,993 L/day in January. It can be noted that, although the quantity of feed water for 23

ACCEPTED MANUSCRIPT 341

June was higher than August (Table 5), the quantity of freshwater produced in August is higher

342

than June. The reason behind the higher freshwater production is the higher feed water

343

temperature in August (43.8 °C) compared to June (41.6 °C). Therefore, it can be concluded

344

that the temperature of the feed water is an important parameter that affects the production of

345

freshwater. In addition, the amount of water produced is adequate to fulfill the freshwater

346

demand of an on-site or a nearby residential area. It is worth noting that DCMD could produce

347

freshwater with very low-grade/waste heat, which cannot be utilized by other thermal

348

desalination systems such as MED and multi-stage flash (MSF).

349 350 351

Table 6 Monthly feed seawater supplied to the DCMD unit and the required number of modules. No. of DCMD Month Feed water (L/min) modules January 1183 59 February

1812

90

March

1568

78

April May

2011 2703

100 135

June

2800

140

July

2446

122

August

2564

128

September

2413

120

October

1884

94

November

1385

69

December

1075

53

352

24

ACCEPTED MANUSCRIPT

353 354 355 356

Fig. 12. Freshwater production through DCMD unit. 3.2.2. Economic analysis

357

The WPC has been calculated to evaluate the economic feasibility of the proposed DCMD

358

unit. The capital cost (CC) of the DCMD unit is the sum of direct capital cost (DCC) and

359

indirect capital cost (ICC).

360

CC  DCC  ICC

(3)

361

DCC consists of the total cost of an MD module (membrane cost, MD assembly cost, and

362

installation cost), land and building costs. Most previous studies considered land cost of MD

363

to be zero [28]. ICC includes legal fees, insurance, labor cost, contingencies, etc. For MD, the

364

ICC is considered to be 10% of DCC [29].

365 366

The total annual cost (ACtotal) is the sum of annual fixed cost (ACfixed) and annual operation & maintenance cost (ACO&M).

25

ACCEPTED MANUSCRIPT 367

ACtotal  AC fixed  ACO& M

(4)

368

To calculate ACfixed, a value of the amortization factor (a) is required. Since it is assumed

369

that the required fund for the proposed DCMD unit is loaned from a bank, the amortization

370

factor (a) accounts for annual interest payments of CC, and it is calculated by

371

i (1  i ) n a (1  i ) n  1

372

where i denotes the annual interest rate, and n is the life of the plant. The interest rate (i) was

373

assumed to be 5% [28, 30]; however, plant life (n) was assumed to be 20 years [28, 30].

374

Therefore, the amortization factor (a) was found to be 0.08/year.

375

ACfixed can be calculated by multiplying the capital cost (CC) by the amortization factor (a)

376

as follows:

377

AC fixed  a * CC

378 379

(5)

(6)

ACO&M can be evaluated from

ACO& M  ACMT  ACMR  ACelectrcity  AClabor  ACBD

(7)

380

where ACMT is the annual maintenance cost, ACMR is the annual membrane replacement cost,

381

AClabor is the annual labor cost, ACBD is the annual brine disposal cost, and ACelectricity is the

382

annual electric cost. These values are listed in Table 7. ACMT can be calculated by

383

ACMT  0.2* AC fixed

(8)

384

26

ACCEPTED MANUSCRIPT 385 386

Table 7 Data and assumptions for WPC. Item

Unit cost

Direct capital cost (DCC) Membrane cost $36/m2 [28] MD equipment [28] Total cost of MD module (membrane cost + MD assembly cost) Installation and buildings [31] Total DCC Indirect capital cost (ICC) [29] Capital cost (CC) = DCC + ICC Annual operating and maintenance cost (ACO&M) Annual maintenance cost (ACMT) [28] Membrane replacement cost (ACMR) [28] Annual labor cost (AClabor) $0.05/m3 [29] Annual brine disposal cost (ACBD) $0.0015/m3 [32] Annual electric cost (ACelectricity) 0.06 kWh/m3 [30] Total ACO&M ACtotal WPC

Quantity 79.8 m2

Estimated cost 2872 $ 8750.45 $ 11578.25 $ 2894.564 $ 14472.82$ 1447.28 $ 15920.1 $

1160.7

255.49 $ 2315.65 $ 522.31 $ 15.66 $ 62.67 $ 3171.80 $ 4449.27 $ $0.425/m3

387 388

Finally, the WPC of the DCMD unit can be calculated from

ACtotal f * Qw *365

389

WPC 

390

where f is the availability of the plant and is assumed to be 90% per year [28, 30]. The WPC

391

was estimated to be $0.425/m3 for an average freshwater production capacity (Qw) of 31.8

392

m3/day. The calculated WPC was found to be in good agreement with the WPC available in

393

the literature [31]. Hence, it can be concluded that the freshwater production through a DCMD

394

unit using waste heat is economically viable.

(9)

395

It is worth mentioning here that the LFR plant integrated DCMD produced adequate amount

396

of freshwater using low-grade heat, and the low WPC revealed that DCMD unit is

397

economically viable. Therefore, integration of DCMD with CSP plant could lead to eradicate 27

ACCEPTED MANUSCRIPT 398

the freshwater and energy crises worldwide. This study opens opportunities for the power

399

plants, where seawater is used as cooling liquid in the condenser, to produce freshwater using

400

DCMD unit.

401 402

4. Conclusions

403

In this paper, we provided a concept description and analysis of a 111 MWe LFR plant

404

integrated DCMD for Abu Dhabi, UAE. An LFR plant was primarily used for electricity

405

generation. Evaporative cooling was proposed for the power plant, and seawater was used as a

406

cooling medium in the condenser. Warm seawater from the condenser was fed to the DCMD

407

unit for freshwater production. The LFR plant analysis were performed using SAM software.

408

Whereas a mathematical model (based on heat and mass transfer equations) was solved in

409

MATLAB for the DCMD unit. The meteorological data of Abu Dhabi was employed to

410

investigate the effects of solar radiation and seawater temperature on system performance. The

411

maximum and minimum electricity production for the LFR plant were 38.33 GWh and 12.08

412

GWh in June and December, respectively. The gross-to-net conversion factor and capacity

413

factor for the LFR plant were found to be 96.1% and 37.2%, respectively. The LCOE(r) was

414

estimated to be 10.34 ¢/kWh. In addition, sensitivity analysis of the LFR plant was carried out

415

with +10% uncertainty in input parameters for both the performance and financial estimations.

416

The analysis revealed that the solar multiple and solar field cost considerably affects the

417

performance of the LFR plant and LCOE, respectively. For the DCMD unit, the EE increased

418

from 39.13% to 50.01% corresponding to a feed water temperature increase from 30 °C to

419

45 °C. In contrast, the STEC of the DCMD unit decreased from 1854 kWh/m3 to 1436 kWh/m3

420

corresponding to a feed water temperature increase from 30 °C to 45°C. The maximum and

421

minimum freshwater production capacities were 56,949 L/day and 9,993 L/day in June and

422

December, respectively. The WPC was estimated to be $0.425/m3. The performance of the

28

ACCEPTED MANUSCRIPT 423

proposed system was found to be quite satisfactory, and the low unit cost of electricity and

424

freshwater revealed the financial viability of the system. Since an LFR plant can utilize solar

425

energy for electricity generation and a DCMD unit can use waste heat for freshwater production,

426

an LFR plant integrated DCMD system could result in reduction in energy and freshwater

427

crises and more sustainable development. Although the STEC by the DCMD unit is high, it is

428

worth mentioning here that the DCMD unit utilized purely low-grade waste heat, which cannot

429

be utilized in thermal desalination units such as MSF and MED. To explore the opportunities

430

for other CSP technologies, assessment of PT and SPT plant integrated DCMD system will be

431

explored in our future work. However, future work may be conducted on the effect of seawater

432

on the fouling, corrosion, and corresponding performance of the condenser.

433 434

Acknowledgment

435

This work was supported by the National Research Foundation of Korea (NRF) Grant

436

funded by the Korean Government (MSIP) (NRF-2017R1D1A1B03031587), the Korea

437

Institute of Energy Technology Evaluation and Planning (KETEP), and the Ministry of Trade,

438

Industry & Energy (MOTIE) of the Republic of Korea (No. 20153010130460).

439

The first author would also like to acknowledge the Higher Education Commission (HEC)

440

and Government of Pakistan for the scholarship under the project titled “HRD Initiative-MS

441

leading to Ph.D. program: Faculty development for UESTPS, Phase-1, and Batch-IV” to

442

Hanyang University, South Korea.

443

29

ACCEPTED MANUSCRIPT Nomenclature Symbols a ACtotal ACfixed ACO&M ACMT ACMR AClabor ACBD ACelectricity Co Cn dr dn f i N n Qn Qw

amortization factor total annual cost annual fixed cost annual operation & maintenance cost annual maintenance cost annual membrane replacement cost annual labor cost annual brine disposal cost annual electric cost project equity investment for LFR plant project annual cost for LFR plant discount rate for real case discount rate for nominal case (with inflation) availability of the DCMD unit annual interest rate analysis period for LFR plant life time of system electricity generated by the LFR plant in N year amount of water produced (m3/s)

Acronyms CC CSP CSP+D DCMD DCC DNI EE GHG HTF ICC LCOE LCOE(r) LCOE(n) LFR MED MENA MD MSF NREL PDS PTC RO SAM SPT SPV STEC TES UAE WPC

capital cost concentrated solar power concentrated solar power and desalination direct contact membrane distillation direct capital cost direct normal irradiance evaporation efficiency greenhouse gases heat transfer fluid indirect capital cost levelized cost of energy real levelized cost of energy nominal levelized cost of energy linear Fresnel reflects multi-effect distillation Middle East and North Africa membrane distillation multi-stage flash National Renewable Energy Laboratory parabolic dish system parabolic trough collector reverse osmosis system advisor model solar power tower solar photovoltaic specific thermal energy consumption thermal energy storage United Arab Emirates water production cost 30

ACCEPTED MANUSCRIPT 444

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ACCEPTED MANUSCRIPT HIGHLIGHTS 

Simulations on integration of DCMD system with LFR plant are presented.



Analysis of LFR plant have been carried out using System Advisor Model (SAM) software.



Investigations of DCMD system have been carried out by solving the mathematical model using MATLAB.



Performance of the proposed system is reported in annual energy production and freshwater production.



Economic analysis is presented in terms of levelized cost of energy and water production cost.