Integrated Optimization of a Solar-Powered ...

Proceedings of the ASME 2012 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference IDETC/CIE 2012 August 12-15, 2012, Chicago, IL, USA

DETC2012-70783

INTEGRATED OPTIMIZATION OF A SOLAR-POWERED HUMIDIFICATIONDEHUMIDIFICATION DESALINATION SYSTEM FOR SMALL COMMUNITIES Karim Hamza Senior Research Fellow Dept. of Mechanical Engineering University of Michigan, Ann Arbor, MI 48109-2102 [email protected] Ashraf O. Nassef Professor Dept. of Mechanical Engineering American University in Cairo, Cairo, Egypt [email protected] Kazuhiro Saitou Sayed Metwalli Professor Professor Dept. of Mechanical Engineering Dept. of Mechanical Design and Production University of Michigan, Ann Arbor, MI 48109-2102 Cairo University, Cairo, Egypt [email protected] [email protected] Mohamed El-Morsi Associate Professor Dept. of Mechanical Engineering American University in Cairo, Cairo, Egypt [email protected]

ABSTRACT Fresh water availability is essential for the economic growth and development, especially in small and semi-isolated communities. In some of these communities fresh water may be scarce, yet brackish water from wells or seawater is often abundantly available. This motivates a need for cost-effective desalination at small scale capacity driven by renewable energy sources. This paper presents an integrated optimization model of a solar powered humidification-dehumidification (HDH) desalination system. The system under investigation is a waterheated system. The design variables include the sizing of solar collectors, storage tank, humidifier and dehumidifier, as well as air circulation flow rate and operating temperature. The objective of the optimization is to minimize the unit cost of the produced fresh water. Thermodynamic performance prediction is done by solving energy and mass balance equations for each of the system components, with consideration to hourly-varying solar irradiance that corresponds to a typical one year cycle. System cost is predicted via first-order estimators. A genetic algorithm is used to obtain the designs optimized for local climate and market. A case study discusses a desalination plant on the Red Sea near the city of Hurgada. NOMENCLATURE Surface area [m2] A

C cp

h

m Q Rp

T tp

U V

Vair V

water

X y F

ω µ

1

Cost [$] Correction factor to account for inflation rate and yearly maintenance costs Specific enthalpy [kJ/kg dry air for moist air, kJ/kg water] Mass flow rate [kg/s] Heat transfer [kW] Recovery ratio (ratio of mass flow rate of distillate to that for brackish water) [kg distillate/kg brackish] Temperature [°C] Time duration of the project lifetime Overall heat transfer coefficient [kW/m2K] Volume [m3] Volume flow rate of air [m3/s] Volume flow rate of water [m3/s] Design variables specific to humidifier-dehumidifier Design variables specific to solar collector Objective function to minimize [$/m3 distillate] Humidity ratio [kg water vapor/kg dry air] Uptime of hot water availability[%]

Copyright © 2012 by ASME

Subscripts B Db cond D humid water wb tank 1, 2, … 6 Acronyms LMTD GA RO SQP SWH TTD

Brine Dry bulb Condenser Distillate Humidifier Seawater pumped through the system Wet bulb Hot water storage tank Location of a physical quantity within the system as per Fig. 1 Logarithmic mean temperature difference Genetic algorithms Reverse osmosis Sequential quadratic programming Solar water heater Terminal temperature difference

1. INTRODUCTION The availability of fresh water is often a concern that hinders economic growth and development of various areas around the world. The issue is further magnified in small, remote semiisolated communities that are not connected to the grid power and water resources of the larger communities, or with limited capacity. While, in some of these communities, salty water from the sea or wells is often available, the lack of grid power hinders implementing desalination processes, which are often expensive and energy intensive. This motivates a need for costeffective desalination plants running on renewable energy sources and at small scale capacity. This paper presents an integrated optimization model of a solar powered desalination system for small communities at coastal locations in Egypt on the Red sea. The Falkenmark water stress index for Egypt is 400 m3 per capita per year indicating that the country is beyond the water barrier of manageable capability [1]. Since solar energy is abundant in Egypt, it will be the primary focus of this study as the renewable power source for desalination. While our model is derived from the geographical and economical data for a specific coastal location in Egypt, it can be relevant to other locations around the world with similar climate. The next section presents a review of relevant work on water desalination processes and solar energy harvesting methods for powering desalination processes. It also discusses the best combination of a desalination process and a solar energy harvesting method for Egyptian coastal locations. The sections that follow present system-level models of a selected technology combination for solar energy harvesting (flat plate) and desalination (humidification-dehumidification). The system models are implemented into an optimization framework, and a case study is presented for plant design for minimum cost per m3 of produced fresh water at a location on the Red sea near the city of Hurgada.

2. RELATED WORK The main technologies for commercial water desalination can be grouped into one of two basic categories: i) reverse osmosis (RO) and ii) distillation. Reverse osmosis relies on pumping the salty water stream at high pressure through semi-permeable membranes that prevent the passage of salt ions, but allow the passage of water molecules. When the pressure in the salty water is sufficiently high, some of the water flows through the membrane and gets collected in a fresh water stream, while the rest is discharged as brine. Distillation relies on heating the water to a temperature sufficient for converting a portion of it into vapor. The vapor is then cooled into fresh water. 2.1. Reverse Osmosis Water Desalination Reverse osmosis was initially developed by Reid and Breton [2] at the University of Florida in the late 1950s. Later, Loeb and Sourajan developed the first asymmetric membrane [3]. Yet, until the development of the composite polyamide membranes, desalination via RO was neither sufficiently robust nor energy-efficient [4]. RO membranes were originally packed in a plate and frame configuration [5], which had low packing density. Mahon [6] suggested the use of hollow fiber configuration, but eventually the spiral wound configuration emerged and became the one mostly used in practice [7]. Mohsen et al. [8] and Abdallah et al. [9] focused their studies on RO desalination systems that are powered via photovoltaic arrays, while Bouguecha et al. [10] focused their studies on small-scale pilot plants. Further studies of pilot plants may be found in the review articles by Greenlee et al. [11] or Alghoul et al. [12], where various brackish water RO designs are examined. Van der Meer et al. [13] modeled multistages of spiral-wound membrane configurations. Voros et al. [14] simplified the RO system model and posed the problem as a nonlinear programming (NLP) problem. Lu et al. [15] presented cost estimation models as well as NLP optimization of spiral-wound RO systems. Multi-objective optimization of these systems was also considered by Guria et al. [16] and Vince et al. [17], and a response surface approach was adopted by Khayet et al. [18] for a solar-powered RO plant model. 2.2. Distillation-based Water Desalination While it is possible to perform distillation by heating salty water till its boiling point at normal atmospheric pressure (approx. 100c), several considerations prevent this from being practical. Heating energy consumption aside, if the salty stream includes dissolved calcium bicarbonate salts (which is the case of seawater), then excessive heating causes the bicarbonates to break into insoluble calcium carbonate, which precipitates and leads to blockages. As such, commercial distillation operate at lower temperature while relying on evaporating only a fraction of the salty water stream through exposure to dry air (air humidification), which is then cooled so that water vapor condensates (dehumidification) and gets collected. Examples of research on humidification-dehumidification (HDH) processes for water desalination include the work of Farsad and Behzadmehr [19], where they conducted a

2

Copyright © 2012 by ASME

sensitivity analysis, using Design of Experiments (DoE), to identify the most sensitive parameters of the HDH cycle. They identified the mass flow rate, the temperature of feed-water, inlet air flow rate, the condenser characteristic parameter, and the total heat flux as the most significant effects on the desalination cycle performance. Mehrgoo and Amidpour [20] presented an optimization study of the performance of the desalination unit using genetic algorithms (GA). The design variables were the humidifier and condenser tubes diameters, the inlet cold and hot water temperatures, and the column heights. The objective was to maximize the productivity of the unit. S. El-Agouz [21] conducted an experimental investigation of the principal operating parameters of a HDH desalination unit. He then studied the effects of the water temperature, water level and air flow rate on the humidification effectiveness, productivity and efficiency of the system. Soufari et al. [22] considered the optimization of a single-stage closed-circulation HDH for three objectives: i) specific thermal energy minimization, ii) productivity maximization and iii) condenser heat recovery maximization. A compilation of solar-powered HDH setups was presented in the review article in [23], which recommended the air-heated closed air open water as the most energy efficient and expected costs of $3-7 per m3 fresh water. 2.3. Solar Energy Harvesting Solar energy harvesting technologies may be categorized into two broad categories based on the energy conversion mechanic [24]: i) photovoltaic and ii) thermal processes. While photovoltaic processes seek the conversion of the energy in solar radiation directly into electricity, thermal processes rely on redirecting solar radiation into a thermal receiver that heats a working fluid. The collected thermal energy may then be used for heating purposes, or to drive a Sterling-engine or steam turbine. Commercial photovoltaic cells have less overall thermodynamic efficiency of energy conversion and higher overall cost per kilowatt hour of electricity than large-scale thermal systems. However, photovoltaic systems have flexible scalability, require far less maintenance, and costs increasingly less through the advances in materials and manufacturing technology. By comparison, thermal systems which rely on conventional power-block elements (e.g. steam turbine, electric generator) are not making such noticeable advancements in cost reduction over the past years. The major drawback of photovoltaic systems is the rapid variation in their output with every passing cloud over the sun. As such, photovoltaic cells can reliably power facilities when either: i) a storage system in batteries is used as a buffer to stabilize the output, or ii) a supplementary power connection is available. Common solar-thermal harvesting technologies include [25]: i) Flat plate collectors, ii) Parabolic-Trough, iii) Linear Fresnel Reflector, iv) Parabolic Dish, and v) Tower with heliostat mirrors. An important characteristic in those systems is known as the Concentration ratio, which is the ratio of effective areas of the collector surfaces (mirrors or lenses) to the area of the receiver [25]. The concentration ratio plays an

important role in the ratio of collected heat to that lost to the atmosphere. In other words, the concentration ratio affects the thermal harvesting efficiency and maximum attainable temperature. While flat plate collectors, whose concentration ratio is equal to one, are cost effective and simple to operate and maintain, they are not efficient at temperatures above 90c, which makes it unsuitable for electric power generation via the conventional thermal engines. Parabolic trough and Linear Fresnel Reflector concentrators operate at concentration ratio between 10 and 100, which is sufficient for steam-based power generation. Parabolic dish and Tower with heliostat systems can reach concentration ratio over 1000, which allows harvesting heat energy at high temperature. Due to the issues of architectural construction, however, tower systems are typically only economical for large scale power generation. 2.4. Technology Selection Based on the review of literature, the most reasonable pairing between solar energy harvesting and small scale desalination technologies seem to be: 1) Photovoltaic cells for powering RO processes, and 2) Stationary non-concentrating “flat plate” thermal collectors for powering HDH processes. This is mainly because the main power consumption in RO is in the form of electric power to operate the high pressure pumps, and the small-scale setting simply rules out the thermal-based electric power generation processes. As for HDH, the main power consumption is in the form of heat to raise the temperate of the intake water, and the operating temperature is fairly low, which means the simplest thermal collectors (flat plate) that have a 1:1 concentration ratio are sufficient. When comparing RO verses HDH for the study in this paper where reliable grid power is not to be assumed, HDH seems like a more plausible choice. This is despite RO having lower cost per m3 fresh water when the cost of electric power is at typical power grid prices. The reasoning for this is: i) Raw cost of electric power generation via photovoltaic cells is still much higher than grid prices, which will inevitably increase the cost of water desalination ii) Power consumption in RO increases significantly at high salinity concentrations in the intake water (which the Red sea is known for), while the power consumption in HDH is almost indifferent to the salinity. iii) RO requires near-continuous electric power availability to maintain turbulent flow within the membrane units and avoid salt precipitation. This means the photovoltaic cells must be augmented with large capacity batteries, or a fossil fuel power source (e.g. Diesel engine generator set), which will add to the effective desalination cost The next section provides the details of the system-level model HDH system and solar collectors. Contributions of this paper include: i) integration of multiple sub-system models, ii) exploration of a simple low investment cost arrangement, and iii) presenting a case study using Hurgada weather data.

3

Copyright © 2012 by ASME

3. SYSTEM MODEL 3.1. Overview A schematic diagram of the solar-powered HDH desalination system is shown in Fig. 1. The system consists of five main components: i) feed pump which pumps seawater through the system, ii) condenser (dehumidifier), in which hothumid air is dehumidified and fresh water is collected, iii) solar water heater (SWH) that provides thermal energy to warm the salty water, iv) humidifier, in which the heated salty water is sprayed through a stream of air coming at ambient conditions, which results in increase in temperature and humidity of the air, and drop in temperature of the un-evaporated water which is discharged as brine, and v) forced draft fan that drives air from the ambient into the humidifier and through the condenser. 6 3

(iii) Solar Water Heater

(ii) Condenser

2

1

Seawater in

(i) Pump

3.2. Dehumidifier Model Eqns. (1) and (2) depict the energy and mass balances for the condenser respectively. Subscripts on the variables indicate location in Fig. 1.

m water ( h2 − h1 ) = m air ( h5 − h6 ) − m D hD

(1)

m air ω5 = m air ω6 + m D

(2)

where the distillate properties are evaluated at the average air wet bulb temperature. Since the inlet conditions for the air and water are known, the terminal temperature difference (TTDcond) between the inlet air dry-bulb temperature and the exit water temperature was chosen as a design variable of the system. Knowing TTDcond, the exit water temperature, and consequently all other variables in Eqns. (1) and (2) can be calculated. The following Eqn. (3) then provides an estimate of the condenser area: Q = A U LMTD (3) cond

cond

cond

  where Q cond = mwater ( h2 − h1 ) and the logarithmic mean temperature difference is defined by Eqn. (4):

5

(iv) Humidifier

Distillate out

4

cond

LMTDcond =

(T

db,5

⎛ T −T ⎞ ln ⎜⎜ db,5 2 ⎟⎟ ⎝ Tdb,6 − T1 ⎠

D Air in

B

− T2 ) − (Tdb,6 − T1 )

(4)

Brine out

FIG. 1: SCHEMATIC DIAGRAM FOR THE SYSTEM

The modeling of the HDH systems involves the application of energy and mass conservation equations on the condenser and humidifier. These equations are applied under the following assumptions: 1. Flows through the system are at steady-state, steady-flow. 2. Both condenser, dehumidifier and piping have negligible heat losses to the atmosphere. 3. Kinetic and potential energy changes, for both the air-side and water-side, are negligible.. 4. Temperature rise due to pumping and fan power are negligible. 5. Air dry-bulb temperature and relative humidity are 30°C and 50% respectively which are conservative values for areas near the Red Sea 6. Thermodynamic properties of seawater are approximated to that of pure water.[26, 27, 28] 7. Water inlet temperature is set to the air inlet wet-bulb temperature. 8. Air is saturated at the condenser and humidifier exits. 9. The overall heat transfer coefficient for the condenser, Ucond, is a typical value of 45 W/m2K. [29] 10. The equivalent overall heat transfer coefficient for the humidifier, Uhumid, is the same as that of the condenser, 45 W/m2K.

3.3. Humidifier Model Eqns. (5) and (6) depicts the energy and mass balances for the condenser, respectively:

m water h3 − m B hB = m air ( h5 − h4 )

(5)

m air ω4 + m water = m air ω5 + m B

(6)

where the water inlet temperature depends on the solar water heater characteristics. The terminal temperature difference (TTDhumid) between the inlet air wet-bulb temperature and the exit water temperature was chosen as a design variable of the system. This allows the calculation of the exit water temperature and other variables in Eqns. (5) and (6). The area of the humidifier is calculated with Eqn. (7): Q =A U LMTD (7) humid

humid

humid

humid

  where Q humid = mair ( h5 − h4 ) and the logarithmic mean temperature difference is defined by Eqn. (8)

LMTD humid =

4

(TB − Twb,4 ) − (T3 − Twb,5 ) ⎛ T −T ⎞ ln ⎜ B wb,4 ⎟ ⎝ T3 − Twb,5 ⎠

(8)

Copyright © 2012 by ASME

3.4. Solar Water Heater Model The model of the solar water heater (SWH) is build from the models of off-shelf components for solar domestic water heating in SAM-2011 software [30]. Given the weather conditions data for the location where the desalination plant is to be built and the water flow rate and sizing of the SWH, SAM estimates the temperature of water in a storage tank for every hour of every day throughout a typical year. An example of the hourly data output from SAM is shown in Fig. 2. The main inputs to SAM that are needed in order to calculate hourly data as in Fig. 2 are listed as: 1. Weather data of the location 2. Water flow rate ( m water ) 3. Total area of the solar receiver panels (ASWH) 4. Volume of the hot water storage tank (Vtank)

TAmbient T2 (intake) Ttank

FIG. 2: EXAMPLE SAM-2011 HOURLY TEMPERATURE ESTIMATES

Based on the hourly estimates of tank temperature, a system uptime operability (µ) is calculated as: Number of hours when Ttank ≥ T3 µ= (9) Number of hours per year An underlying assumption in this model is that the desalination system shuts down when the solar energy supply and hot storage buffer from the tank are unable to reach the desirable operating temperature, and immediately starts back up at full capacity when the temperature is reached. While it may be somewhat inaccurate to ignore the transients, it is noted that an actual system can still produce fresh water (albeit, at less productivity) at lower water flow rate or operating temperature during hours of insufficient solar energy. As such, the binary state (operable or not) approximation in fact provides a conservative estimate of fresh water productivity.

3.5. Cost Models An estimate of the investment costs is obtained from typical values of chemical plant equipment [31], corrected with economic indicators [32] adjusted to September 2011. The costs of the system components are estimated by Eqns. (10) – (13):

Ccond = 4059+251Acond

(10)

Cfan = 2812+1270Vair

(11)

Cpump = 3714+1.2 × 10 Vwater − 3.6 ×10 +06

+07

V

2 water

(12)

C SWH = 750 + 220 × ASWH + 1100 × V tank

(13)

3.6. Optimization Problem Formulation The objective of the optimization is to minimize the cost per m3 of fresh water. This is summarized as: C cond + C fan + C pump + C SWH f (T3 , x , y ) = (1 + c p ) (14) µV t

(

)

D p

where: cp is a correction factor to account for inflation rate and yearly maintenance costs tp is the time duration of the project lifetime T3 is the heating temperature for the salty water before entering the humidifier unit x are design variables specific to HDH y are design variables specific to SWH x1 is the mass flow ratio of water to air: m x1 = water (15) m air x2 is the humidifier terminal temp. difference (TTDhumid) x3 is the dehumidifier terminal temp. difference (TTDcond) y1 is a dimensionless variable for sizing of the solar collector panels: × Average yearly Irradience A y1 = SWH (16) h2 × m water (T3 − TAverageAmbient ) y2

is a dimensionless variable for sizing of the hot water storage tank: V tank y2 = (17) Volume flow per day

Estimation of the objective value for a candidate design is calculated via the following steps: 1.

2.

5

Values of T3 and x are input to engineering equation solver (EES) [33], which solves the mass and energy balance equations (Eqns. (1) – (8)) in order to calculate the sizing of the HDH components and flow rates of salty water intake and product fresh water distillate while the system is running Water flow rate, y and desirable operating temperature T3 are input to a SamUL script [30] that calculates the components sizing of the SWH and runs SAM-2011 to

Copyright © 2012 by ASME

3.

obtain the hourly temperature data, then calculates the system uptime (µ) A MatLab [34] model uses component sizing, system uptime and distillate flow rate to calculate the investment cost via Eqns. (10) – (13), then the cost per m3 via Eqn. (14)

In the actual, implementation a MatLab function was implemented as the cost estimator which places calls to EES for the HDH calculations. SAM-2011 simulations were conducted offline for various combinations of (T3, y1, y2) and implemented as a lookup-table with an interpolating function in MatLab. 4. CASE STUDY This study considers a small scale desalination plant on a coastal location on the Red sea near the city of Hurgada. Average climate conditions are imported to SAM-2011 from [35]. The study assumes the sizing of all components (based on choices of the design variables) will be relative to a mass flow rate of air ( m air ) of 1.0 kg/s, which is a typical value for a small-scale desalination plant with productivity between 5 and 10 m3 per day. Inflation and yearly maintenance cost factor (cp) was assumed to be 10% and the plant lifetime was assumed to be 30 years, which are all typical values for the type of equipment considered [30]. The ranges of the design variables are listed in Table 1. Other than T3, whose upper limit is set by calcium carbonate within the system, the ranges of the design variables are set based on common choices for design of similar equipment [30, 31]. Table 1. RANGES OF DESIGN VARIABLES Design Variable

T3 [°C] x1 (water to air mass flow ratio) x2 (TTDhumid) [°C] x3 (TTDcond) [°C] y1 (SWH collector area ratio) y2 (SWH tank volume ratio)

Minimum 50.0 0.7 3.0 3.0 0.8 0.1

Maximum 60.0 2.7 10.0 10.0 2.8 8.0

Attempts to optimize the problem via sequential quadratic programming (SQP) revealed the existence of multiple local optima, as evident by SQP runs returning different results for different starting points as shown in Table 2. This motivated the testing a genetic algorithm (GA) [36] to solve the problem. The implemented genetic algorithm used a population size of 100 and was run for 100 generations. Genetic operators followed the best practices as recommended by Michalewiz’s real-coded GA [36]. Summary of the parameters are provided in Table 3. Since GA is a stochastic algorithm that isn’t necessarily guaranteed to produce the same result every time, a total of 10 runs were conducted. It was then observed that all the runs produced the same final result (with accuracy up to the considered significant digits). Details of the final system design suggested by GA are shown in Table 4.

Table 2. SAMPLE RESULTS FROM SQP Start-1

Result-1

60.00 2.00 10.00 10.00 2.80 8.00 43.31

T3 [°C] x1 x2 [°C] x3 [°C] y1 y2 f [$/m3]

Start-2

59.86 2.00 10.0 10.0 1.00 0.20 12.38

Result-2

55.00 1.35 6.50 7.00 1.80 4.05 32.75

53.75 1.35 6.50 7.00 0.80 0.10 14.91

Table 3. GENETIC ALGORITHM DETAILS Parameter

Population size Number of Generations Crossover #1 (applied 2 times) Crossover #2 (applied 2 times) Crossover #3 (applied 2 times) Mutation #1 (applied 4 times) Mutation #2 (applied 4 times) Mutation #3 (applied 4 times) Mutation #4 (applied 4 times)

Value 100 100 Heuristic Crossover Uniform Crossover Arithmetic Crossover Boundary Mutation Uniform Mutation Non-uniform Mutation Whole non-uniform Mutation

It is noted that the optimal values of the design variables indeed converged to interior values within the search space except T3, which converged to the maximum reasonable limit, thereby justifying the choice of variable ranges from common practices. It is also observed that the unit cost of fresh water is in-line with the reported values of 3-7 $/m3 in [24] as best attainable despite the conservative estimates adopted in this modeling and study. Table 4. GA RESULTS AND OPTIMAL SYSTEM DETAILS Base Result

T3 [°C] x1 x2 [°C] x3 [°C] y1 y2 Ahumid [m2] Acond [m2] ASWH [m2] Vtank [m3] VD [m3/day] f [$/m3]

Uhumid 10%

Uhumid 20%

60.0 2.0 5.3 7.3 1.2 0.4 1183.6 496.8 1434.7 69.3 6.8

Increase 60.0 2.0 5.1 7.3 1.2 0.4 1110.5 492.2 1457.7 69.3 6.8

Increase 60.0 2.0 4.9 7.3 1.2 0.4 1051.7 494.0 1434.7 69.3 6.9

8.2

8.1

8.0

A sensitivity study is conducted for one of model parameters with conservative estimates, which is the equivalent heat transfer coefficient UHumid. UHumid was assumed to be equal to that of the condenser, when in fact it is usually more because of the direct mixing between air and hot water. Studies were conducted for an increase by 10% and 20% of UHumid. Results

6

Copyright © 2012 by ASME

of GA (as summarized in Table 4) show only minor changes in the values of optimal design variables, but the unit cost of fresh water decreases. This is because higher heat transfer coefficient in the humidifier results in a smaller sized (and less costly) humidifier unit to achieve the same performance.

7

8 9

5. CONCLUSION An integrated optimization model of a solar-powered desalination system humidification-dehumidification (HDH) is presented. The system uses a simple water-heated setup for small-scale plant. Design variables included the sizing of solar collectors, storage tank, humidifier and dehumidifier, as well as air circulation flow rate and operating temperature. Simulation of system performance is performed through linking systemlevel models in Engineering Equation Solver, SAM-2011 and MatLab. An optimization objective is formulated for the minimization of unit cost of the produced fresh water. Initial optimization attempts revealed the existence of multiple local optima, which motivated the use of genetic algorithm. The cost of unit volume of fresh water for a prospective plant near the city of Hurgada was estimated to be in-line with typical values in the literature. Further work in this research may include detailed models of some of the components of the desalination system. Detailed models allow relaxing some conservative assumptions on the system performance, and should lead to lowering the estimated cost of fresh water. ACKNOWLEDGEMENT This research was supported by the U.S. Department of Agriculture and Egypt Science and Technology Development Fund. STDF Project #3832.

10

11

12

13

14

15

16

REFERENCES 1

2

3

4

5

6

P. Lawrence, J. Meigh and C. Sullivan, (2003) “The Water Poverty Index: an International Comparison,” Keele Economics Research Papers KERP 2002/19, Centre for Economic Research, Keele University, revised. Reid, C. and Breton E. (1959). “Water and ion flow across cellulosic membranes,” J. of Applied Polymer Science, 1: 133-143. Sourirajan S. (1977). Reverse Osmosis and Synthetic Membranes: Theory-Technology-Engineering, National Research Council, Ottawa, Canada. Peterson, R., Cadotte, J. and Buttner, J. (1982). “Final Report: development of FT-30 membrane in spiral wound modules,” Contract No. 14-34-0001-8547 for US department of Interior. Bray, D. and Menzel, H. (1966). “Design study of reverse osmosis pilot plant,” Office of Saline Water Research and Development Progress, Report No. 176. Mahon, H. and Lipps, B. (1971). Encyclopedia of Polymer Science and Technology, 15, Interscience publishers, New York. USA.

17

18

19

20

21

22

7

Wilf, M. (2007). The Guidebook to Membrane Desalination Technology, 1st Edition, Desalination Publications, L’Aquila, Italy. Mohsen M., and Jaber J. (2001). “A photovoltaic-powered system for water desalination,” Desalination, 138:129–36. Abdallah S., Abu-Hilal M., and Mohsen M. (2005). “Performance of a photovoltaic powered reverse osmosis system under local climatic conditions,” Desalination, 183: 95–104. Bouguecha S., Hamrouni, B., and Dhahbi, M. (2005). “Small scale desalination pilots powered by renewable energy sources: case studies,” Desalination, 183:151–65. Greenlee, L., Lawler, D., Freeman, B., Marrot, B. and Moulin, P., 2009, “Reverse Osmosis Desalination: Water Sources, Technology, and Today’s Challenges,” Water Research, 43(9): 2317–2348. Alghoul, M, Poovanaesvaran, P., Sopian, K. and Sulaiman, M. (2009). “Review of brackish water reverse osmosis (BWRO) system designs,” Renewable and Sustainable Energy Reviews, 13: 2661–2667. Van der Meer, W., Riemersma, M. and Van Dijk, J. (1998). “Only two membrane modules per pressure vessel? Hydraulic optimization of spiral-wound membrane filtration plants,” Desalination, 119: 57-64. Voros, N. (1996). “Optimization of reverse osmosis networks for seawater desalination,” Comp. Chem. Eng., 20: 345–350. Lu, Y. (2007). “Optimum design of reverse osmosis system under different feed concentration and product specification,” J. Membrane. Science, 287: 219–229. Guria, C., Bhattacharya, P. and Gupta, S. (2005). “Multiobjective optimization of reverse osmosis desalination units using different adaptations of the non-dominated sorting genetic algorithm”, Comp. Chem. Eng., 29: 1977– 1995. Vince, F., Marechal, F., Aoustin, E. and , Bréant, P. (2008). “Multi-objective optimization of RO desalination plants,” Desalination, 222: 96-118. Khayet, M., Essalhi, M., Armetna-Du, C, Cojucaru, C. and Hilal, N. (2010). “Optimization of solar-powered reverse osmosis desalination pilot plant using response surface methodology,” Desalination, 261: 284-292. Farsad, S. and Behzadmehr, A. (2011). “Analysis of a Solar Desalination Unit with HumidificationDehumidification Cycle Using Doe Method”, Desalination, 278(1-3): 70-76. Mehrgoo, M. and Amidpour M. (2011). “Derivation of Optimal Geometry of a Multi-Effect HumidificationDehumidification Desalination Unit: A Constructal Design”, Desalination, 281: 234-242. El-Agouz, S. (2010). “Desalination Based on Humidification–Dehumidification by Air Bubbles Passing Through Brackish Water”, Chemical Engineering Journal, 165: 413–419. Soufari, S., Zamen, M. and Amidpour, M. (2009). “Performance Optimization of the Humidification–

Copyright © 2012 by ASME

23

24

25

26

27

28

29

30 31

32 33 34 35

36

Dehumidification Desalination Process Using Mathematical Programming,” Desalination, 237(1-3): 305-317. Narayan, G.., a, Sharqawy, M., Summers, E., Lienhard, J., Zubair, S. and Antar, M. (2010). “The potential of solardriven humidification–dehumidification desalination for small-scale decentralized water production,” Renewable and Sustainable Energy Reviews, 14: 1187-1201. Garma, S., Wayman, E. and Bradford, T. (2008). Concentrating Solar Power – Technology, Cost and Markets, Green-Tech Media 2008 Industry Report. Pitz-Pal, R. (2009). Solar Energy Conversion and Photoenergy Systems, Chapter 6: High Temperature Solar Concentrators, Encyclopedia of Life Support Systems. S. Eftekharzadeh, M.M. Baasiri, P.A. Lindahl Jr., (2003) “Feasibility of Seawater Cooling Towers for Large-Scale Petrochemical Development,” Technical Report TP03-17. Cooling Technology Institute, San Antonio, Texas. M.H. Sharqawy, J.H. Lienhard V, S.M. Zubair, (2010) “Thermophysical Properties of Seawater: A Review of Existing Correlations and Data,” Desalination and Water Treatment 16, 354-380. M.H. Sharqawy, J.H. Lienhard V, S.M. Zubair, (2011), “On Thermal Performance Of Seawater Cooling Towers,” Journal of Engineering for Gas Turbines and Power, 133, 043001. Juan-Jorge Hermosillo, Camilo A. Arancibia-Bulnes, Claudio A. Estrada, (2011), “Water Desalination by Air Humidification: Mathematical Model and Experimental Study,” Solar Energy. National Renewable Energy Laboratory (2011), System Advisor Model, https://sam.nrel.gov/ Max S. Peters, Klaus Timmerhaus, Ronald E. West, (2003), “Plant Design and Economics for Chemical Engineers,” 5/e, McGraw-Hill Higher Education, ISBN 9780071240444. “Economic Indicators,” Chemical Engineering, (2012), 119, (1), 56-56. S.A. Klein, Engineering Equation Solver, Academic Professional Version 8.940, http://www.fchart.com. www.mathworks.com US Department of Energy, (2011). EnergyPlus Simulation Software, Weather Data: http://apps1.eere.energy.gov/buildings/energyplus/cfm/wea ther_data.cfm Michalewiz, Z. and Fogel, D. B., 2000, How to Solve it: Modern Heuristics, Springer-Verlag Berlin Heidelberg, New York

8

Copyright © 2012 by ASME