Comparative Simulative Studies Using PHREEQC-Interactive and

Visual MINTEQ Model for Understanding Metal-NOM

Complexation Occurring in Cooling and Raw Water and the Effects

on Saturation Indices

Speaker/Author: H. Madhav*

Co-authors: G. Gericke*, S. Mishra**, J.C. Ngila***, O. Bosire***

*Sustainability Department, Applied Chemistry and Microbiology, Eskom, Private Bag X40175,

Cleveland, 2022, South Africa **Nanotechnology and Water Sustainability Unit, College of Science, Engineering and Technology,

University of South Africa, Florida Campus, Johannesburg, South Africa

***Department of Applied Chemistry, University of Johannesburg, P.O Box 17011, Doornfontein

2028, Johannesburg, South Africa

*Corresponding author, Tel +27 11 629 5124; Fax +27 1186 218 3748;

Email: Heena.madhav@eskom.co.za

Abstract

Open evaporative cooling water systems at Eskom power stations continuously face the

problem of metals such as calcium (Ca) and magnesium (Mg) precipitating out in the

condenser tubes. These metals however also form complexes with natural organic matter

(NOM) in the raw and cooling water. The present investigation was carried out using both

PHREEQC-interactive (PhreeqcI) and Visual MINTEQ 3.1 for understanding metal-natural

organic matter complexation (M-NOM) in both raw and cooling water. The effect of

temperature on the saturation indices (SIs) for both waters was also simulated using both

models. The raw and cooling water which contain natural organic matter (NOM) was

characterized and quantified by the liquid chromatography-organic carbon detection (LC-

OCD) technique. The total quantities of scaling cations and anions in both types of waters

were also determined using inductively coupled plasma-optical emission spectrometry (ICP-

OES) and the ion chromatography technique, respectively. From the LC-OCD results, the

humic substances were characterised as fulvates. The LC-OCD results shown by specific UV

absorbance at 254nm (SUVA254) values, revealed a difference in the aromaticity of the

fulvates in both raw and cooling water i.e. the fulvates in the raw water was higher in

aromaticity (SUVA254 = 4.1) as compared to the fulvates in the cooling water (SUVA254 =

2.22). Also, the LC-OCD established significant differences in fulvates as well as building

blocks (referred to as the break down product of the fulvates) in raw and cooling water. The

percentage fulvic acid present in both the raw and cooling water was very similar, yet it was

observed that a higher percentage of Ca and Mg was bound to the organics (from Visual

MINTEQ) in the cooling water. The SIs of the various mineral phases using the Visual

MINTEQ model as well as the PhreeqcI model, showed very similar trends, except for

calcite. The precipitation and dissolution of anhydrite, calcite, dolomite and gypsum mineral

phases were dependent on input concentrations, equilibrium phases and temperature. This

study provides possible solutions to the scaling problems: simulations, discussions and

predictions which can improve the efficiency of the cooling water (CW) system.

Keywords: Equilibrium phases, Mineral phases, PHREEQC-interactive, SUVA254, Visual

MINTEQ

1. Introduction

The cooling systems at Eskom power stations utilise the raw water as feed water. The quality

of raw water however that is available for industrial and commercial use has greatly

deteriorated in terms of the natural organic matter (NOM) content. This NOM is a complex

mixture of organic components which are formed through the degradation of plant and

animal matter well as from agricultural and human activity [1, 2]. Majority of dissolved

organic matter in an aquatic environment is made up of humic substances (HS). Humic

substances are either classified as humic acids (HA), which are soluble under alkaline

conditions and insoluble under acidic conditions (pH < 2) or fulvic acids (FA), which are

soluble under all pH conditions [3].The quality of the raw water has an impact on various

processes at Eskom Power Stations (i.e. portable water production, cooling system and ultra-

pure water production) and hence the need to study the NOM and its interactions with various

components.

In particular, the interaction between NOM and calcium (NOM-Ca) and NOM and

magnesium (NOM-Mg) is very important as this affects the amount of scaling in the

condenser tubes in the cooling systems [4]. When scaling metals such as Ca and Mg complex

with NOM, there is less of the “free” metal in solution available to form scale (increases the

efficiency of the cooling system) in cooling water condenser tubes. Metal-NOM (M-NOM)

complexation is affected by temperature, pH, nature of the organics present as well as the

metals present in the water [5, 6, 7]. For a better understanding of M-NOM complexation, the

identification as well as quantification of the NOM is very important. There are many

techniques available to identify organics to a certain extent and these include flourescence

excitation emission matrices (FEEM), polarity rapid testing method (PRAM), specific ultra

violet absorption (SUVA), high performance size exclusion chromatography (HPSEC) [1, 8].

Size exclusion chromatography (SEC) for organic carbon detection is also one of the oldest

techniques that have been used for the analysis of NOM [9]. More recently however, S. A

Huber et al., (2011), modified this NOM analysing technique such that not only organic

carbon could be analysed, but also organic nitrogen and hence size exclusion

chromatography- organic carbon detection- organic nitrogen detection (LC-OCD-OND)

technique [10].

Even though there has been an improvement in analytical techniques for the analysis of

humic acids, the use of chemical modelling programmes such as PhreeqcI, Visual MINTEQ,

WHAM, EQ 3/6, Geochem and NICA-Donnan [11, 12, 13] greatly assist in understanding the

fundamentals with regards to chemical reactions. A recent study using Visual MINTEQ has

discussed copper (Cu) complexation with the Suwannee River fulvic acid (SRFA). This study

indicated that pH affects Cu complexation to organics. Further, the approach that was used in

this study could also prove useful to theoretically determine other metal complexation in the

environment [12]. Extensive research has also been carried out by Rémi Marsac et al

regarding metal complexation to NOM using PHREEQC [14, 15, 16]. The studies indicated

that the light rare earth elements (LREE) are preferentially bound to the carboxylic groups of

the humics whereas the heavy rare earth elements (HREE) are bound to the phenolic groups

of the humics [14].

The objective of this study is to investigate the effect of Ca and Mg complexation to NOM as

this affects the saturation index of the cooling water at Eskom power stations. Data from two

models i.e. Visual MINTEQ 3.1 and PhreeqcI will be compared. The benefits of using

modeling as done in this article, can guide researchers to better their understanding of metal

interactions with organic species in cooling water. Furthermore, the saturation indices assist

in predicting the precipitation and dissolution of mineral phases and therefore the possibility

of projecting if condenser tube scaling will occur or not. Thus modeling saves time, cost and

efforts required for water analysis and therefore a quick and cost effective way of monitoring

and managing scale in condenser tubes.

2. Materials and methods

Sampling and preparation

The raw and cooling water sample was collected from Lethabo power station in 1000 mL

sampling bottles. The pH and alkalinity of the samples were recorded at the laboratory and

samples were then refrigerated immediately. Also, the cooling water sampled here is the water

obtained from the hot duct, i.e. water that has just passed through the cooling water condenser

tubes. These samples were cooled to 25°C before being analysed.

2.1 Experimental

2.1.1 Reagents and standard solutions

All reagents were of analytical grade and Millipore water (0.05 µS/cm) was used to prepare

the standards. The calibration standards were purchased from Spectrascan (South Africa) and

Merck (South Africa). The following metals: Al, Ba, Be, B, Cd, Cu, Fe, K, Mn, Pb, Ni, Sr

and Zn were purchased from Spectrascan and the standards for Ca, Co, Cr, Mg, Na were

purchased from Merck. The alkalinity of the sample was determined by electrometric

titration, 25.00 ml of the sample was titrated with a standardized solution of 0.02 N nitric acid

(65%) until the end point was reached, The nitric acid was purchased from Associated

Chemical Enterprises Sodium carbonate anhydrous (99.5%, Merck) was used as the

calibration standard. The buffers (pH 4, 7, 9) used for the calibration of the pH meter were

obtained from Metrohm (South Africa). The quality control standard (pH 7) was obtained

from Merck (South Africa). For the TOC analysis, the samples were first filtered through a

0.45 µm filter before being analysed. Potassium Hydrogen Phthalate (99.5%, Associated

Chemical Enterprises) was used to prepare the calibration standards. The potassium hydrogen

phthalate (99.5%, AR grade), used for the quality control standard was obtained from Merck.

For the analysis of anions, the eluent used was a mixture of 3.5 mM anhydrous sodium

carbonate (99.5%, Merck) and 1.0 mM sodium bicarbonate (99.0%, Merck). The calibration

standards for the anion analyses were purchased from Merck, South Africa and these include

NaF, NaNO3, NaNO2, anhydrous Na2SO4 and NaCl. For the UV254 measurements, the

samples were first filtered through a 0.45 µm syringe filter (purchased from Separations).

Sodium metasilicate pentahydrate (55.5% dry solids, AR grade, Associated Chemical

Enterprises) was used to prepare the calibration standards and sodium metasilicate

nonahydrate (47.5% total solids, AR grade, Associated Chemical Enterprises) was used for

the quality control. For the LC-OCD analysis, the procedure described in Huber et al., 2011,

was followed. Part of the procedure includes acidification (orthophosphoric acid, 85 %, AR

grade) of the sample at the inlet of the OCD at a flow rate of 0.2 mL/ min) to convert

carbonates to carbonic acid. Accordingly the organic carbon detector (OCD) calibration was

based on potassium hydrogen phthalate (99.5 %, AR grade). In this case, the carbon mass

was used to calibrate the OCD and its extinction coefficient, ɛ = 1.683x10-3 Lmol-1cm-1 used

to calibrate the ultraviolet detector (UVD) (Huber et al 2011).

2.1.2 Instrumentation

The metal ions were determined using Inductively Coupled Plasma Atomic Emission

Spectrometry (ICP). The ICP model used was a Perkin Elmer Optima, 4300 DV. Prior to

analysis the samples were filtered through a 0.22 µm syringe filter (purchased from

Separations, South Africa). The alkalinity and pH measurements were carried out using the

Metrohm 862 compact Titrosampler. The anions in the sample were analysed using ion

chromatography (IC). The IC model used was the Dionex ICS. The column used for the

analyses was the Dionex IonPacTM AS914 (Analytical 4x250 mm) and the Guard column

used was Dionex IonPacTM AG14 (Guard 4x50 mm). The TOC and DOC analyses were

carried out on the ElementarVario. Reactive silica was analysed using the Genesys 10UV

scanning spectrophotometer from Thermo Scientific. The LCOCD analyses were carried out

on DOC-Labor LCOCD instrument, Mode 8, Version 2012-08-27.

3. Results and Discussion

The physico-chemical properties of the raw and cooling water were obtained as these

parameters were used for modeling the saturation indices (SI). Characterisation and

quantification of NOM fractions in both the raw and cooling water were also obtained using

the LCOCD technique. The simulative modelling for both waters were carried using Visual

MINTEQ 3.1 and PhreeqcI. The modelled results gave an indication of the mineral phases in

the waters as well as predicted Ca/Mg-NOM interactions. The saturation indices obtained for

the various mineral phases in both waters gave an indication of the potential of the water to

form scale.

3.1 Characteristics of raw and cooling water

Physico–chemical properties for the raw and cooling water are presented in Table 1. The total

cation and anion concentrations are also presented in Table 2 and Table 3 respectively. The

results indicated that the concentration of species in the CW is higher than the concentrations

in the raw water. This conforms to what is expected as the cooling water system is a closed

loop system that operates using open evaporative cooling (Fig 1). This open evaporative

cooling system, causes the components in the CW to concentrate. For example the dissolved

organic carbon (DOC) concentration in the raw is 4.83 mg/L and for CW, the DOC increased

to 42.2 mg/L. Metals such as Ca (170 mg/L) and Mg (25 mg/L) in the CW form complexes

with the DOC and hence decrease the concentration of the “free” metal in the CW (Fig 2).

This decrease in concentration of the ‘free metal’ results in a decrease in scale formation in

the condenser tubes (only the “free” metal in solution causes scaling) and causes a decrease

in the saturation index of the CW, hence decreasing the potential of the CW to form scale in

the condenser tubes. Reactive minerals example reactive silica (16.60 mg/L, CW) also play

an important and critical role in dissolution, complexation and precipitation of various

saturates [17, 18, 19]. The compleaxation of metals with natural organic matter are pH [20,

21] and temperature dependent.

Fig 1. Conventional open evaporative cooling system (cooling system used at Lethabo

Power Station)

Ca (aq) + CO3 (aq) Ca(CO)3 (s)

(Fulvic acid) [22]

(aq)

Fig 2. Calcium complexation with NOM (Some of the calcium in solution is complexed to

the NOM, hence only the ‘free’ calcium reacts with the carbonate to form a precipitate)

Table 1. Physico-chemical characteristics of raw and cooling water

Physico-chemical

characteristics

Raw water Cooling water

Alkalinity as CaCO3

(mg/L)

72.14 117.00

pH at 25°C 6.75 7.39

Temperature (°C) 25.00 25.00

Conductivity at 25°C

(µS/cm)

211.00 2660.00

TDS (mg/L) 122.40 2219.90

TOC (mg/L) 5.38 43.10

DOC (mg/L) 4.83 42.20

Reactive silica (mg/L)

(UV-Vis)

7.54 16.60

Table 2. Distribution of metals in raw and cooling water (superscripts a and b represent

raw and cooling water respectively)

Metal ICP Concentration (mg/L)

Ca 15a, 170b

Cu 0.02a, 0.12b

Fe 1.4a, 1.4b

K 3.1a, 81b

Mg 8.4a, 25b

Ni/Cr <0.005a, <0.005b

Be <0.005a, <0.005b

Cd/Co <0.005a, <0.005b

Zn 0.02a, 0.04b

Mn 0.04a, 0.09b

Na 11a, 540b

Pb 0.01a, 0.01b

Sr 0.11a, 0.83b

Table 3. Distribution of anions in raw and cooling water (superscripts a and b represent

raw and cooling water respectively)

Anion (IC) Concentration (mg/L)

Cl- 8.49a, 410b

F- 0.1a, 0.82b

N- 0.57a, 24.86b

SO42- 17.55a, 1230b

3.2 Characterization and quantification of NOM

Characterisation and quantification of the NOM in both the raw and cooling water is

important as the nature of the organics affect complexation with the metals which in turn

affects the saturation index of the water.

Both the raw and cooling water from Lethabo power station were analysed using the

LCOCD. The raw water from Lethabo originates from the Vaal River and as shown in the

HS-diagram (Fig 3(I)), this water contains natural organic matter that has high molecular

weight organic compounds that are hydrophobic. According to the founders of the HS-

Diagram i.e. S.A Huber et al, this Vaal Raw Water contains organic compounds that are

composed of fulvic acid [10]. The natural organic matter in the cooling water is also

represented on the HS-diagram (Fig 3(I), and illustrates that the natural organic matter is also

fulvic acid. The diagram does however indicate that the fulvic acid compounds in the CW are

lower in molecularity and aromaticity as compared to the fulvic acid compounds in the raw

water. This result is also evident from the SUVA values calculated (from LCOCD) for the

raw and cooling water i.e. 4.10 and 2.21 L/(mg*m) (Table 4), respectively.

The LCOCD chromatogram also characterises and quantifies the degradation products of the

NOM in the sample and these products are referred to as building blocks, low molecular

weight organic acids as well as low molecular weight organic neutral compounds (Fig 3 (II)).

Fig 3. (I) HS-Diagram showing the aromaticity/molecularity relationship of NOM in raw

and cooling waters; (II) OCD, UVD and OND NOM detection signals of raw and cooling

water (obtained from LCOCD analysis)

Bio

poly

mers

Hum

ics

Build

ing B

locks

LM

W A

cid

s a

nd H

S

LMW Neutrals

Nitra

te

Am

moniu

m

Bypass

Vaal Raw Water (Lethabo)

rel.

Sig

na

l R

es

po

nse

Retention Time in Minutes

-- OCD-- UVD-- OND

Cooling Water

I

II

Table 4. Comparison of SUVA values in Raw Water (Vaal) and Cooling Water

Type of Water SUVA

L/(mg*m)

Composition

Raw Water 4.10 Mostly aquatic humic, high hydrophobicity

Cooling Water 2.22 Mixture of aquatic humic and other NOM,

Mixture of hydrophobic and hydrophylic NOM,

large range of molecular weights

3.3 Geochemical Modeling

The raw and cooling water from Lethabo power station was analysed as explained in section

3.1 and 3.2 and the data obtained was entered in the two models i.e. Visual MINTEQ 3.1 and

PhreeqcI. For both the raw and the cooling water (using the modelled results), the percantage

metal bound (complexed) to the organics in the water was calculated using formula 1.

% M-DOC = mass of metal bound to DOC x 100 [1]

mass of total metal

From Visual MINTEQ (Table 5), the results indicated that the percentage of Ca and Mg

bound to fulvates in the CW is higher (i.e. 0.49 % and 1.03 % respectively) as compared to

the raw water (i.e. 0.05 and 0.09 respectively). The LCOCD results however indicate that the

percentage humic substances (in this case fulvic acid) in both the raw water as well as the

CW is very similar i.e 53% and 55% respectively but for the building blocks (from LCOCD),

there is a significant increase in the percentage from the RW to the CW i.e. 13.6% to 19.3%

respectively.This is an indication that the less aromatic (as compared to the raw water) fulvic

acid compounds present in CW react more easily with the Ca to form Ca organic complexes,

unlike the results that were obtained from Stern J.C et al (2007) where it was evident that

humic substances that have a higher aromaticity and a strong proton affinity are associated

with strong metal-humic substance complexation [23]. Stern also mentioned that pH plays a

major role regarding metal-humic substance complexation and has a stronger influence in

terms of metal-organic complexation as compared to the HS composition.

The results obtained from PhreeqcI [24] with regards to Ca and Mg complextion to the

organics are also shown in Table 5. The database, within its Ca-organic complexation

definitions, showed relatively small complexed moles compared to the total in bulk. The

values using this modification for Ca-fulvate were 1.71E-16 M and 5.03E-15 M for raw and

cooling water respectively. The Mg-fulvate complexes showed a similar trend in values.

Table 5. Comparison of the percentage of various organic species in Raw and Cooling

Water

Organic Species Percetage (%), RW

(Vaal water) Percetage (%), CW

Ca-Fulvate (Visual MINTEQ 3.1) 0.05 0.49

Mg-Fulvate (Visual MINTEQ 3.1) 0.09 1.03

Ca-Fulvate (PHREECQCI) 3.74E-03 4.25E-03

Mg-Fulvate (PHREEQCI) 3.46E-04 1.03E-03

Humic substances (LCOCD) 53 55

Building blocks (LCOCD) 13.6 19.3

The saturation index (SI) of the various mineral formed in both the RW and CW using the

two models i.e. PhreeqcI and Visual MINTEQ 3.1 is shown below in Table 6. The general

trend for both Visual MINTEQ 3.1 and PhreeqcI was that the SI indices of the minerals

increased in the CW as compared to the RW. Also, as explained, the percentage metal

organic complexation from Visual MINTEQ 3.1 is greater than that obtained using PhreeqcI

(Table 5). The SI trends observed (Table 6), indicated that the SI for the minerals using

Visual MINTEQ 3.1 is lower than the SI values for minerals obtained using the PreeqcI

model. This is an indication that the greater the percentage metal complexed, the lower the

saturation index and therefore the mineral is less likely to precipitate out of solution.

From Table 6, four mineral phases for the CW were selected for simulitive modeling namely

calcite (CaCO3), gypsum (CaSO4.2H2O), anhydrite (CaSO4) and dolomite (ordered)

(CaMg(CO3)2). Figure 4 shows how the SI of the various minerals are affected with changes

in temperature. The trend observed from both models for the minerals gypsum, dolomite

(ordered) and anhydrite were very similar i.e. for dolomite and anhydrite, as the temperature

increased, the SI increased and for gypsum, the SI of the mineral decreased as the

temperature increased. For calcite however, the results obtained from models are

contradictory. The modeled values from PhreeqcI shows a decreasing trend for calcite as

compared to Visual MINTEQ 3.1 where the SI for calcite is shown to increase with

temperature. Generally, the formation of calcite increases with increasing temperature [25].

Table 6. Saturation indices (at 25°C) of scaling mineral phases using modified PHREEQC

interactive and Visual MINTEQ 3.1

Mineral phase Formula SIs using modified

PhreeqcI

SIs using Visual

MINTEQ 3.1

RW CW RW CW

Aragonite CaCO3 0.34 1.38 -1.70 -0.26

Calcite CaCO3 0.48 1.52 -1.56 -0.11

Anhydrite CaSO4 -3.06 -0.82 -3.08 -0.87

Gypsum CaSO4.2H2O -2.84 -0.60 -2.83 -0.62

Chlorite14A Mg5Al2Si3O10(OH)8 20.98 17.21 - -

Chlorite7A Mg5Al2Si3O10(OH)8 17.61 13.84 - -

Clinoenstatite MgSiO3 1.19 0.64 - -

Diopside CaMgSi2O6 5.25 4.70 - --

Dolomite

(ordered)

CaMg(CO3)2 1.01 2.54 -3.02 -0.69

Dolomite

(dis-ordered)

CaMg(CO3)2 0.46 1.99 -3.57 -1.24

Forsterite Mg2SiO4 1.89 -0.08 - -

Goethite FeOO2H3 7.32 8.00 - -

Hausmannite Mn3O4 11.56 5.61 - -

Hematite Fe2O3 16.64 18.00 - -

Kmica KAl3Si3O10(OH)2 3.14 7.37 - -

Leonhardite Ca2Al4Si8O24:7H2O 6.38 13.13 - -

Montmorillon

ite-Aberdeen

(HNaK)0.14Mg0.45Fe0.33Al1.4

7Si3.82O10(OH)2

0.82 4.49 - -

Also, as shown in Table 6, some of the minerals defined in PhreeqcI are not found in the

Visual MINTEQ database. The reason for this is that the two models have distinct differences

in their database definitions. The Visual MINTEQ 3.1 programme has a comprehensive

organic data base [6] that is built into the programme and therefore many metal-organic

complexes can be modelled. The PhreeqcI model however, has well defined inorganic

equilibrium phases. Previous studies have shown utilization of Visual MINTEQ

thermodynamic definitions into PhreeqcI. Huber et al., in their simulations, used

thermodynamic definitions of volatile fatty acids (acetate, propionate, butyrate and lactate)

from MINTEQ.V4.DAT and included them in the PhreeqcI database [26].

Fig 4. Extrapolated saturation indices verses temperature for calcite, dolmite,anhydrite and

gypsum using Visual MINTEQ 3.1 and PhreeqcI

4. Conclusions

It is evident from this study that organic compounds present in the Vaal river (raw water) are

more aromatic in nature as compared to the organics present in the cooling water (Lethabo

power station). The results also indicate that the bulk of the organic matter can be classifieded

as fulvic acid in both instances. The percentage fulvic acid present in both the raw and

cooling water is very similar, yet there is a higher percentage of Ca and Mg bound to the

organics (from Visual MINTEQ 3.1) in the cooling water. The SI of the various minerals

using both the Visual MINTEQ model as well as the PhreeqcI model, showed very similar

trends, except for calcite. Results of SI obtained from Visual MINTEQ however were

consistantly lower than the SI values from the PhreeqcI model and this is due to the extensive

organic database that is incorporated into Visual MINTEQ, allowing for more comprehensive

metal organic complexation reactions as compared to those of PhreeqcI model. Even though

PhreeqcI is not the most ideal tool in terms of metal complexation with organic compounds

for the determination of SI of various minerals, this program has a very extensive inorganic

database.

5. Acknowledgements

The authors would like to thank the National Research Foundation (NRF) and Eskom for

funding this research work

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