1. 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
By: Heena Madhav
July 2017

2. CONTENTS
Introduction
Experimental
Results
Conclusion
Acknowledgements

3. INTRODUCTION – Schematic of power plant

4. INTRODUCTION – Eskom open evaporative cooling tower

5. INTRODUCTION – Current Problem

6. INTRODUCTION What is scale?
Scale formation in steam generating systems results from the fact that the solubility of the scale forming salts decreases with increasing temperature and concentration.
Some of the more common constituents associated with scaling are: Calcium (Ca), Phosphate (PO4), Magnesium (Mg), Sulfate (SO4), Bicarbonate (HCO3) Silicate (SiO2), Carbonate (CO3), Iron (Fe)
The formation of scale, in particular CaCO3, in cooling water (CW) condenser tubes at Eskom power stations is of concern.
Scale decreases the heat exchange capacity of the condenser tubes and hence decreases the efficiency of the cooling water (CW) system
For scaling tendency to decrease CW chemistry needs to be corrected.

7. INTRODUCTION – Organics and scaling
Natural organic matter (NOM) compounds easily form complexes with scaling metals such as Ca and Mg and therefore affect the scaling potential of the water.
NOM consists of hydrophobic and hydrophilic substances and is further broken down into and humic substances, building blocks, low molecular weight acids and neutral compounds.
In order to evaluate the effect of metal complexation to organics on the saturation indices in cooling water, experimental work as well modelling (Visual MINTEQ and PHREEQC-Interactive) was used

8. EXPERIMENTAL
Cooling and raw water from Lethabo power station was sampled
The samples were analysed for the following: pH
Conductivity
Alkalinity
Cations – ICP-OES
Anions – IC
Aromaticity of organics –Specific ultra violet absorption (SUVA)
Organic species – Liquid chromatography organic carbon detector (LC-OCD)

9. EXPERIMENTAL
Langelier Saturation Index is calculated (LSI) is mostly used for calculating the saturation of the water LSI = pH - pHs pHs = {9.3 + (log [TDS] -1/10) + ( x log(temp)+34.55} – {log [Ca2+ as CaCO3] – (log [alkalinity as CaCO3]}

10. EXPERIMENTAL
LSI Results
Table1: Results for cooling water from Lethabo Power Station

11. RESULTS
SUVA RESULTS
Comparison of SUVA254 values in Raw Water (Vaal) and Cooling Water (Lethabo)
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 hydrophilic NOM, large range of molecular weights

12. RESULTS
LCOCD RESULTS
LCOCD chromatogram for CW and RW

13. RESULTS cont
LCOCD Humic substance (HS) Diagram

14. Both models calculate Saturation Index (SI)
RESULTS: Modelling
Visual MINTEQ: Chemical equilibrium model that is used for metal speciation, solubility equilibria, complexation reactions as well as incorporates the organic compounds through the NICA-Donnan Model. A powerful and easy to use model Phreeqc-Interactive: Geochemical model for simulating chemical reactions and transport processes in natural or contaminated water. Allows for incorporation of species into the model. A very powerful inorganic model
Both models calculate Saturation Index (SI)

15. RESULTS – Comparison of models
Comparison of the percentage of various organic species in Raw and Cooling Water

16. RESULTS cont.
Saturation indices (at 25°C) of scaling mineral phases using modified PhreeqcI interactive and Visual MINTEQ 3.1

17. Conclusions
When using LSI for calculating precipitation potential , always use Ca value obtained by titration
When using Visual MINTEQ the Ca obtained by ICP must be utilized when calculating saturation indices
The Vaal raw water has NOM that are more aromatic in nature as compared to the cooling water.
From the LC-OCD data, the humic substances were identified as fulvates.
SI values obtained from Visual MINTEQ were consistently lower than that obtained from the PhreeqcI model.
Reason being that PhreeqcI is a powerful model in terms of the inorganic database but for the organic components, the organic definitions have to be added to the database to model chemical interaction with the organics.

19. ACKNOWLEDGMENTS Eskom (AC&M), Jenny Reeves and Gerhard Gericke
UJ (Department of Applied Chemistry), Prof S. Mishra and Prof J.C. Ngila
NRF, Eskom and UJ for funding this work