SUPPLEMENTARY MATERIALS

Reverse Osmosis Model

The RO model implemented for the present work is able to simulate a single-stage or double-

stage plant (i.e. one or two units in series) depending on the recovery, given by the ratio

between permeates and feed flow rate. In particular, for any recovery higher than 50% a

double stage is automatically selected (Dow Water and Process Solutions). The model follows

the typical structure of the stage, given by the arrangement of a number of pressure vessels in

parallel. A single pressure vessel is schematically represented in Figure S 1. A pressure vessel

contains a number of RO units in series (typically between 6 and 8), which have usually a

spiral-wound configuration. The brine produced by a unit constitutes the feed of the following

unit, while the permeate solutions are mixed together. The number of pressure vessels in

parallel is calculated on the basis of the total recovery, given by the ratio between the required

permeate solution flow rate and the feed flow rate.

Figure S 1. Scheme of a RO pressure vessel.

Firstly, the model calculates the membrane properties and the fluxes of water and salt through

the RO membrane, under an applied pressure, through a two-parameter solution-diffusion

model (RO membrane equations in Table S 1) (Wilf 2007). The concentration polarization is

taken into account in the feed channel through the adoption of a concentration polarization

factor, while it is neglected in the permeate (Wilf 2007). Concerning the RO unit, a spiral-

wound geometry is typically used. However, for the modelling purposes, as widely reported

in literature, the channel curvature is negligible and the wound membranes are treated as

unwounded, since the channel thickness is much lower than the module radius, so the

configuration is analogous to the plate and frame system (Senthilmurugan et al. 2005).

Moreover, the variation along the permeate flow direction is neglected and we implemented a

one-dimensional model by discretizing the unit along the feed main flow direction (Geraldes

et al. 2005), (Al-Obaidi and Mujtaba 2016). For every discretization element, the membrane

solver is applied to calculate the trans-membrane flux and consequently the permeate flow

rate and composition. Thus, mass balances are employed for the evaluation of the

concentration and flow rate on the retentate and permeate sides (RO unit equations in Table S

1) (Malek et al. 1996). Finally, the RO units are interconnected to simulate the pressure

vessel, where the retentate of one unit constitutes the feed of the following unit, while the

permeate solutions produced by each unit are mixed together. On the basis of the recovery,

the total membrane area, which corresponds to a number of vessels in parallel, is estimated. In

this case the feed pressure is not given as an input but it is calculated through the

minimization of the difference between the required and the calculated recovery. The model

can be also run in the operation mode: at a fixed membrane area and feed pressure, the total

recovery is calculated. The equations for the estimation of the membrane area (design mode)

and for the estimation of the total recovery (operation mode) are reported in Table S 1,

together with the ones for the calculation of the outlet permeate composition and the

electricity demand. In particular, a reference case of RO unit without brine Energy Recovery

Device (ERD) is considered. For the sake of completeness, also a scenario with pressure

exchangers, with an efficiency of 98% (Gebel and Yuce 2008), was investigated. As a matter

of fact, the pressure exchanger allows recovering most of the pressure of the outlet brine. This

reduces the total energy consumption of the RO unit on the one hand, but it leads to an

increase the capex on the other. In the present case, it was observed that the high recovery

ratio of the RO unit and the consequent low brine volumetric flow rate, from which the

pressure energy can be recovered, make the increase of the capex more critical than the

reduction of the opex. For this reason, only the reference case without ERD has been reported

among results, as the presence of ERD results in a slight increase of overall costs.

Model validation

The validation of the model was performed by comparing the results with the ones of the

commercial software Wave, developed by Dow (www.dow.com/water-and-process-

solutions/resources/design-software). The results were collected considering a single stage

RO plant, with a fixed membrane area, and calculating the feed pressure required to obtain a

certain recovery and the corresponding average water flux. The validation charts are reported

in Figure S 2.

Figure S 2. Feed pressure and average water flux trends vs. the plant recovery obtained with the present model and with the commercial software Wave.

Table S 1. Main equations of the Reverse Osmosis model

RO membrane equations

(A1) Fw=Amembr ( ∆ P−∆ Π )Water flux through the

membrane

(A2) F s=Bmembr ( X f , w−X p ) 10−6 Salt flux through the

membrane

(A3) ∆ Π=2 RT ρM NaCl

10−5 ( X f , w−X p )10−6 Transmembrane osmotic

pressure difference

(A4) TCF= 1

eCmembr ( 1

T− 1

298 )Temperature correction

factor

(A5) MAFw=(1−∆ Φw )agemembrMembrane ageing factor-

water flux

(A6) MAF s=1+∆ Φs , coeff ×agemembr

Membrane ageing factor-salt

flux

(A7) CPF=X f , w

X f=k p e

( 2 Ri

2−R i) Concentration polarization

factor (Wilf 2007)

RO unit equations

(A8) M perm [ i ]=( Fw [i ]+F s [ i ] )Aelem

ndiscr

Permeate flow rate produced

in the i-th element

(A9) X perm [ i ]=F s [ i ]Fw [i ] 106 Permeate concentration (of

NaCl) in the i-th element

(A10) M brine [ i ]=M f [ i ]−M perm [ i ]Brine flow rate produced in

the i-th element

(A11) X brine [i ]=X f [i ] M f [i ]−X perm [ i ] M perm [i ]

M brine [i ]Brine concentration (of

NaCl) in the i-th element

(A12) M f [ i+1 ]=M brine [ i ] Mass balances between two

following elements(A13) X f [i+1 ]=Xbrine [ i ]

(A14) M perm ,out unit=∑i

M perm [i ] Permeate flow rate produced

by the RO unit

(A15) X perm , out unit=∑

iM perm [i ] X perm [i ]

∑i

M perm [ i ]

Concentration of the

permeate produced by the

RO unit

RO plant equation

(A16) Atot=Rplant M feed

Fw

Total membrane area for a

given plant recovery

(A17) Rplant=M perm , out

M feed×100 Plant recovery

(A18) X perm , out=∑elem

M perm ,out elem X perm, out elem

M perm ,out

Outlet composition of the

permeate solution

(A19) PHP=P feed 105 M perm

ρ ηpump

Power demand of the high

pressure pump

(A20) PBP=(P¿¿ feed−PERD ,out )105 M feed

ρ ηpump¿

Power demand of the booster

pump (if the ERD is present)

Membrane Distillation Model

The MD model presents also a hierarchical structure, which follows the structure of the MD

plant. In this case, a particular attention has been devoted to the modelling of heat and mass

transfer mechanisms through the membrane. The DCMD configuration, as it is reported in

Figure S 3, presents two channels separated by the membrane: one channel is crossed by the

feed solution, while the other by a permeate solution used to keep a low temperature on one

side of the membrane. Therefore, the temperature difference gives rise to a vapour pressure

difference, which constitutes the driving force of the system.

Figure S 3. Schematic representation of the MD membrane, with the temperature and concentration profiles.

The equations used to describe the transfer mechanisms through the membrane are reported in

Table S 2: concerning the heat transfer phenomena, the heat fluxes from the bulk of the two

channels to the bulk-membrane interfaces and the heat flux through the membrane are

defined. The calculation of the temperature at the two interfaces is derived by equating Qconv,hot

to Qm and by equating Qm and Qconv,cold (Khayet et al. 2004). The convective heat fluxes

depend on the convective heat transfer coefficient h, which is estimated starting from the

Nusselts number, defined in the equations A29-A30 (Hitsov et al. 2017). The transmembrane

heat flux is given by the heat transferred by conduction (Qcond,m [W/m2]) and the latent heat

associated to the vapour flux (Qevap,m [W/m2]) (Khalifa et al. 2017). The conductive heat flux

through the membrane depends on the conductivity of the membrane, given by the

combination of the conductivity of the polymeric structure and the one of air in the pores, as

reported in equation A28. Concerning the mass transfer phenomena, the vapour flux Jw

depends on the vapour pressure difference and on the mass transfer coefficient Bm. Bm

depends on the main transport mechanism through porous membranes among the Knudsen

diffusion, the molecular diffusion or a combination of these two (Qtaishat et al. 2008). The

most significant transport mechanism can be identified on the basis of the Knudsen

coefficient, given by the ratio between the molecular mean free path λ and the pore diameter.

Finally, the concentration at the feed-membrane interface (Cm,hot) usually results higher than

the concentration in the bulk, because of the concentration polarization. This effect can be

estimated via equation A36.

Concerning the MD unit, a counter-current flow arrangement is selected. Therefore, we set up

an iterative procedure, which simulates the whole MD unit until the error between the

calculated and the given values of permeate flow rate and temperature results lower than a

fixed tolerance. The MD unit is divided into a certain number of discretization steps along the

feed flow direction and for each unit the vapour and the heat flux through the membrane are

calculated. Then, mass and energy balances (Equations A37-A41) are applied to evaluate the

flow rate and temperature profiles in the two channels and the concentration profile in the

feed channel, along the MD unit length. Commercial MD modules are given by a number of

MD membranes wounded together in a spiral-wound fashion. Thus, each commercial module

is given by a number of feed and permeate channels, arranged in parallel, and is typically

crossed by a feed flow rate between 500 and 1500 l/h (Hitsov et al. 2018).

The MD plant is designed taking into account the total feed flow rate to be treated and the

plant recovery and it is given by MD modules arranged in parallel and in series (Ali et al.

2018). Between each module and the following one, a heater and a cooler have to be

accounted to ensure the same inlet temperatures in every module. In order to reduce the

thermal energy demand, recovery heat exchangers are used to realize a thermal integration

between the streams. As in the RO plant, three arrangements are considered depending on the

total recovery: single stage for recovery values lower than 50%, two stages for recovery

between 50 and 75% and three stages for recovery higher than 75%.

The knowledge of the plant recovery (or of the required concentration of the outlet brine)

allows calculating the outlet flow rates of the brine and the distillate (equations A42-A43).

The number of branches in parallel depends on the total flow rate and on the design flow rate

of the module (equation A44). Furthermore, the number of modules in series depends on the

plant recovery and it is estimated via an iterative procedure, in which the vapour flux of each

MD unit is recalculated based on the inlet flow rate and concentration of the module. The

starting value for the number of modules in series is calculated taking into account the

average flux produced in a module with an operating concentration and flow rate equal to the

average values between the inlet and the required output (equation A45). Therefore, the whole

series is simulated considering that the outlet flow rate and concentration of a module

correspond to the inlet flow rate and concentration of the following module in the series.

Conversely, the inlet temperatures are always the same, thanks to the employment of

intermediate heaters, coolers and recovery heat exchangers. The thermal energy demand of

the MD plant is calculated as the sum of the demands of the intermediate heaters plus the heat

needed to raise the temperature of the feed to the required inlet temperature.

Table S 2 Main equations of the Membrane distillation model

MD membrane equations

(A21

)Qconv ,hot=hf (T bulk , hot−T m,hot )

Convective heat flux-feed

side

(A22

)Qm=Qcond ,m+Q evap,m

Heat flux through the

membrane

(A23

)Qcond , m=hm (T m, hot−T m,cold )

Conductive heat flux

through the membrane

(A24

)Qevap ,m=J w ∆ H evap[T m, hot+T m , cold

2 ] Latent heat with the

vapour flux

(A25

)Qconv ,cold=hp (T m,cold−Tbulk ,cold )

Convective heat flux –

permeate side

(A26

)T m,hot=

k m

δm(T b , cold+

h f

hpT b ,hot )+hf T b , hot−J w ∆ H evap

km

δm+hf +km

h f

hp δm

Temperature at the

feed-membrane

interface

(A27

)T m ,cold=

k m

δ m(Tb, hot+

hp

h fT b ,cold)+hp T b ,cold+Jw ∆ H evap

k m

δm+hp+km

hp

hf δ m

Temperature at the

permeate-membrane

interface

(A28

)k m=ε k air+ (1−ε ) kmembr , pol

Conductivity of the

membrane

(A29

)Nu=0.13 ℜ0.64 Pr0.38

Nu for laminar flow

(Andrjesdóttir et al. 2013)

(A30

)Nu=0.22 ℜ0.69 Prbulk

0.13( Prbulk

Prmembr)

0.25 Nu for turbulent flow

(Hitsov et al. 2017)

(A31

)Jw=Bm ( Pm, hot−Pm, cold ) Water flux

(A32

)λ=

KbT

√2 π Ppore dwat2

Molecular mean free

path

(A33

)Dw , k=

2r pore ε3 τ √ 8R T

π M w , wat

Knudsen diffusion

coefficient

(A34

)Dw , m=4.46 E−6 ε

τT 2.334 Molecular diffusion

coefficient

(A35

)Bm=

1R T

Dw , k Dw, m

Dw , m+Pair Dw, k

M w ,wat

δ m

Mass transfer

coefficient

(A36

) Cm,hot=Cbulk , hot eJw

k f ,mass ρ

Concentration at the

feed-membrane

interface

MD unit equations

(A37

)mf ,out [ i ]=mf ,∈¿[i ]−J w[ i]A elem 10−3¿

Outlet feed flow rate in

the i-th element

(A38

)mp ,∈¿[i ]=m p ,out[i ]−Jw [i] Aelem 10−3¿

Inlet permeate flow rate

(i-th element)

(A39

)T f , out [ i]=mf ,∈¿[i]Cp , f ,∈¿[i] ρ

f ,∈¿ [i]

Tf ,∈¿ [i]−Qm [i ]Aelemm f ,out [i]C p, f ,out [i] ρf , out[ i]¿

¿¿¿ Outlet feed temperature

(i-th element)

(A40

)T

p ,∈¿[i]=m p,out [i ]C p ,p ,out[ i]ρ p,out [i]T p, out[i ]−Qm [ i] Aelem

mf ,∈¿[i]Cp, f ,∈¿ [i] ρf ,∈¿ [i]

¿¿¿¿

Outlet permeate

temperature (i-th

element)

(A41

)C f , out [ i]=mf ,∈¿[ i] ρ

f ,∈¿[i]Cf ,∈¿ [i ]

m f ,out[i]ρf , out [i]¿¿¿

Outlet feed

concentration (i-th

element)

MD plant equations

(A42

)M brine , plant=

M feed , plant C feed

Cbrine , plantOutlet brine flow rate

(A43

)M dist , plant=M feed, plant−M brine , plant

Outlet distillate flow

rate

(A44

)N ¿=∫( M feed , plant ρ feed , plant

M feed ,∈−design−lh× 1000 ) Number of branches in

parallel

(A45

)N guess

modules , series=∫( M dist , plant

Jw , aver N ¿ Amodule 3600 )Guess value of the

number of modules in

series

Model validation

The MD model was validated comparing the results of a commercial MD module at different

feed temperatures and concentrations with the ones reported in literature by Hitsov et al.

(Hitsov et al. 2017). The simulations were performed with a DCMD module, with a feed flow

rate of 1500 l/h and an inlet temperature of the permeate solution of 20°C. In analogy with

what reported in literature, the effect of the pressure drops on the membrane properties is here

reported by increasing the membrane conductivity by 50%. The resulting water fluxes and

outlet temperatures in the hot and in the cold channels are reported in Figure S 4.

Figure S 4. Trends of the water flux vs. the feed concentration (on the left) and of the outlet temperature in the feed and in the permeate channels vs. the feed concentration (on the right) at two different inlet feed temperatures. The lines correspond to the results of the model, the markers to the experimental results reported in (Hitsov et al. 2017).

Implementation and numerical details

The tools developed for the simulation of the single units are integrated and interconnected in

a simulation platform called Remote Component Environment (RCE), internally developed by

DLR and freely available (http://rcenvironment.de/). The units are represented by customized

blocks, which call the relevant models, and are connected in order to exchange data. This

leads to the construction of a workflow, as the one reported in Figure S 5, which is relevant to

the RO-MD chain.

Figure S 5. Workflow of the treatment chain composed by NF, crystallizer, RO and MD as implemented in RCE. The dark-blue blocks represent the units in the pre-treatment step and the red blocks the units in the concentration step; the green units indicate the input values or files; the light blue blocks indicate the outputs; the light orange blocks are the intermediate tools, representing mixing units.

In the reported example, each process included in the chain is represented by a block. The

block consists in a customized script, which imports the model file and calls the solver

procedure. The models have been re-shaped in Python through the implementation of suitable

wrappers. Each block is connected to a certain number of “input provider” blocks, which

supply the main inputs required by the model, while all the other parameters are reported in

the relevant “config file”. Some of the inputs of the blocks are provided by the previous ones,

such as the feed flow rate or the feed composition. In some cases, it is necessary to position

intermediate scripts in order to adjust the units of measurement or to apply intermediate mass

balances (e.g. the block “mass bal.” which represents the mixing of the effluent of the

crystallizer and the permeate of the nanofiltration and the block “Dist calc” which represents

the mixing of the distillates coming from the RO and the MD units). Finally, for what

concerns the output generated by the workflow, every block corresponding to a process

produces an excel file, where all the system outputs are collected. Moreover, some

customized scripts are used to merge the files and to produce directly the main figures of the

treatment chain. These units are not reported in Figure S 5 in order to simplify the workflow.

PV-battery plant model and outputs

The integrated PV-battery system is simulated through a tool implemented in INSEL (Moser

et al. 2014). Each INSEL simulation firstly requires as input the specific location

meteorological data of a typical year with hourly resolution. In particular, the global

horizontal irradiance (GHI), the diffuse horizontal irradiance (DHI) and the ambient

temperature are needed. With such inputs, the model is able to produce a matrix with the

values of the power produced by the PV-battery system varying the PV installed power [MW]

and the full load hours of the battery [h]. Overall, the produced power depends on the given

PV installed power, on the given full load hours of the battery and on the solar potential of the

location, based on the meteorological data. Parametric analyses varying the installed PV

power and capacity of the battery give rise to a scatter of LCOE values as a function of the

CO2 emission factor, which are due to the share of the demand covered by the grid. Figure 7

in the paper shows the values of the global LCOE as a function of the CO2 emissions, when

the ratio between the installed PV power and the power demand is varied between 0.5 and 10

and, for each Pinst/Pdemand ratio, the full load hours of the battery are varied between 0 and 17.5

h. In the chart A, the emissions are supposed to be subjected to no taxation, while in chart B

the CO2 emissions are taxed with a price of 80 €/ton.

The curves with the same colour correspond to configurations with equal Pinst/Pdemand ratio and

variable full load hours of the battery. Values of Pinst/Pdemand ratio lower than 1 (curves on the

right) correspond to small PV plants, which are never able to produce a power higher than the

load. For this reason, the presence of the battery is completely irrelevant for the power

produced and it only determines an increase of the costs. As a consequence, the trends are

vertical. When the Pinst/Pdemand ratio increases, the plants become bigger and the produced

power increases. Thus, the battery can play a more relevant role, because it can store the

surplus of energy produced in some hours, which can be reused when the produced power

decreases. Therefore, the increase of the full load hours of the battery causes a growth of the

share of the total load covered by the renewables, which corresponds to a decrease of the

emission factor. However, in presence of lower emissions, it is necessary to build larger fields

with an installed power much higher than the demand and this leads to higher system costs

and higher LCOE values. Conversely, at higher emissions, the LCOE mostly decreases

because of the relatively low cost of the electricity purchased from the grid. These effects are

evident in the black line reported in both charts to highlight the lower envelope of the scatter

plots. Interestingly, both curves present a minimum value of LCOE at a given CO2 emission

factor: on the left side, the LCOE increases with the renewable share because of the higher

investments required for larger PV and battery; conversely, on the right side, the LCOE

increases because the cost of the electricity from the grid results higher than the LCOE given

by the combined electricity supply. In chart A, the minimum corresponds to a configuration

with very low share of renewables, because of the relatively low cost of the electricity

purchased from the grid, while in chart B the minimum occurs at a slightly higher share,

because of the taxation considered in the second case, which makes the configurations

dominated by the grid less convenient.

NOMENCLATURE

GHI global horizontal irradiance DHI diffuse horizontal irradianceLCOE levelized cost of electricityPV photovoltaicP power [MW]

Membrane Distillation

T temperature [K]m volume flow rate [m3/s]C concentration [ppm]Q heat flux [W/m2]h heat transfer coefficient [W/(m2 K)]Jw water flux [kg/(m2 s)]ΔHevap latent heat of vaporization of water [J/kg]km membrane thermal conductivity [W/(m K)]kair air thermal conductivity [W/(m K)]kmembr,pol polymeric structure thermal conductivity [W/(m K)]k thermal conductivity of the solution [W/(m K)]Nu Nusselt number [-]Re Reynolds number [-]Pr Prandtl number [-]Dh hydraulic diameter of the channel [m]v fluid velocity in the channel [m/s]Cp fluid specific heat [J/(kg K)]Bm mass transfer coefficient [kg / (m2 s Pa)]R ideal gas constant (8.314 J/(K mol))kB Boltzmann constant (1.38066 x 10-23 J/K)dwat collision diameter of water vapour [m]Ppore pressure within the pores [Pa]rpore pore radius [m]Pair air pressure inside the pores [Pa]Dw,k Knudsen diffusion coefficient [m2/s)]Dw,m molecular diffusion coefficient [m2/s]DNaCl,watdiffusivity of NaCl in water [m2/s]kf,mass mass transfer coefficient in the feed channel [m/s]Nparallel number of branches in parallel [-]Ntot,modules total number of modules present in the plant [-]Amodule membrane area of a single module [m2]Subscriptsm membranef feedp permeatem,hot feed membrane interface

m,cold permeate membrane interfacebulk,hot feed bulkbulk,cold permeate bulkconv,hot convective flux, feed sideconv,cold convective flux, permeate sidecond,m conductive flux, membraneevap,m latent heat, membranein inlet in the elementout outlet of the elementGreek lettersδm membrane thickness [m]ε membrane porosity [-]ρ solution density [kg/m3]μ dynamic viscosity of the fluid [kg/(m s)]λ molecular mean free path [m]τ membrane tortuosity [-]Reverse Osmosis

Amembr pure water permeability in the membrane [kg/(s m2 bar)]Bmembr solute permeability in the membrane [kg/(s m2)]Fw water flux [kg/(s m2)]Fs salt flux [kg/(s m2)]T operating temperature [K]ΔP transmembrane pressure difference [bar]kp membrane constant [-]Ri recovery of the i-th element [-]X concentration [ppm]Rsalt salt rejection [%]A membrane active area [m]Cmembr membrane constant for the temperature correction factor [K]M flow rate [kg/s]ndiscr number of discretization intervals [-]Rplant plant recovery [%]Pfeed feed pressure [bar]PRO electric power consumption [W]CPF concentration polarization factorSubscriptsf,w feed side at the membrane interfacef feed sidep permeate sidefeed feed solutionbrine brine solutionperm permeate solutionperm,out unit permeate produced by the RO unitperm,out permeate produced by the plant

w waters saltelem discretization elementGreek lettersΔΠ transmembrane osmotic pressure difference [bar]ΔΦ relative variation of the flux with time [-]ρ density of the solution [kg/m3] ηpump efficiency of the high pressure pump [-]AcronymsTCF temperature correction factorMAF membrane ageing factor

BIBLIOGRAPHY

Al-Obaidi, M. A., and I. M. Mujtaba. 2016. Steady state and dynamic modeling of spiral wound wastewater reverse osmosis process. Computers & Chemical Engineering 90:278-299.

Ali, A., J.-H. Tsai, K.-L. Tung, E. Drioli, and F. Macedonio. 2018. Designing and optimization of continuous direct contact membrane distillation process. Desalination 426:97-107.

Andrjesdóttir, Ó., C. L. Ong, M. Nabavi, S. Paredes, A. S. G. Khalil, B. Michel, and D. Poulikakos. 2013. An experimentally optimized model for heat and mass transfer in direct contact membrane distillation. International Journal of Heat and Mass Transfer 66:855-867.

Dow Water and Process Solutions. Filmtec Reverse Osmosis Membrane. Technical Manual.Gebel, J., and S. Yuce. 2008. An Engineer's Guide to Desalination. VBG PowerTech.Geraldes, V., N. E. Pereira, and M. N. De Pinho. 2005. Simulation and Optimization of

Medium-Sized Seawater Reverse Osmosis Processes with Spiral-Wound Modules. Ind. Eng. Chem. Res. 44:1897-1905.

Hitsov, I., L. Eykens, W. D. Schepper, K. D. Sitter, C. Dotremont, and I. Nopens. 2017. Full-scale direct contact membrane distillation (DCMD) model including membrane compaction effects. Journal of Membrane Science 524:245-256.

Hitsov, I., K. D. Sitter, C. Dotremont, and I. Nopens. 2018. Economic modelling and model-based process optimization of membrane distillation. Desalination 436:125-143.

Khalifa, A., H. Ahmad, M. Antar, T. Laoui, and M. Khayet. 2017. Experimental and theoretical investigations on water desalination using direct contact membrane distillation. Desalination 404:22-34.

Khayet, M., A. Velázquez, and J. I. Mengual. 2004. Modelling mass transport through a porous partition: Effect of pore size distribution. Journal of Non-Equilibrium Thermodynamics 29.

Malek, A., M. N. A. Hawlader, and J. C. Ho. 1996. Design and economics of RO seawater desalination. Desalination 105:245-261.

Moser, M., F. Trieb, T. Fichter, J. Kern, and D. Hess. 2014. A flexible techno-economic model for the assessment of desalination plants driven by renewable energies. Desalination and Water Treatment:1-15.

Qtaishat, M., T. Matsuura, B. Kruczek, and M. Khayet. 2008. Heat and mass transfer analysis in direct contact membrane distillation. Desalination 219:272-292.

Senthilmurugan, S., A. Ahluwalia, and S. K. Gupta. 2005. Modeling of a spiral-wound module and estimation of model parameters using numerical techniques. Desalination 173:269-286.

Wilf, M. 2007. The Guidebook to Membrane Desalination Technology. Balaban Desalination Publications.