1

THE SCALING OF TUBES IN MSF EVAPORATORS: A CRITICAL

REVIEW ACROSS 20 YEARS OF OPERATIONAL EXPERIENCE

Authors:

E. Ghiazza, A.M. Ferro (Fisia Italimpianti)

ABSTRACT:

The accumulation of heat resistive deposits inside MSF evaporator tube bundles, deriving from

scaling and fouling, significantly reduces plant thermal efficiency up to 25% and more, thus

resulting in increased steam requirements to maintain the same distillate production rate.

Furthermore fouling contributes to tube corrosion and failure, badly affecting maintenance costs

and overall plant life.

An extensive overview of the results of scale control through 20 years of MSF evaporators

operational experience shows how best results have been achieved by the regular application of

sponge ball cleaning as an integral part of the overall chemical dosing and process control system to

maintain condenser tubes status near optimum.

On this basis, the paper proposes a new update of one of the most renowned scaling accumulation

models, and introduces a method for tubes scaling evaluation in running plants, leading to an

algorithm for actual fouling estimation from feed back data.

Fouling factor figures evaluated on long term plants feedback data confirm the revised updated

model, thus suggesting possible future influences on MSF operation and design optimisation.

2

CONTENTS

CONTENTS 2

1. INTRODUCTION 2

2. SCALING: PROBLEMS AND SOLUTIONS 3

2.1. Nature and causes 3

2.2. Problems caused by the fouling 3

2.3. Weapons against it 3

3. SCALE GROWTH MODELING: A NEW PERSPECTIVE 4

4. HOW DOES FOULING BEHAVE? 6

4.1. Methods for fouling factor evaluation 6

4.2. Time based fouling recording 7

5. NEW TRENDS AND CONCLUSIONS 8

FIGURES INDEX

Figure 1: How P.R. can be influenced by fouling 4

Figure 2: The original and the updated models: different results 6

Figure 3: Performance Ratio and recovery section fouling factor: a strong link 7

Figure 4: Fouling behaviour vs. time 8

Figure 5 The validation of the updated model: actual ff from plants feedback data 9

1. INTRODUCTION

The accumulation of any heat resistive deposits within the heat transfer tube bundles of an MSF

evaporator will rapidly reduce plant thermal efficiency and result in increased steam requirements

to maintain a set distillate production rate. Scale formation, hindering heat transfer process,

increases operating costs and causes frequent shut down of the plant for cleaning. For these reasons

and due to severe economic constraints imposed by the need of a low water cost scaling prevention

and control is of utmost importance in desalination plants.

At present the most widely used method to control scale is by the addition of chemicals additives

(antiscalants) combined with a continuous operation of sponge ball cleaning system.

Present understanding of basic phenomena involved in heat exchange surfaces fouling allows a

more precise modelling of the scaling deposition, better fitting the very satisfactory results reached

with these combined actions and confirmed by feed back data of plants running since long.

This leads to the firm belief that future improvements on MSF design are possible through a heat

transfer surface optimisation.

3

2. SCALING: PROBLEMS AND SOLUTIONS

2.1. Nature and causes

The rate of scale formation is usually influenced by temperature, pH, concentration of ions (e.g.

HCO3-, Ca

2+, Mg

2+), rate of CO2 release, total dissolved solids.

The main kinds of scales taking place can be summarised as follows:

Alkaline scales (Calcium carbonate, magnesium hydroxide) generated during periods of

chemicals under dosing and/or over-concentration of brine.

Non-alkaline scales (Calcium sulphate tenacious deposits): formed in supersaturated seawater,

at high concentration factors (1.8-2.0) and temperatures above 110-120°C.

Particulate deposits: fouling by fine silt can occur especially when the seabed is disturbed and

suspended solids content increases.

Other: chemical compounds (iron oxides and hydroxides) from corrosion, pollution deposits or

agglomerated suspended matter (silica, biological organism, and sludge).

2.2. Problems caused by the fouling

The presence of scaling directly involves the following effects:

formation of insulating film, that constitutes an additional resistance to heat transfer, thus

resulting in a reduction of plant thermal efficiency;

narrowing of passageways, that means either a flow rate reduction for the higher head loss in the

tubes or an increasing in energy consumption to keep the same flow rate;

risk of corrosion under deposits, with the consequent risk of pitting priming.

2.3. Weapons against it

Efforts to fight with fouling may be aimed to prevent all the possible scaling phenomena from

taking place (chemistry based methods: acid treatment or antiscalants dosing) or to remove the

scales once they are formed (chemistry based methods: acid cleaning, or mechanical methods:

physical removal by means of sponge ball).

To prevent the formation of solid deposits the following measures can be adopted:

Avoidance of temperature and concentration ranges in which solid phase can form.

Partial or complete removal of solution components that may form solid deposits (acid

treatment). This method involves the addition of acid (hazardous and corrosion initiator)

requires an additional decarbonator to remove the large volumes of CO2 released.

Inhibition of crystal growth by means of specific chemical additives. The first ones used in MSF

plants are polyphosphates, replaced later on by antiscalant polymeric compounds.

Once scaling is formed actions must be taken to remove it, by:

Chemical cleaning (acid cleaning): off-line method that removes even hard scales.

Mechanical cleaning, (sponge ball cleaning system): on-line method that removes any soft

scales.

The use of high temperature improved antiscalants together with the use of ball cleaning system at a

relatively high cycle frequency definitely prevents from tube sheet and tubes clogging, allows to

reduce additive dosage, lowers heat consumption and substantially reduces tubes corrosion.

Moreover, allowing an almost complete elimination of manual and /or off line cleaning, it leads to

very high plant availability.

4

In fig. 1 the advantages achieved in terms of both plant availability and energy saving are

summarised.

In fact, typical performance ratio very quick decline (curve C) causes the need for acid cleaning

after very short periods of plant operation (about 4 months), to bring back the performance ratio

(curve D) above the design figure. The continuous use of improved antiscale + sponge ball cleaning

system (curve E) allows, on the contrary, an extended operation keeping the performance ratio near

to the one characteristic of the clean plant (curve A) and usually far above the design figure (curve

B).

Figure 1: How fouling can influence P.R.

3. SCALE GROWTH MODELING: A NEW PERSPECTIVE

In the last years many studies have been carried out trying to correlate and rationalise heat exchange

surfaces scaling phenomena, correlating crystal growth data on the basis of different surface

reaction models. Nowadays the most borne out model for MSF evaporators seems to be the

asymptotic one. It was first proposed by Kern and Seaton [1] and it is based on the assumption that

the net scale deposition flux is given by the difference between the scale specific flux reaching the

exchange surface (.

m ) and the scale removal flux. This second term is assumed to be directly

proportional to the scale layer thickness (x) and to the shear stress ( exerted by the flowing liquid

on the scale layer, and inversely proportional to a parameter (B) characterising the adhesion of the

scale layer to the exchange surface. The variables and B are influenced by both the geometrical

characteristics and the operating condition of a single plant, and can be considered as plant

representative parameters.

According to these assumptions the scale layer growing rate is expressed by the following

differential equation:

PERFORMANCE RATIO DECLINE WITH FOULING

A

B

C

E

D

332,3

244,8

258,4

273,6

290,8

310,1

7

7,5

8

8,5

9

9,5

0 2 4 6 8 10 12

Operating Time [Months]

Pe

rfo

rma

nc

e r

ati

o [

kg

dis

t/23

26k

J]

Sp

ec

ific

He

at

Co

ns

um

p.

[kJ

/kg

dis

t]

A: Completely clean plant B: Design performance ratio

C: Typical decline by tube fouling E: P.R. with continuous spongeball cleaning

D: Off load acid cleaning

5

xB

md

dxss

.

Equation 3-1

where dt

dx represents the scale growth velocity and

dt

dxs the transport rate of scale constituents.

Solving this equation with the constraint of having zero fouling in the beginning, the trend of scale

layer thickness vs time is represented by the following expression:

)1( cexx

Equation 3-2

where the : characteristic time constant is defined as

B

c while the scale layer thickness

asymptotic value is given by

s

cm

x .

The fouling resistance commonly used for overall heat exchange coefficients evaluation is given by:

xff Equation 3-3

From the combination of equations 3-2 and 3-3, the fouling resistance vs time is given by:

)1( ceffff

Equation 3-4

The Kern & Seaton model dates back to the early sixties, when only polyphosphates were used in

desalination plants as antiscalant compound and no ball cleaning system was in operation. Using the

values of .

m , and B characteristic of this kind of operation, the classical figures for c and ff

were evaluated.

At present, things have become different, due to the development of high efficiency synthetic

antiscale compounds and to the well-established use of ball cleaning systems. The enhanced

antiscalants have a double effect, lowering both the scale flux (by a better inhibition of scale

formation) and the scale adhesion – described by the parameter B - (acting as an impurity which

avoids large scale crystals growth) [2]. The ball cleaning system strongly affects shear stress –

described by the parameter -, due in this case not only to the flowing liquid, but also to the balls

scraping effect. This comes true only in case of high frequency operation of the system itself.

Recent studies have roughly quantified the variation of the scale deposition flux (.

m ) and of the

plant parameters (and B), being respectively: .

m - 5% (1)

B - 10% (2)

+ 30% (3)

As a consequence, the characteristic time c and the asymptotic value of fouling resistance ff

drop

down to about 60-70% of their corresponding values predicted by the original model. For more

details about these figures see Appendix A.

The difference between the original and the updated model are outlined in the following diagram,

while in the next chapters a description of how the proposed updated model is well supported by

several plants feedback data will be outlined.

6

Figure 2: The original and the updated models: different results

4. HOW DOES FOULING BEHAVE?

4.1. Methods for fouling factor evaluation

Unfortunately, fouling is not a directly measurable variable; hence in order to estimate tubes scaling

in a running plant it is necessary to infer it in some way from field data. Since measurements are

often unreliable, we have to choose a stable and valid reference parameter to rely on. The plant

performance ratio, easily deducible from distillate production, condensate extraction flow and

temperature measurements, seems to be a suitable figure for this purpose due to the higher accuracy

of the measurements involved in its calculation and to the remarkable effect fouling has on it. In this

respect, all heat exchange surfaces (brine heater, heat reject, heat recovery) are in principle

involved, but not all of them can be related to plant performance ratio.

As brine heater tubes fouling increases, equilibrium condition in the exchanger will become

different to account for a reduced heat transfer coefficient, leading, to keep the same brine

temperature increase, to an increase of LMTD, and thus of condensation temperature. This effect

only slightly affects steam consumption, and the influence on plant performance ratio is negligible.

This concept also applies to the heat reject section, since the fouling increase in this section would

result only in a blow down temperature increase, again only slightly influencing distillate

production rate or steam consumption.

On the contrary, as the fouling in heat recovery stages increases, exchange phenomena in this

section will worsen leading to a brine temperature at brine heater inlet reduction. This would cause

a top brine temperature (and consequently distillate production) quick decline. To keep the same top

brine temperature (since this is the controlled variable fixed by distillate production demand) it

follows that a larger amount of steam will be required, thus directly bad affecting performance ratio.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 10 20 30 40 50 60 70time [days]

fracti

on

of

ori

gin

al K

&S

ff h

Polyphosphates / No B.C.S.(original K&S model)

Improved chemicals / No B.C.S.

Polyphosphates + B.C.S.

Improved chemicals + B.C.S.(modified K&S model)

7

For what above said, only heat recovery section fouling factor significantly affects plant

performance, so our efforts will concentrate on it as a representative index for plant performance

monitoring.

To calculate the recovery fouling from performance ratio data, it is possible to build up a set of

curves of fouling versus performance ratio for several different plants, as shown in fig. 3.

Figure 3: Performance Ratio and recovery section fouling factor: a strong link

These curves, based on plant heat & mass balances data, are built up by means of several computer

runs carried at nominal condition (the same used during plants performance tests), with different

percentages of design fouling factor. The resulting data are then interpolated to obtain the best

fitting continuous curve describing the plants behaviour, with lower fouling corresponding to higher

performance ratio, at the same reference running condition.

4.2. Time based fouling recording

The curves in fig. 2 allow only to evaluate punctually plant fouling from the feedback data of a

running plant. In addition to that it is necessary to build up a couple of curves describing the fouling

assessment with time (see fig. 3).

The first of these curves, built to cross 0 at the beginning and to cross the design figure for fouling

factor after a given time, represents the theoretical trend of fouling vs. time. The shape of the curve

as well as the design value for fouling factor are in accordance with the original model discussed in

chapter 3; it’s no accident that fouling asymptotic value is close to the figures still commonly

imposed in most desalination plant tender specifications.

In addition to the theoretical curve it is possible to build up a second curve from feed back data after

a certain number of running hours. This second curve, crossing again 0 at the beginning and

interpolating the feed back data at the various measurement times, represents the expected trend of

fouling vs. time, allowing a prediction of plant behaviour after a certain running time, knowing the

plant behaviour at present time (see Appendix A).

Both curves have a typical quick initial increase and will be later flat. The main highlight arising

from the comparison of these two curves, describing the plant behaviour as time goes by, is that the

actual/expected trend is always considerably lower than the theoretical one, as shown in the

following chapter, and in good accordance with the updated model.

Plant 1

Plant 2

Plant 3

Plant 4

0,00

0,02

0,04

0,06

0,08

0,10

0,12

0,14

0,16

0,18

0,20

7,0 7,5 8,0 8,5 9,0 9,5 10,0

performance ratio [kg/2326kJ]

reco

very

secti

on

fo

uli

ng

[m

2°C

/kW

]

8

Figure 4: Fouling behaviour vs. time

5. NEW TRENDS AND CONCLUSIONS

From the review of different plants feedback data [3], a rich data base has been created in order to

better understand the plants behaviour through their whole operating life. Extracting from this data

base the figures necessary to calculate the desalination units performance ratio, and applying the

procedure written above, the fouling trend for several plants in the periods between two subsequent

acid cleanings have been plotted.

The results for three units, quite different as far as capacity, materials, design performance ratio,

ambient conditions are concerned, are shown in fig. 4.

From the diagram, as a matter of fact, the following two basic issues clearly appear:

1. not depending on the specific plant, the fouling behaviour is the same, settling to an

approximately constant value after a relatively short number of running hours;

2. the asymptotic value of the fouling is always remarkably lower than the corresponding

design value.

It must be noted that all the reported data refer to desalination units where the ball cleaning system

was kept in almost continuous operation, coupled with a proper dosage of improved chemicals for

scale prevention, which is, according to our experience, the best way to operate desalination plants.

Desalination Unit fouling condition assessment curve

800070006000500040003000200010000 9000 10000 11000 12000 13000 14000 15000 16000 17000 18000 19000 20000

b

a

0,00

0,05

0,10

0,15

0,20

0,25

time [hours]

reco

very

fo

uli

ng

facto

r [m

2°C

/kW

]

6,8 7,0 7,2 7,4 7,6 7,8 8,0 8,2 8,4 8,6 8,8 9,0 9,2 9,4 9,6 9,8 10,0 10,2 10,4 10,6 10,8

evaporator unit performance ratio [kg/2326kJ]

c

theoretical fouling (design)

after running hours

foreseen before acid cleaning

expected fouling

after running hours

foreseen before acid

cleaning

a. actual P.R. value

b. actual plant running hours

c. actual fouling factor value

-----------------------------------------------

e. plant running hours before a.c.

f. expected P.R. value before a.c.

e

f

9

Figure 5 The validation of the updated model: actual ff from plants feedback data

This leads to the following conclusions:

The common fouling factor values still used nowadays in the design of desalination units date

back to several years ago, when antiscalants were poorly effective and the ball cleaning systems

were still not available, or used at very low frequencies. Today these values appear to be

extremely overestimated with respect to the real ones.

As a consequence, the design of desalination plants is far from being optimised, since the actual

steam and condensate flows result to be in the reality much lower than the expected ones. This leads

to the over sizing of several parts of the desalination units (piping, equipment, etc.), and to a non-

optimised link between the water production and power production plants.

It would be advisable to take into consideration these new trends for the design of the future plants,

since the confidence in reaching the desired performances together with the best possible

knowledge of the margins you keep when carrying out a design are among the key parameters for a

real design optimisation.

REFERENCES

[1] Kern, D.Q. and R.E. Seaton , Surface Fouling – How to calculate Limits, Chem. Eng. Prog. ,

55 (1959) 6, 71-73.

[2] D.Hasson– Scale formation and prevention, Proceedings Workshop on Scaling in Seawater

Desalination, Lutherstadt Wittenberg (2001).

[3] R.Borsani, A. Barone – Four Years operation of the largest single train MSF desalination

plant, Proceedings of World Congress on Desalination and Water Sciences, San Diego (1999), IV,

123-141.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 2000 4000 6000 8000 10000 12000

Plant running hours

% o

f s

tan

da

rd d

es

ign

fo

uli

ng

10

Appendix A

A good agreement has been found interpolating the fouling data vs. time with curves corresponding

to the following equations and using the parameters of the updated K&S model:

ceffff

1 Equation A-1

The asymptotic fouling represents the settling value of this variable, while the characteristic time

represents the number of hours necessary to reach about 63% of the fouling asymptotic value.

The shape of this curve fits both the theoretical and the expected one.

For the theoretical curve the value of ff is fixed by the constraint that after the number of hours

theoretically foreseen before an acid cleaning the fouling must reach the design value, while the

value of c is chosen according to experience. Both ff and c are usually in accordance with the

K&S original model.

For the expected curve the value of ff and c are fixed by the two values of fouling measured at the

actual time and expected after the number of hours theoretically foreseen before an acid cleaning. .

Both ff and c are usually in accordance with the K&S revised model. The expected value of

fouling ffexp (close to ff) can be estimated from the actual one according to the following formula:

11

1

exp

T

n

ACD

baff Equation A-2

A comparison between the values of c and ff in the original K&S model and in its updated version

can be derived by the following relations:

3

2

1

..

1

1

)1(

originalupdated

BoriginalBupdated

moriginalmupdated

Since

B

c it results that 3

2

1

1

cc originalupdated while being

s

cmff

, it results

that

3

21

1

11

fforiginalffupdated

11

LEGEND

a, b parameters of fouling vs. p.r. curves in fig.3 (ff = a+b/) [-]

B adhesion characteristic parameter [Ns/m2]

dt

dx scale growth velocity [m/s]

ff fouling resistance [m2°C/kW]

ff asymptotic fouling [m2°C/kW]

ffexp expected fouling before acid cleaning [m2°C/kW]

.

m gross scale deposition flux [kg/s m2]

n empirical exponent [-]

x scale layer thickness [m]

s

cm

x scale layer thickness asymptotic value [m]

Greek symbols:

actual performance ratio [kg/2326kJ]

D design performance ratio [kg/2326kJ]

T theoretical performance ratio [kg/2326kJ]

time [s]

AC time before acid cleaning [s]

Bc characteristic time constant [s]

s scale bulk density [kg/m3]

dt

dxs transport rate of scale constituents [kg/sm

2]

scale thermal conductivity [kW/m°C]

flowing liquid shear stress [N/m2]

IDA WORLD CONGRESS Desalination & Water Reuse

Manama, Bahrain

March 8th – 13th, 2002

1

THE SCALING OF TUBES IN MSF

EVAPORATORS:

A critical review across 20 years of

operational experience

E. Ghiazza, A.M. Ferro - Fisia Italimpianti

2

Types of fouling in

desalination plants

FOULING

BIOLOGICAL CORROSION SCALE

PARTICULATE CHEMICAL

•Silt •Calcium carbonate

•Magnesium hydroxide

•Calcium sulphate

3

Problems caused by scale

SCALING

Insulating film Narrowing of

passageways

Possibility of

corrosion

under deposits

Heat transfer

reduction

Flow rate reduction

Energy consumption increasing

Pitting

4

Solid Deposits

Prevention & Removal

Avoidance of risky

temperature and

concentration ranges

Removal of risky

components (acid treatment)

Inhibition of crystal growth

(chemical additives)

Hazardous, corrosion risk, high CO2

release

Effective on non-alkaline scaling

costly

Mechanical cleaning

SPONGEBALL CLEANING

SYSTEM (on line)

Chemical cleaning

ACID CLEANING

SYSTEM (off line)

5

The optimal combination

high temperature improved

antiscalants

+

continuous ball cleaning

system

• clogging prevention

• additive dosage reduction

• heat consumption lowering

• tube corrosion reduction

•high plants availability

PERFORMANCE RATIO DECLINE WITH FOULING

A

B

C D

E

7

7,5

8

8,5

9

9,5

0 2 4 6 8 10 12Operating Time [Months]

Pe

rfo

rma

nc

e R

ati

o [

kg

dis

t/2

32

6 k

J]

A: Completely clean plant B: Design performance ratio

C: Typical decline by tube fouling D: Off load acid cleaning

E: P.R. with continuos spongeball clean.

6

Scale growth modeling

Scale Growth Asymptotic model

(Kern & Seaton)

xB

md

dxss

Net Scale Deposition Flux

=

Scale Specific Flux – Scale Removal Flux

s scale bulk density [kg/m3]

dx/d scale growth velocity [m/s]

m gross scale deposition flux [kg/s m2]

flowing liquid shear stress [N/m2]

B adhesion parameter [Ns/m2]

x scale layer thickness [m]

s

cmx scale layer thickness asymptotic value

[m]

characteristic time constant

[s]

Bc

)1( cexx

@ = 0 (initial condition) md

dxs

7

Fouling Resistance vs Time

Fouling Factor for Overall H.E.C. evaluation

xff

)1( ceffff ff asymptotic fouling [m2

C/kW]

ff fouling resistance [m2

C/kW]

x scale layer thickness [m]

scale thermal conductivity [kW/m

C]

polyphosphates

no ball cleaning system m, , B Classical c and ff

8

Times they’re a-changing

high efficiency synthetic

antiscalant

ff down to 60-70% original model value

Nowadays:

continuous ball cleaning

- 5% ( 1)

B - 10% ( 2)

+ 30% ( 3)

m

scale flux m

scale adhesion B

New c and ff

shear stress

9

Different results of original and

updated model

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 10 20 30 40 50 60 70

time [days]

fra

cti

on

of

orig

ina

l K

&S

ff

Polifosphates / No B.C.S: original K & S

model)Improved chemicals / No B.C.S.

Polifosphates + B.C.S.

Improved chemicals + B.C.S. (modified

K & S model)

10

Plants Feed Back Support:

Fouling Evaluation Heat Recovery Section Fouling

Factor =

Monitoring Index

Set of Fouling Factor Curves vs.

Performance Ratio by fitting the results of different computer runs

Performance Ratio =

Reliable Indirect Parameter

11

Fouling vs. Performance Ratio

Performance Ratio [kg/2326 kJ]

Plant 1Plant 2

Plant 3

Plant 4

0

0,02

0,04

0,06

0,08

0,1

0,12

0,14

0,16

0,18

0,2

7 7,5 8 8,5 9 9,5 10re

co

ve

ry s

ec

tio

n f

ou

lin

g [

m2°C

/kW

]

12

Fouling Assessment

curves describing fouling trend vs. time

together with fouling evaluation

from feedback data

Theoretical (design figure)

Expected (feed back data fitting)

13

Time Based Fouling Recording

Desalination Unit fouling condition assessment curve

f

0

0,05

0,1

0,15

0,2

0,25

6,8 7 7,2 7,4 7,6 7,8 8 8,2 8,4 8,6 8,8 9 9,2 9,4 9,6 9,8 10 10,2 10,4 10,6 10,8

time [hours]

reco

ve

ry f

ou

lin

g f

acto

r [m

2°C

/kW

]

020000

a. actual P.R. value

b. actual plant running hours

c. actual fouling factor value

-----------------------------------------------

e. plant running hours before a.c.

f. expected P.R. value before a.c.

theoretical fouling (design)

after running hours

foreseen before acid

cleaning

expected fouling

after running hours

foreseen before

acid cleaning

14

Different Plants Review

(based on Fisia Italimpianti experience in MSF Deaslination since 1970)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 2000 4000 6000 8000 10000 12000

Plant running hours

% o

f s

tan

da

rd d

es

ign

fo

ulin

g

15

CONCLUSIONS

Fouling behavior :

• not depending on the specific

plant (capacity, p.r., ..)

• settling to almost constant value

after a relative short number of

running hours

• asymptotic value always

remarkably lower than the

design

16

New Trends

Standard f.f. values

(overestimated)

Actual f.f. values

Optimized Desalination Plant

Design