KATHOLIEKE UNIVERSITEIT LEUVEN FACULTEIT INGENIEURSWETENSCHAPPEN DEPARTEMENT CHEMISCHE INGENIEURSTECHNIEKEN W. DE CROYLAAN 46 B 3001 HEVERLEE BELGIË
MODELLING LEACHING OF INORGANIC CONTAMINANTS FROM CEMENTITIOUS WASTE MATRICES
Promotor: Prof. dr. R. Swennen Eindwerk ingediend tot het behalen van de graad van Co promotor: Dr. ir. T. Van Gerven burgerlijk scheikundig ingenieur door
Evelien Martens
Juni 2007 KATHOLIEKE UNIVERSITEIT LEUVEN FACULTEIT INGENIEURSWETENSCHAPPEN DEPARTEMENT CHEMISCHE INGENIEURSTECHNIEKEN W. DE CROYLAAN 46 B 3001 HEVERLEE BELGIË
MODELLING LEACHING OF INORGANIC CONTAMINANTS FROM CEMENTITIOUS WASTE MATRICES
Promotor: Prof. dr. R. Swennen Eindwerk ingediend tot het behalen van de graad van Co promotor: Dr. ir. T. Van Gerven burgerlijk scheikundig ingenieur door
Assessoren: ir. G. Cornelis Prof. dr. ir. E. Smolders Evelien Martens
Juni 2007
© Copyright by K.U.Leuven – Deze tekst is een examendocument dat na verdediging niet werd gecorrigeerd voor eventueel vastgestelde fouten. Zonder voorafgaande schriftelijke toestemming van de promotoren en de auteurs is overnemen, kopiëren, gebruiken of realiseren van deze uitgave of gedeelten ervan verboden. Voor aanvragen tot of informatie in verband met het overnemen en/of gebruik en/of realisatie van gedeelten uit deze publicatie, wendt u zich tot de K.U.Leuven, Dept.Chemische Ingenieurstechnieken, de Croylaan 46 B-3001 Heverlee (België), tel. 016/322676. Voorafgaande schriftelijke toestemming van de promotor is vereist voor het aanwenden van de in dit afstudeerwerk beschreven (originele) methoden, producten, toestellen, programma’s voor industrieel nut en voor inzending van deze publicatie ter deelname aan wetenschappelijke prijzen of wedstrijden.
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Dankwoord
Het is zover, na lang zwoegen met verschillende tegenslagen – wanneer PHREEQC niet convergeerde, de modellering niet overeenkwam met de resultaten of mijn pc crashte – maar ook veel leuke momenten – wanneer alles eindelijk bleek te werken – is mijn thesis af. Tijd dus om even iedereen te bedanken die hieraan heeft meegeholpen. Een eerste woord van dank voor Prof. Rudy Swennen om als promotor te willen fungeren bij dit eindwerk. Ondanks zijn drukke agenda heeft hij steeds geprobeerd tijd vrij te maken voor de vergaderingen. Daarnaast dien ik uiteraard mijn co promotor, Tom Van Gerven, te bedanken die mij het onderwerp heeft aangeboden. Doorheen het jaar volgde hij de evolutie van mijn thesis nauwgezet op, waarvoor dank! Verder wens ik Diederik Jacques, Dirk Mallants en Lian Wang oprecht te bedanken voor de uitstekende begeleiding vanuit het SCK, zowel tijdens mijn twee weken stage in september als doorheen het voorbije jaar. De sfeer op het SCK was altijd zeer aangenaam, wat maakte dat ik er graag langskwam om een dagje te modelleren. In het bijzonder ben ik veel dank verschuldigd aan Diederik die heel wat uren vrijmaakte voor het verhelpen van mijn modelleerproblemen. Zonder zijn hulp was het wellicht niet gelukt, heel erg bedankt dus! Naast mijn “officiële” begeleiders, was er ook nog Geert Cornelis, bij wie ik altijd even mocht binnenlopen om iets te vragen. Zijn ervaring met PHREEQC en zijn geochemische kennis hebben mij dikwijls vooruit geholpen, bedankt daarvoor! Verder wens ik Prof. Jan Elsen, Gilles Mertens en Lieven Machiels te bedanken voor de XRD metingen en Sebastiaan voor de hulp in het labo bij de bepaling van de adsorptieparameters. I would like to extend my gratitude to Dr. Kulik of the Paul Scherrer Institute for providing me his GEM data and for his quick and extensive response to all my questions. A word of thanks also goes to Janez Perko for his hydrus 3D simulation. Mijn ouders wil ik bedanken voor alle steun, niet alleen tijdens mijn thesisjaar, maar gedurende de vijf jaren van mijn studies. Verder wens ik uiteraard m’n broers en zussen, andere familieleden, vrienden, jaargenoten en kotgenoten te bedanken. Ik wens ook mijn vriend, Maarten, te bedanken voor enkele praktische zaken zoals het voorbereiden van de stalen voor de XRD metingen en het gebruik van zijn laptop toen de mijne het begaf. Verder heb ik zijn interesse in mijn eindwerk steeds geapprecieerd. Zijn geologische achtergrond leverde vaak interessante discussies op. Tot slot ben ik ook dankbaar voor zijn enorme steun op momenten dat het niet wou vlotten en begrip wanneer ik – vooral naar het einde toe – minder tijd kon vrijmaken voor ons.
Aan iedereen, nogmaals bedankt en veel leesplezier!
Evelien
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List of abbreviations
AAM amorphous aluminium minerals ARD advection reaction dispersion C/S calcium silicium ratio CSH calcium silicate hydrate CZSH a Zn bearing calcium silicate hydrate phase DL diffuse layer DOC dissolved organic carbon EN12457 European standardized extraction test HFO hydrous ferric oxides HMO hydrous manganese oxides IAP ion activity product IAWG International Ash Working Group ICP MS inductively coupled plasma mass spectrometry KUL Katholieke Universiteit Leuven llnl Lawrence Livermore National Laboratory L/S liquid to solid ratio MSWI municipal solid waste incinerator NEN 7345 Dutch standardized diffusion test OPC ordinary Portland cement RH relative air humidity (%) SC surface complexation SCK Studiecentrum voor Kernenergie SI saturation index S/S solidification/stabilization S/W solid water ratio w/c water to cement ratio XRD X ray diffraction
v
List of symbols
αL longitudinal dispersivity (m) A specific surface area (m²/g) or temperature dependent constant in Davies equation th Ai i aqueous species
Aij shared surface area of cell i and j (m²)
γi activity coefficient of species i in water ( ) Γ sorption density (mol/m²) C concentration in water (mol/kg water) G 0 standard Gibbs free energy change for a reaction (J/mol) G E excess free energy of mixing (J/mol) H0 reaction enthalpy change (J/mol) S0 entropy change (J/mol.K)
De effective diffusion coefficient in a porous medium (m²/s)
DL hydrodynamic dispersion coefficient (m²/s) fbc correction factor for boundary cells ( ) F Faraday constant (96485 C/mol) η porosity ( )
ηij smallest of the two porosities of cell i and j ( ) θ water content (total porosity) ( ) hij distance between the midpoints of cell i and j (m) I ionic strength (mol/l or dimensionless if divided with the standard state) K equilibrium constant ( ) λ activity coefficient in solid solution ( ) mix f mixing factor ( ) ν stoichiometric coefficient in a reaction ( ) n number of discretization ( ) P coulombic correction factor ( ) th Pi i product q concentration in the solid phase (mol/kg water) r side of a cell (m) R molar gas constant (8.314 J/mol.K) th Ri i reactant σ surface charge density (C/m²) S solid concentration (g/l)
vi t time (s) T absolute temperature (K) v pore water flow velocity (m/s)
Vj volume of cell j (m³)
Vm volume of the mobile zone (m³)
Vbc volume of the boundary cell (m³) w thickness of water layer surroundig the cement block (m) φ potential (V) x distance (m) X mole fraction ( ) saturation state ( ) z charge number ( )
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TABLE OF CONTENT
SUMMARY ...... 1 SAMENVATTING...... 2 CHAPTER 1: INTRODUCTION AND OBJECTIVES ...... 3 1. TREATMENT OF WASTE MATERIALS BY SOLIDIFICATION /STABILIZATION ...... 3 2. LEACHING TESTS ...... 4 2.1. Extraction tests ...... 4 2.2. Diffusion tests ...... 4 2.3. Experimental data ...... 5 3. LEACHING MODELLING ...... 7 4. OBJECTIVES AND THESIS OUTLINE ...... 9 CHAPTER 2: THERMODYNAMIC CONCEPTS ...... 10 1. INTRODUCTION ...... 10 2. PRECIPITATION /DISSOLUTION ...... 10 2.1. Law of mass action ...... 10 2.2. Aqueous speciation modelling ...... 11 2.3. Mineral equilibrium...... 11 3. SURFACE COMPLEXATION ...... 12 4. SOLID SOLUTIONS ...... 14 5. TRANSPORT ...... 15 CHAPTER 3: MINERALOGICAL COMPOSITION OF CEMENT AND BOTTOM ASH...... 17 1. INTRODUCTION ...... 17 2. ORDINARY PORTLAND CEMENT ...... 17 3. MUNICIPAL SOLID WASTE INCINERATOR BOTTOM ASH ...... 20 4. PRESENCE OF LEAD IN A CEMENTITIOUS WASTE MATRIX ...... 21 4.1. Dissolution/precipitation of Pb containing minerals ...... 22 4.2. Solid solutions of Pb in matrix minerals...... 22 4.3. Surface complexation of Pb to matrix minerals...... 23 4.4. Remarks...... 23 5. CONCLUSION ...... 23 CHAPTER 4: DESCRIPTION OF PHREEQC AND BENCHMARKING ...... 24 1. INTRODUCTION ...... 24 2. METHOD , DATABASES AND DATA REQUIREMENTS ...... 24 3. TRANSPORT MODELLING ...... 26 3.1. Finite difference approximation ...... 26 3.2. Three dimensional model...... 27 3.3. Testing the implemented 3D model ...... 28 4. SOLID SOLUTION BENCHMARKING STUDY ...... 32 4.1. Introduction...... 32 4.2. Conversion of GEM input data to PHREEQC input data ...... 33 4.3. Model results ...... 34 4.3.1. Binary solid solutions...... 34 4.3.2. Ternary solid solutions...... 37 4.3.2.1. Modelling the impact of leaching on CZSH ...... 38 4.3.2.2. Modelling the impact of carbonation on CZSH...... 43 5. CONCLUSION ...... 46 CHAPTER 5: DATA COLLECTION...... 47 1. INTRODUCTION ...... 47 2. PRECIPITATION /DISSOLUTION ...... 47 3. SURFACE COMPLEXATION ...... 49
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3.1. Sorbent mineral concentration ...... 49 3.1.1. Hydrous ferric oxide content...... 49 3.1.2. Amorphous aluminium oxide content...... 50 3.2. Specific surface area of HFO ...... 51 3.3. Concentration of binding sites on HFO...... 51 4. SOLID SOLUTIONS ...... 51 5. TRANSPORT ...... 52 CHAPTER 6: EXTRACTION TEST MODELLING...... 53 1. INTRODUCTION ...... 53 2. METHOD AND INPUT DATA ...... 53 3. LEACHING OF MAJOR ELEMENTS ...... 54 3.1. Fresh concrete...... 54 3.2. Carbonated concrete ...... 64 3.2.1. Sample B14...... 65 3.2.2. Sample B30...... 69 3.2.3. Sample B60...... 71 3.3. General model ...... 73 3.4. Remark concerning solid solutions...... 79 3.5. Remark concerning Lothenbach and Winnefeld model ...... 82 4. LEACHING OF TRACE ELEMENTS ...... 82 4.1. Precipitation/dissolution only...... 82 4.2. Precipitation and solid solutions...... 88 4.2.1. Solid solution of calcite and cerrusite...... 88 4.2.2. Solid solution of gibbsite, ferrihydrite and litharge ...... 89 4.3. Precipitation, solid solutions and surface complexation...... 90 4.3.1. Surface complexation on hydrous ferric oxides ...... 90 4.3.2. Surface complexation on amorphous aluminium minerals ...... 90 4.3.3. Discussion and remarks ...... 91 5. CONCLUSION ...... 93 CHAPTER 7: DIFFUSION TEST MODELLING...... 94 1. INTRODUCTION ...... 94 2. LEACHING OF SODIUM AND POTASSIUM ...... 94 3. LEACHING OF CALCIUM AND LEAD ...... 96 4. CONCLUSION ...... 98 CHAPTER 8: CONCLUSION AND PERSPECTIVES FOR FUTURE RESEARCH...... 99 REFERENCES...... 101 APPENDICES...... 110 APPENDIX 1: OVERVIEW OF THE MINERALS INCLUDED IN EACH MODEL ...... 110 APPENDIX 2: GENERAL MODEL WITH WAIRAKITE ...... 112 APPENDIX 3: LOTHENBACH AND WINNEFELD MODEL ...... 115 APPENDIX 4: PHREEQC INPUTFILE FOR THE GENERAL MODEL FOR THE EXTRACTION TEST (MODEL 21*) ...... 121
ix
Summary
Solidification/stabilization is a technique for immobilizing hazardous wastes in mostly cement based binding materials, to delay dissolution and release of toxic components to the environment. Lead is a potential toxic component. Van Gerven (2005) studied lead leaching from cementitious waste matrices by performing extraction and diffusion tests. This thesis attempts to model his experimental results using the geochemical code PHREEQC. The composition of Ordinary Portland Cement (OPC) and municipal solid waste incinerator (MSWI) bottom ash was studied, in preparation of the modelling. The geochemical processes influencing the leaching of lead from cement/waste matrices were identified. Experimental and literature data were collected to model these processes. The new PHREEQC feature which makes it possible to model solid solutions was tested and approved in a benchmarking study. A PHREEQC input file for three dimensional diffusion was constructed and tested in another benchmarking study. This file provided satisfying results for the test cases. The model for the extraction tests was gradually constructed, starting with the simulation of the leaching of major elements. Model predictions obtained by defining a single set of pure mineralogical phases for both the uncarbonated and carbonated samples are in good agreement with the experiments in terms of leached concentrations for Ca, Mg, and Al. This indicates leaching of major elements is mainly solubility controlled. Moreover, the positive model results are confirmed by the decrease in Ca leaching with increasing carbonation that is observed in both experiments and model predictions. In a second step, leaching of Pb was simulated. Model predictions revealed that leaching of Pb is not only controlled by dissolution/precipitation of pure Pb containing minerals. Solid solutions (i.e. calcite cerrusite and gibbsite ferrihydrite litharge solid solutions) and adsorption reactions on amorphous Fe and Al oxides also appear to have a significant impact on the modelled results for the leaching of Pb. Addition of both solid solutions and adsorption reactions to the model provided a model curve that approaches the experimentally observed amphoteric leaching profile of Pb from the cementitious waste material both quantitatively and qualitatively. pH dependent Pb leaching decreases as carbonation proceeds. This trends was also visible in the model predictions. Moreover, the model results provide a possible explanation for this behaviour. Formation of cerrusite as a member of calcite cerrusite solid solution is responsible for lower predicted Pb release for the carbonated sample. In a last phase, the diffusion test was modelled. Good predictions were obtained for the cumulative release of sodium and potassium from the fresh sample. Carbonation decreases leaching of sodium and potassium. This trend was also predicted by the model. For lead, good model predictions were obtained by including only diffusion and using the maximum leachable amount as input. Calcium release was overestimated. If diffusion and chemical processes are coupled, predictions for calcium release remain unsatisfying. As such, it can be concluded that although the coupling between geochemical and transport processes still leaves place for further improvement, a lot of progress is made in the modelling of three dimensional diffusional transport.
1
Samenvatting
Solidificatie/stabilisatie is een techniek om gevaarlijk afval te immobiliseren, gewoonlijk door het afval te binden met cement, om oplossing en vrijkomen van gevaarlijke componenten in de omgeving te vertragen. Lood is een potentieel gevaarlijke component. Van Gerven (2005) bestudeerde de uitloging van lood uit cement/afval matrices door het uitvoeren van extractie en diffusietesten. Deze thesis heeft tot doel zijn experimentele resultaten te modelleren met de geochemische code PHREEQC. Ter voorbereiding van de modellering werd de samenstelling van Portland cement en bodemassen van afvalverbranding bestudeerd. De geochemische processen die de uitloging van lood uit een cement/afval matrix beïnvloeden werden geïdentificeerd. Experimentele en op de literatuur gebaseerde data nodig voor de modellering werden verzameld. De nieuwe PHREEQC optie die het mogelijk maakt vaste oplossingen te modelleren werd getest en goedgekeurd. Een PHREEQC input file voor 3D diffusie werd geconstrueerd en getest in een andere benchmarking studie. Dit bestand leverde goede resultaten voor de test cases. Het model voor de extractietesten werd gradueel opgebouwd. Hiervoor werd eerst de uitloging van de hoofdelementen gesimuleerd. Modelvoorspellingen verkregen door één set mineralen te definiëren voor zowel de ongecarbonateerde als gecarbonateerde stalen zijn in goede overeenkomst met de experimentele data voor wat betreft de uitgeloogde concentraties voor Ca, Mg en Al. Dit duidt aan dat de uitloging van hoofdelementen hoofdzakelijk gecontrolleerd wordt door oplosbaarheid. De goede modelvoorspellingen werden bovendien bevestigd door de daling in Ca uitloging bij stijgende graad van carbonatatie die zowel in experimentele als modelresultaten zichtbaar was. In een tweede stap werd de uitloging van lood gesimuleerd. Modelresultaten wezen uit dat de uitloging van lood niet enkel gecontroleerd wordt door oplossing/precipitatie van zuivere loodmineralen. Vaste oplossingen (nl. calciet cerrusiet en gibsiet ferrihydriet loodoxide vaste oplossingen) en adsorptie op amorfe ijzer en aluminiumoxides blijken ook een significante impact te hebben op de modelresultaten voor de uitloging van lood. Toevoegen van zowel vaste oplossingen als adsorptiereacties aan het model leverde een modelcurve op die het experimenteel waargenomen amfoteer uitloogprofiel van lood uit de cement/afval matrix zowel kwantitatief als kwalitatief goed benadert. De pH afhankelijke uitloging van lood is lager naarmate carbonatatie verder gevorderd is. Deze trend was ook zichtbaar in de modelvoorspellingen. Bovendien bieden de modelresultaten een mogelijke verklaring voor dit gedrag. Vorming van cerrusiet als component van een calciet cerrusiet vaste oplossing is verantwoordelijk voor de lagere voorspelling voor de lood uitloging voor het gecarbonnateerd staal. In een laatste fase werd de diffusietest gemodelleerd. Goede voorspellingen werden bekomen voor de cumulatieve uitloging van natrium en kalium voor het ongecarbonateerd staal. Carbonatatie verlaagt de uiloging van Na en K. Deze trend werd eveneens voorspeld door het model. Voor lood werden goede modelvoorspellingen bekomen door enkel diffusie in rekening te brengen en de maximaal uitloogbare concentratie als input te nemen. Calcium uitloging werd echter overschat. De calciumresultaten verbeteren niet door diffusie en geochemische reacties te koppelen. Er kan dus besloten worden dat, hoewel de koppeling tussen geochemische processen en transport nog kan verbeterd worden, er heel wat vooruitgang geboekt is op het gebied van de modellering van drie dimensioneel diffusioneel transport.
2 CHAPTER 1: INTRODUCTION AND OBJECTIVES
1. Treatment of waste materials by solidification/stabilization
Solid waste is produced in various municipal and industrial processes. Solid waste ranges from rather inert waste, e.g. glass bottles and fractions of building and demolition waste, to hazardous waste with high concentrations of heavy metals, toxic organic compounds, and the like. Examples of solid waste that contains heavy metals are metallurgical slag, incinerator bottom ash and fly ash. Depending on the heavy metal concentration and the physico chemical characteristics of the waste, landfilling or recycling may be viable management options (Sabbas et al., 2003). Waste can be recycled as such in granular applications or by solidification in monolithic form (Van Gerven, 2005). Solidification/stabilization (S/S) is the worldwide used process whereby a variety of hazardous materials are treated in a way to prevent dissolution of the toxic components and their release to the environment. S/S treatment involves mixing a binding reagent into the contaminated substance. Cementitious material, and in particular, ordinary Portland cement (OPC), is the most common used binding reagent (Conner, 1990; Malviya and Chaudhary, 2006b). This is attributable to its low cost, applicability to a wide variety of waste types and ease of operation in the field (Means et al., 1995). Moreover, the alkalinity of OPC inhibits microbiological processes (Glasser, 1997). Although the terms solidification and stabilization are frequently used interchangeably, they describe different effects that the binding reagents create to immobilize hazardous constituents (Conner, 1990): • Solidification refers to techniques that encapsulate the waste in a monolithic solid of high structural integrity. The encapsulation may be of fine waste particles or of a large block or container of wastes. Solidification does not necessarily involve a chemical interaction between the wastes and the solidifying reagents, but may mechanically bind the waste into the monolith. Contaminant migration is restricted by vastly decreasing the surface area exposed to leaching and/or by isolating the wastes within an impervious capsule. • Stabilization refers to those techniques that reduce the hazard potential of a waste by converting the contaminants into their least soluble, mobile or toxic form. The physical nature and handling characteristics of the waste are not necessarily changed by stabilization. S/S is used for non radioactive as well as for radioactive waste. Although, S/S does provide additional shielding of radioactivity immobilized within contaminated material, note that S/S does not reduce radioactivity of a material contaminated with radionuclides. The principle action of S/S on nuclear wastes is to physically and chemically immobilize the radionuclides within the treated material. Immobilization of the radioactive material prevents release of those materials into the environment. Over time, the level of radioactivity emitted from the immobilized radionuclides reduces itself through the process of radioactive decay. S/S treatment thus contributes to radioactive waste confinement and containment so that the contaminated material can be disposed of safely until the process of radioactive decay reduces the level of radiation emitted from the treated material to an acceptable level (The Portland Cement Organization, 2007).
3 CHAPTER 1: INTRODUCTION AND OBJECTIVES
The treated wastes are generally stored on land with or without a barrier system around it, where they are exposed to rain, soil water drainage, groundwater flow and/or other forms of weathering . If OPC is used as binding reagent, an important example of such a weathering process is carbonation. This is the uptake of carbon dioxide and the subsequent formation of carbonate containing minerals (e.g. calcite), with one of main the results being a decrease of pH in the pore solution. Weathering can have significant effects on the properties of the S/S wastes, particularly in the near surface region (Malviya and Chaudhary, 2006b). The main environmental concern with respect to these treated wastes is the release of constituents to soil and groundwater by leaching . This is the process in which constituents in the solid phase are transferred to a mobile liquid phase that is in contact with the solid phase. In the case of porous monoliths, such as cement matrices, this is followed by transport of these constituents from the pore liquid out of the monolith into the surroundings by diffusion and/or convection. This process may continue for thousands of years. Evaluation of leaching behaviour is needed to comply with regulation. Therefore an extensive array of leaching tests has been developed. In this study, a set of experimental data resulting from various leaching tests on a cementitious waste matrix is analysed by geochemical modelling. The experimental data are from Van Gerven (2005) and the analysis focuses on the main constituents (Ca, Si, Al, Mg) and one trace element (Pb). Lead was chosen because it is a toxic element that is present in municipal solid waste incinerator (MSWI) bottom ash as well as in low level radioactive waste. In the remaining of this chapter, the experimental data and previous modelling studies are discussed in paragraphs 2 and 3, respectively. The specific objectives of this study are given in paragraph 4.
2. Leaching tests
In leaching tests the material is extracted with a contact solution (i.e. the leachant) during a certain amount of time, after which the constituent concentrations are measured in the leachate. To investigate the various processes governing the extent and rate of leaching, endless variations can be introduced by changing test variables, such as leachant composition, method of contact, liquid to solid (L/S) ratio, contact time and system control (pH, E h, temperature). On the basis of leachant renewal, a broad distinction is made between single or successive extraction tests and continuous extractions, also termed ‘dynamic leaching tests’ (diffusion tests).
2.1. Extraction tests
In single extraction tests the leachant is not renewed. Equilibrium is assumed to have been reached at the end of the test. The leaching concentration is therefore assumed to equal the solubility of the particular compound in this particular matrix.
2.2. Diffusion tests
Dynamic tests include all tests where the leachant is continuously or intermittently renewed. Information on the kinetics of solid phase dissolution and constituent flux is thus obtained. These tests are useful to
4 CHAPTER 1: INTRODUCTION AND OBJECTIVES determine the long term waste evolution and release of toxic components. Dynamic tests can be either flow through tests or flow around tests. The first applies to granular material through which flow is mainly convective. The flow around test is generally used for porous monolithic samples; constituent transport will be mainly controlled by diffusion. The Dutch NEN 7345 procedure is a standardized flow around test, usually called “diffusion test”.
2.3. Experimental data
Mortars were produced by mixing 548 kg/m³ of OPC, 1096 kg/m³ dried bottom ash from a municipal solid waste incinerator and 281 kg/m³ distilled water, giving a water to cement ratio w/c = 0.5. The mixtures were poured in moulds of 150 x 150 x 150 mm and vibrated. After 24 hours of setting time, the samples were demoulded and cured for 28 days in a humid room (20°C, > 95% RH, 0.035% CO 2). At the end of the curing period, approximately 1.5 cm of material was cut from the edges of the monoliths to obtain a fresh uncarbonated surface and the remaining cubes were cut into samples of 40 x 40 x 40 mm using a dry cutting technique. One set of samples was dried in a vacuum oven at 40°C and subsequently stored at room temperature in a bag filled with nitrogen gas, awaiting further treatment (uncarbonated samples, “B0”). Another set was placed in a closed chamber with the atmosphere at a temperature of 37°C, RH over 90% and containing
20% of CO 2. Some samples were carbonated for 14 days (“B14”), others for 30 days (“B30”) and yet another set for 60 days (“B60”). At the end of the carbonation period, it was concluded, based on measurements, that B0 was almost not carbonated, B60 was almost completely carbonated, and B14 and B30 presented intermediate cases with a carbonated shell covering an uncarbonated core. When the B14 and B30 monoliths were particle size reduced (<125 m), uncarbonated and carbonated mineral phases became mixed. An extraction test , analogous to the EN12457 test, was conducted on 10 g of particle size reduced material in 100 ml of distilled water acidified with different volumes of concentrated HNO 3. After 24 hours, the pH and the composition of the leachate were analyzed. For Ca, Mg, Al, Si and Pb the results of this analysis are shown in Figure 1.1.
1.0E+06 1.0E+04
1.0E+05 1.0E+03
1.0E+04 1.0E+02
1.0E+03 1.0E+01
B0 B0 1.0E+02 B14 1.0E+00 B14 B30 B30 Ca leached out (mg/kg) out leached Ca B60 (mg/kg) leached out Mg B60 1.0E+01 1.0E-01 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 pH pH a b
Figure 1.1: Solubility of Ca (a), Mg (b), Al (c), Si (d) and Pb (e) from uncarbonated (B0), partially (B14, B30) and fully carbonated samples (B60) (data from Van Gerven, 2005).
5 CHAPTER 1: INTRODUCTION AND OBJECTIVES
1.0E+05 1.0E+05
1.0E+04
1.0E+03 1.0E+04 1.0E+02 1.0E+01 1.0E+00 1.0E+03 1.0E-01 B0 B14 1.0E-02 B30 Si leached out (mg/kg) leached out Si
Al leached out (mg/kg) out leached Al B0 B60 1.0E-03 1.0E+02 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 pH pH c d
1.0E+03
1.0E+02
1.0E+01
1.0E+00 B0 1.0E-01 B14 B30 Pb leached out (mg/kg) out leached Pb B60 1.0E-02 0 2 4 6 8 10 12 14 pH e
Figure 1.1 (continued): Solubility of Ca (a), Mg (b), Al (c), Si (d) and Pb (e) from uncarbonated (B0), partially (B14, B30) and fully carbonated samples (B60) (data from Van Gerven, 2005).
The NEN 7345 procedure forms the basis for the diffusion tests carried out. A monolithic sample with minimum dimensions of 40 mm was placed in a leaching tank filled with distilled water, acidified to pH 4. The volume of the leachant was five times the volume of the sample. The leachate was frequently replaced by fresh leachant to prevent a concentration build up of constituents in the leachate. The renewal times are based on the assumption of diffusional transport. The leachant is renewed after cumulative leaching times of 6, 24, 54, 96 hours and 9, 16, 25, 36, 49, 64, 81, 100, 121, 144, 169, 225 days. After each renewal an aliquot of the leachate is filtered through a 0.45 m membrane and conserved for measurement. For Na, K, Ca, Mg, Al and Pb the results of these measurements are shown in Figure 1.2.
30000 35000 30000 25000 25000 20000 B0 20000 B14 15000 15000 B30 B0 10000 B60 B14 10000
B30 (mg/m²) release K (mg/m²) release Na 5000 5000 B60 0 0 0 50 100 150 200 250 0 50 100 150 200 250 time (days) time (days) a b
Figure 1.2: Cumulative release of Na (a), K (b), Ca (c), Mg (d), Al (e) and Pb (f) from uncarbonated (B0), partially (B14, B30) and fully carbonated samples (B60) (data from Van Gerven, 2005).
6 CHAPTER 1: INTRODUCTION AND OBJECTIVES
35000 1200 B0 30000 B14 1000 25000 B30 B0 800 B60 B14 20000 600 B30 15000 B60 400 10000
Ca release (mg/m²) release Ca 200 5000 (mg/m²) release Mg
0 0 0 50 100 150 200 250 0 50 100 150 200 250 time (days) time (days) c d
1400 7
1200 6 1000 5 B0 B0 800 4 B14 B14 600 B30 3 B30 B60 B60 400 2 Al release (mg/m²) release Al 200 (mg/m²) release Pb 1 0 0 0 50 100 150 200 250 0 50 100 150 200 250 time (days) time (days) e f
Figure 1.2 (continued): Cumulative release of Na (a), K (b), Ca (c), Mg (d), Al (e) and Pb (f) from uncarbonated (B0), partially (B14, B30) and fully carbonated samples (B60) (data from Van Gerven, 2005).
3. Leaching modelling
The development of geochemical modelling codes has made it possible to model leaching behaviour. This offers a lot of possibilities: (i) modelling can help in understanding complex leaching behaviour; (ii) the models can be used to predict changes in leaching behaviour over much longer time frames than possible in a leaching test; and (iii) the models can also be used to predict changes in leaching behaviour under different management or treatment scenarios.
Former modelling work of the data set Modelling of the results of the extraction test was initiated by Van Gerven (2005) and continued by Swinnen (2006), who modelled these experimental data with MINTEQA2 (Allison et al., 1990), PHREEQC 2 (Parkhurst and Appelo, 1999) and GEM PSI (Kulik, 2002) in order to compare these different geochemical modelling codes. Results for Ca and Pb are given in Figures 1.3 & 1.4, which clearly show that the three modelling programs give almost identical results. Nevertheless, there are important differences between the three models, of which Swinnen (2006) mentioned the following: (i) MINTEQA and PHREEQC calculate equilibrium by simultaneous solving sets of nonlinear mole balance and mass action equations, while GEM PSI calculates equilibrium by Gibbs free energy minimization; (ii) MINTEQA gives the most convergence problems; (iii) PHREEQC has much more options than MINTEQA and GEM PSI, the most important being the option to model transport processes.
7 CHAPTER 1: INTRODUCTION AND OBJECTIVES
Figure 1.3: Leaching of Ca: comparison between experimental values of the extraction test and MINTEQA, GEM-PSI, and PHREEQC modelling results (from Swinnen, 2006).
Figure 1.4: Leaching of Pb: comparison between experimental values of the extraction test and MINTEQA and PHREEQC modelling results (from Swinnen, 2006).
Table 1.1: Comparison between observed and predicted release of Na, K, Ca, Al, Mg and Pb after the first diffusion step (from Swinnen, 2006). Component Leached out (model) [mg/m²] Leached out (experimental) [mg/m²] Na 8905 3528 K 8232 5649 Ca 0.052 594 Al 27 169 Mg 0.000022 0 Pb 0.00012 1
8 CHAPTER 1: INTRODUCTION AND OBJECTIVES
This last remark explains why only PHREEQC is able to model diffusion tests. Swinnen (2006) also started one dimensional modelling of the diffusion tests with PHREEQC. Some of her results are shown in Table 1.1.
4. Objectives and thesis outline
As mentioned in the previous paragraph, Swinnen (2006) already started modelling the extraction and diffusion tests from the PhD of Van Gerven (2005). Her results still leave place for further improvement (cf. Figures 1.3 & 1.4 and Table 1.1) since the quantitative match between model predictions and experimental results is not excellent. Moreover, precipitation was the only mechanism brought into account. The objectives of this thesis are (i) to identify the geochemical processes that are key to the mobility of lead in cementitious matrices and (ii) to implement the results from this study in a coupled reactive transport code (PHREEQC 2) to make improved model predictions for lead leaching from cemented waste forms and the potential impact of the waste on the environment. As such the study contributes to the realisation of the mission of both the Katholieke Universiteit Leuven (KUL) and the Belgian Nuclear Research Centre (Studiecentrum voor Kernenergie, SCK) in the field of industrial and radioactive waste management. To achieve this objective, following steps will be undertaken: • An extensive literature study on the composition of OPC and MSWI bottom ash, and on the way lead might be present in a cement/waste matrix will be performed (Chapter 3). Lead can be either precipitated in a pure mineral due to exceeding of the solubility product, included in minerals as a solid solution, or adsorbed to the solid phases by surface complexation. Thermodynamic data for Pb containing minerals that are relevant to a cementitious environment (for fresh concrete with an alkaline pH, aged concrete with a near neutral pH and partially carbonated concrete with an intermediate pH), needed for implementation in PHREEQC 2 will be collected (Chapter 5). • In PHREEQC 1 it was impossible to model solid solutions. In PHREEQC 2, this modelling capability has been added. A benchmarking study is needed to verify if PHREEQC offers correct solid solution calculations (Chapter 4). This benchmarking is performed in comparison with the GEM software, a modelling package that has already been successfully used for solid solution calculations. • PHREEQC is able to perform three dimensional (3D) diffusive transport calculations needed to model the diffusion tests. However, the implementation of a 3D diffusive problem is not straightforward. Therefore, a second benchmark simulation is performed (Chapter 4). • Extraction data from Van Gerven (2005) will then be calculated with PHREEQC (including solid solution and surface complexation, but excluding the transport term) (Chapter 6). Fresh, as well as partially and fully carbonated concrete will be considered. • The diffusive transport of dissolved elements from within the pore water of a cement monolith to the surrounding environment is then modelled with PHREEQC (Chapter 7). 9 CHAPTER 2: THERMODYNAMIC CONCEPTS
1. Introduction
In this chapter, the basic concepts of precipitation/dissolution reactions, surface complexation and solid solutions are summarized. In a last paragraph, transport of aqueous species is addressed.
2. Precipitation/dissolution
If the aqueous phase is undersaturated for a given mineral, this mineral will dissolve or remain in solution. On the other hand, a mineral will precipitate or remain precipitated when the aqueous phase is oversaturated with respect to that mineral. Under or oversaturation of a solution is calculated based on thermodynamic principles. The basic concepts of thermodynamic equilibrium modelling are extensively described in different publications (e.g. Stumm and Morgan, 1996; Appelo and Postma, 2005). Only a short overview will be given here. Pure equilibrium reactions will be assumed for the precipitation and dissolution of all minerals (i.e. no kinetics will be included).
2.1. Law of mass action
The most fundamental equation for geochemical equilibrium modelling is the law of mass action. This law states that the ratio of the product of the activities of the reaction products to the product of the activities of the reactants is constant. For a generalized reaction: K K ν r 1, R1 + ν r 2, R2 + + ν r ,nr Rnr ↔ ν p 1, P1 + ν p 2, P2 + + ν p,np Pnp (2.1) the law of mass action is written as
np ν p,i ∑ Pi i=1 (2.2) K = nr ν r,i ∑ Ri i=1 where K is the equilibrium constant , [ ] denotes activity, Ri and Pi are the reactants and products, respectively, nr and np are the number of reactants and products, respectively, and νr,i and νp,i are the stoichiometric coefficients of the reactants and products, respectively. The reactants and products can be aqueous species, surface complexes, and minerals.
The behaviour of a species is thus determined by its activity and not by its concentration. Activities are measures for the effective concentration of species. They are expressed as a fraction relative to a standard state and are, therefore, dimensionless. For dilute electrolyte solution, the activity of water [H 2O] is equal to 1.
10 CHAPTER 2: THERMODYNAMIC CONCEPTS
2.2. Aqueous speciation modelling
The standard state for an aqueous species is defined as an ideal solution with solute concentration of
1 mol / kg H 2O = 1 molal. The relation between activity and molal concentration for an aqueous species Ai is
[Ai ]= γ i (Ai ) (2.3)
1 th where ( ) denotes molality (mol kg H2O) and γi is the unitless activity coefficient of the i aqueous species. The activity becomes dimensionless by division with the standard state. The activity coefficient is related to the ionic strength I, which describes the number of electrical charges in the solution:
1 (2.4) I = ()A z 2 2 ∑ i i
th where zi is the charge of the i aqueous species (dimensionless). Similarly to the definition of activity, the ionic strength becomes dimensionless by division with the standard state. Different equations exist to calculate the activity coefficient . In this thesis, the Davies equation will be used, which is applicable for 0 < I < 0.5 and is probably the most appropriate for use with dilute leaching solutions (IAWG, 1997):
(2.5) logγ = −Az2 I − 3.0 I i i 1 + I where A is a temperature dependent coefficient. Under ideal conditions in very dilute systems, the ionic strength I ~ 0 and, consequently, activity and concentration are equal: [Ai ] =(Ai ). In leaching solutions, however, ionic strength is typically higher than zero so that the solution must be classified as non ideal.
2.3. Mineral equilibrium
The standard state of a mineral is a pure solid. Therefore, the activity of a pure solid phase is equal to one and can be omitted from the mass action equation, which can then be written as:
ν Na m,i (2.6) K m = ∑[]Ai i=1
th where Km is the equilibrium constant for the m mineral (which is in this case often called the solubility product ), Na is the number of aqueous species (minus 1 for H 2O) and νm,i is the stoichiometric coefficient of the ith aqueous species for the mth mineral. When the actual activities of the aqueous species are put in equation (2.6) (which can be different from the activities at equilibrium), the ion activity product ( IAP ) is obtained. The ratio of the ion activity product and the solubility constant is the saturation state :
= IAP / K (2.7)
Or, in a logarithmic scale, the saturation index SI :
SI = log(IAP / K ) (2.8)
11 CHAPTER 2: THERMODYNAMIC CONCEPTS
For equilibrium geochemical conditions between the mineral and the aqueous solution, it is required that = 1 or SI = 0. Supersaturated conditions prevail when > 1 or SI > 0 (precipitation), while < 1 or SI < 0 indicate undersaturation (no precipitation). The magnitude of the saturation index describes the driving force towards remaining dissolved (a large negative number) or precipitation (a large positive number).
3. Surface complexation
Besides chemical bonding, sorption of ions on mineral surfaces also depends on the charge characteristics of the surfaces. Mineral surfaces can be positively, neutrally or negatively charged, depending on the solution pH and composition. For example, charged mineral surfaces enhance sorption of counter ions (i.e. ions with a charge opposite to the surface) but hinder the sorption of co ions (i.e. ions with the same charge as the surface) because of an electrostatic effect. The influence of surface charge on sorption can be described by surface complexation (SC) models. The generalized two layer model will be used here. This is an example of an electrostatic surface complexation model. This kind of model simulates adsorption to mineral surfaces while taking into account electrostatic interactions between charged surfaces and solutes. For a detailed description of different surface complexation models, see Davis and Kent (1990), Goldberg (1992) and Langmuir (1997). The generalized two layer model is described in Dzombak and Morel (1990), of which a brief summary is given here.
The generalized two-layer model of Dzombak and Morel (1990) The diffuse layer (DL) model employs a simple two layer description of the oxide/water interface consisting of one surface layer and a Gouy Chapman diffuse layer of counterions in solution (Figure 2.1). All specifically sorbed ions are assigned to one surface layer, and all non specifically sorbed counterions are assigned to the diffuse layer. A Gouy Chapman distribution of ions is assumed for the solution side of the interface. The charge on an oxide surface is determined by proton transfer reactions and by surface coordination reactions with other cations and anions. In general, the net surface charge density σ (in C/m²) is given by
σ = F[ΓH − ΓOH + ∑ (zM ΓM )+ ∑ (z AΓA )] (2.9)
where F is the Faraday constant (96485 C/mol), z the valence of a sorbing ion, ΓH and Γ OH the sorption densities (mol/m²) of protons and hydroxyl ions, respectively, and Γ M and ΓA the sorption densities of specifically sorbed cations and anions, respectively. As shown in Figure 2.1, in the DL model this charge is assumed to reside in one surface layer. Because a diffuse layer of counter ion charges is assumed for the solution side of the interface, the relationship between surface charge and potential is fixed by electrical double layer theory. According to the Gouy Chapman theory (for a symmetrical electrolyte with valence z), the surface charge density ( σ) is related to the electrical potential at the surface ( φ, in volts) by
12 CHAPTER 2: THERMODYNAMIC CONCEPTS
σ = .0 1174 I 2/1 sinh( zϕ ×19.46) (2.10) Surface charge density and electrical potential are thus related by the square root of the ionic strength (in mol/l). For a mono monovalent solution the ionic strength equals the concentration of the electrolyte. The amount of surface charge that can be developed on an oxide surface is limited by the number of binding sites.
Figure 2.1: a) Schematic representation of ion binding on an oxide surface. This conceptualization is used in the diffuse layer surface complexation model. b) Potential decay in the diffuse layer. (Dzombak and Morel, 1990)
Surface acidity Surface protonation and deprotonation reactions that are basic to a surface complexation model are written in the form + + app ≡ SOH 2 = ≡ SOH + H K a1 (2.11) − + app ≡ SOH = ≡ SO + H K a2 (2.12) + − where ≡ SOH 2 , ≡ SOH and ≡ SO represent positively charged, neutral and negatively charged app app + surface hydroxyl groups, and K a1 and K a2 are apparent acidity constants. If H is the only specifically sorbing ion in the system, then the net surface charge density is given by + − σ = [F / AS ][(≡ SOH 2 ) − (≡ SO ]) (2.13) where ( ) represent molar concentrations, A the specific surface area (m²/g) and S the solid concentration (g/l). The mass law constants for reactions (2.11) and (2.12) are listed as “apparent” because they include long range coulombic interactions of hydrogen ions with the surface and hence are functions of the surface charge, which varies with solution conditions such as pH and ionic strength. The apparent dissociation constants can be measured experimentally and can be written as a function of a constant intrinsic dissociation constant by introducing a coulombic correction factor: K app = K int exp(− zFϕ / RT ) = K int P z (2.14) where P is the coulombic correction factor [exp( F φ/RT)], z the net change in the charge of the surface species, R the molar gas constant (8.314 VC/mol.K), and T the absolute temperature (K).
13 CHAPTER 2: THERMODYNAMIC CONCEPTS
The laws of mass action then become
+ int (≡ SOH )[H ] Fϕ K a1 = .exp z (2.15) (≡ SOH + ) RT 2
− + int (≡ SO )[H ] Fϕ K = .exp z (2.16) a 2 (≡ SOH) RT
Cation surface complexation Cation sorption is modelled using two types of binding sites: (i) a small amount of high affinity sites, and (ii) a large amount of relatively low affinity sites. Surface complexation reactions for cation sorption are typically of the form: s 2+ s + + app int ≡ S OH + M = ≡ S OM + H K1M = K1M P (2.17) w 2+ w + + app int ≡ S OH + M = ≡ S OM + H K 2M = K 2M P (2.18) where ≡ S sOH and ≡ S wOH represent high affinity and low affinity binding sites, M2+ is a divalent app app cation and K1M and K 2M are apparent equilibrium constants. As in the case of proton exchange reactions, coulombic effects are taken into account by including a coulombic correction factor P in the apparent constants. The mass law equations then become
s + + int (≡ S OM )[H ] Fϕ K = .exp z (2.19) 1M (≡ SsOH)(M2+ ) RT w + + int (≡ S OM )[H ] Fϕ K = .exp z (2.20) 1M (≡ SwOH)(M2+ ) RT and the net surface charge density is given by s + s + w + w + s − w − σ = [F / AS][(≡ X OH 2 ) + (≡ X OM ) + (≡ X OH 2 ) + (≡ X OM ) − (≡ X O ) − (≡ X O ]) (2.21)
Anion surface complexation This is completely analogous to the above described cation surface complexation and is therefore not treated here.
4. Solid solutions
In paragraph 2, minerals have been considered as pure phases, with activity equal to one. However, the solids occurring in nature are seldom pure solid phases. Isomorphous replacement by a foreign constituent in the crystalline lattice is an important factor by which the activity of the solid phase may be decreased. The thermodynamics of solid solutions have been treated in detail by Vaslow and Boyd (1952). A brief summary will be given here, based on Stumm and Morgan (1996) and Appelo and Postma (2005).
14 CHAPTER 2: THERMODYNAMIC CONCEPTS
To express the relationship theoretically, we consider a two phase system such as calcite otavite, where some of the CdCO 3(s) (otavite) becomes dissolved in CaCO 3(s) (calcite). The reaction may be characterized by the equilibrium: 2+ 2+ CaCO 3 (s) + Cd ↔ CdCO 3 + Ca (2.22) and its equilibrium constant is defined by
2+ 2+ − .8 37 [CdCO (s)][Ca ] X CdCO λCdCO [Ca ] K CaCO 10 3 K = 3 = 3 3 = 3 = ≅ 10 (2.23) []CaCO (s) []Cd 2+ X λ []Cd 2+ K 10 −11 3. 3 CaCO 3 CaCO 3 CdCO 3 where the activity ratio of the solids is replaced by the ratio of the mole fractions ( X) multiplied by activity coefficients ( λ) which correct for non ideal behaviour. For an ideal solid solution λ = 1. Commonly, solid solutions show deviations from ideal behaviour and the activity coefficients λ become a function of the excess free energy of mixing ( non-ideal solid solutions ). For a binary solid solution the excess free energy of mixing, G E, can be modelled with the Guggenheim series expansion:
E 2 G = ( X 1 X 2)RT (a0 + a1 ( X 1 − X 2 ) + a2 ( X 1 − X 2 ) + ... (2.24)
where a0, a1, … are empirical coefficients (dimensionless), called Guggenheim mixing parameters .
When a0 is non zero, and the other coefficients are zero, the solid solution is classified as regular . The activity coefficients are found from the excess energy with the Gibbs Duhem relation, and are in the regular model given by: 2 ln λ1 = X 2 a 0 (2.25) and 2 ln λ2 = X 1 a0 (2.26)
5. Transport
The transport of aqueous species is described using the advection-reaction-dispersion (ARD) equation which follows from the principle of conservation of mass (Figure 2.2) (Parkhurst and Appelo, 1999): ∂C ∂C ∂ 2C ∂q = −v + D − ∂t ∂x L ∂x 2 ∂t (2.27) where C is the concentration in water (mol/kg water), t is time (s), v is the pore water flow velocity (m/s), x is distance (m), DL is the hydrodynamic dispersion coefficient [m²/s, DL = De + αLv, with De the effective 1 2 diffusion coefficient (m²/s), and αL the dispersivity (m)], and q is concentration in the solid phase ∂C ∂ 2C (expressed as mol/kg water in the pores). The term − v represents advective transport, D ∂x L ∂x 2
1 Compared to diffusion in “free” water, solutes diffusing through a porous medium must travel an extra distance because they have to circumnavigate the solid particles. The effective diffusion coefficient corrects the molecular diffusion coefficient in free water for the additional pathway by multiplying with a tortuosity factor. 2 The dispersivity brings into account the twisting of the pores. 15 CHAPTER 2: THERMODYNAMIC CONCEPTS
∂q represents dispersive transport, and is the change in concentration in the solid phase due to reactions ∂t (q in the same units as C). The latter term thus couples the transport processes with the chemical reactions defined in the previous paragraphs. It is assumed that v and DL are equal for all solute species. As such, C represents the total dissolved concentration for an element.
For the diffusion tests, the pore water velocity is supposed to be zero. As such, there is no advective transport, and the ARD equation simplifies to ∂C ∂ 2C ∂q (2.28) = D − ∂t e ∂x 2 ∂t This equation is known as Fick’s second law . As we are dealing with a porous medium, a porosity factor must be included: ∂θC ∂ 2C ∂q (2.29) = ϑD − ∂t e ∂x 2 ∂t were θ is the water content, which equals the porosity η for the saturated conditions considered here. The three dimensional solute diffusion equation needed to describe the diffusion tests is then: ∂ηC ∂ 2C ∂ 2C ∂ 2C ∂q (2.30) = ϑD + + − e 2 2 2 ∂t ∂x ∂y ∂z ∂t where x, y, and z are the three directions in a Cartesian coordinate system assuming thus an isotropic, spatial and time invariant diffusion coefficient.
Figure 2.2: Terms in the advection-reaction-dispersion equation (Parkhurst and Appelo, 1999).
16 CHAPTER 3: MINERALOGICAL COMPOSITION OF CEMENT AND BOTTOM ASH
1. Introduction
This chapter focuses on the mineralogical composition of cement and MSWI bottom ash. An extensive literature study was performed to determine: (i) the main cement minerals (for fresh as well as (partially) carbonated cement); (ii) the main minerals present in MSWI bottom ash; and (iii) the presence and speciation of lead in a cementitious waste matrix: inclusion of lead in minerals, surface complexation and solid solutions.
2. Ordinary Portland cement
Ordinary Portland cement (OPC) is the standard type of cement produced by burning limestone and clay in a kiln at high temperatures (>1400°C) and grinding the product – also known as clinker – to a powder.
OPC consists of 50 70 wt% tricalcium silicate (alite, 3CaO.SiO 2), 15 30 wt% dicalcium silicate (belite,
2CaO.SiO 2), 5 12 wt% tricalcium aluminate (3CaO.Al 2O3), and 5 12 wt% calcium aluminoferrite
(4CaO.Al 2O3.Fe 2O3). A number of other minerals such as gypsum (CaSO 4 2H 2O, 2 5 wt%, added to the clinker in order to slow down hydration), calcium oxide (lime, CaO, <2 3 wt%), magnesium oxide, Na and K sulphates are usually also present. When OPC is brought into contact with water ( hydration ), a range of reactions starts. The dominant reactions are those of alite and belite with water to form calcium silicate hydrate (CSH) and calcium hydroxide (Ca(OH) 2, portlandite), according to equations 3.1 and 3.2 (e.g., Cougar et al., 1996; Van Gerven, 2005).
2 3( CaO.SiO2 ) + 6 H 2O → 3CaO 2. SiO2 3. H 2O + 3 Ca(OH )2 (3.1)
2 2( CaO.SiO2 ) + 4 H 2O → 3CaO 2. SiO2 3. H 2O + Ca(OH )2 (3.2)
Lothenbach and Winnefeld (2006) conducted experiments in which the composition of an OPC during its hydration was determined. XRD patterns of the solid phase during hydration very clearly show this disappearance of alite and formation of portlandite (Figure 3.1). The samples, used in the experiments that will be modelled in this thesis, hydrated for 28 days and were then stored in a N 2 bag or carbonated, after which they were leached. Consequently, for our modelling purpose, it is important to know the mineral distribution of the hydrated cement after 28 days (= 672 hours). The main constituent of hydrated cement is calcium silicate hydrate, CSH , which is an amorphous solid and is often referred to as a gel. The structure is not well defined, although it is structurally related to 1.4 nm tobermorite and jennite (e.g. Clodic and Meike, 1997; Nonat, 2004). The basic structure of both crystalline minerals is one of alternating CaO layers and SiO 2 chains. CSH is primarily responsible for the microporous structure of the cement matrix and is considered the major strength providing component of
17 CHAPTER 3: MINERALOGICAL COMPOSITION OF CEMENT AND BOTTOM ASH hydrated OPC. CSH accounts for 60 to 70 wt% of the hydrated cement, whereas portlandite , the second major constituent, accounts for 20 to 25 wt% (Glasser, 1993). The remaining minerals include hydrated calcium sulphoaluminates (e.g. calcium monosulfo-aluminate, ettringite), hydrated calcium aluminates (e.g. hydrogarnet) and ferrites . In addition, aluminium and iron hydroxides form. MgO will dissolve and precipitate as brucite or as hydrotalcite , depending on the chemical conditions. Portlandite and, to a lesser extent, CSH are alkaline minerals and give hydrated cement its alkaline character (i.e. typical pore water pH of 12 13).
Figure 3.1: Semi-quantitative evaluation of XRD patterns of the solid phase after different hydration times (Lothenbach and Winnefeld, 2006).
It is important to note that when the pH is forced to decrease, as in the extraction test by adding HNO 3, the minerals typically present in hydrated cement may dissolve and other minerals will form. As such, it will be necessary not only to include the minerals present in hydrated cement but also those that form at lower pH values into our model. For instance, gypsum , which is not present anymore after hydration by the fast formation of ettringite (cf. Figure 3.1), must be included in our geochemical model because ettringite will dissolve again and gypsum will reappear when pH is lowered in the extraction test. The cement mineral phases commonly mentioned in literature are summarized in Table 3.1. For each phase, some authors that use this phase in their model are indicated.
18 CHAPTER 3: MINERALOGICAL COMPOSITION OF CEMENT AND BOTTOM ASH
Table 3.1: Overview of common fresh cement mineral phases. Phase Chemical formula References CSH phases and portlandite Afwillite Ca 3Si 2O4(OH) 6 [1], [3], [6] CSH_1.8 Ca 1.8 SiO 9H10.4 [1], [3], [6] Portlandite Ca(OH) 2 [1], [2], [3], [4], [6]
Sulphate minerals Ettringite Ca 6Al 2(SO 4)3(OH) 12 26H 2O [1], [3], [4], [5] Anhydrite CaSO 4 [5] Gypsum CaSO 4 2H 2O [1], [2], [5] Monosulfoaluminate Ca 4Al 2SO 10 12H 2O [1], [4]
Hydrated calcium aluminates (CAH) C4AH13 (CaO) 4(Al 2O3) 13H 2O [1], [4], [5], [6] Hydrogarnet Ca 3Al 2O12 H12 [1], [2], [3], [5], [6]
Hydrated calcium ferrites (CFH) C4FH13 (CaO) 4(Fe 2O3) 13H 2O [5]
Aluminium and iron hydroxides Al(OH) 3(am) Al(OH) 3 [5], [6] Gibbsite Al(OH) 3 [2], [5], [6] Ferrihydrite Fe(OH) 3 [2] Fe(OH)3(am) Fe(OH) 3 [5] Fe(OH)3(microcr) Fe(OH) 3 [5]
Magnesium phases Brucite Mg(OH) 2 [2], [5], [6] Hydrotalcite Mg 4Al 2O7 10H 2O [1], [3], [6]
SiO 2 Amorphous SiO 2 SiO 2 [5], [6] Quartz SiO 2 [6] Chalcedony SiO 2 [6]
Other phases Katoite Ca 3Al 2SiO 12 H8 [1], [6] Straëtlingite Ca 2Al 2SiO 7 8H 2O [1], [6] Hematite Fe 2O3 [3] Brownmillerite Ca 4Al 2Fe 2O10 [2] References: [1] (Gaucher, 2004); [2] (Halim et al., 2005); [3] (Wang, 2006); [4] (Matschei et al., 2007); [5] (Lothenbach and Winnefeld, 2006); [6] (Swinnen, 2006)
Cement carbonation Carbonation is considered to be one of the most important weathering processes. Carbonation involves the chemical reaction of cement hydration products (e.g. portlandite and CSH) with carbon dioxide gas from the atmosphere leading to calcite, CaCO 3 (Bin Shafique et al., 1998; Bary and Sellier, 2004; Garrabrants et al., 2004; Van Gerven et al., 2004). The overall reaction is exothermic and is written as:
2+ − Ca + 2 OH + CO2 → CaCO 3 (s) + H 2O (3.3)
19 CHAPTER 3: MINERALOGICAL COMPOSITION OF CEMENT AND BOTTOM ASH
Other phases may also be converted to carbonates (e.g. brucite may convert to magnesite, MgCO 3). The most common mentioned carbonates are provided in Table 3.2. The overall effects of Portland cement carbonation are (i) neutralization of pore water alkalinity (i.e. the pH in the pore water is lowered from pH 13 to ultimately about pH 8), (ii) formation of carbonates, (iii) progressive decalcification of the hydrated cement (causing a reduction of the Ca/Si ratio of the CSH gel) and (iv) reduction of the porosity of the monolith (Van Gerven et al., 2006). As the structure of CSH gel is not well defined, CSH gel and its decalcification are represented differently throughout literature. The representation can be quite complex involving a solid solution model or simplified by using CSH phases of different Ca/Si ratio. The solid solution model developed by Kulik and Kersten (2001) was applied by Lothenbach and Winnefeld (2006) to model the hydration of Portland cement. Stronach and Glasser (1997) proposed to use three CSH phases of different Ca/Si ratio according to their experimental results. This model was implemented by e.g. De Windt et al. (2004). When only one amorphous CSH phase or a crystalline phase afwillite is used to represent the CSH gel (e.g. afwillite by Wang, 2006), the decalcification process is completely neglected. The latter was already represented in Table 3.1, the first two options are provided in Table 3.2.
Table 3.2: Additional mineral phases for carbonated concrete with two representations for the decalcification of the CSH-phases. Phase Chemical formula References Carbonates Calcite CaCO 3 [1], [2], [3], [4], [5], [7] Magnesite MgCO 3 [5] Monocarbo aluminate Ca 4Al 2CO 9 10H 2O [1], [4], [7] Hemicarbo aluminate Ca 8Al 4CO 16 21H 2O [1], [4], [7]
CSH solid solutions CSH I SiO 2(am) SiO 2 [5] Tobermorite I Ca 2Si 2.4 H8O10.8 [5] CSH II Jennite Ca 1.5 Si 0.9 H4.8 O5.7 [5] Tobermorite II Ca 1.5 O4.5 H6Si 1.8 O3.6 [5]
CSH phases CSH_0.8 Ca 0.8 SiO 5H4.4 [1], [6], [7] CSH_1.1 Ca 1.1 SiO 7H7.8 [1], [6], [7] CSH_1.8 Ca 1.8 SiO 9H10.4 [1], [6], [7] References: [1] (Gaucher, 2004); [2] (Halim et al., 2005); [3] (Wang, 2006) ; [4] (Gaucher, 2004); [5] (Lothenbach and Winnefeld, 2006); [6] (De Windt et al., 2004) ; [7] (Swinnen, 2006)
3. Municipal solid waste incinerator bottom ash
Bottom ash is a very heterogeneous material, containing numerous mineral phases. The composition has been analyzed by different authors (Kirby and Rimstidt, 1993; Eighmy et al., 1994; Speiser et al., 2000; Freyssinet et al., 2002; Polettini and Pomi, 2004). Their studies revealed that the coarse fraction of fresh bottom ash is essentially composed of glass (~ 40%) and silicate minerals (quartz, gehlenite, etc.) plus oxides (magnetite, hematite, lime, etc.). The fine fraction is chiefly composed of sulphates (anhydrite, ettringite, gypsum, etc.) and carbonates (e.g. calcite). Portlandite and a fraction of unburned organic
20 CHAPTER 3: MINERALOGICAL COMPOSITION OF CEMENT AND BOTTOM ASH matter were also detected. Minerals that were detected by at least two of the cited authors are summarized in Table 3.3. Note that most of these minerals are also present in cement (cf. previous paragraph). The minerals indicated in Table 3.3 are generally in accordance with the findings of Meima (1997), who performed a very extensive literature study on the mineral phases identified in MSWI bottom ash. Aging will affect the mineralogy of bottom ash. The most important changes occurring during weathering of MSWI bottom ash that have been identified are a drop in pH, accompanied by the formation of calcite, the dissolution of metastable (hydr)oxides (e.g. portlandite, ettringite), the precipitation of gypsum and aluminosilicates, and the neoformation of Al and Fe oxides (Meima, 1997). This is again analogous to the above mentioned effect of weathering on OPC.
Table 3.3: Mineral phases in fresh bottom ash. Phase Chemical formula References Silicate minerals quartz SiO 2 [1], [2], [3], [4] gehlenite Ca 2Al 2SiO 7 [1], [2], [3], [4]
Oxides wustite FeO [1], [2] magnetite Fe 3O4 [2], [3], [4] hematite Fe 2O3 [1] , [2], [3], [4] corundum Al 2O3 [1] , [2], [5] lime CaO [1], [2], [3]
Sulfates anhydrite CaSO 4 [1], [2], [3], [4], [5] ettringite Ca 6Al 2(SO 4)3(OH) 12 26H 2O [4], [5] gypsum CaSO 4 2H 2O [1], [2], [3]
Carbonates calcite CaCO 3 [1], [2], [3], [4], [5]
Hydroxides portlandite Ca(OH) 2 [3], [4]
Salts Sylvite KCl [1], [3] Halite NaCl [1], [2], [3] References: [1] (Kirby and Rimstidt,1993); [2] (Eighmy et al., 1994); [3] (Speiser et al., 2000); [4] (Freyssinet et al., 2002); [5] (Polettini and Pomi, 2004)
4. Presence of lead in a cementitious waste matrix
MSWI bottom ash is typically contaminated with lead with contents varying between 98 to 13700 mg/kg (IAWG, 1997). This means lead is very enriched in these materials relative to lithospheric and soil materials 3. Halim et al. (2004) observed that lead is evenly distributed throughout the cement matrix. Lead
3 The average lead concentration in the Earth’s crust is estimated to be approximately 16 mg/kg (Thornton, 1995), though it is not evenly distributed. 21 CHAPTER 3: MINERALOGICAL COMPOSITION OF CEMENT AND BOTTOM ASH can be either precipitated in a pure mineral due to exceeding of the solubility product, included in minerals as a solid solution, or adsorbed to the solid phases by surface complexation.
4.1. Dissolution/precipitation of Pb-containing minerals
A list of Pb solid species that can possibly form in solid waste systems is given in Table 3.4; aqueous species are represented in Table 3.5.
Table 3.4: Pb-containing solid species. Solid species Chemical formula References Fresh samples Lead hydroxide Pb(OH) 2 [1], [2], [4] Alamosite PbSiO 3 [1], [3] Litharge PbO [1], [3], [4] Massicot PbO Pb 2SiO 4 Pb 2SiO 4 [1], [3] Laurionite PbOHCl [1], [4] Pb 2(OH) 3Cl Pb 2(OH) 3Cl [1], [4] Anglesite PbSO 4 [1], [4]
Additional mineral phases for carbonated samples Cerrusite PbCO 3 [1], [2], [3], [4] Hydrocerrusite Pb 3(CO 3)2(OH) 2 [1], [2], [3], [4] References: [1] (Geysen, 2004); [2] (Jing et al., 2004); [3] (Halim et al., 2005); [4] (De Windt and Badreddine, 2006)
Table 3.5: Pb-containing aqueous species. Aqueous species References Fresh concrete PbOH + [1], [2], [3], [4] Pb(OH) 2 (aq) [1], [2], [3], [4] Pb(OH) 3 [1], [2], [3], [4] 2 Pb(OH) 4 [2], [3], [4] 3+ Pb 2(OH) [2], [3], [4] PbCl + [2], [3] PbSO 4 [2], [3]
Additional species for carbonated samples 2 Pb(CO 3)2 [2], [3], [4] PbCO 3 (aq) [1], [2], [3], [4] + PbHCO 3 [3], [4] References: [1] (Kersten et al., 1997); [2] (Pierrard et al., 2002); [3] (Geysen, 2004); [4] (Jing et al., 2004)
4.2. Solid solutions of Pb in matrix minerals
Formation of solid solutions with lead is often suggested in literature. However, formulation of exact compositions is scarce. Two solid solution systems were found in literature: (i) litharge (PbO) is assumed to form a solid solution with Fe- and Al-hydroxides (Halim et al., 2005); and (ii) formation of a calcite- cerrusite solid solution is suggested by Kirby and Rimstidt (1994), Godelitsas et al. (2003) and Rouff et al. (2005).
22 CHAPTER 3: MINERALOGICAL COMPOSITION OF CEMENT AND BOTTOM ASH
4.3. Surface complexation of Pb to matrix minerals
Lead adsorbs on hydrous ferric oxides (HFO) (Dzombak and Morel, 1990; Meima and Comans, 1998a; van Benschoten, 1998; Jing et al., 2004; Halim et al., 2005; Xu et al., 2006) and on amorphous aluminium oxides (also called amorphous aluminium minerals, AAM) (Meima and Comans, 1998a; Yoshida et al., 2003; Xu et al., 2006). Some authors (Fan et al., 2005; Xu et al., 2006) also mention adsorption of lead on hydrous manganese oxides (HMO), but as the amount of Mn in a cementitious waste sample typically is extremely small, this is supposed not to be significant for a cementitious waste sample.
4.4. Remarks
1) Many authors indicate ettringite and CSH as prime candidates for heavy metal binding because of their abundance and appropriate structures (e.g., Cougar et al., 1996; Johnson and Kersten, 1999; Rose et al., 2000; Johnson, 2002; Badreddine et al., 2004; Halim et al., 2004; Mijno et al., 2004). For ettringite, it is generally accepted that chemical substitution of Ca2+ by divalent metals like Pb 2+ can take place, but it is not clear whether in real systems the mineral ettringite contributes to the overall decreased heavy metal mobility (Geysen, 2004). As host for heavy metals in real cement based waste systems, CSH finds more support than ettringite although the binding mechanisms are still not determined unequivocally. Moreover, thermodynamic data are generally lacking or incomplete. As such, further research is needed before implementation in a geochemical model is possible. 2) Model predictions of Dijkstra et al. (2006) suggest that metal complexation with dissolved organic carbon (DOC) might also be an important factor controlling Pb leaching. However, Pb DOC complexes will not be considered in this thesis, as no DOC measurements were performed on the available samples.
5. Conclusion
In this chapter an overview of the formation, hydration and carbonation of OPC was presented. The main cement mineral phases were identified. Many cement minerals are also present in MSWI bottom ash. Lead is very enriched in bottom ash. A list of common Pb minerals was given. Pb can also be included in minerals as a solid solution. Litharge – gibbsite – ferrihydrite and calcite – cerrusite are two possible lead containing solid solutions. Surface complexation on HFO and AAM is the last process potentially influencing Pb leaching which will be considered in this thesis.
23 CHAPTER 4: DESCRIPTION OF PHREEQC AND BENCHMARKING
1. Introduction
PHREEQC version 2, the modelling software selected for this thesis, is a computer program written in C++, capable to perform a wide variety of low temperature aqueous geochemical calculations. PHREEQC has capabilities for (i) speciation and saturation index calculations; (ii) batch reaction and one dimensional (1D) advective diffusive or three dimensional (3D) diffusive transport calculations involving reversible reactions, which include aqueous, mineral, gas, solid solution, surface complexation, and ion exchange equilibria, and irreversible reactions, which include specified mole transfers of reactants, kinetically controlled reactions, mixing of solutions, and temperature changes; and (iii) inverse modelling, which finds sets of mineral and gas mole transfers that account for differences in composition between waters, within specified compositional uncertainty limits (Parkhurst and Appelo, 1999). The code is used all over the world in research and to predict the long term effects of pollution and radioactive waste storage (e.g. Gaucher et al., 2004).
This chapter starts with a short overview of how PHREEQC works, the databases that are included in the package and the data requirements relevant for the modelling that will be performed in this thesis (all the data are then collected in the subsequent chapter). A full description of how to use PHREEQC and the mathematical backgrounds can be found in the very detailed manual of the program by Parkhurst and Appelo (1999) and on the webpages (http://wwwbrr.cr.usgs.gov/projects/GWC_coupled/phreeqc/). The next part of the chapter focuses on how PHREEQC calculates transport processes. One dimensional transport simulations are straightforward in PHREEQC. However, construction of an input file for 3D diffusion is quite complex. A file which makes it possible to perform three dimensional processes is written and tested. In a last paragraph, the new PHREEQC feature which makes it possible to model solid solutions, is tested in a benchmarking study.
2. Method, databases and data requirements
For speciation and batch reaction calculations, a model run starts with an initial activity guess of components present in the system and then calculates the activities of possible aqueous and gaseous species, the amount of precipitated phases, solid solution components, etc. by solving equilibrium equations and mass and charge balances. The resulting activities are converted to concentrations, using activity coefficients deduced from the ionic strength. As long as the mole balance between the input components and the output of calculated species is not equal, calculations are repeated but with a new estimate of component activity using a modification of the Newton Raphson method. At present, the numerical method has proven to be relatively robust. The method used for transport modelling is described in the third paragraph of this chapter.
24 CHAPTER 4: DESCRIPTION OF PHREEQC AND BENCHMARKING
In the PHREEQC package, several databases are available. These databases contain the definition of aqueous species, exchange species, surface species, and mineral phases for a set of elements. The database phreeqc.dat (Parkhurst and Appelo, 1999) contains information for only a limited set of elements, but it is the most consistent database. The database wateq4f.dat (Ball and Nordstrom, 1991) contains some additional constituents. The database minteq.dat (Allison et al., 1990) is derived from the thermodynamic data of the program MINTEQA2. The database llnl.dat (Wolery, 1992) is a huge database containing many elements and with a large temperature range, which was developed for the program EQ3/6. The database phreeqc.dat will be used in this thesis. If additional elements, species, or phases are needed, it is possible to extend each of the described databases. In this case, chemical reaction, log K, and eventually also data for the temperature dependence of log K are needed for each additional species and phase.
Proper use of the program requires adequate knowledge of geochemistry and a proper formulation of the problem. A PHREEQC input file is organized in “keywords” and associated data blocks, which can appear in any order. An overview of all possible keywords and how to use them, is given in the manual of the program (Parkurst and Appelo, 1999). For batch reaction modelling the initial solution composition is required (SOLUTION or MIX data block). Other equilibrium reactants may be defined with EQUILIBRIUM_PHASES, EXCHANGE, SURFACE, GAS_PHASE, and SOLID_SOLUTION data blocks. Data requirements for the processes that will be modelled in this thesis, are as follows (Parkhurst and Appelo, 1999):
• Precipitation/dissolution Mineral phases that are not included in the database can be added using the PHASES data block. For each new phase the name of the phase, dissociation reaction and accompanying log K are the minimum required input. If new phases contain elements that are not specified in the database, these elements must be added in a SOLUTION_MASTER_SPECIES data block. The EQUILIBRIUM_PHASES data block is then used to define the amounts of an assemblage of pure phases that can react reversibly with the aqueous phase. When the phases included in this keyword data block are brought into contact with an aqueous solution, each phase will dissolve or precipitate to achieve equilibrium or will dissolve completely.
• Solid solutions PHREEQC allows multiple solid solutions, to exist in equilibrium with the aqueous phase, subject to the limitations of the Gibbs’ phase rule. Modelling of non ideal solid solutions is limited to two component (binary) solid solutions; ideal solid solutions may have two or more components. The dissocation reaction and accompanying log K of each component of a solid solution have to be defined with a PHASES data block. Initial composition of a solid solution assemblage and Guggenheim parameter(s) for nonideal solid solutions have to be defined with the SOLID_SOLUTIONS data block.
25 CHAPTER 4: DESCRIPTION OF PHREEQC AND BENCHMARKING
• Surface complexation PHREEQC incorporates the generalized two layer model of Dzombak and Morel (1990) as well as a database with surface complexation reactions and associated equilibrium constants for the sorption of ions on HFO. A complete and systematic database for sorption reactions on AAM does not exist but HFO can be used as a surrogate sorbent for AAM, as proposed by Meima and Comans (1998a). They mentioned different reasons to justify this approach. However, this approach might introduce uncertainty into the model predictions. For instance, Fan et al. (2005) report a lower surface area for AAM than for HFO. The amount and composition of each surface in a surface assemblage have to be defined in the SURFACE data block. Additions and/or modifications to the database can be included using the SURFACE_MASTER_SPECIES and SURFACE_SPECIES data blocks.
3. Transport modelling
The advection reaction dispersion (ARD) equation (and, in case of no advection, the reaction diffusion equation or Fick’s second law which will be used in this thesis) is implemented in PHREEQC in the TRANSPORT function. Necessary input data for this function are the number of cells in the 1D column, length of the column, size and number of time steps, boundary conditions, dispersivity of each cell and diffusion coefficient. The PHREEQC code allows the use of only one effective diffusion constant. PHREEQC solves the ARD equation by using a sequential non iterative approach (Parkhurst and Appelo, 1999). The transport part of the ARD equation is solved with an explicit finite difference scheme that is forward in time, central in space for dispersion, and upwind for advective transport. In a single time step, first advective transport is simulated, then all equilibrium and kinetically controlled geochemical reactions, then dispersive transport and finally again the geochemical reactions. The transport described so far is only one dimensional because that is where PHREEQC is designed for. However, we would like to model three dimensional transport. To make this possible in PHREEQC, the finite difference method will be used.
3.1. Finite difference approximation
The finite difference approximation of Fick’s second law (without geochemical reactions) for an arbitrarily shaped cell j is:
n (De + De ) A η t 2 t1 i j ij i j t1 t1 C j = C j + t∑ ()Ci − C j fbc i≠ j 2 hijV jη j (4.1)
t1 t 2 where C j is the concentration in cell j at the current time, C j is the concentration in cell j after the time step, t is the time step, i is an adjacent cell, Aij is shared surface area of cell i and j (m²), hij is the distance between the midpoints of cells i and j (m), Vj is the volume of cell j (m³), ηij is the smallest of the two
26 CHAPTER 4: DESCRIPTION OF PHREEQC AND BENCHMARKING porosities of cell i and j and fbc is a correction factor for boundary cells ( ). The summation is for all cells (up to n) adjacent to j.
The correction factor fbc depends on the ratio of the volume of the mobile zone, Vm, to the volume of the boundary cell which contacts the mobile zone, Vbc . When the two volumes are equal, fbc = 1. It can be shown that fbc → 2 when Vm → ∞. Likewise, fbc = 0 when Vm = 0. To a good approximation therefore,
Vm (4.2) fbc = 2 Vm +Vbc Equation (4.1) can be restated in terms of mixing factors for combinations of adjacent cells. For an adjacent cell, the mixing factor contains the terms which multiply the concentration difference ( Ci – Cj),
(De + De ) t A η f (4.3) mix f = i j ij i j bc ij 2 hijV jη j and for the central cell, the mixing factor is n (De + De ) A η f (4.4) mix f =1− t i j ij i j bc jj ∑ i≠ j 2 hijV jη j which give in equation (4.1): n t 2 t1 t1 (4.5) C j = mix f jj C j + ∑ mix fijCi i≠ j It is necessary that 0 < mix f < 1 to prevent numerical oscillations. If any mixing factor is outside this range, the grid of cells or timesteps must be adapted. In PHREEQC, the mixing factors have to be defined in MIX data blocks.
3.2. Three dimensional model Because of symmetry, only 1/8 part of the cement cube used in the diffusion experiments has to be modelled. This part of the cube is divided into n³ smaller cubes (“cells”), with n being the number of discretization in a single direction. The water surrounding the cube is modelled as an extra cell. Exchange of water and solutes between two adjacent cells occurs through diffusion (no advection, v = 0). This mixing is specified with MIX data blocks. The mixing factors mix fij , needed as input for the MIX data blocks, are calculated using equation (4.3). In this equation f bc = 1, Aij = r², hij = w/2 + r/2 for cell faces which are in contact with the surrounding water (with w the thickness of the surrounding water layer) and hij = r for the others cell faces, and Vj = r³, with r the side of a cell, also called “grid size”. The diffusion D + D coefficient D = block water for cell faces which are in contact with the surrounding water and e 2
De = Dblock for the other cell faces. The time step t depends on the grid size. It is chosen in such a way that none of the mixing factors is outside the valid range between 0 and 1. The mixing factors fjj are calculated using equation (4.4), using the same values as above. The only adjustable parameters for defining the mixing factors are t and r because all other factors like the diffusion coefficient are dictated by the experimental conditions. Changing the grid size (dividing the
27 CHAPTER 4: DESCRIPTION OF PHREEQC AND BENCHMARKING cement cube in more or less smaller cubes) (and thus r) leads to another number of mixing factors. For example, a discretization of 8 cells in each direction leads to 512 cells of which most of them have six adjacent cells with corresponding mixing factors. An Excel spreadsheet was created to calculate the extended list of mixing factors.
3.3. Testing the implemented 3D-model
To verify the calculations of the mixing factors for a 3D diffusion problem, a simple test was set up. In the centre of a cube of side 0.05m, 1423.833 mmol/kg 4 Cl was added (in a cube of side 0.01m) as represented in Figure 4.1. Leaching of Cl over a time period of 5 days was modelled with our 3D PHREEQC model. 9 A diffusivity of De = 1*10 m²/s and a discretization number ( n) of 10 was used (i.e. grid size r = 0.25). The cube was modelled as a closed volume, which means that only the transport in the pore water of the cube was modelled and no liquid was added around the cube (closed boundary conditions; mixing factors for cells at the outer surface in contact with the environment equal zero). At equilibrium, the Cl concentration is equal throughout the cube. This equilibrium concentration can easily be calculated and equals 0.0114 mol/l 5. With time, the concentration predicted by the PHREEQC 3D model indeed approaches this value (Figure 4.2).
Figure 4.1: Schematic representation of the test case. Arrows indicate the directions of leaching, dotted lines indicate the modelled part (1/8 of the cube).
Note that the concentration in cell 993 given in Figure 4.2 first exceeds the equilibrium concentration. This is a logical consequence of the way the 3D model works: the directions of leaching are perpendicular to the Cl cube as indicated in Figure 4.1. Mixing never happens directly diagonally. Cell 993 (for which the concentration is given in Figure 4.2) is situated on a line perpendicular to the Cl source (Figure 4.1),
4 Initially, the purpose was to add 1000 mmol/kg Cl. However, in HYDRUS 3D the concentration must be specified at different points and the program then makes an interpolation. As such, the concentration turned out to be equal to 1423.833 mmol/kg. As later in this paragraph our PHREEQC model will be compared with HYDRUS 3D, the PHREEQC input concentration was also set to 1423.833 mmol/kg. 5 ³5.0 cm *³ .1 424 mol / L Calculation of the equilibrium concentration: = .0 0114 mol / kg. ³5.2 cm³ 28 CHAPTER 4: DESCRIPTION OF PHREEQC AND BENCHMARKING which means that Cl will reach this point rather fast. Afterwards, there will be mixing between cell 993 and the cell just above it, where the Cl concentration is still lower. As a result, the Cl concentration in cell 993 will decrease again.
0.014
0.012
0.010
0.008
0.006
0.004
Clconcentration cel in 993 (mol/l) PHREEQC simulation 0.002 calculated concentration at equilibrium 0.000 0 1 2 3 4 5 time (days)
Figure 4.2: PHREEQC simulation for 5 days, Cl concentration in cel 993 as a function of time (solid line). The calculated Cl equilibrium concentration is indicated with a dotted line.
The model predicts the right equilibrium concentration, but there is not yet certainty about the correctness of the concentrations predicted between start and equilibrium. The best way to test the reliability of the 3D model is to compare the results with the results of another model (benchmarking). Therefore, HYDRUS 3D (Simunek et al., 2007), a model that is specifically developed to model three dimensional flow processes, will be used for comparison. The same test case is used again. Predictions of the two models are very similar (Figure 4.3). If a dimensionless property (Cl concentration at a certain time/Cl concentration after 2 days) is plotted, results of the two models are almost identically (Figure 4.4).
Different reasons may be given for the small differences between the PHREEQC and HYDRUS 3D results. First of all, the grid size concept is different. In PHREEQC, the grid size ( r) determines the number of small cubes ( n³) while in HYDRUS 3D the grid size is defined in terms of layers, as represented in Figure 4.5. The most important factor explaining the small differences between the two simulations is probably the fact that different numerical solvers are used in the two programs. A last reason might be the fact that HYDRUS 3D is designed to model flow processes, while in this case there is no bulk flow ( v = 0).
29 CHAPTER 4: DESCRIPTION OF PHREEQC AND BENCHMARKING
0.014
0.012
0.010
0.008
0.006
0.004 concentration in cel 993(mol/l)
0.002 PHREEQC HYDRUS 0.000 0 1 2 3 4 5 time (days) Figure 4.3: Comparison PHREEQC – HYDRUS-3D simulation: Cl concentration in cel 993 as a function of time.
1.400 993 1.200
1.000
0.800
0.600 after 2 2 after days)
0.400
0.200 PHREEQC
(concentration in in cel in (concentration 993)/cell (concentration HYDRUS 0.000 0 1 2 3 4 5 time (days)
Figure 4.4: Comparison PHREEQC – HYDRUS-3D simulation: (Cl concentration at a certain time/Cl concentration after 2 days) as function of time.
30 CHAPTER 4: DESCRIPTION OF PHREEQC AND BENCHMARKING
Figure 4.5: Grid size concept in HYDRUS 3D.
The influence of the grid size is investigated too. The same test case is used again, with discretization numbers n = 5 and 10 (i.e. grid sizes r = 0.5 and 0.25). Results are shown in Figure 4.6. The finer grid converges to the solution obtained with HYDRUS 3D which was calculated with a very fine grid. 0.014
0.012
0.010
0.008
0.006
0.004 concentration concentration in cel (mol/l) 993 PHREEQC n=5 0.002 PHREEQC n=10 HYDRUS 0.000 0 1 2 3 4 5 time (days) Figure 4.6: Concentration of Cl in cel 993 as a function of time, calculated with HYDRUS-3D and PHREEQC (for different grid sizes, indicated by the discretization number n).
To model the experimental diffusion test, the 3D model will be used to calculate the concentration of different elements in the water surrounding a cement cube. This is tested by using a cube of side 0.04 m, containing 1 mmol Cl/l (equal concentration in every cell of the cube) and surrounded by 320 ml H2O. 9 A diffusivity of De = 1*10 m²/s was used. The grid size was varied. The equilibrium Cl concentration of
31 CHAPTER 4: DESCRIPTION OF PHREEQC AND BENCHMARKING the surrounding water can easily be calculated 6 and equals 0.167 mmol/kg water or 58 mg/kg solid. Results are shown in Figure 4.7. It is observed again that using a finer grid reaches this equilibrium. However, once a certain accuracy attained, taking a smaller grid size has only little effect anymore (Figure 4.7: there is almost no difference between the results for n = 8 and 10). Since a finer discretization results in more accurate solutions, convergence of simulations with finer grids indicate accuracy. At this point, it is advised to stop reducing grid size because this makes the calculations more complicated and, consequently, more time consuming. For the simple test case used here, the difference in calculation time between n = 8 and 10 is small (order of magnitude 10² s, but of course depending on the performance of the computer used), but for more complicated systems that will be modelled in the future, this might be significant. Unless otherwise specified, n = 8 is used in all subsequent modelling.
Figure 4.7: Cl concentration in water surrounding the cube as a function of time, for different grid sizes.
4. Solid solution benchmarking study
4.1. Introduction
Given the relative new and specialized function of solid solution in PHREEQC, a benchmarking study must be performed to verify the solid solution calculations in PHREEQC. The study will be based on the series of papers “Aqueous Solubility Diagrams for Cementitious Waste Stabilization Systems” from Kulik and Kersten (2001 and 2002). In these papers, Kulik and Kersten report solid solution results obtained
6 Calculation of the Cl concentration in the water at equilibrium: 1 mmol / l * ³4 cm³ = .0 167mmol / l and .0 167mmol / l *35g / mol = .5 83mg / l or 58 3. mg / kg solid ()³4 + 320 cm³
32 CHAPTER 4: DESCRIPTION OF PHREEQC AND BENCHMARKING with GEM software that are assumed to reflect ‘real’ solid solution very well. In part 2 of this paper (“End Member Stoichiometries of Ideal Calcium Silicate Hydrate Solid Solutions”, 2001) ideal binary CSH solid solutions are modelled. In part 4 (“A Carbonation Model for Zn Doped Calcium Silicate Hydrate by Gibbs Energy Minimization”, 2002) the binary solid solution model is extended to a ternary system to incorporate end members for Zn. Results of both binary and ternary solid solutions will now be reproduced using PHREEQC. Note that, although this thesis focuses on modelling the leaching of Pb, the benchmarking study will be done for Zn. This is for the simple reason that there are not yet solid solutions results reported for Pb within the GEM literature.
4.2. Conversion of GEM input data to PHREEQC input data
Kulik and Kersten have performed GEM thermodynamic modelling using the GEM Selektor code (Kulik, 2002). The GEM Selektor PSI is a Gibbs Energy Minimization program package for interactive thermodynamic modelling of heterogeneous aquatic (geo)chemical systems, especially those involving metastability and dispersity of mineral phases, solid solution aqueous solution equilibria, and adsorption/ion exchange. It includes a built in (default) thermodynamic database in both thermochemical and reaction formats. GEM finds chemical potentials of elements and mole quantities of species that minimize the total Gibbs energy function of the system at the temperature and pressure of interest, subject to mass balances of chemical elements and charge balance. Input data include the system bulk elemental composition in total moles of elements and zero charge and the standard thermodynamic properties (Gibbs free energy, enthalpy, entropy, heat capacity and volume) of aqueous species, gases, solids and solid solution end members. More information about GEM can be found on the website (http://les.web.psi.ch/Software/GEMS PSI) or in the master’s thesis of Swinnen (2006). As mentioned in paragraph 2 of this chapter, PHREEQC needs log K–values as input data. Consequently, the thermodynamic data used by Kulik and Kersten in GEMS PSI have to be converted to log K–values. This requires simple thermodynamic calculations, which are briefly described here (based on Kotz et al., 2003 and Baelmans and Wollants, 2004). The standard Gibbs free energy change G 0 (J/mol) for a reaction is given by
G 0 = H 0 − T * S 0 (4.6) with H 0, the reaction enthalpy change in J/mol:
H 0 = H 0 − H 0 ∑ products ∑ reactants (4.7) S 0, the entropy change in J/(mol.K):
0 0 0 S = ∑ S products − ∑ Sreac tants (4.8) and T, the temperature of the reaction (in Kelvin). At chemical equilibrium:
33 CHAPTER 4: DESCRIPTION OF PHREEQC AND BENCHMARKING
G 0 = −R T ln K (4.9) where R is the ideal gas constant and K is the equilibrium constant of the reaction. From (4.6) and (4.9) it follows that: H 0 −T * S 0 K = exp R T (4.10)
2+ 2 As an example, the equilibrium constant K for the calcite dissolution reaction CaCO3(s) ↔ Ca + CO 3 is calculated. Thermodynamic data for this calculation are given in Table 4.1.
Table 4.1: Standard thermodynamic properties (at T = 298K) to calculate K of calcite equilibrium (data from Kulik and Kersten, 2002). Reactants Products 2+ 2 CaCO 3 Ca CO 3 H 0 (kJ/mol) 1206.67 543.083 675.235 S 0 (J/K.mol) 91.7 56.484 50.0
Equation (4.7) gives:
0 0 0 0 2− 0 H = H products − H reac tan ts = ( H 2+ + H 2− )− ( H CaCO ) ∑ ∑ Ca CO3 3 = ()− 543.083*103 − 675.235*103 − (−1206.67 *103 ) = −11645 Equation (4.8) gives:
0 0 0 0 2− 0 S = S products − S reac tan ts = ( S 2+ + S 2− )− ( SCaCO ) ∑ ∑ Ca CO3 3 = ()− 56.484 − 50 − 91 7. = −198.184 Now, equation (4.10) can be filled in:
0 0 H − T * S −11645 − 298 (* −198.184) 8 K = exp− = exp− = − .2 045*10 R T .8 3145* 298 which results in log K = 8.31. All the thermodynamic data used in the model calculations by Kulik and Kersten are converted this way and put together in a new database called “ GEM.dat ” that will be used for the model calculations in PHREEQC.
4.3. Model results 4.3.1. Binary solid solutions Kulik and Kersten (2001) developed a solid solution model for the CSH phases. In this model, the CSH system is described as a system of two concurrent ideal binary solid solution phases: CSH I solid solution system with the end members SiO 2 and tobermorite and CSH II solid solution system with the end members jennite and tobermorite.
34 CHAPTER 4: DESCRIPTION OF PHREEQC AND BENCHMARKING
The ultimate test of the reliability of a model is its ability to provide independent and reasonable predictions of existing experimental data. Therefore, Kulik and Kersten (2001) used their ideal solid solution model to simulate experiments of Greenberg and Chang (1965) with GEMS PSI. The experimental systems of Greenberg and Chang consisted of 8.05 g of SiO 2 per 1 kg of H 2O at varying additions of CaO. Equilibria for these systems at 1 bar and 25°C were calculated at Ca/Si ratios increasing from 0.01 to 2.0 at step 0.05. Portlandite as a pure phase was always present in the system formulation. The aqueous, gaseous, and relevant solid phases were included. These solid phases with their thermodynamic properties are given in Table 4.2. The stoichiometry of the solid solution end members
(n Si , n Ca and n H) was changed until the calculated GEM model equilibria predictions did fit the experimental values very well. This is called inverse modelling. Thermodynamic data for the optimized end members found this way are given in Table 4.3. GEM model equilibria predictions are compared with the experimental data in Figure 4.8.
Table 4.2: Standard-state thermodynamic properties of solids used in GEM calculations (data from Kulik and Kersten, 2001). Component G 0 H 0 S 0 Cp 0 V 0 kJ.mol 1 kJ.mol 1 J.K 1mol 1 J.K 1mol 1 cm³mol 1
SiO 2 (quartz) 856.30 910.70 41.5 44.6 22.69 SiO 2 (amorphous silica) 848.88 903.28 41.5 44.6 23.0 CaO (lime) 603.10 635.10 38.1 42.07 16.76 Ca(OH) 2 (portlandite) 897.0 984.66 83.4 87.51 33.06 Ca 3Si 2O4(OH) 6 (afwillite) 4405.54 4783.62 312.1 328.4 122.3 Ca 5Si 6H11 O22.5 (tobermorite) 9880.31 10696.05 611.5 698.5 274.0 Ca 5Si 6O6.5 (OH) 21 (plombierite) 11076.30 12181.14 808.1 890.0 319.2 CaSi 2O5(H 2O) 2 (nekoite) 2871.90 3136.50 171.1 162.1 81.65
Table 4.3: Thermodynamic data for the ideal binary solid solutions (data from Kulik and Kersten, 2001).
Solid solution end members nSi nCa nH log K CSH I amorphous silica (SH) 1.0 0 0 2.81 A tobermorite I (Tob I) 2.4 2.0 2.0 27.36
CSH II tobermorite II (Tob II) 1.8 1.5 1.5 20.52 jennite (Jen) 0.9 1.5 0.9 26.45
Coefficients n Si , n Ca and n H refer to the bulk formulae n Si (SiO 2).n Ca [Ca(OH) 2].n H(H 2O). Values of log K refer to the reaction: + 2+ nSi (SiO 2).n Ca [Ca(OH) 2].n H(H 2O) + 2 n Ca H ↔ (2n Ca + n H) H 2O + n Ca Ca + n Si SiO 2. A: reaction: SiO 2 (SH) ↔ SiO 2 (aq.)
Table 4.4: Calculated log K values for the solids used in PHREEQC calculations. Component Reaction log K
Quartz SiO 2 (quartz) = SiO 2 (aq) 4.012 Amorphous silica SiO 2 (am.silica) = SiO 2 (aq) 2.81 + 2+ Lime CaO + 2 H = Ca + H 2O 32.69 2+ Portlandite Ca(OH) 2 = Ca + 2 OH 5.19 + 2+ Afwillite Ca 3Si 2O4(OH) 6 +6 H = 2 SiO 2 + 3 Ca + 6 H 2O 60.081 + 2+ Tobermorite Ca 5Si 6H11 O22.5 +10 H = 5 Ca + 6 SiO 2 + 10.5 H 2O 65.683 + 2+ Plombierite Ca 5Si 6O6.5 (OH) 21 + 10 H = 6 SiO 2 + 5 Ca + 15.5 H 2O 63.883 + 2+ Nekoite CaSi 2O5(H 2O) 2 +2 H = 2 SiO 2 + 1 Ca + 3 H 2O 10.388
35 CHAPTER 4: DESCRIPTION OF PHREEQC AND BENCHMARKING
Figure 4.8: Comparison between pH values, aqueous Ca and Si concentrations calculated with GEM and experimental values of Greenberg and Chang (1965) (from Kulik and Kersten, 2001).
Kulik and Kersten their binary solid solution model for the CSH phases, with the optimized values from Table 4.3, is now implemented in PHREEQC and verified against the experimental data of Greenberg and Chang (1965). Thermodynamic data for aqueous species, gases, solids and solid solution end members are converted to log K data as described in paragraph 4.2. The calculated log K data for the solid phases are given in Table 4.4. Figure 4.9 shows that PHREEQC is capable of making predictions that are as good as those obtained with GEM.
20
18
16
14
12 pH 10 Ca_aq 8 Ca_aq, Si_aq (mmol)
6
Si_aq 4
2
0 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 Total C/S ratio in the system
Figure 4.9: pH values, aqueous Ca and Si concentrations calculated with GEM (points) and PHREEQC (solid lines) (GEM data from Kulik and Kersten, 2001). 36 CHAPTER 4: DESCRIPTION OF PHREEQC AND BENCHMARKING
Now that the PHREEQC model results are confirmed, a closer look can be taken to see if PHREEQC in fact does predict exactly the same composition for the two solid solutions. Therefore, the mole fractions of the solid solution end members calculated with GEM as well as with PHREEQC are shown in Figure 4.10. Although from this figure, GEM and PHREEQC results seem to be exactly the same, a small remark may be made. For the GEM results there is a small region (between C/S ratio 0.80 and 0.85) where the two solid solutions are both formed. For the PHREEQC results, in contrast, CSH I and CSH II solid solution never exist at the same time. GEM thus predicts a more gradual transition from CSH I to CSH II, which seems to be more realistic.
Figure 4.10: Mole fractions of CSH solid solution end-members calculated with GEM (points) and PHREEQC (solid lines). The presence of portlandite is indicated by a horizontal line (out of scale). The formation of the CSH solid solutions is also indicated above the graph with a horizontal line (solid line for PHREEQC results and dotted line for GEM results) (GEM data from Kulik and Kersten, 2001).
4.3.2. Ternary solid solutions Kulik and Kersten (2002) further extended their CSH ideal binary solid solution model to a ternary ideal solid solution system by introduction of another end member for a minor trace metal (Zn in this case). The ideal model appeared preferable for an extension to a CZSH system (CZSH = a Zn bearing Calcium Silicate Hydrate phase) because of the lack of experimental data precise enough to determine the interaction parameters needed for any non ideal solid solution model. A first step in this study was therefore to search for the Zn end member stoichiometry and its Gibbs free energy value consistent with
37 CHAPTER 4: DESCRIPTION OF PHREEQC AND BENCHMARKING
Raoultian ideal mixing behavior. Estimations of Kulik and Kersten (2002) found using dual thermodynamic calculations are shown in Table 4.5.
Table 4.5: End-member stoichiometry and thermodynamic data for the ideal ternary solid solutions (data from Kulik and Kersten, 2002).
Solid solution end members nSi nCa nZn nH log K CZSH I amorphous silica (SH) 1.0 0 0 0 2.81 A tobermorite I (Tob I) 2.4 2.0 0 2.0 25.42 hardystonite I (Hds I) 1.0 1.0 0.5 0 19.94
CZSH II tobermorite II (Tob II) 1.0 0.83 0 0.83 10.59 Jennite (Jen) 1.0 1.667 0 1.0 27.35 Hardystonite II (Hds II) 1.0 1.0 0.5 0 19.94
Coefficients n Si , n Ca , n Zn and n H refer to the bulk formulae n Si (SiO 2).n Ca [Ca(OH) 2].n Zn [Zn(OH) 2].n H(H 2O). Values of log K refer to + 2+ 2+ the reaction: n Si (SiO 2).n Ca [Ca(OH) 2].n H(H 2O) + (2n Ca + 2n Zn ) H ↔ (2n Ca + 2n Zn + nH) H 2O + n Ca Ca + n Zn Zn + n Si SiO 2. A: reaction: SiO 2 (SH) ↔ SiO 2 (aq.)
In a next step, the ideal ternary solid solution model was tested in GEM calculations to follow how the composition of Zn doped CSH phases reacts upon leaching or carbonation of the cement system.
4.3.2.1. Modelling the impact of leaching on CZSH Leaching of Zn from a cement matrix by carbonate free water was first simulated. Therefore, available experimental solubility data from Johnson and Kersten (1999) are used. Initial bulk composition of the system included 1 kg of H 2O, 4 mol of N 2, 1 mol of O 2 and 1 mol of solid SiO 2.(2 z)Ca(OH) 2.zZn(OH) 2 with z = 0.01. Log(S/W) was stepwise changed from 0 to 4.2 with step 0.2. The aqueous, gaseous, and relevant solid phases (Table 4.6) were included, plus the two ternary ideal CZSH solid solutions given in Table 4.5.
Table 4.6: Standard-state thermodynamic properties of solids used in GEM calculations (data from Kulik and Kersten, 2002). Component G 0 H 0 S 0 Cp 0 V 0 kJ.mol 1 kJ.mol 1 J.K 1mol 1 J.K 1mol 1 cm³mol 1 ZnO (zincite) 320.4 350.5 43.2 40.42 14.34 β Zn(OH) 2 (sweetite) 554.57 642.88 81.2 85.8 29.9 CaO (lime) 603.1 635.1 38.1 42.07 16.76 Ca(OH) 2 (portlandite) 897.0 984.66 83.4 87.505 33.06 CaZn 2(OH) 6.2H 2O (Ca zincate) 2501.82 2829.39 500.1 526.4 118.8 SiO 2 (am.silica) 849.45 903.85 41.5 44.6 23.0
GEM model results of dissolved Zn aq , Ca aq , Si aq , equilibrium pH and CZSH end member mole fractions are plotted in Figures 4.11 & 4.12. From the comparison of these two figures, it can be seen that at log
S/W > 1.2 mol/(kg H 2O), pH, dissolved Ca aq and Zn aq are fixed by presence of excess portlandite and calcium zincate (CaZn 2(OH) 6.2H 2O). These solids disappear at log S/W < 1.8, where Ca aq and Zn aq become controlled by the CZSH II phase of drastically changing composition. Note that this change in solid solution composition coincides with a trough like Zn aq minimum of 2 3 orders of magnitude. Experimental data from Johnson and Kersten (1999) (error bars in Figure 4.11) are close to this minimum, which indicates consistent behavior of the CZSH ideal solid solution model in the GEM model results.
38 CHAPTER 4: DESCRIPTION OF PHREEQC AND BENCHMARKING
Figure 4.11: GEM model simulation. Total aqueous solubilities for Ca, Zn and Si and pH. Bars at a S/W 10 -3 correspond to the uncertainty intervals of experimental solubility data from Johnson and Kersten (1999) (figure from Kulik and Kersten, 2002).
Figure 4.12: GEM model simulation. Mole fractions of CZSH-II end-members and presence of single- component solids; hexagonal dots correspond to the solid-phase composition in the solubility experiment of Johnson and Kersten (1999) (figure from Kulik and Kersten, 2002).
The same is now modelled in PHREEQC. Therefore, thermodynamic data for aqueous species, gases, solids and solid solution end members are converted to log K data as described in paragraph 4.2. The calculated log K data for the solid phases are given in Table 4.7. Results of the simulations are plotted in Figures 4.13 & 4.14. The resemblance between the results obtained with GEM and PHREEQC is almost perfect.
39 CHAPTER 4: DESCRIPTION OF PHREEQC AND BENCHMARKING
0
Ca_aq
-2
-4 Zn_aq
-6 Si_aq
log molal log -8
-10
-pH -12
-14 -4.5 -4 -3.5 -3 -2.5 -2 -1.5 -1 -0.5 0 log S/W (moles Si kg(H2O)-1)
Figure 4.13: GEM (points) and PHREEQC (solid lines) model simulation. Total aqueous solubilities for Ca, Zn and Si and pH (GEM data from Kulik and Kersten, 2002).
100 Jen 90
Tob 80
70 Portlandite 60
50 Ca-zincate 40 Mole Mole fraction, %
30
20
10 Hds
0 -4 -3.5 -3 -2.5 -2 -1.5 -1 -0.5 0 log S/W (moles Si kg(H2O)-1)
Figure 4.14: GEM (points) and PHREEQC (solid lines) model simulation. Mole fractions of CZSH-II end- members and presence of single-component solids (out of scale) (GEM data from Kulik and Kersten, 2002).
40 CHAPTER 4: DESCRIPTION OF PHREEQC AND BENCHMARKING
Table 4.7: Calculated log K values for the solids used in PHREEQC calculations. Component Reaction log K
+ 2+ Zincite ZnO +2 H = H 2O + Zn 11.23 2+ Sweetite Zn(OH) 2 = Zn + 2 OH 16.25 + 2+ Lime CaO + 2 H = Ca + H 2O 32.69 2+ Portlandite Ca(OH) 2 = Ca + 2 OH 5.19 + 2+ 2+ Ca zincate CaZn 2(OH) 6:2H 2O + 6 H = Ca + 2 Zn + 8 H 2O 42.59 Amorphous silica SiO 2 = SiO 2 2.8
It is also tested if PHREEQC still gives the same predictions as GEM when the initial Zn concentration is changed. Therefore, the same experiment is now also run for z = 0.001, 0.05 and 0.1 (with z determining the initial composition of the solid: 1 mol of SiO 2.(2 z)Ca(OH) 2.zZn(OH) 2). The predicted aqueous solubility of Zn for different initial Zn concentrations are given in Figure 4.15. At low Zn loadings, results are very satisfying, but at the highest Zn loading (10%) there is a mismatch at log S/W = 4 and 4.2. The drop in the GEM data suggests that there is another phase that precipitates. Therefore, in a new model run, precipitation of ZnO is included (Figure 4.16). Now, PHREEQC indeed predicts a drop of the Zn aq concentration, but this drop appears too early. There is even a drop predicted for the 5% Zn curve. This problem was discussed in depth with Dr. Kulik. Different possible causes for the mismatch were investigated. The conclusion was that the mismatch is most probably not due to the way PHREEQC calculates solid solutions. For their GEM calculations, Kulik and Kersten used an intermediate GEM Selektor version which does not perform refinement for the mass balance as PHREEQC and more recent versions of GEM do, which probably clarifies why zincite appears earlier in the dilution profile in PHREEQC calculations than it happened in GEM calculations.
-4
-5
-6
-7 log molal log
PHREEQC Zn 10% -8 GEM Zn 10% GEM Zn 5% PHREEQC Zn 5% GEM Zn 1% -9 PHREEQC Zn 1% GEM Zn 0.1% PHREEQC Zn 0.1%
-10 -4.5 -4 -3.5 -3 -2.5 -2 -1.5 -1 -0.5 0 log S/W (moles Si kg(H2O)-1)
Figure 4.15: GEM (points) and PHREEQC (solid lines) model simulation runs for different Zn loadings (GEM data from Kulik and Kersten, 2002). 41 CHAPTER 4: DESCRIPTION OF PHREEQC AND BENCHMARKING
-4
-5
-6
-7 log molal log PHREEQC Zn 10% -8 GEM Zn 10% GEM Zn 5% PHREEQC Zn 5% GEM Zn 1% -9 PHREEQC Zn 1% GEM Zn 0.1% PHREEQC Zn 0.1%
-10 -4.5 -4 -3.5 -3 -2.5 -2 -1.5 -1 -0.5 0 log S/W (moles Si kg(H2O)-1)
Figure 4.16: GEM (points) and PHREEQC (solid lines) model simulation runs for different Zn loadings (with precipitation of ZnO, log K = 11.23) (GEM data from Kulik and Kersten, 2002).
In a last test of the PHREEQC model without carbonation, one of the solid solution end members is changed, more specifically the hardystonite like end member (SiO 2.Ca(OH) 2.0.5Zn(OH) 2) is replaced by a clinohedrite like end member (SiO 2.Ca(OH) 2.Zn(OH) 2). The Zn loading is also changed again. Results are given in Figure 4.17. For the 10 mol% Zn curve, the same remark as in the previous case may be made. Further on, there is one more unexpected result at log S/W = 3.4 and 0.1 mol% Zn, but overall results obtained with GEM and PHREEQC are the same.
42 CHAPTER 4: DESCRIPTION OF PHREEQC AND BENCHMARKING
-4 -4.5 -4 -3.5 -3 -2.5 -2 -1.5 -1 -0.5 0
-5
-6
-7 log molal log
PHREEQC Zn 10% -8 GEM Zn 10% GEM Zn 5% PHREEQC Zn 5% GEM Zn 1% -9 PHREEQC Zn 1% GEM Zn 0.1% PHREEQC Zn 0.1%
-10 log S/W (moles Si kg(H2O)-1)
Figure 4.17: GEM (points) and PHREEQC (solid lines) model simulation runs for different Zn loadings with clinohedrite-like CZSH-II end-member (GEM data from Kulik and Kersten, 2002).
4.3.2.2. Modelling the impact of carbonation on CZSH In the following chapters, experiments for fresh as well as for aged concrete will be modelled. Therefore, it is also necessary to check if PHREEQC offers correct solid solution calculations for carbonated concrete. To model the impact of carbonation, Kulik and Kersten (2002) added a regular non ideal
(Ca,Zn)CO 3 solid solution (Table 4.8) to their model system definition. The Guggenheim mixing parameter for this non ideal solid solution is a0 = 2.3. Beside the extra solid solution, also extra aqueous species, gases and solids had to be added to the database. The extra solids included with their thermodynamic properties (for simulation in GEM) and calculated log K values (for simulation in PHREEQC) are given in Tables 4.9 & 4.8.
Table 4.8: (Ca,Zn)CO 3 non-ideal binary solid solution. Solid solution Components Chemical formula and reaction log K 2+ 2 (Ca,Zn)CO 3 calcite CaCO 3 = Ca + CO 3 8.31 2+ 2 smithsonite ZnCO 3 = Zn + CO 3 10.28
Table 4.9: Standard-state thermodynamic properties of the extra solids used in GEM calculations (data from Kulik and Kersten, 2002). Component G 0 H 0 S 0 Cp 0 V 0 kJ.mol 1 kJ.mol 1 J.K 1mol 1 J.K 1mol 1 cm³mol 1
CaCO 3 (calcite) 1128.21 1206.67 91.7 83.47 36.93 ZnCO 3 (smithsonite) 733.94 815.50 81.2 80.05 28.28
43 CHAPTER 4: DESCRIPTION OF PHREEQC AND BENCHMARKING
The bulk system composition is defined by adding 0.1 mol of CZSH stoichiometry,
SiO2 .(2 − z)Ca(OH ) 2 .zZn(OH ) 2 with z = 0.01. Evolution of this system at constant S/W is simulated by titration with CO 2 at a stepwise log(CO 2) addition from 0.2 to 2.3 at increment 0.1 (in log mol). GEM and PHREEQC simulation results for this system are shown in Figures 4.18 & 4.19. It is clear that
GEM and PHREEQC model results are the same. A Ca Zn carbonate phase with very small X Zn is predicted to occur throughout the whole equilibrium profile. In the presence of excess portlandite, aqueous concentrations of Ca, C, Si and Zn remain constant. On further addition of CO 2 to the system, excess portlandite and calcium zincate disappear, and the values of pH, Zn aq and C aq drastically change due to a complex incongruent dissolution of concurrent CZSH I and CZSH II solid solution phases. This development ultimately ends up in a total re partitioning of Ca and Zn into the carbonate solid solution coexisting with almost pure amorphous silica. Concurring with this quite complex phase precipitation/dissolution pattern, a Zn aq curve with a minimum at ~ 7.5 log molal is predicted, quite similar in shape to that in the previous CO 2 free leaching model (Figures 4.11 & 13). This minimum, in turn, coincides with the maxima in mole fractions of Tob I or Tob II end members at about
0.1 mol of CO 2/kg of H 2O. 0 0 0.5 1 1.5 2 2.5 Ca_aq
-2
-4
Zn_aq C_aq
-6 Si_aq
logmolal -8
-10
pH
-12
-14 -log CO2 added (moles kg(H2O)-1) Figure 4.18: GEM (points) and PHREEQC (solid lines) simulation of a weathering scenario (with 1% Zn and hardystonite-like end-members). Total aqueous concentrations and pH (GEM data from Kulik and Kersten, 2002).
A last model run is performed at higher Zn loading (10% Zn or thus z = 0.1) and with clinohedrite like end members in both CZSH phases (in stead of the hardystonite like end members). GEM and PHREEQC results are compared in Figures 4.20 & 4.21. Portlandite did not appear at all because all excess calcium was kept in CHZ phase already at very early stages of CO 2 titration. The models produced a Zn aq concentration curve again with a minimum at about ~ 6 log molal. This time, however, excess 44 CHAPTER 4: DESCRIPTION OF PHREEQC AND BENCHMARKING
zincite (ZnO) appears together with CZSH I at intermediate process stages (log CO 2 = 0.75), just before the ultimate change to Ca Zn carbonate dominated (X ZnCO3 < 6%) equilibrium. In the PHREEQC simulation, CHZ is already formed at log CO 2 = 1.40 while in the GEM simulation it starts appearing at log CO 2 = 1.48. Nevertheless, the results of the two simulations are very similar. 120
CaCO3 100
SH-I Jen-II 80
Portlandite 60
Mole % fraction, CHZ 40
Tob-I Tob-II 20
ZnCO3 Hd-I Hd-II 0 0 0.5 1 1.5 2 2.5 -log CO2 added (moles kg(H2O)-1) Figure 4.19: GEM (points) and PHREEQC (solid lines) simulation of a weathering scenario (with 1% Zn and hardystonite-like end-members). Composition of solid phases (GEM data from Kulik and Kersten, 2002). 0
C_aq Ca_aq -2
-4
Zn_aq
-6 Si_aq
logmolal -8
-10
pH
-12
-14 0 0.5 1 1.5 2 2.5 -log CO2 added (moles kg(H2O)-1) Figure 4.20: GEM and PHREEQC simulation of a weathering scenario (with 10% Zn and clinohedrite-like end-members). Total aqueous concentrations and pH (GEM data from Kulik and Kersten, 2002). 45 CHAPTER 4: DESCRIPTION OF PHREEQC AND BENCHMARKING
120
SH-I CaCO3 100
Jen-II 80 Tob-II
60 ZnO
Mole fraction, % CHZ 40
Tob-I
20 Cln-II ZnCO3 Cln-I
0 0 0.5 1 1.5 2 2.5 -log CO2 added (moles kg(H2O)-1)
Figure 4.21: GEM and PHREEQC simulation of a weathering scenario (with 10% Zn and clinohedrite-like end-members). Composition of solid phases (GEM data from Kulik and Kersten, 2002).
5. Conclusion
In this chapter a short description of PHREEQC was presented. The data required to model the extraction and diffusion test in subsequent chapters were indicated. An input file for three dimensional diffusion was written and approved. Finally, the new PHREEQC feature which makes it possible to model solid solutions, was tested in a benchmarking study. From the numerous simulations performed, it can be concluded that both the GEM and PHREEQC geochemical modelling code seem to be able to handle solid solutions.
46 CHAPTER 5: DATA COLLECTION
1. Introduction
The main relevant processes for this thesis and the data requirements to model these processes in PHREEQC were identified in Chapter 3 and 4, respectively. Consequently, in this chapter, all the data needed for modelling (i) precipitation/dissolution; (ii) surface complexation; (iii) solid solutions; and (iv) transport processes with PHREEQC are collected. Some data can be found in literature, others need to be determined experimentally.
2. Precipitation/dissolution
The chemical formulas, dissolution reactions and equilibrium constants of the common cement mineral phases (as specified in Chapter 3) can be found in literature and are given in Table 5.1. Remark: Some sources mention a rather high log K value for ettringite (e.g., in the llnl database a value of 62.54 is given). However, most authors use a value in the range 54 57 (e.g. Meima, 1997; Gaucher, 2004; De Windt et al., 2006; Wang, 2006). As such, it is decided to use the log K value of 56.7 (Atkins et al., 1991).
Table 5.1: Thermodynamic data for concrete. Phase reaction log K ref CSH phases and portlandite + 2+ CSH_0.8 Ca 0.8 SiO 5H4.4 + 1.6 H = 0.8 Ca + H 4SiO 4 + H 2O 11.1 [1] + 2+ CSH_1.1 Ca 1.1 SiO 7H7.8 + 2.2 H = 1.1 Ca + H 4SiO 4 + 3 H 2O 16.7 [1] + 2+ CSH_1.8 Ca 1.8 SiO 9H10.4 + 3.6 H = 1.8 Ca + H 4SiO 4 + 5 H 2O 32.6 [1] + 2+ Afwillite Ca 3Si 2O4(OH) 6 + 6 H = 3 Ca + 2 H 4SiO 4 + 2 H 2O 50 [2] + 2+ Portlandite Ca(OH) 2 + 2 H = Ca + 2 H 2O 22.80 [2], [5]
Sulphate minerals + 3+ 2 2+ Ettringite Ca 6Al 2(SO 4)3(OH) 12 26H 2O +12 H = 2 Al + 3 SO 4 + 6 Ca + 38 H 2O 56.7 [3] 2+ 2 Anhydrite CaSO 4 = Ca + SO 4 4.41 [5] 2+ 2 Gypsum CaSO 4 2H 2O = Ca + SO 4 + 2 H 2O 4.58 [4] + 2+ 3+ 2 Monosulfoaluminate Ca 4Al 2SO 10 12H 2O + 12 H = 4 Ca + 2 Al + SO 4 + 18 H 2O 71.0 [2]
Hydrated calcium aluminates (CAH) and ferrites (CFH) 2+ C4AH13 (CaO) 4(Al 2O3) 13H 2O = 4 Ca + 2 Al(OH) 4 + 6 OH + 6 H 2O 25.56 [5] + 2+ 3+ Hydrogarnet Ca 3Al 2O12 H12 + 12 H = 3 Ca + 2 Al + 12 H 2O 78 [2] 2+ C4FH13 (CaO) 4(Fe 2O3) 13H 2O = 4 Ca + 2 Fe(OH) 4 + 6 OH + 6 H 2O 29.88 [5]
Aluminium and iron hydroxides Al(OH) 3(am) Al(OH) 3 + OH = Al(OH) 4 0.24 [5] + 3+ Gibbsite Al(OH) 3 + 3 H = Al + 3 H 2O 7.76 [5] + 3+ Ferrihydrite Fe(OH) 3 + 3 H = Fe + 3 H2O 4.89 [6] + 3+ Fe(OH)3(am) Fe(OH) 3 + 3 H = Fe + 3 H2O 5.00 [5] + 3+ Fe(OH)3(microcr) Fe(OH) 3 + 3 H = Fe + 3 H2O 3.00 [5]
Magnesium phases + 2+ Brucite Mg(OH) 2 + 2 H = Mg + 2 H 2O 16.84 [5] + 2+ 3+ Hydrotalcite Mg 4Al 2O7 10H 2O + 14 H = 4 Mg + 2 Al + 17 H 2O 75 [2] 47 CHAPTER 5: DATA COLLECTION
SiO 2 Amorphous SiO 2 SiO 2 + 2 H 2O = H 4SiO 4 2.71 [4] Quartz SiO 2 + 2 H 2O = H 4SiO 4 3.98 [4] Chalcedony SiO 2 + 2 H 2O = H 4SiO 4 3.55 [4]
Other phases + 2+ 3+ Katoite Ca 3Al 2SiO 12 H8 + 12 H = 3 Ca + 2 Al + H 4SiO 4 + 8 H 2O 69.4 [2] + 2+ 3+ Straëtlingite Ca 2Al 2SiO 7 8H 2O + 10 H = 2 Ca + 2 Al + H 4SiO 4 + 11 H 2O 49.5 [2] + 2+ Hematite Fe 2O3 + 6 H = 3H2O + 2 Fe 4.0 [4] + 3+ 3+ 2+ Brownmillerite Ca 4Al 2Fe 2O10 +20 H = 2 Al + 2 Fe + 4 Ca + 10 H 2O 140.5 [8]
Carbonates 2+ 2 Calcite CaCO 3 = Ca + CO 3 8.48 [4] 2+ + Magnesite MgCO 3 = Mg H + HCO 3 2.04 [5] + 2+ 3+ Monocarbo aluminate Ca 4Al 2CO 9 10H 2O + 13 H = 4 Ca + 2 Al + HCO 3 + 16 H 2O 80.33 [2] + 2+ 3+ Hemicarbo aluminate Ca 8Al 4CO 16 21H 2O + 27 H = 8 Ca + 4 Al + HCO 3 + 34 H 2O 182.33 [2] References: [1] (Stronach and Glasser, 1997); [2] (Bourbon, 2003); [3] (Atkins et al., 1991); [4] (PHREEQC database, Parkhurst and Appelo, 1999); [5] (Lothenbach and Winnefeld, 2006); [6] (MINTEQA database, Allison et al., 1990); [7] (De Windt and Badreddine, 2006); [8] (llnl database, Wolery, 1992)
The additional data for MSWI bottom ash are presented in Table 5.2. The data specific for Pb containing minerals and aqueous species are given in Tables 5.3 & 5.4.
Table 5.2: Additional thermodynamic data for MSWI bottom ash. Phase reaction log K ref gehlenite Ca 2Al 2SiO 7 + 10 H+ = SiO2 + 2 Al3+ + 2 Ca2+ + 5 H2O 56.30 [1] + 2+ wustite Fe 0.947 O + 2H = 0.947Fe + H 2O 11.69 [2] + 2+ 3+ magnetite Fe 3O4 + 8 H = Fe + 2 Fe + 4 H 2O 10.47 [1] + 3+ corundum Al 2O3 + 6 H = 2 Al + 3 H 2O 18.31 [1] + 2+ lime CaO + 2 H = Ca + H 2O 32.58 [1] sylvite KCl = Cl + K + 0.85 [1] halite NaCl = Cl + Na + 1.59 [1] References: [1] (llnl database, Wolery, 1992); [2] (MINTEQA database, Allison et al., 1990)
Table 5.3: Thermodynamic data for Pb-containing solid species. Solid species Solubility reaction log K ref Fresh samples + 2+ Lead hydroxide Pb(OH) 2 + 2 H = Pb + 2 H2O 11 [3] + 2+ Alamosite PbSiO 3 + 2 H = H 2O + Pb + SiO 2 5.6733 [1] + 2+ Litharge PbO + 2 H = H 2O + Pb 12.6388 [1] + 2+ Massicot PbO + 2 H = H 2O + Pb 12.8210 [1] + 2+ Pb 2SiO 4 Pb 2SiO 4 + 4 H = SiO 2 + 2 H 2O + 2 Pb 18.0370 [1] + 2+ Laurionite PbOHCl + H = Pb + Cl + H 2O 0.623 [2] + 2+ Pb 2(OH) 3Cl Pb 2(OH) 3Cl + 3 H = 2Pb + 3 H2O + Cl 8.793 [2] 2+ 2 Anglesite PbSO 4 = Pb + SO 4 7.79 [2] + 2+ 2 Pb 4O3SO 4 Pb 4O3SO 4 + 6 H = 4 Pb + SO 4 + 3 H2O 22.1 [2] + 2+ 2 Pb 3O2SO 4 Pb 3O2SO 4 + 4 H = 3 Pb + SO 4 + 2 H2O 10.4 [2]
Additional data for carbonated samples + 2+ Cerrusite PbCO 3 + H = HCO3 + Pb 3.2091 [1] + 2+ Hydrocerrusite Pb 3(CO 3)2(OH) 2 +4 H = + 2 H 2O + 2 HCO 3 + 3 Pb 1.8477 [1] + 2+ 2 Pb 2OCO 3 Pb 2OCO 3 + 2 H = 2 Pb + H 2O + CO 3 0.5 [2] + 2+ 2 Pb 3O2CO 3 Pb 3O2CO 3 + 4 H = 3 Pb + CO 3 + 2 H2O 11.02 [2] References: [1] (llnl database, Wolery, 1992); [2] (MINTEQA database, Allison et al., 1990); [3] (De Windt and Badreddine, 2006)
48 CHAPTER 5: DATA COLLECTION
Remark: Published values of the solubility products of Pb(OH) 2 vary between log K = 8.15 and 13.6 (Pierrard et al., 2002). The intermediate value log K = 11, as suggested in the publications of De Windt (e.g. De Windt and Badreddine, 2006; De Windt et al., 2006) will be used. The influence of the exact value for this log K will be investigated in Chapter 6.
Table 5.4: Thermodynamic data for the Pb-containing aqueous species. Aqueous species Solubility reaction log K ref Fresh samples + 2+ + + PbOH Pb + H 2O = PbOH + H 7. 71 [1] 2+ + Pb(OH) 2 (aq) Pb + 2 H2O = Pb(OH) 2 + 2 H 17.12 [1] 2+ + Pb(OH) 3 Pb + 3 H2O = Pb(OH) 3 + 3 H 28.06 [1] 2 2+ 2 + Pb(OH) 4 Pb + 4 H2O = Pb(OH) 4 + 4 H 39.7 [1] 3+ 2+ 3+ + Pb 2(OH) 2 Pb + H 2O = Pb 2(OH) + H 6.36 [1] PbCl + Pb 2+ + Cl = PbCl + 1.6 [1] 2+ 2 PbSO 4 Pb + SO 4 = PbSO 4 2.75 [1]
Additional species for carbonated samples 2 2+ 2 2 Pb(CO 3)2 Pb + 2CO 3 = Pb(CO 3)2 10.64 [1] 2+ 2 PbCO 3 (aq) Pb + CO 3 = PbCO 3 7.24 [1] + 2+ + PbHCO 3 Pb + HCO 3 = PbHCO 3 2.9 [1] References: [1] (PHREEQC database, Parkhurst and Appelo, 1999)
3. Surface complexation
Surface complexation on hydrous ferric oxides (HFO) and amorphous aluminium minerals (AAM) will be modelled. HFO will be used as a surrogate for AAM, as was already mentioned in the previous chapter. The sorbent mineral concentration is determined experimentally while the specific surface area and the concentration of binding sites are found in literature.
3.1. Sorbent mineral concentration
The amount of sorbent mineral needed as input in PHREEQC is estimated by ‘selective’ chemical extractions. 3.1.1. Hydrous ferric oxide content If a weak reducing agent is added to a sample of solidified waste, amorphous iron oxides (also called hydrous ferric oxides, HFO) are reduced and Fe 2+ ions are leached. The dissolved amount of iron then provides an indication of the amount of amorphous iron oxides. By using a selective reducing agent, only the amorphous iron oxides (and not the crystalline ones) are attacked. The selectivity of various chemicals for iron minerals has been evaluated by Kostka and Luther (1994). They concluded that HFO are most selectively dissolved by extraction with ascorbate, according to the method of Ferdelman (1988, cited by Kostka and Luther, 1994). This method was already used by different authors (e.g. Meima and Comans, 1998a and b; Dijkstra et al., 2002; Cornelis et al., 2006) to determine the amount of HFO in MSWI bottom ash. Description of the method: Prior to the chemical extraction, 2.5 g of the sample was mixed with distilled, deionized water at a L/S of 10. This suspension was subsequently equilibrated for 24 hours at pH = 8
49 CHAPTER 5: DATA COLLECTION
(obtained by adding HNO 3), filtered and dried in a vacuum oven at 40°C. The extractant was prepared as follows: 50 g of sodium citrate, 50 g sodium bicarbonate and 4 g of ascorbic acid were added to 1000 ml of distilled, deionized water. The solid material remaining after drying was then extracted with 20 ml of the ascorbate mixture for each gram of solid material for 2 h at room temperature while shaking at 145 mvt/min. The samples were filtered over a 0.45 m filter and the concentration of Fe in the extracts was measured by ICP MS. The molecular weight of 89 g of HFO/ mol of Fe, recommended by Dzombak and Morel (1990), was used to calculate the amount of HFO from the extracted Fe. For the carbonated sample (B60) this procedure was repeated twice, for the fresh sample (B0) only one measurement could be made as there was not enough sample left for a second measurement. Results: The amount of HFO (in g/l) is given in Table 5.5. Note that there are twice as much HFO present in the uncarbonated sample. For cement/waste matrices no data were found in literature, but for bottom ash, Cornelis et al. (2006) did not observe a significant change in the amount of HFO between differently carbonated fractions.
Table 5.5: HFO content for sample B0 and B60. HFO (g/l) in B0 HFO (g/l) in B60 Measurement 1 1.31 0.62 Measurement 2 / 0.61 Average 1.31 0.62
3.1.2. Amorphous aluminium oxide content The amorphous aluminium oxide (also called amorphous aluminium minerals, AAM) content is measured by oxalate extraction in the dark (Jackson et al., 1996). This extraction must be carried out in the dark because in the presence of light, oxalate dissolves amorphous as well as crystalline aluminium oxides. Description of the method: The ammonium oxalate extractant (0.2 mol/L) is prepared by dissolving 28.4 g reagent grade ammonium oxalate monohydrate in 900 ml distilled, deionized water. HCl is added to attain a pH = 3. Subsequently, 50 ml of this extractant is added to 250 mg of the sample. This suspension is shaken for 48 hours in the dark. The samples were filtered over a 0.45 m filter and the amount of Al in the extracts was measured by ICP MS. One mol of Al was assumed to be representative of 1 mol of Fe and the molecular weight of 89 g of HFO/ mol of Fe recommended by Dzombak and Morel (1990) was used to calculate the corresponding concentration of HFO from the extracted amount of Al. This procedure was repeated three times for the fresh (B0) as well as for the carbonated sample (B60). Results: The amount of AAM (in g/l) is given in Table 5.6.
Table 5.6: AAM content for B0 and B60. AAM (g/l) in B0 AAM (g/l) in B60 Measurement 1 3.04 3.22 Measurement 2 3.08 3.31 Measurement 3 3.09 3.22 Average 3.07 3.25
50 CHAPTER 5: DATA COLLECTION
3.2. Specific surface area of HFO
The general value of 600 m²/g recommended by Dzombak and Morel (1990) is used.
3.3. Concentration of binding sites on HFO
Sorption sites on HFO are divided into two types, i.e., low capacity/high affinity sites (HFO_s) and high capacitiy/low affinity sites (HFO_w). Dzombak and Morel (1990) recommend 5.10 3 mol/mol Fe for the HFO_s sites and 0.2 mol/mol Fe for the HFO_w sites. Now that the amount of Fe and Al extracted (cf. paragraph 3.1.) and the concentration of each binding site are known, the total number of strong and weak sites can be calculated using again the molecular weight of 89 g of HFO/ mol of Fe from Dzombak and Morel (1990). The average values are displayed in Table 5.7.
Table 5.7: Total number of sites. HFO_w (mol/l) AAM_w (mol/l) Total weak sites (mol/l) (HFO_w + AAM_w) B0 2.95*10 3 6.89*10 3 9.84*10 3 B60 1.38*10 3 7.31*10 3 8.69*10 3 HFO_s (mol/l) AAM_s (mol/l) Total strong sites (mol/l) (HFO_s + AAM_s) B0 7.37*10 5 1.72*10 4 2.46*10 4 B60 3.46*10 5 1.83*10 4 2.17*10 4
4. Solid solutions
The chemical formulas, dissolution reactions and equilibrium constants of all components of the solid solutions are summarized in Table 5.8.
Table 5.8: Thermodynamic data for the solid solutions. Phase reaction log K ref CSH I solid solution SiO 2(am) SiO 2 + 2 H 2O = H 4SiO 4 2.81 [1] + 2+ Tobermorite I Ca 2Si 2.4 H8O10.8 + 4 H = 2.4 H 4SiO 4 + 2 Ca + 1.2 H 2O 27.36 [1]
CSH II solid solution + 2+ Jennite Ca 1.5 Si 0.9 H4.8 O5.7 + 3 H = 0.9 H 4SiO 4 + 1.5 Ca + 2.1 H 2O 26.445 [1] + 2+ Tobermorite II Ca 1.5 O4.5 H6Si 1.8 O3.6 + 3 H = 1.8 H 4SiO 4 + 1.5 Ca + 0.9 H 2O 20.52 [1]
(Pb,Ca)CO 3 solid solution + 2+ Cerrusite PbCO 3 + H = HCO3 + Pb 3.2091 [2] 2+ 2 Calcite CaCO 3 = Ca + CO 3 8.48 [3]
PbO,Al(OH) 3,Fe(OH) 3 solid solution + 2+ Litharge PbO +2 H = H2O + Pb 12.64 [2] + 3+ Gibbsite Al(OH) 3 + 3 H = Al + 3 H 2O 7.76 [2] + 3+ Fe(OH) 3 Fe(OH) 3 + 3 H = Fe + 3 H 2O 5.66 [2] References: [1] (Kulik and Kersten, 2001); [2] (llnl database, Wolery, 1992); [3] (PHREEQC database, Parkhurst and Appelo, 1999)
51 CHAPTER 5: DATA COLLECTION
5. Transport
To model transport, porosity is needed. Values for the open or effective porosity are used as this refers to the fraction of the total volume in which fluid flow is effectively taking place (excluding dead end pores or non connected cavities). The data are represented in Table 5.9 (from Van Gerven, 2005).
Table 5.9: Open porosity of the samples. Sample Porosity (%) B0 26.37 B14 25.15 B30 23.23 B60 20.74
The effective diffusion constant is also required. For diffusion in the free water surrounding the monolith, a value of 1*10 9 m²/s is used (Appelo and Postma, 2005). For diffusion in the pore water of the monolith, the effective diffusion constant will be obtained by performing a sensitivity analysis for sodium (Chapter 7).
52 CHAPTER 6: EXTRACTION TEST MODELLING
1. Introduction
Results of the extraction test (Van Gerven, 2005) are simulated using PHREEQC. First, leaching of Ca, Al, Si and Mg from the cement/waste samples is modelled as a function of pH. Major elements control important variables such as pH and ionic strength. Ca minerals in MSWI bottom ash, for example, mainly control leachate pH, which in turn has been identified as a major parameter controlling the leaching of many other elements from bottom ash (Meima and Comans, 1997; Polettini and Pomi, 2004) and S/S waste (van der Sloot, 2002). It is therefore necessary that the model works well for the major elements before trying to model minor elements. Leaching of major elements from fresh concrete, and in a second step also from partially and fully carbonated concrete is modelled. The subsequent modelling of the leaching of minor elements focuses on the leaching of lead.
2. Method and input data
PHREEQC is used to calculate leachate composition in equilibrium with solubility controlling minerals. The concentrations were experimentally measured after 24 hours leaching. In these waste materials 24 hours of leaching particle size reduced samples in a horizontal shaking device is generally considered to result in equilibrium conditions (Van Gerven, 2005). In the lab longer leaching (48, 72 hours) has been previously studied for similar cement waste samples and this assumption appeared valid. A simple equilibrium calculation in which kinetic reactions are not considered will therefore be performed. PHREEQC input files are thus composed of: (i) total concentrations 7 of all the elements measured in the solid samples at the start of the extraction test (Table 6.1), (ii) the pH 8, which is varied between 1 and 13 with step 0.5. The initial solution is fixed at pH = 0 and increasing amounts of NaOH are added. In the experiment, initial solution was
at high pH and HNO 3 was added. HNO 3 addition is also possible in PHREEQC, but then the model turns out to be much more instable. No convergence was obtained for some pH values and the input had to be simplified (sometimes the model does not work if for example sulphate and chlorine are present in the input solution). Simulations obtained by
adding NaOH or HNO 3 in case of convergence gave equal results. As such, the more stable approach of NaOH addition is used.
7 Cation concentrations were obtained from digestion with three acids (HNO 3, HClO 4 and HF) and subsequent measurement of the concentrations in the digestate with ICP MS (Van Gerven, 2005). The Cl concentration was obtained by leaching the solid sample at pH 2 (24h, L/S=10) and measuring the Cl concentration in the leachate with AgNO 3 titration. H 4SiO 4 concentration is calculated from mass balance equations with measured concentrations as input.
8 Results for the entire pH range will be shown. However, the pH range 7 to 11 is most relevant from an environmental point of view. 53 CHAPTER 6: EXTRACTION TEST MODELLING
(iii) the temperature, which is set to 25°C, and (iv) the solids that are allowed to precipitate when oversaturated. These solubility controlling minerals are selected from the list of minerals generally observed in concrete and/or bottom ash (cf. Chapter 3). Only the minerals for which the model predicted curve shapes follow the laboratory data well are retained. Some solids are imported as solid solutions. For modelling Pb leaching, surface complexation is also considered. However, no surface complexation is considered for the major elements, as leaching of these elements is mainly controlled by dissolution/precipitation reactions (Meima and Comans, 1997). Model leaching predictions for Ca, Si, Mg, Al and Pb together with the experimental data will be presented in graphs of log concentration versus pH.
Table 6.1: Input concentrations in extracts at L/S = 10. Component Total concentration (mol/l) Na + 0.029 K+ 0.017 Ca 2+ 0.476 Al 3+ 0.128 Mg 2+ 0.041 H4SiO 4 0.917 Pb 2+ 0.00049 Cl 0.045 2 SO4 0.022 Fe 3+ 0.101
3. Leaching of major elements
3.1. Fresh concrete
The minor element Pb is not yet considered, consequently the input simplifies (Table 6.2).
Table 6.2: Input concentrations in extracts at L/S = 10 for the simplified model. Component Total concentration (mmol/l) Na + 29 K+ 17 Ca 2+ 476 Al 3+ 128 Mg 2+ 41 H4SiO 4 917 Cl 45 2 SO 4 22 Fe 3+ 101
If all the minerals from the extended list in Chapter 3 are immediately included, it will be complicated to investigate the influence of each individual mineral on the leaching of the different elements. Therefore, it is decided to start with a ‘simple model’ (model 1), containing a limited number of minerals. More
54 CHAPTER 6: EXTRACTION TEST MODELLING specific, this model contains the same minerals as used by Wang (2006) 9 i.e., portlandite, CSH_1.8, hydrogarnet and hydrotalcite. Appendix 1 gives an overview of all the models that will be discussed in this chapter. Thermodynamic data used for the phases contained in this ‘simple model’ and for those that will be contained in all subsequent models were already given in the previous chapter.
Results and discussion
1.E+06
1.E+05
1.E+04
1.E+03
1.E+02 Caleached out (mg/kg) experimental 1.E+01 model 1 model 2 model 3 model 1* 1.E+00 0 2 4 6 8 10 12 14 pH Figure 6.1: Comparison between predicted Ca concentrations with models 1, 2, 3 and 1* and experimental results for sample B0.
Calcium and Silicon: Calcium results obtained with model 1 differ from the experimental data (model 1 in Figure 6.1). However, removing the silicon containing phase (CSH_1.8) (model 2) considerably improves the prediction. The predicted calcium concentrations of this Si free model approach those of the extraction test (model 2 in Figure 6.1). This suggests that only a small fraction of the high initial Si concentration (917 mmol/l) reacts with calcium. The remaining Si is then considered to be incorporated in inert phases that do not react during leaching. Therefore, amorphous SiO 2, which is believed to be an inert Si phase, is added to the initial model (model 1), but the results remain deficient (model 3 in Figure 6.1). Less CSH_1.8 is predicted to form and the formation starts at higher pH values (Table 6.3). At low pH values, model 3 predicts (almost) all silicon to be incorporated in amorphous SiO 2 (Table 6.3), while at high pH values, model 3 predicts dissolution of this phase. The dissolution of amorphous SiO 2 results in an increase in Si concentration at high pH which contradicts the Si measurements (Figure 6.2)10 . Results
9 Except for sulfate and iron containing minerals, which will be added afterwards (sulfate as well as iron leaching are considered afterwards because no experimental leaching data are available). 10 At low pH only 1 mmol Si is dissolved (917 mmol – 916 mol, cf. Table 6.2 and 6.3). This corresponds with a leachate concentration of 280 mg/kg (1 mmol/L * 28 g/mol * 10 L/kg = 280 mg/kg, cf. Figure 6.2). 55 CHAPTER 6: EXTRACTION TEST MODELLING
are similar when quartz (crystalline SiO 2) or chalcedony (a cryptocrystalline variety of the mineral quartz) were included. Meima and Comans (1997) suggested that at strongly alkaline pH, Si leaching from bottom ash is likely to be controlled by a (not yet identified) Si mineral that is less soluble than amorphous SiO2. Consequently, another way to deal with the problem of inert Si phases is needed. Vinckx (2003) experimentally determined the amount of silicon that can maximally dissolve (for the same samples that are being used here), giving a value of 29876.3 mg Si/kg or 106 mmol/l (L/S = 10 and pH = 2). Based on this, the input concentration for Si is changed from 917 mmol/l to 106 mmol/l. This results in a better prediction of the experimental measured Ca concentrations (Figure 6.1, model 1*). In all subsequent simulations, 106 mmol/l instead of 917 mmol/l will thus be used as input for the Si concentration.
Table 6.3: Amounts of Si-containing phases at different pH's for models 1 & 3. Model 1 Model 3 (model without amorphous (model with amorphous SiO 2) SiO 2) pH CSH_1.8 (mol) CSH_1.8 (mol) SiO 2 (am.) (mol) 1.0 0 0 0.916 1.5 0 0 0.916 2.0 0 0 0.916 2.5 0 0 0.916 3.0 0 0 0.916 3.5 0 0 0.916 4.0 0 0 0.916 4.5 0 0 0.916 5.0 0 0 0.916 5.5 0 0 0.916 6.0 0 0 0.916 6.5 0 0 0.916 7.0 0 0 0.916 7.5 0 0 0.916 8.0 0 0 0.916 8.5 0 0 0.916 9.0 0 0 0.916 9.5 0 0 0.916 10.0 0.176 0 0.915 10.5 0.217 0.082 0.829 11.0 0.235 0.168 0.731 11.5 0.249 0.184 0.681 12.0 0.263 0.204 0.556 12.5 0.264 0.250 0.238 13.0 0.264 0.264 0
The Si prediction is also improved (Figure 6.2, model 1*), but the observed decrease in Si concentrations between pH 2 and 4 is not predicted by the model. Zevenbergen et al. (1996 and 1998) have observed that, during weathering, metastable glassy constituents in MSWI bottom ash decompose and an amorphous hydrous aluminosilicate (allophane) in a first stage and clay minerals (e.g. illite,
K0.6 Mg 0.25 Al 2.3 Si 3.5 O10 (OH) 2) in a next stage are formed. An allophane solubility constant is not found in literature; illite, however, is present in the database. Although illite is mainly observed in severly weathered (12 years) bottom ash samples (Zevenbergen et al., 1996 and 1998), inclusion of this mineral significantly improves the prediction (Figure 6.2, model 4). Even though illite might not be the exact
56 CHAPTER 6: EXTRACTION TEST MODELLING mineral actually formed in fresh samples, it can be used as a surrogate for another yet unidentified mineral phase. A considerable amount of illite is formed between pH 2.5 and 11 (Figure 6.3a). As the chemical formula of illite is K 0.6 Mg 0.25 Al 2.3 Si 3.5 O10 (OH) 2, formation of illite will also influence leaching of potassium, aluminium and magnesium. Illite can only be used as a surrogate if it does also adequately predict leaching of these elements. The influence on the leaching of potassium is unacceptably high (Figure 6.3b) so that adding illite to the model is not a good option.
1.E+06
1.E+05
1.E+04
1.E+03
1.E+02
1.E+01 experimental Si leached outSi (mg/kg) leached model 3 1.E+00 model 1* model 4 1.E-01 0 2 4 6 8 10 12 14 pH Figure 6.2: Comparison between predicted Si concentrations with models 3, 1* and 4, and experimental results for sample B0. Experimental data are from Vinckx (2003).
0.140 1.E+05 portlandite 1.E+02 0.120 1.E-01 0 2 4 6 8 10 12 14 0.100 1.E-04 CSH_1.8 1.E-07 0.080 1.E-10 hydrogarnet 1.E-13 0.060 1.E-16 1.E-19 0.040 illite 1.E-22
totalamount (mol/L) 0.020 1.E-25 hydrotalcite Kleached (mg/kg) out 1.E-28 experimental 0.000 1.E-31 PHREEQC 1 3 5 7 9 11 13 1.E-34 a pH b pH
Figure 6.3: Predicted (model 4) pure-phase assemblage (a) and K-leaching profile (b) for sample B0.
Magnesium: Mg leaching is almost pH independent at pH < 9, but decreases strongly at alkaline pH (Figure 6.4). This behaviour suggests solubility control at alkaline pH, whereas at pH < 9 the solubility controlling mineral may have dissolved completely from the matrix. A first model run using model 1* which includes hydrotalcite as the only Mg containing phase does not provide a good prediction of the experimental data (Figure 6.4). This indicates that hydrotalcite is not the solubility controlling mineral.
57 CHAPTER 6: EXTRACTION TEST MODELLING
Indeed, hydrotalcite does not immediately dissolve when pH becomes smaller than 9 (Figure 6.5a). Various authors indicate that Mg leaching from bottom ash (Meima and Comans, 1997; Dijkstra et al.,
2006) and from cement (van der Sloot, 2000) is mainly solubility controlled by brucite (Mg(OH) 2). Miyamoto et al. (2006) experimentally observed that brucite was present in their fresh cement samples, while hydrotalcite was not. Including both hydrotalcite and brucite in the model (model 5) gives the same poor result because hydrotalcite has the lowest solubility. Replacing hydrotalcite by brucite (model 6) gives fairly good results (Figure 6.4). At pH < 9.5 brucite is dissolved and the leached amount of Mg is limited by the total Mg content of the sample (41 mmol/l = 1.0*10 4 mg/kg), while at higher pH values brucite precipitates (Figure 6.5b).
1.0E+04
1.0E+03
1.0E+02
1.0E+01
1.0E+00
1.0E-01 Mg leached out (mg/kg) Mgleached experimental 1.0E-02 model 1* and 5 model 6 1.0E-03 0 2 4 6 8 10 12 14 pH Figure 6.4: Comparison between predicted Mg concentrations with models 1*, 5 and 6 and experimental results for sample B0.
0.140 0.120 portlandite portlandite 0.120 0.100 CSH_1.8 CSH_1.8 0.100 0.080 0.080 hydrogarnet 0.060 hydrogarnet 0.060 brucite 0.040 0.040
totalamount (mol) 0.020 total total amount (mol) 0.020 hydrotalcite 0.000 0.000 1 3 5 7 9 11 13 1 3 5 7 9 11 13 a pH b pH
Figure 6.5: Predicted pure-phase assemblage for the models containing (a) hydrotalcite (model 1* and 5) and (b) brucite (model 6) for sample B0.
Aluminium: Except for hydrogarnet, no aluminium containing phases are yet included in the model. Many authors indicate that the strongly pH dependent leaching of Al from bottom ash is adequately
58 CHAPTER 6: EXTRACTION TEST MODELLING described over a large pH range by the solubility behaviour of Al(hydr)oxide forms (Dijkstra et al., 2006;
Kirby and Rimstidt, 1994; Meima and Comans, 1997). As such, amorphous Al(OH) 3 and gibbsite
(crystalline Al(OH) 3) are added to the model (model 7), which provides fairly good agreement with the experimental data (Figure 6.6, model 7). Al appears to precipitate as crystalline gibbsite and not as amorphous Al(OH) 3 (Figure 6.7). Amorphous Al(OH) 3 will however not be removed from the model, as it might probably be solubility controlling under slightly different conditions. For pH 6 to 10, no experimental data are available because Al concentrations were below detection limit. Model 7 indeed predicts very low Al concentrations in this pH range (Figure 6.6). The zigzag pattern at high pH values might seem a bit unusual, but can easily be explained. From pH = 11 and higher, hydrogarnet
(Ca 3Al 2O12 H12 ) forms (Figure 6.7), decreasing the Al concentration. At pH = 12.5, portlandite forms (Figure 6.7). A small part of the hydrogarnet dissolves to provide additional Ca ions for the formation of portlandite and thus Al ions are released into the solution. Of course, in reality the evolution goes from high pH to low pH but the reasoning remains the same.
1.0E+05
1.0E+04
1.0E+03
1.0E+02
1.0E+01
1.0E+00
1.0E-01 Al leached out (mg/kg) out Alleached 1.0E-02 experimental 1.0E-03 detection limit model 7 1.0E-04 0 2 4 6 8 10 12 14 pH Figure 6.6: Comparison between predicted Al concentrations with model 7 and experimental results for sample B0. Open circles indicate results below the detection limit (0.1 mg/kg).
portlandite 2 0.140 gibbsite 0.120 1 gib 0.100 0 CSH_1.8 0 2 4 6 8 10 12 14 0.080 -1 hydrogarnet Al(OH)3 0.060 -2 bruc 0.040 -3 brucite
total total amount(mol) 0.020 SaturationIndex -4 port Al(OH)3 0.000 -5 1 3 5 7 9 11 13 CSH hydr -6 pH a b pH
Figure 6.7: Predicted pure-phase assemblage (a) and saturation indices (b) as a function of pH for model 7. 59 CHAPTER 6: EXTRACTION TEST MODELLING
Last modifications to the model for fresh concrete:
Although iron is known to be a major element in cementitious waste matrices (e.g., van der Sloot et al., 2005), iron minerals were not yet included because experimental leaching data was not available, except for the input concentration. Several authors (Dijkstra et al., 2006; Eighmy et al., 1995; Meima and Comans, 1997) have observed that Fe leaching curves (concentration as a function of pH) for MSWI ash approach a V shape and modelling indicated this pattern is characteristic for the solubility of
Fe(hydr)oxides; more specific for the solubility of ferrihydrite (Fe(OH) 3). As such, it is decided to include ferrihydrite in the model (model 8). As was indicated in Chapter 5 (Table 5.1), this mineral is not present in the PHREEQC database, but it was found in the MINTEQA database (which was also used by the + 3+ above referenced authors). The log K value equals 4.89 (for the reaction Fe(OH) 3 + 3H = Fe + 3H 2O). As mentioned before, Fe leaching predictions can not be verified, but (i) model 8 predictions indeed approach a V shape (Figure 6.8) and (ii) including ferrihydrite does not have any negative influence on the previously obtained Ca, Al, Mg and Si leaching predictions (results not shown). As such, addition of ferrihydrite is considered as being a good choice. Remark: As indicated in Chapter 5 (Table 5.1) Lothenbach and Winnefeld (2006) include two iron hydroxides in their model for the hydration of Portland cement: microcrystalline Fe(OH) 3 with a log K =
3.00 and amorphous Fe(OH) 3 with a log K = 5.00 (this last one being close to the above used value of 4.89 for ferrihydrite). If these two phases are included in our model (model 9), microcrystalline Fe(OH) 3 is the only one that is predicted to form. The Fe leaching profile has the same shape, but predicted Fe leaching concentrations are 1 to 2 orders of magnitude lower (Figure 6.8). As the mineral ferrihydrite is approved by much more authors (cf. above) model 8 is used in subsequent modelling.
1.0E+05
1.0E+04
1.0E+03
1.0E+02
1.0E+01
1.0E+00
1.0E-01
1.0E-02 Feleached out (mg/kg) 1.0E-03 model 8 1.0E-04 model 9 1.0E-05 0 2 4 6 8 10 12 14 pH
Figure 6.8: Leaching of Fe as predicted by model 8 and 9.
60 CHAPTER 6: EXTRACTION TEST MODELLING
A second species that was not yet included for the same reason (no experimental data available) is sulphate. However, sulphate mineral phases may play a non negligible role as many authors indicate ettringite (Ca 6Al 2(SO 4)3(OH) 12 26H 2O) as a possible solubility controlling mineral for sulphate and calcium leaching from MSWI bottom ash (Meima and Comans, 1997 Polettini and Pomi, 2004), from MSWI air pollution control residues (Astrup et al., 2006) and from cement (van der Sloot, 2000) at pH larger than 9. Ettringite is thus included. For the lower pH values, the mineral that is most often indicated as solubility controlling, for calcium as well as for sulphate, for MSWI bottom ash (Meima and Comans,
1997; Dijkstra et al., 2006) and for cement (van der Sloot, 2000), is gypsum (CaSO 4 2H 2O). Finally, the cement mineral monosulfo aluminate (Ca 4Al 2SO 10 12H 2O) is included because this mineral is more stable at high pH compared to ettringite (Chrysochoou and Dermatas, 2006).
An overview of the minerals that are included in this final model for fresh concrete (model 10) is given in Appendix 1. The thermodynamic data were already given in Chapter 5 (Table 5.1). Predictions with model 10 are compared with experimental data in Figure 6.9. Predicted pure phase assemblage and a plot of the saturation indices as a function of pH for this simulation are shown in Figure 6.10 and 6.11, respectively.
1.0E+06 1.0E+04
1.0E+03 1.0E+05 1.0E+02 1.0E+01
1.0E+00 1.0E+04 1.0E-01 experimental experimental 1.0E-02
Ca leached out (mg/kg) leachedCa model 10 model 10 Mgleached (mg/kg) out 1.0E+03 1.0E-03 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 pH pH a b
1.0E+05 1.0E+05
1.0E+04 1.0E+04 1.0E+03 1.0E+02 1.0E+03
1.0E+01 1.0E+02 1.0E+00 1.0E+01 1.0E-01 1.0E-02 experimental 1.0E+00 experimental Al(mg/kg) out leached
1.0E-03 leached out Si (mg/kg) model 10 model 10 1.0E-01 1.0E-04 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 pH pH c d Figure 6.9: Comparison between predicted Ca (a), Mg (b), Al (c) and Si (d) concentration with model 10 (model for fresh concrete) and experimental results for sample B0 as a function of pH.
61 CHAPTER 6: EXTRACTION TEST MODELLING
0.140
gibbsite portlandite 0.120 CSH_1.8 ferrihydrite 0.100
0.080
0.060 hydrogarnet
total amount (mol/L) total 0.040
gypsum ettringite 0.020
brucite 0.000 1 3 5 7 9 11 13 pH
Figure 6.10: Predicted pure-phase assemblage using model 10 (fresh concrete model) for sample B0.
1
1 3 5 7 9 11 13 -1
-3
-5
-7 SI
-9 portlandite CSH_1.8 brucite -11 Al(OH)3(am) gibbsite ferrihydrite -13 hydrogarnet ettringite gypsum monosulfoaluminate -15 pH
Figure 6.11: Saturation indices as a function of pH for model 10 (sample B0).
Experimental data and model predictions are in fairly good agreement. Leaching of silicon remains least satisfying. As such, this problem is addressed again. Several authors (Meima and Comans, 1997; Dijkstra et al., 2006) use the zeolite wairakite (CaAl 2Si 4O12 2H 2O) to model Si leaching from fresh MSWI bottom
62 CHAPTER 6: EXTRACTION TEST MODELLING ash. Wairakite has not been observed in bottom ash but quite good modelling results have been obtained by these authors. This mineral is present in the Lawrence Livermore National Laboratory (llnl) database and added to the model (model 11). When using wairakite, model 11 predicts a decrease of the Si concentration from pH 3 which qualitatively corresponds better to the experimental results although the decrease is too fast compared with the experimental data. Modelled Al and Ca leaching curves remain largely unchanged, while the prediction for Ca is a little bit worse (Figures 6.12 & 6.9). Note that there is no problem with leaching of potassium as was the case for illite. Wairakite is predicted to form between pH 3 and 11 (Figure 6.13). Comparison of Figures 6.13 and 6.10 indicates that, due to the formation of wairakite, less gibbsite forms and CSH formation starts at higher pH (10.5 instead of 9.5).
1.0E+06 1.0E+04
1.0E+03 1.0E+05 1.0E+02 1.0E+01
1.0E+00 1.0E+04 1.0E-01 experimental experimental 1.0E-02 model 11
Ca out (mg/kg) leached model 11 Mgleached (mg/kg) out 1.0E+03 1.0E-03 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 pH pH a b
1.0E+05 1.0E+05 1.0E+04 1.0E+04 1.0E+03 1.0E+02 1.0E+03 1.0E+01 1.0E+02 1.0E+00 1.0E-01 1.0E+01 1.0E-02 experimental 1.0E+00 experimental 1.0E-03
model 11 leached out Si (mg/kg) Alleached(mg/kg) out model 11 1.0E-04 1.0E-01 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 c pH pH d
Figure 6.12: Comparison between predicted Ca (a), Mg (b), Al (c) and Si (d) concentration with model 11 (model for fresh concrete with wairakite) and experimental results for B0 -sample as a function of pH.
Although wairakite is probably not the exact mineral present in reality, the better agreement between the model curve and the data may point to the presence of other zeolites. Recently, gismondine
(Ca 2Al 4Si 4O16 9H 2O) and laumontite (CaAl 2Si 4O12 4H 2O) have been identified in MSWI bottom ash, but only in weathered samples (Piantone et al., 2004). Further research is thus needed for fresh concrete.
63 CHAPTER 6: EXTRACTION TEST MODELLING
0.120
CSH_1.8 ferrihydrite 0.100
0.080 gibbsite
portlandite
0.060
hydrogarnet brucite 0.040 total amount totalamount (mol/L) wairakite
ettringite 0.020 gypsum
0.000 1 3 5 7 9 11 13 pH Figure 6.13: Predicted pure-phase assemblage using model 11 (fresh concrete model with wairakite for B0).
3.2. Carbonated concrete
For (partially) carbonated concrete, some additions have to be made to model 10 (the model for fresh concrete): • Carbonate phases have to be added:
o Calcium carbonate (CaCO 3): calcite is the only mineral form of CaCO 3 stable at 25°C
(Clodic and Meike, 1997). As such, no other CaCO 3 mineral forms (e.g. vaterite, aragonite) are taken into account.
o Magnesite (MgCO 3) is added as van der Sloot (2000) suggests that Mg leaching from cement may partly be controlled by this mineral. o Lothenbach and Winnefeld (2006) have stated that in the presence of calcite, calcium
mono carbo aluminate (Ca 4Al 2CO 9 10H 2O) is more stable than the hydrogarnet phases and thus no hydrogarnet is expected to form. Therefore, it is logical to replace hydrogarnet by calcium mono carbo aluminate. However, by including both minerals in the model, the chemical code will choose the most stable mineral based on the thermodynamic equilibrium constant and the prevailing geochemical calculations. • The Ca/Si ratio of the CSH gel continuously lowers as carbonation proceeds. This is known as the decalcification process of the CSH phases (e.g.; Bonen and Sarkar, 1995; Garrabrants et al., 2004). To simulate this degradation, CSH phases with Ca/Si ratios of 0.8 and 1.1 are incorporated in the model, in addition to the CSH phase with a Ca/Si ratio of 1.8 which was already present. When the decalcification process proceeds, the geochemical model is then able to include progressively CSH phases with lower Ca/Si ratios.
64 CHAPTER 6: EXTRACTION TEST MODELLING
All thermodynamic data for the (partially) carbonated concrete model (model 12) was already provided in the previous chapter (Table 5.1). Note that a Si concentration of 106 mmol/l is used again.
3.2.1. Sample B14 Table 6.4 shows the input data for the 14 days – long carbonated sample.
Table 6.4: Input concentrations in extracts at L/S = 10 for B14. Component Total concentration (mmol/l) Na + 29 K+ 17 Ca 2+ 476 Al 3+ 128 Mg 2+ 41 H4SiO 4 106 Cl 45 2 SO 4 22 Fe 3+ 101 2 CO3 330
Results and discussion
Calcium: Results of the simulation indicate that the formation of calcite leads to a decrease of the calcium concentration in the leachate compared with the non carbonated simulation (Figure 6.14). Observed leached Ca was also smaller for the carbonated than for the non carbonated sample (experimental B0 versus B14 in Figure 6.14). However, the predicted decrease is much larger than the observed one.
1.0E+06
1.0E+05
1.0E+04
Ca leached out (mg/kg) leached out Ca 1.0E+03 experimental B0 experimental B14 predictions for B0 with model 10 predictions for B14 with model 12 1.0E+02 0 2 4 6 8 10 12 14 pH
Figure 6.14: Comparison between observed and predicted (model 10 and 12) Ca leaching curve for samples B0 and B14.
65 CHAPTER 6: EXTRACTION TEST MODELLING
Such underpredictions of the leached amount of calcium were also observed in many other studies (Astrup et al., 2006; Johnson et al., 1999; Meima and Comans, 1997). It may indicate that the thermodynamic data do not reflect the characteristics of typical calcite phases in incinerator ash systems (Astrup et al., 2006) or the influence of slow reaction kinetics (Astrup et al., 2006; Johnson et al., 1999). One possible approach to deal with this problem is to allow calcite oversaturation during equilibrium geochemical calculations, as was done by Halim et al. (2005) by specifying a saturation index of 0.75 for calcite. An alternative approach is as follows: assuming that free portlandite is transformed into calcite by carbonation, the calcite content can be deduced from the difference between the free portlandite in uncarbonated and carbonated material. The remaining carbonate, i.e. the difference between the total measured carbonate concentration and the calculated value, is possibly incorporated in stable cement minerals and does not participate in reactions. The experimental measured carbonate concentrations are almost three times higher than those calculated this way (Van Gerven, 2005). Garrabrants et al. (2004) report a 2.5 ratio between measured and calculated values. If the average between these two ratios (3 and 2.5) is used (2.75), the calculated amount of carbonate for sample B14 is 120 mmol/l. If the input concentration for carbonate is set to 120 mmol/l (calculated amount) instead of 330 mmol/l (measured amount), model 12 provides much better predictions (Figure 6.15). Less calcite is predicted to form and thus more calcium leaches out (Figures 6.16 & 6.17). Because of the improved predictions, the carbonate concentration will be changed this way in all following model runs. Also note that all portlandite is dissolved in the model simulation and as pH decreases, CSH_1.8 is transformed into CSH_1.1 and afterwards into CSH_0.8 (Figure 6.17). It is experimentally observed that the carbonation process is indeed initiated with the preferential dissolution of portlandite (e.g. Miyamoto et al., 2006).
1.0E+06
1.0E+05
1.0E+04
Caleached out (mg/kg) 1.0E+03
experimental B14 model 12 with measured carbonate concentration (330 mmol/L) model 12 with calculated carbonate concentration (120 mmol/L) 1.0E+02 0 2 4 6 8 10 12 14 pH
Figure 6.15: Comparison between predicted Ca leaching curve with model 12 using measured or calculated carbonate concentrations as input and experimental data for sample B14.
66 CHAPTER 6: EXTRACTION TEST MODELLING
0.35 1) Gibbsite 2) Calcite 0.30 3) CSH_0.8 4) CSH_1.1
0.25 5) CSH_1.8 6) Ferrihydrite 7) Brucite 0.20 8) Hydrogarnet 2 9) Ettringite 0.15 10) Gypsum 1 4
total amount (mol) amount total 6 0.10
3 0.05 7 10 9 8 0.00 1 3 5 7 9 11 13 pH Figure 6.16: Predicted pure-phase assemblage using model 12 and measured carbonate concentration (330 mmol/l) for sample B14.
0.14 1) Gibbsite 1 2) Calcite 0.12 3) CSH_0.8 2 4 4) CSH_1.1 6 0.10 5) CSH_1.8
6) Ferrihydrite 0.08 7) Brucite
8) Hydrogarnet 0.06 9) Ettringite 3 10) Gypsum 8
totalamount (mol) 0.04 11) Monosulfo- aluminate 7 5 11 0.02 10 9
0.00 1 3 5 7 9 11 13 pH Figure 6.17: Predicted pure-phase assemblage using model 12 and calculated carbonate concentration (120 mmol/l) for sample B14.
Silicon: Results for Si leaching are not presented due to lack of experimental data. Magnesium: Magnesite was never formed (Figure 6.17); consequently Mg leaching was again predicted to be solubility controlled by brucite. As the input concentration for Mg was the same as for sample B0 and, as magnesite and brucite are the only Mg containing phases, Mg model predictions for B0 and B14 67 CHAPTER 6: EXTRACTION TEST MODELLING are identical. This is acceptable, as the experimental Mg leaching data for B0 and B14 also lie on the same curve (Figure 6.18).
1.0E+04
1.0E+03
1.0E+02
1.0E+01
1.0E+00
1.0E-01
Mg leached out out (mg/kg) Mgleached experimental B0
1.0E-02 experimental B14 model 12 1.0E-03 0 2 4 6 8 10 12 14 pH
Figure 6.18: Comparison between predicted Mg leaching curve using model 12 and experimental results for samples B0 and B14.
Magnesite is only slightly undersaturated (SI ≈ 1.3) between a pH of 5 and 9.5 (Figure 6.19). Possibly, magnesite will form at higher carbonate concentrations (thus in sample B30 and/or B60).
1
1 3 5 7 9 11 13 -1
-3
-5
-7 SI
gypsum ferrihydrite -9 gibbsite Al(OH)3(am) calcite magnesite -11 CSH_0.8 CSH_1.1 brucite ettringite CSH_1.8 -13 portlandite monosulfoaluminate hydrogarnet monocarboaluminate -15 pH Figure 6.19: Saturation indices as a function of pH using model 12 (sample B14).
68 CHAPTER 6: EXTRACTION TEST MODELLING
Aluminium: Al leaching is again controlled by gibbsite (Figure 6.17). The SI profile for amorphous
Al(OH) 3 follows the same trend as the one for gibbsite, but the former remains undersaturated for the whole pH interval (Figure 6.19). Analog to the modelling of Mg leaching, Al leaching predictions for sample B0 and B14 are almost the same. For Al, they are not exactly identical as there is a very minor difference in the amount of hydrotalcite formed at pH = 11 and 11.5 (0 mol for B14 versus 0.0026351 mol for B0 at pH = 11 and 0.063922 mol for B14 versus 0.063962 mol for B0 at pH = 11.5). This small difference explains the difference in the leaching curves of Figure 6.20 (note the log scale and the units of the y axis).
Finally, note that although Lothenbach and Winnefeld (2006) stated that in the presence of calcite, calcium mono carbo aluminate (Ca 4Al 2CO 9 10H 2O) is more stable than the hydrogarnet phases, model 12 predicts only hydrogarnet to form (Figure 6.17). The SI profile for mono carbo aluminate follows the same shape as the profile for hydrogarnet but mono carbo aluminate stays undersaturated for the whole pH interval (Figure 6.19).
1.00E+05
1.00E+04
1.00E+03
1.00E+02
1.00E+01
1.00E+00
experimental B0 1.00E-01
Al(mg/kg) leached out experimenta B14 1.00E-02 predictions for B0 with model 10 1.00E-03 predictions for B14 with model 12 1.00E-04 0 2 4 6 8 10 12 14 pH Figure 6.20: Comparison between predicted Al concentrations by model 10 for B0 and model 12 for B14 and experimental results for sample B0 and B14 as a function of pH.
3.2.2. Sample B30 Input data are the same as for sample B14 (Table 6.4), except for the carbonate concentration which is now set to 145 mmol/l. This is the calculated amount from the measured concentration of 400 mmol/l. Simulations are again performed with model 12.
Results and discussion:
69 CHAPTER 6: EXTRACTION TEST MODELLING
Calcium: Results turn out to be fairly good (Figure 6.21a). The pH at which the saturation index of calcite becomes zero (and thus calcite formation starts) is the same for sample B14 and B30 (cf. Figures 6.19 & 6.22). However, the amount of calcite formed is logically higher for sample B30 (cf. Figures 6.17 & 6.21d). By analogy, less CSH_1.8 is formed for B30. Silicon: Results for Si leaching are not presented due to lack of experimental data. Magnesium: Same remarks as for the model predictions for sample B14 are made. Magnesite was slightly less undersaturated than for B14 (cf. Figure 6.19 & 6.22), but again never formed (Figure 6.21d); consequently Mg leaching was again predicted to be solubility controlled only by brucite. Thus, simulated Mg leaching profiles are the same for B0, B14 and B30 which is supported again by the experimental data: Mg leaching data for B30 lie on the same curve as the data for B0 and B14 (Figure 6.21b).
Aluminium: Al leaching is again controlled by gibbsite (Figure 6.21d) as amorphous Al(OH) 3 remains undersaturated (Figure 6.22). Experimental measured concentrations in the pH range 7 9 are underestimated by 1 to 2 orders of magnitude. If gibbsite is removed from the model (model 13), amorphous Al(OH) 3 becomes the solubility controlling mineral and a better estimate for the concentrations at pH 7 9 is obtained (Figure 6.21c). Since the goal is to develop a model applicable to all samples, it is better to keep gibbsite in the model (i.e. to use model 12 instead of model 13) as it provides good predictions for B0 and B14. An individual model for each sample would provide better predictions but is not generally applicable. As such, compromises have to be made and thus gibbsite and amorphous
Al(OH) 3 are both kept in the model and the least soluble will be formed (apparently this is gibbsite).
1.0E+06 1.0E+04
1.0E+05 1.0E+03
1.0E+02 1.0E+04 1.0E+01
1.0E+03 1.0E+00 experimental B0 1.0E-01 experimental B14 1.0E+02 experimental experimental B30 1.0E-02 model 12 (mg/kg) out Mg leached Ca leached out (mg/kg) leachedCa out model 12 1.0E+01 1.0E-03 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 pH a b pH
1.0E+05 0.160 calcite 1.0E+04 0.140 gibbsite 1.0E+03 0.120 1.0E+02 ferrihydrite CSH_0.8 CSH_1.1 1.0E+01 0.100 1.0E+00 0.080 1.0E-01 0.060 hydrogarnet experimental 1.0E-02 brucite 0.040
model 12 amounttotal (mol/l) Al leached Al (mg/kg) out monosulfo- 1.0E-03 ettringite aluminate model 13 0.020 gypsum 1.0E-04 CSH_1.8 0 2 4 6 8 10 12 14 0.000 1 3 5 7 9 11 13 pH c d pH
Figure 6.21: Observed and predicted (model 12) Ca (a), Mg (b) and Al (c) concentrations and predicted pure- phase assemblage (d) for sample B30. In the Mg graph (b), experimental data for B0 and B14 are also shown. In the Al graph (c), results are shown for the mo del with and without gibbsite (i.e. model 12 and 13, respectively). 70 CHAPTER 6: EXTRACTION TEST MODELLING
1
1 3 5 7 9 11 13 -1
-3
-5
-7 SI
gypsum ferrihydrite -9 gibbsite Al(OH)3(am) calcite magnesite -11 CSH_0.8 CSH_1.1 brucite ettringite CSH_1.8 -13 portlandite monosulfoaluminate hydrogarnet monocarboaluminate -15 pH
Figure 6.22: Saturation indices as a function of pH as simulated with model 12 (sample B30).
3.2.3. Sample B60 Input data are the same as for sample B14 (Table 6.4), except for the carbonate concentration which is now set to 218 mmol/l. This is the calculated amount from the measured concentration of 600 mmol/l. Simulations are again performed with model 12.
Results and discussion:
Calcium: Simulation results for the fully carbonated sample predict that there is no formation of CSH_1.8 anymore (Figure 6.24). This is in agreement with the decalcification process of CSH. However, simulation results for Ca do not completely fit the experimental results (Figure 6.23a). Probably there are still some unidentified mineral phases left. Silicon and aluminium: Same remarks as for B30 can be made. Experimental and simulated Al leaching profiles are displayed in Figure 6.23b.
1.0E+06 1.0E+05 1.0E+04 1.0E+05 1.0E+03 1.0E+04 1.0E+02 1.0E+01 1.0E+03 1.0E+00 1.0E-01 1.0E+02 experimental experimental 1.0E-02
Ca leached out (mg/kg) leached Ca out model 12 Alleached (mg/kg) out model 12 1.0E+01 1.0E-03 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 pH pH a b
Figure 6.23: Observed and predicted (model 12) Ca (a) and Al (b) leaching profile for sample B60. 71 CHAPTER 6: EXTRACTION TEST MODELLING
0.25 1) Gibbsite
2) Calcite
3) CSH_0.8 2 0.20 4) CSH_1.1
5) CSH_1.8
6) Ferrihydrite 0.15 7) Brucite 1
8) Hydrogarnet 6 4 0.10 9) Ettringite 10) Gypsum total amount (mol) total 3 11) Monosulfo- 8 aluminate 0.05 7 10 9 11 0.00 1 3 5 7 9 11 13 pH
Figure 6.24: Predicted pure-phase assemblage using model 12 for sample B60.
1
1 3 5 7 9 11 13 -1
-3
-5
-7 SI
gypsum ferrihydrite -9 gibbsite Al(OH)3(am) calcite magnesite -11 CSH_0.8 CSH_1.1 brucite ettringite CSH_1.8 -13 portlandite monosulfoaluminate hydrogarnet monocarboaluminate -15 pH
Figure 6.25: Saturation indices as a function of pH as simulated with model 12 for sample B60.
Magnesium: Again, model 12 predicts no magnesite to form and, consequently, Mg solubility is controlled by brucite only (Figures 6.24, 6.25 & 6.26). Unfortunately, there are no experimental data between pH 7.4 and 12.2, but the data between pH 6 and 7.4 might suggest that for the fully carbonated sample, brucite is not the only solubility controlling mineral. The experimental data suggest a decrease in
72 CHAPTER 6: EXTRACTION TEST MODELLING
Mg concentration starting at pH 6, whereas Mg concentration decreases only at pH 9 for model 12 (Figure 6.26). As a test, calcite formation is suppressed. As expected, magnesite now forms and prediction of Mg leaching is better (model 14 in Figure 6.26). Of course, excluding calcite from the model is unrealistic. However, this test demonstrates that Mg leaching might be partially solubility controlled by magnesite, as suggested by van der Sloot (2000), but that all carbonate is used for the formation of calcite, so no magnesite forms. Possibly another solubility constant for magnesite and/or calcite might solve this problem. Therefore, as a second test the magnesite solubility constant is changed until an optimal fit is obtained. This is found for log K = 0.5 (instead of 2.04) (model 12 with log K (magnesite) = 0.5, called model 12* in Figure 6.26). As the results of these tests can not be confirmed by experimental data, these changes are only illustrative and model 12 will be kept for subsequent simulations.
1.0E+05
1.0E+04
1.0E+03
1.0E+02
1.0E+01
1.0E+00
experimental B60 1.0E-01 experimental B0
Mgleachedout (mg/kg) model 12 1.0E-02 model 14 model 12* 1.0E-03 0 2 4 6 8 10 12 14 pH
Figure 6.26: Observed and predicted Mg leaching curves using model 12, model 14 (no calcite) and model 12* (log K (magnesite) = 0.5) for sample B60. Experimental results for sample B0 are also shown.
3.3. General model
Two models were built, one for fresh and another for (partially) carbonated concrete. However, we need to have one model that provides adequate predictions for all samples. A combination of the two models is thus made. The carbonate phases (calcite and magnesite) are included; this will not change the results for the fresh concrete predictions since these phases will simply not form. Hydrogarnet and monocarbo aluminate are both included. Further, portlandite, brucite, amorphous Al(OH) 3, gibbsite, ferrihydrite, monosulfo aluminate, ettringite and gypsum and all CSH phases are included. Of course, not all these phases will be formed, but the model will select the most stable minerals from this list of typical phases as a function of the geochemical conditions. Wairakite is not included because (i) this mineral is only used
73 CHAPTER 6: EXTRACTION TEST MODELLING for fresh samples (Meima and Comans, 1997) and (ii) verification of a model with wairakite is not possible due to a lack of experimental silicon data for the carbonated samples. In short, this means that the general model contains all mineral phases from the fresh and carbonated model, except wairakite and is thus the same as model 12. The mineral phases are summarized in Appendix 1. Figures 6.27 6.30 give an overview of all experimental data and modelling results (leaching curves and pure phase assemblages).
Results and discussion
Sample B0: Leaching curves are more or less the same as those predicted by the fresh concrete model (model 10) (cf. Figure 6.27 & 6.9). The Ca curve is even better as it follows the experimental data more smoothly. The general model predicts all CSH phases to form while the fresh concrete model only included CSH_1.8. This is probably the reason why the Ca curve predicted by the general model is more accurate. Sample B14, B30 and B60: As the general model is in fact the same as the model for carbonated samples, results for B14, B30 and B60 are the same as discussed above. Overall agreement between experimental data and model predictions: The concentration levels of Ca are predicted to within one order of magnitude. For Mg the same can be said, except for the highest pH value. The Al predictions are getting worse as carbonation increases. Overall, predictions are fairly good. Remark: A test run was performed with the same general model but this time including wairakite. Results and remarks are given in Appendix 2.
1.0E+06 1.0E+04 1.0E+05 1.0E+03 1.0E+02 1.0E+04 1.0E+01 1.0E+03 1.0E+00 experimental 1.0E-01 1.0E+02 experimental general model 1.0E-02
Caleached out (mg/kg) general model Mgleached out (mg/kg) 1.0E+01 1.0E-03 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 pH pH a b
0.140 1.0E+05 gibbsite portlandite
1.0E+04 0.120 CSH_0.8 CSH_1.1 CSH_1.8 ferrihydrite 1.0E+03 0.100 1.0E+02 0.080 hydrogarnet 1.0E+01 0.060 1.0E+00 0.040 1.0E-01 brucite experimental ettringite
1.0E-02 amounttotal (mol/L) 0.020 gypsum
Alleached out (mg/kg) general model 1.0E-03 0.000 0 2 4 6 8 10 12 14 1 3 5 7 9 11 13 pH pH monosulfo- c d aluminate Figure 6.27: Observed and predicted (general model) Ca (a), Mg (b) and Al (c) concentrations and predicted pure-phase assemblage (d) for sample B0.
74 CHAPTER 6: EXTRACTION TEST MODELLING
1.0E+06 1.0E+04 1.0E+05 1.0E+03 1.0E+02 1.0E+04 1.0E+01 1.0E+03 1.0E+00 1.0E-01 1.0E+02 experimental experimental 1.0E-02 general model
Ca leached out (mg/kg) leachedCa out general Mgleached out (mg/kg) 1.0E+01 1.0E-03 model 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 pH pH a b 0.140 1.0E+05 gibbsite 0.120 calcite 1.0E+04 CSH_1.1 ferrihydrite CSH_0.8 1.0E+03 0.100 1.0E+02 0.080 hydrogarnet 1.0E+01 0.060 1.0E+00 0.040 1.0E-01 experimental ettringite brucite
1.0E-02 amounttotal (mol/L) 0.020 gypsum
leached outAl (mg/kg) general 1.0E-03 model 0.000 0 2 4 6 8 10 12 14 1 3 5 7 9 11 13
pH pH monosulfo- CSH_1.8 c d aluminate Figure 6.29: Observed and predicted (general model) Ca (a), Mg (b) and Al (c) concentrations and predicted pure-phase assemblage (d) for sample B14.
1.0E+06 1.0E+04
1.0E+05 1.0E+03 1.0E+02 1.0E+04 1.0E+01
1.0E+03 1.0E+00
1.0E-01 1.0E+02 experimental experimental 1.0E-02
Ca leached out (mg/kg) leachedCa out general model general Mgleached out (mg/kg) 1.0E+01 1.0E-03 model 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 pH pH a b
0.160 1.0E+05 calcite 0.140 1.0E+04 gibbsite 0.120 1.0E+03 CSH_0.8 CSH_1.1 ferrihydrite 0.100 1.0E+02 hydrogarnet 1.0E+01 0.080 1.0E+00 0.060 1.0E-01 0.040 experimental ettringite brucite
amounttotal (mol/L) 1.0E-02 0.020 gypsum
Al leached outAl (mg/kg) general 1.0E-03 model 0.000 0 2 4 6 8 10 12 14 1 3 5 7 9 11 13 pH pH monosulfo- CSH_1.8 c d aluminate
Figure 6.28: Observed and predicted (general model) Ca (a), Mg (b) and Al (c) concentrations and predicted pure-phase assemblage (d) for sample B30.
75 CHAPTER 6: EXTRACTION TEST MODELLING
1.0E+06 1.0E+04
1.0E+05 1.0E+03 1.0E+02 1.0E+04 1.0E+01 1.0E+03 1.0E+00 1.0E-01 experimental 1.0E+02 experimental 1.0E-02 general
Ca leached out (mg/kg) leachedCa out general model Mgleached out (mg/kg) 1.0E+01 1.0E-03 model 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 pH pH a b
0.250 1.0E+05 calcite 1.0E+04 0.200 1.0E+03 hydrogarnet 1.0E+02 0.150 gibbsite 1.0E+01 CSH_1.1 ferrihydrite 0.100 1.0E+00 CSH_0.8 1.0E-01 experimental 0.050 ettringite brucite 1.0E-02 amounttotal (mol/L) gypsum
Al leached outAl (mg/kg) general 1.0E-03 model 0.000 0 2 4 6 8 10 12 14 1 3 5 7 9 11 13 monosulfo- pH d pH aluminate c Figure 6.30: Observed and predicted (general model) Ca (a), Mg (b) and Al (c) concentrations and predicted pure-phase assemblage (d) for sample B60.
Trends: To evaluate if observed trends due to carbonation are reflected in the modelling results, all experimental data and modelling results are plotted on Figure 6.31a, c and e, and Figure 6.31b, d and f, respectively. For calcium, decreased leaching as a result of carbonation is well predicted by the model, although less pronounced. Remember that initially the model overestimated this trend (cf. Figure 6.14). Possibly, the correction factor for the carbonate input concentration is too high. Another possibility is that certain Ca containing mineral phases have not yet been identified. For magnesium it was already indicated that the same prediction is obtained for all samples and consequently the decreased Mg leaching as a result of carbonation is not predicted by the model, probably due to the fact that no magnesite is predicted to form. Furthermore, at low pH the model predicts a higher leaching than experimentally observed, which indicates that there is still a minor mineral phase that precipitates at low pH which is not yet included in the model. Finally, for aluminium there is no real trend visible in the experimental data. The modelled curve which is for all samples more or less the same can thus be considered as satisfying.
Carbonation process and distribution of elements: The carbonation process and related decalcification are clearly illustrated (Figure 6.32): initially portlandite as well as all CSH phases are present (B0), then portlandite dissolves (B14) and the amount of CSH_1.8 gradually decreases (B14 and B30) until it is completely dissolved too (B60). The Ca ions that are released this way are used to form calcite. The figures show that the amount of calcite formed increases from B0 to B60. In addition to the decalcification
76 CHAPTER 6: EXTRACTION TEST MODELLING of the CSH gel, part of the monosulfo aluminate and hydrogarnet dissolve to supply extra Ca ions for the formation of calcite. Part of the sulphate released from monosulfo aluminate will contribute to the formation of additional ettringite (Matschei et al., 2007), which is also visible on Figure 6.32. Distribution of aluminium solid species is rather independent of carbonation level (Figure 6.33). However, as part of the hydrogarnet and monosulfo aluminate dissolve, gibbsite is formed at higher pH values. Finally, predictions for magnesium are very simple: Mg is present in solution or precipitated as brucite (Figure 6.34).
1.0E+06 1.0E+06 1.0E+05 1.0E+05
1.0E+04 1.0E+04
1.0E+03 1.0E+03 B00 B00 1.0E+02 B14 1.0E+02 B14 B30 B30 Ca leachedCa out (mg/kg) B60 Ca leached (mg/kg) out B60 1.0E+01 1.0E+01 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 pH pH a b
1.0E+04 1.0E+04
1.0E+03 1.0E+03
1.0E+02 1.0E+02
1.0E+01 1.0E+01 B00 B00 1.0E+00 B14 1.0E+00 B14 B30 B30
Mgleached out (mg/kg) B60 Mgleached out (mg/kg) B60 1.0E-01 1.0E-01 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 pH pH c d 1.0E+05 1.0E+05 1.0E+04 1.0E+04
1.0E+03 1.0E+03
1.0E+02 1.0E+02
1.0E+01 1.0E+01 1.0E+00 1.0E+00 1.0E-01 B00 1.0E-01 B00 B14 B14 1.0E-02 B30 1.0E-02 B30
Al leached (mg/kg) out Al leached (mg/kg) out B60 B60 1.0E-03 1.0E-03 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 pH pH e f Figure 6.31: Comparison of the Ca- (a and b), Mg- (c and d) and Al- (e and f) leaching curves for samples B0, B14, B30 and B60. Experimental data are shown on the left side (a, c and e) and simulated curv es using the general model on the right side (b, d and f).
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B0 B14 200000 200000 180000 180000 CSH_0.8 160000 CSH_1.1 160000 gypsum calcite 140000 gypsum 140000 ettringite 120000 120000 CSH_0.8 monosulfo- CSH_1.1 100000 100000 aluminate hydrogar- net ettringite 80000 80000 Ca in solution Ca in solution Ca (mg/kg) Ca (mg/kg) Ca 60000 60000 monosulfo- aluminate hydrogar- CSH_1.8 40000 40000 net 20000 20000 portlandite CSH_1.8 0 0 1 3 5 7 9 11 13 1 3 5 7 9 11 13 pH pH
B30 B60 200000 200000 180000 180000
160000 160000 gypsum calcite gypsum 140000 140000 calcite
120000 CSH_0.8 120000 ettringite 100000 CSH_1.1 100000 CSH_0.8 80000 80000 Ca in solution ettringite Ca in solution CSH_1.1 Ca (mg/kg) Ca (mg/kg) 60000 60000 monosulfo- 40000 aluminate hydrogar- 40000 net monosulfo- 20000 20000 aluminate hydro- garnet 0 0 1 3 5 7 9 11 13 1 3 5 7 9 11 13 pH CSH_1.8 pH
Figure 6.32: Calcium distribution in solution or mineral phases for each sample predicted with the general model.
B0 B14 35000 35000
30000 30000
25000 25000 hydro- garnet 20000 gibbsite hydro- 20000 garnet gibbsite Al in 15000 15000 Al in solution Al (mg/kg) Al solution (mg/kg) Al 10000 10000 ettringite mono-sulfo- mono-sulfo- 5000 5000 ettringite aluminate aluminate 0 0 1 3 5 7 9 11 13 1 3 5 7 9 11 13 pH pH
B30 B60 35000 35000
30000 30000
25000 25000 hydro- gibbsite garnet 20000 20000 gibbsite Al in Al in hydro- 15000 solution 15000 solution garnet Al (mg/kg) Al Al (mg/kg) Al mono-sulfo- 10000 10000 aluminate
5000 ettringite mono-sulfo- 5000 ettringite aluminate 0 0 1 3 5 7 9 11 13 1 3 5 7 9 11 13 pH pH Al in solution
Figure 6.33: Aluminium distribution in solution or mineral phases for each sample predicted with the general model.
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12000
10000
8000 brucite 6000 Mg in solution 4000 Mg (mg/kg) Mg
2000
0 1 3 5 7 9 11 13 pH Figure 6.34: Magnesium distribution in solution or mineral phases predicted with the general model. Mg predictions are the same for the different samples.
3.4. Remark concerning solid solutions
The binary CSH solid solution model 11 from Kulik and Kersten (2001) consisting of an ideal solid solution with end members amorphous silica and tobermorite I (CSH I solid solution) and another with end members jennite and tobermorite II (CSH II solid solution) is now used instead of the three CSH phases (CSH_0.8, CSH_1.1 and CSH_1.8). The effect of this replacement is investigated for samples B0 and B60.
1.0E+06 1.0E+04
1.0E+05 1.0E+03 1.0E+02 1.0E+04 1.0E+01 1.0E+03 1.0E+00 experimental 1.0E-01 experimental 1.0E+02 model 12 (CSH phases) 1.0E-02 model 12 (CSH phases)
Ca leachedCa out (mg/kg) model 15 (CSH solid solutions)
Mgleached out (mg/kg) model 15 (CSH solid solutions) 1.0E+01 1.0E-03 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 pH pH a b
1.0E+05 1.0E+05 experimental 1.0E+04 model 12 (CSH phases) 1.0E+04 1.0E+03 model 15 (CSH solid solutions) 1.0E+03 1.0E+02 1.0E+01 1.0E+02
1.0E+00 1.0E+01 experimental 1.0E-01 1.0E+00 1.0E-02 model 12 (CSH phases) Si leached out Si (mg/kg) Alleached out (mg/kg) 1.0E-03 1.0E-01 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 pH pH c d Figure 6.35: Comparison between predicted Ca (a), Mg (b), Al (c) and Si (d) leaching curves with the general model (model 12) and model 15 (general model with solid solutions) and the experimental results for sample B0.
11 which was already tested in the solid solution benchmarking study (Chapter 4). 79 CHAPTER 6: EXTRACTION TEST MODELLING
The predicted Si leaching profile does not fit the experimental data (Figure 6.35). This is normal as the Si concentration that was used as input is the total Si concentration minus the Si concentration present in
SiO 2 and other inert minerals (cf. paragraph 3.1). As one of the end members of the CSH I solid solution is amorphous SiO 2, it is not really justified to use the CSH solid solution model for this case. However, predictions for Ca, Mg as well as for Al are as good as those obtained by using the general model with three CSH phases (model 12) (cf. Figure 6.35 and 6.36). For the fresh sample (B0) as well as for the carbonated one (B60), there is a transition from solid solution CSH I to CSH II at pH 10 (Figures 6.37 & 6.38). The amount of CSH I end members formed is more or less the same for the two samples. For CSH II, there are some small differences: more tobermorite II and less jennite is formed for B60. Tobermorite II ((Ca(OH) 2)1.5 (SiO 2)1.8 (H 2O) 1.5 ) has a Ca/Si ratio of 0.83 while this ratio is twice as large (1.67) for jennite ((Ca(OH) 2)1.5 (SiO 2)0.9 (H 2O) 0.9 ). The difference in amounts is thus in agreement with the decalcification process of CSH. Comparison of the pure phase assemblage obtained by the model with and without solid solutions, learns that the same amounts of gibbsite, calcite, ferrihydrite, brucite, hydrogarnet, gibbsite, ettringite and monosulfo aluminate are predicted (cf. Figure 6.37 versus 6.27 and Figure 6.38 versus 6.30).
1.0E+06 1.0E+04
1.0E+05 1.0E+03 1.0E+02 1.0E+04 1.0E+01
1.0E+03 1.0E+00 1.0E-01 1.0E+02 experimental experimental model 12 (CSH phases) 1.0E-02 model 12 (CSH phases)
Ca leached out(mg/kg) leachedCa model 15 (CSH solid solutions) Mgleached out (mg/kg) model 15 (CSH solid solutions) 1.0E+01 1.0E-03 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 pH pH a b
1.0E+05 1.0E+05 experimental 1.0E+04 model 12 (CSH phases) model 15 (CSH solid solutions) 1.0E+04 1.0E+03 1.0E+02 1.0E+03 1.0E+01 1.0E+00 1.0E+02 1.0E-01 1.0E+01 model 12 (CSH phases) 1.0E-02 model 15 (CSH solid solutions) Si leached (mg/kg) out Al Al leached (mg/kg) out 1.0E-03 1.0E+00 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 pH pH c d Figure 6.36: Comparison between predicted Ca (a), Mg (b), Al (c) and Si (d) leaching curves with the general model (model 12) and model 15 (general model with solid solutions) and the experimental results for sample B60.
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0.25
0.20 hydrogarnet
portlandite 0.15 gibbsite Jennite SiO2(am) 0.10 totalamount (mol/L) ferrihydrite Tob-II brucite 0.05 Tob-I
gypsum
0.00 1 3 5 7 9 11 13 monosulfo- pH ettringite aluminate Figure 6.37: Predicted pure-phase and solid solution assemblage using model 15 for sample B0.
0.25
calcite
0.20 hydrogarnet
0.15 gibbsite
SiO2(am) 0.10 total amount (mol/L) total ferrihydrite Tob-II brucite 0.05 Tob-I
gypsum Jennite
0.00 1 3 5 7 9 11 13 monosulfo- pH ettringite aluminate
Figure 6.38: Predicted pure- phase and solid solution assemblage using model 15 for sample B60.
As leached concentrations and amounts of precipitated phases are more or less the same for the model including the three CSH phases and the model using the solid solutions, it can be concluded that although the solid solution model is more realistic (because it enables a gradual transition of the Ca/Si ratio), use of the three CSH phases turns out to be a very good approximation.
81 CHAPTER 6: EXTRACTION TEST MODELLING
3.5. Remark concerning Lothenbach and Winnefeld model
Lothenbach and Winnefeld (2006) developed a model to simulate the hydration of Portland cement. Their model consists of an extended list of pure mineral phases and solid solutions and was implemented in GEM (Kulik, 2002). In Appendix 3, the applicability of this model for the simulation of the extraction tests with PHREEQC is investigated. Model simulations revealed that the Lothenbach and Winnefeld model is not stable in PHREEQC and that it does not provide satisfying results for all samples.
4. Leaching of trace elements
In order to be able to make predictions for the leaching of lead, the general model (model 12) is now extended in three steps: (i) by including lead minerals to account for precipitation/dissolution reactions; (ii) by adding solid solutions; and finally (iii) by adding surface complexation. The data collected in Chapter 5 are used.
4.1. Precipitation/dissolution only
Many authors (e.g. Jing et al., 2004; Halim et al., 2005; cf. Table 3.3) include only very few lead minerals in their model to simulate Pb leaching. Hydroxides and carbonates are the most commonly assumed solid phases. However, this kind of simple model does not (always) provide satisfying results. To illustrate this statement, the limited set of the Pb minerals cerrusite (PbCO 3), hydrocerrusite (Pb 3(CO 3)2(OH) 2) and lead hydroxide (Pb(OH) 2) (used by Jing et al., 2004) is added to our model (model 16). The results for sample B0 and B60, represented in Figure 6.39, show that agreement between experimental data (points) and model 16 predictions (dotted lines) is indeed poor. Not surprisingly, Johnson et al. (1996) state that it is not accurate to assume that the solubility of Pb is controlled by the precipitation of pure carbonates and (hydr)oxides alone.
10000 10000 1000 1000 100 100
10 10
1 experimental 1 experimental model 16 0.1 model 16 0.1
Pb leached out (mg/kg) out leached Pb model 17 model 17 (mg/kg) out leached Pb 0.01 0.01 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 pH pH a b Figure 6.39: Comparison between predicted Pb concentrations using model 16 and model 17 and experimental results for sample B0 (a) and B60 (b).
As such, all Pb minerals from the more extensive list given in Table 3.4 (data presented in Table 5.3) will be considered as potential candidates to control lead solubility (model 17). The effect of adding all these minerals is visualized in Figure 6.39 (solid line). It is clear that these results are much better.
82 CHAPTER 6: EXTRACTION TEST MODELLING
Discussion and remarks:
1) Lead is known to have an amphoteric character (e.g., Sanchez et al., 2002; Garrabrants et al., 2004), i.e. the leaching curve shows a solubility minimum between pH 8 and 10, combined with sharp solubility increases in both acidic and alkaline conditions. This character is shown in the experimental as well as in the model predictions of the extraction test (cf. Figure 6.39). Note that including only lead hydroxide and lead carbonates (model 16) already provides model predictions that reflect the amphoteric behaviour. However, in that case, quantitative match is poor.
2) Published values of the solubility products of Pb(OH) 2 vary between log K = 8.15 and 13.6 (Pierrard et al., 2002). The intermediate value log K = 11, as suggested in the publications of De Windt (e.g. De Windt and Badreddine, 2006; De Windt et al., 2006) is used here. Lower log K values (e.g. log K = 8.15, model 17**) provide much worse results while using a higher log K value (e.g. log K = 13.6, model 17*) provides the same results (Figure 6.40a). If a low log K value is used, Pb(OH) 2 forms at high pH values
(Figure 6.40b), while for high log K values, no Pb(OH) 2 is predicted to form and Pb 2(OH) 3Cl is the controlling solid phase (Figure 6.41a). Pb 2(OH) 3Cl solubility control at alkaline pH and improved model predictions by a using a higher log K value for Pb(OH) 2 (i.e., log K = 12.69) were also reported by Geysen (2004) for MSWI flue gas cleaning residues.
10000 7.E-04
1000 6.E-04 alamosite PbSiO3 Pb(OH)2 100 5.E-04
10 4.E-04 1 3.E-04 0.1 2.E-04 experimental laurionite (PbClOH) 0.01 model 17 or model 17* (mol/l) amount total 1.E-04
Pb leached out (mg/kg) out leached Pb model 17** 0.001 0.E+00 0 2 4 6 8 10 12 14 1 3 5 7 9 11 13 pH pH a b
Figure 6.40: a) Pb leaching curve for sample B0, using different log K (Pb(OH) 2) values; b) Predicted mineral composition using model 17** (log log K (Pb(OH) 2) = 8.15) for sample B0.
7.E-04 7.E-04
6.E-04 alamosite 6.E-04 alamosite PbSiO3 PbSiO3 5.E-04 5.E-04
4.E-04 4.E-04 Pb2(OH)3Cl Pb2(OH)3Cl 3.E-04 3.E-04 2.E-04 2.E-04 laurionite laurionite (PbClOH) (PbClOH) total amount total (mol/l)
1.E-04 total(mol/l) amount 1.E-04 0.E+00 0.E+00 1 3 5 7 9 11 13 1 3 5 7 9 11 13 pH pH a b
Figure 6.41: Predicted mineral composition using model 17 for sample B0 (a) and B60 (b). 83 CHAPTER 6: EXTRACTION TEST MODELLING
3) The solubility controlling minerals predicted by the model are: (i) laurionite (PbClOH) at pH = 6; (ii) alamosite (PbSiO 3) for 6.5 < pH < 9.5; and (iii) blixite (Pb 2(OH) 3Cl) for 10 < pH < 13 (Figure 6.41). It is remarkable that the only (very minor) differences (almost not visible on the figures) between the fresh and carbonated sample are the amounts of the minerals formed. Not one carbonate containing lead mineral
(neither cerrusite, PbCO 3 nor hydrocerrusite, Pb 3(CO 3)2(OH) 2) is predicted to form, while for model 16 hydrocerrusite was predicted to form between pH 7 and 9 (Figure 6.42). If model 17 is used, cerrusite is however only very slightly undersaturated at mildly acidic pH values (Figure 6.44). Maybe defining cerrusite as a solid solution with calcite (CaCO 3) might cause cerrusite to form and improve predictions for the carbonated sample. This will be the subject of next paragraph. Note also that the saturation index of cerrusite is always higher than that of hydrocerrusite (Figure 6.44) in accordance with Meima and Comans (1999) who indicate cerrusite as the most stable Pb carbonate mineral. If only the minerals indicated by Jing et al. (2004) are included (model 16), the model predicts hydrocerrusite instead of cerrusite to form (Figure 6.42). Again this shows the low applicability of this approach.
7.E-04 2 6.E-04 0 Pb(OH)2 1 3 5 7 9 11 13 5.E-04 -2
4.E-04 -4
3.E-04 -6 SI 2.E-04 -8 Hydrocerrusite total amounttotal (mol/l) 1.E-04 -10 Pb(OH)2 Cerrusite 0.E+00 -12 1 3 5 7 9 11 13 Hydrocerrusite -14 pH pH
Figure 6.42: Mineral composition and saturation indices as a function of pH for model 16 (sample B60).
4) Some authors indicate anglesite (PbSO 4) as a solubility controlling mineral in the acidic pH domain for MSWI ash (Eighmy et al., 1995) and for S/S waste (De Windt et al., 2006). However, there is not really agreement in literature. For instance, Meima and Comans (1999), observed undersaturation with respect to anglesite for their MSWI bottom ash samples. The model does not predict anglesite formation, but this mineral is only slightly undersaturated for pH < 6, for the fresh as well as for the carbonated sample (Figures 6.43 & 6.44).
5) De Windt and Badreddine (2006) suggest that at the lowest pH values, lead concentration in S/S waste leachate is not solubility controlled but rather limited by the total Pb content of the waste. This might explain part of the flat curves predicted for the lowest pH values (the part below pH ≈ 2) in Figure 6.39. In Figure 6.45, total Pb concentration and model predictions are plotted together. For low pH values these two curves coincide. This was also clear from predicted phases (Figure 6.41) and saturation indices (Figures 6.43 & 6.44) as below pH 6, no minerals are predicted to form.
84 CHAPTER 6: EXTRACTION TEST MODELLING
1
1 3 5 7 9 11 13 -1
-3
-5
-7 SI
-9 Pb2SiO4 Pb(OH)2 -11 Litharge Laurionite
-13 Anglesite Alamosite Pb2(OH)3Cl -15 pH Figure 6.43: Saturation indices as a function of pH using model 17 (sample B0).
1
1 3 5 7 9 11 13 -1
-3
-5
-7 SI
-9 Pb2SiO4 Pb(OH)2 Litharge -11 Laurionite Anglesite Alamosite -13 Pb2(OH)3Cl Cerrusite Hydrocerrusite -15 pH Figure 6.44: Saturation indices as a function of pH using model 17 (sample B60).
85 CHAPTER 6: EXTRACTION TEST MODELLING
10000 10000
1000 1000 100 100 10 10 experimental 1 experimental 1 model 17 0.1 model 17 0.1 total Pb
(mg/kg) out leached Pb total Pb concentration (mg/kg) out leached Pb concentration 0.01 0.01 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 pH pH a b Figure 6.45: Observed and modelled Pb leaching curve (model 17) and total Pb concentration for sample B0 (a) and B60 (b).
6) Experimental data indicate that carbonation leads to lower Pb leaching for pH < 7 and pH > 11 (at 7 < pH < 11 the trend is not clear since there is only one measurement for the carbonated sample, cf. Figure 6.46). As there is almost no difference between model predictions for the fresh and carbonated sample (Figure 6.46), no explanation for the phenomenon can be given at this stage.
10000
1000
100
10 experimental B0 1 experimental B60 B0 predicted with Pb leached outPb leached (mg/kg) 0.1 model 17 B60 predicted with model 17 0.01 0 2 4 6 8 10 12 14 pH
Figure 6.46: Observed and predicted (model 17) Pb leaching curves for the fresh (B0) and carbonated (B60) sample.
2 7) Modelling indicates that aqueous hydroxide complexes, Pb(OH) 2, Pb(OH) 3 and Pb(OH) 4 are the main aqueous species under alkaline conditions (Figure 6.47 and 6.48). This is in agreement with modelling results of Halim et al. (2005) and Malviya and Chaudhary (2006a) who observed Pb(OH) 2 and Pb(OH) 3 + to be the main aqueous species at alkaline pH. At pH < 10, the chloride species PbCl 3 , PbCl 2, PbCl and 2 + PbCl 4 dominate. For the carbonated Pb aqueous species, PbHCO 3 dominates at pH ≤ 6 while PbCO 3 is the dominating aqueous species at pH > 6.
86 CHAPTER 6: EXTRACTION TEST MODELLING
1.E-01 1.E-03 1.E-05 1.E-07 1.E-09 1.E-11 1.E-13 1.E-15 1.E-17 1.E-19 1.E-21 1.E-23 1.E-25 molality molality (mol/l)
1.E-27 Pb+2 PbOH+ 1.E-29 Pb(OH)2 Pb(OH)3- 1.E-31 Pb(OH)4-2 Pb2OH+3 1.E-33 PbCl+ PbCl2 1.E-35 PbCl3- PbCl4-2 1.E-37 PbSO4 Pb(SO4)2-2 1.E-39 0 2 4 6 8 10 12 14 pH
Figure 6.47: Predicted concentration of aqueous species for B0 (model 17).
1.E-01 1.E-03 1.E-05 1.E-07 1.E-09 1.E-11 1.E-13 1.E-15 1.E-17 1.E-19 1.E-21 1.E-23 1.E-25 molality(mol/l) 1.E-27 Pb+2 PbOH+ Pb(OH)2 1.E-29 1.E-31 Pb(OH)3- Pb(OH)4-2 Pb2OH+3 1.E-33 PbCl+ PbCl2 PbCl3- 1.E-35 PbCl4-2 PbSO4 Pb(SO4)2-2 1.E-37 PbCO3 Pb(CO3)2-2 PbHCO3+ 1.E-39 0 2 4 6 8 10 12 14 pH
Figure 6.48: Predicted concentration of aqueous species for B60 (model 17).
87 CHAPTER 6: EXTRACTION TEST MODELLING
4.2. Precipitation and solid solutions 4.2.1. Solid solution of calcite and cerrusite
If the ideal (Ca,Pb)CO 3 solid solution is added to the model (model 18), predictions for the carbonated sample improve considerably (Figure 6.49a). With this kind of formulation, the model predicts cerrusite to form for pH > 4.5 (Figure 6.49b). Remember that in the previous paragraph (model 17), cerrusite never formed, which might indicate that cerrusite does not form as a pure solid but only as a solid solution with calcite. This seems realistic as in natural systems, pure minerals are the exception rather than the rule (cf. Chapter 2).
10000 7.E-04 cerrusite in 1000 6.E-04 SS alamosite PbCO3(SS) PbSiO3 5.E-04 100 4.E-04 10 Pb2(OH)3Cl 3.E-04 1 2.E-04 experimental 0.1 model 17 total amount (mol/l) amount total 1.E-04
Pb leached out (mg/kg) out leached Pb model 18 0.01 0.E+00 0 2 4 6 8 10 12 14 1 3 5 7 9 11 13 a pH pH b Figure 6.49: a) Observed and predicted Pb leaching curves for sample B60 using model 17 and model 18, and b) predicted mineral composition with model 18 for sample B60.
Mole fractions of the two solid solution end members are shown in Figure 6.50. The total amount of Pb present in the system is 0.49 mmol/l, while the amount of Ca is equal to 476 mmol/l (cf. Table 6.1).
Therefore, it is logic that the mole fraction of calcite in the (Pb,Ca)CO 3 solid solution is always greater than 99%.
0.40 100 0.35 99 98 0.30 97 0.25 96 0.20 95 0.15 94 0.10 93 Mole fraction, % Mole % fraction, 92 0.05 91 0.00 90 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 a pH b pH Figure 6.50: Mole fraction of cerrusite (fig a) and calcite (fig b) in (Pb,Ca)CO 3 solid solution predicted with model 18 (for sample B60). The formation of cerrusite in a solid solution with calcite provides a possible explanation for the lower Pb leaching from the carbonated sample at pH < 8. It is also interesting to note that saturation indices of all Pb minerals are affected by addition of the
(Pb,Ca)CO 3 solid solution to the model (Figure 6.51 in comparison with Figure 6.44). In general, it can be
88 CHAPTER 6: EXTRACTION TEST MODELLING said that saturation indices are the same or smaller. Hydrocerrusite, for instance, has become much more undersaturated by the precipitation of cerrusite in a solid solution.
1
1 3 5 7 9 11 13 -1
-3
-5
-7 SI
-9 Pb2SiO4 Pb(OH)2 -11 Litharge Laurionite Anglesite -13 Alamosite Pb2(OH)3Cl Hydrocerrusite -15 pH
Figure 6.51: Saturation indices as a function of pH using model 18 (sample B60).
4.2.2. Solid solution of gibbsite, ferrihydrite and litharge The second solid solution that is added to the model is the ternary ideal solid solution consisting of end members gibbsite (Al(OH) 3), ferrihydrite (Fe(OH) 3) and litharge (PbO) (model 19). Model results are only marginally improved (Figures 6.52a & 6.53a). It is again observed, that a mineral (litharge in this case) that did not form as a pure solid (cf. paragraph 4.1), does form as member of a solid solution (Figures 6.52b & 6.53b).
10000 7.E-04 alamosite 1000 6.E-04 litharge in SS PbSiO3 PbO (SS) 100 5.E-04 10 4.E-04
1 3.E-04
0.1 experimental 2.E-04 laurionite Pb2(OH)3Cl model 18 (PbClOH) 0.01 (mol/l) amount total 1.E-04 model 19 Pb leached out (mg/kg) out leached Pb 0.001 0.E+00 0 2 4 6 8 10 12 14 1 3 5 7 9 11 13 pH pH a b Figure 6.52: a) Observed and predicted Pb leaching curves for sample B0 using model 18 and model 19, and b) predicted mineral composition with model 19 for sample B0.
89 CHAPTER 6: EXTRACTION TEST MODELLING
10000 7.E-04
6.E-04 1000 cerrusite alamosite Litharge in SS PbCO3 PbO (Ss) 5.E-04 PbSiO3 100 4.E-04 10 3.E-04 1 2.E-04 experimental Pb2(OH)3Cl 0.1 model 18 (mol/l) amount total 1.E-04
(mg/kg) out leached Pb model 19 0.01 0.E+00 1 3 5 7 9 11 13 0 2 4 6 8 10 12 14 a pH pH b
Figure 6.53: a) Observed and predicted Pb leaching curves for sample B60 using model 18 and model 19, and b) predicted mineral composition using model 19 for sample B60.
4.3. Precipitation, solid solutions and surface complexation
Last step in the improvement of Pb leaching modelling is the addition of surface complexation to the model. The model is first run under the assumption that HFO are the only sorbent minerals and second, under the assumption that HFO as well as AAM play a role in the sorption process. 4.3.1. Surface complexation on hydrous ferric oxides If only HFO are included as possible sorbent minerals (model 20), there are only very minor differences with the Pb leaching predictions obtained with model 19. This small differences are almost invisible on a logarithmic plot and are therefore not shown. Surface complexation reactions and accompanying log K values for sorption on HFO present in the PHREEQC database were used in this calculation. However, Meima and Comans (1998a) mention that it is better to replace the log K value of 0.3 for surface complexation on the weak sites by the higher value of 1.7 to be consistent with the general trend of an approximately 3 log unit difference between the sorption constants for the high and low affinity sites (Dzombak and Morel, 1990). After modifying the database this way, slightly better modelling predictions are obtained (Fig. 6.54a, model 20*) for the uncarbonated sample. For the carbonated sample, differences are still too small to be visible on a logarithmic plot. This indicates that surface complexation is more important for uncarbonated samples, which was already clear from the HFO adsorption parameters, which are up to one order of magnitude greater for the uncarbonated samples (cf. Chapter 5). Cornelis et al. (2006) state that during weathering, HFO are transformed to more crystalline iron oxides. The latter are not measured by ascorbate extraction, which might explain the difference in HFO amount. 4.3.2. Surface complexation on amorphous aluminium minerals If AAM are added as sorbent minerals, model predictions improve. With log K = 0.3 for surface complexation on the weak sites (model 21), small improvement is obtained, while using log K = 1.7 (model 21*) provides considerably better results (Figure 6.54). This is the same trend as observed for HFO, but much more pronounced. From Figure 6.54 it is clear that the influence of AAM adsorption is much stronger than that of HFO. There is even a substantial influence on the carbonated sample now (Figure 6.54b). However, it was already indicated that AAM is assumed to have a lower surface area
90 CHAPTER 6: EXTRACTION TEST MODELLING
(Chapter 4). As such, using HFO as a “surrogate” for AAM, might overestimate the effect of AAM sorption. A test run was thus performed, using a surface area of 411 m²/g instead of 600 m²/g for AAM (this is the value reported by Fan et al., 2005). This had no significant influence. The small differences were even not visible on a logarithmic plot and are therefore not shown. Consequently, it can be concluded that surface complexation on AAM has the strongest influence on Pb leaching.
10000 10000 1000 1000
100 100
10 10
1 1 experimental experimental model 19 model 20* model 19
0.1 Pb (mg/kg) leached 0.1 model 21 model 21 Pb leachedPb (mg/kg) out model 21* model 21* 0.01 0.01 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 a pH b pH
Figure 6.54: Observed and modelled Pb leaching curves (model 19, 20*, 21 and 21*) for sample B0 (a) and B60 (b).
4.3.3. Discussion and remarks 1) As surface complexation has a considerable influence on the Pb leaching curves, it is advisable to check the experimental determined adsorption data by comparing them with data reported in literature. However, no data for cement/waste mixtures could be found. In Table 6.5 the data for the fresh sample are compared with data reported by Meima and Comans (1998b) for fresh MSWI bottom ash. It is clear that our values are lower, but globally, within the same order of magnitude. Bottom ash only constitutes 57 wt% of our cement/waste matrix, which is probably the reason for the (small) difference.
Table 6.5: Surface complexation data for HFO + AAM. Own experimental data Meima and Comans, 1998b (fresh sample B0) (sample of fresh MSWI bottom ash) HFO+AAM content (g/l) 4.38 5.06 Total number of strong sites (mol/l) 2.46*10 4 2.84*10 4 Total number of weak sites (mol/l) 9.84*10 3 1.14*10 2
2) In an ascorbate extraction only the amount of amorphous Fe oxides (and thus not the more crystalline forms) is measured. HFO might have partially transformed to crystalline Fe oxides, especially for the carbonated sample. Crystalline iron oxides have a less strong adsorption capacity due to their much lower specific surface area (e.g., Dijkstra et al. (2006) mention a surface site density of 600 m²/g for the amorphous and 100 m²/g for the crystalline iron oxide surfaces). However, this adsorption capacity might still be significant. As such, the ascorbate extraction might provide an underestimation of the total adsorption capacity. van der Sloot et al. (2005) performed a dithionite extraction, in which the amorphous as well as the crystalline iron (hydr)oxides are measured (Kostka and Luther, 1994), instead of an ascorbate extraction to determine the amount of HFO needed to model surface complexation for their
91 CHAPTER 6: EXTRACTION TEST MODELLING waste matrix. The same approach was used by Dijkstra et al. (2006) for their MSWI bottom ash samples. As there was no time left to perform a dithionite extraction, as a simple test, a new model run is performed under the assumption that all Fe is present as Fe oxides (model 21**). This amount of Fe is added to the measured amount of amorphous aluminium minerals to obtain the total amount of HFO. Results are shown in Figure 6.55 and are still better than all previous model results. As a last test, the same assumption can be made for Al (i.e. all Al present as AAM) (model 21***). It is clear that these calculations are overestimations, as (i) part of the Fe and Al will dissolve, and (ii) for adsorption on crystalline surfaces, a lower value for the surface site density must be used (as mentioned above, Dijkstra et al. (2006), for example, use a surface site density of 600 m²/g for the amorphous and 100 m²/g for the crystalline surfaces). However, results obtained with the simplified test case almost perfectly simulate the experimental data, especially for the fresh sample (Figure 6.55) which indicates that it might be interesting to perform a dithionite extraction in the future in order to investigate the influence of adsorption on crystalline surfaces thoroughly.
10000 10000 1000 1000
100 100
10 10
1 experimental 1 model 21* experimental model 21*
0.1 Pb (mg/kg) leached 0.1 model 21** model 21**
Pb outleached (mg/kg) model 21*** model 21*** 0.01 0.01 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 a pH b pH Figure 6.55: Influence of surface complexation using different data sets on the Pb leaching curve for sample B0 (a) and B60 (b).
3) There is competition for sorption sites on the (hydr)oxide minerals among the metals and between the metals and, in particular, the major elements Ca and SO 4 (Dijkstra et al., 2002). This is illustrated (Figure 6.56) by removing the surface complexation reactions for all elements, except for Pb, from the database (model 21****).
10000 1000 100 10 1 0.1 0.01 0.001 experimental model 21* 0.0001 Pb leached out (mg/kg) model 21**** 0.00001 0 2 4 6 8 10 12 14 pH
Figure 6.56: Illustration of the effect of competition for sorption sites (sample B0).
92 CHAPTER 6: EXTRACTION TEST MODELLING
5. Conclusion
In this chapter, a model was developed to simulate the extraction test. This model is able to predict the concentration levels of the major elements (Ca, Mg and Al) leached from fresh, partially and fully carbonated samples to within one or two orders of magnitude. These results were achieved by incorporating only equilibrium precipitation/dissolution reactions, which indicates that leaching of major elements is mainly solubility controlled. For each major element, the main mineral phase(s) controlling solubility were identified. Moreover, the carbonation process and related decalcification are very clearly illustrated by the model predictions. The small remaining deviations between data and model predictions may be due to: (i) 24 h was possibly not sufficient to reach a complete equilibrium state which involves better model predictions might be obtained by including kinetics; (ii) uncertainties on thermodynamic data; (iii) mixing thermodynamic data from different sources involves a risk that the newly compiled database becomes internally inconsistent; (iv) still unidentified minerals. Further modelling indicated dissolution and precipitation of carbonates and (hydr)oxides are not sufficient to explain pH dependent lead leaching. Taking into account more minerals in a first step, and formation of solid solutions in a second step, improved model predictions. The formation of cerrusite as end member of a calcite cerrusite solid solution was put forward as explanation for the observed decreased Pb leaching from the carbonated sample. In a last step, surface complexation on HFO and AAM was added, which further improved model predictions. It was found that there is a strong dependency of the model results on the amount of Fe and Al (hydr)oxides. This indicates that the measured input parameters used for adsorption modelling represent a source of uncertainty that warrants careful consideration. It was proposed to perform a dithionite extraction in order to obtain more information concerning the proportion of crystalline and amorphous iron oxides. Moreover, taking HFO as a ‘surrogate’ for AAM in the model may also introduce uncertainty. Modelling results also indicated that AAM are more important than HFO in the retention of lead. A last remark is that DOC complexation as well as lead binding by CSH or ettringite probably also influence Pb leaching but could not be included, since needed data are not available and/or the mechanisms are not clear. However, model results correctly simulate the amphoteric Pb leaching profile ánd agree relatively well with the whole experimental data set.
93 CHAPTER 7: DIFFUSION TEST MODELLING
1. Introduction
To model the diffusion test, coupling between geochemical processes and transport is needed. Geochemical processes were already identified and included in the model for the extraction tests (Chapter 6). As such, this model only needs to be extended by bringing into account diffusive transport. In literature, Park and Batchelor (2002), Garrabrants et al. (2003), Islam et al. (2004) and Tiruta Barna et al. (2005) addressed the modelling of dynamic leaching tests on monolithic cement based waste, but only in one dimension. To our knowledge, only De Windt and Badreddine (2006) developed a three dimensional model. In this chapter, an attempt is undertaken to create a three dimensional model for simulation of the diffusion test, by using the created 3D diffusion file (Chapter 4).
2. Leaching of sodium and potassium
In the extraction test, pH was regulated by addition of HNO 3. In the PHREEQC file, pH was fixed to the desired values (i.e. pH was varied between 1 and 13 with steps of 0.5). In the diffusion test, there is no pH control. As such, leaching of Na and K ions may have a significant influence on the pH level (as these ions are charge balanced by hydroxide ions) and thus need to be predicted accurately. As no Na and K containing minerals were included in the extraction test model, the maximum leachable concentration (determined by performing a NEN7341 test, Van Gerven (2005)) will be used as input value (i.e., 2720 and 2613 mg/kg, respectively for sample B0) and the approach of Wang (2006) is used: the log K values of alkali oxides (Table 7.1) are chosen so that dissolution of Na 2O and K 2O in pore water will result in dissolved Na and K concentrations of 0.14 and 0.37 molal respectively. These are the experimental observed average Na and K concentrations in an OPC pore fluid (Brouwers and van Eijk, 2003).
Table 7.1: Thermodynamic data. Reaction log K llnl database log K used (Wolery, 1992) (Wang, 2006) + + Na 2O + 2 H = H 2O + 2 Na 67.43 24.94 + + K2O + 2 H = H 2O + 2 K 84.04 25.71
Furthermore, the approach of De Windt and Badreddine (2006) is followed: the effective diffusion coefficient, De is fit by modelling the cumulative release profile of sodium. The diffusion coefficient of Na is then assigned to all of the other elements as an approximation. Figure 7.1 reports a sensitivity analysis 12 11 on De ranging from 3*10 to 3*10 m²/s. The release of Na is clearly too fast with the largest value and too slow with the smallest one. The best fit is obtained with an intermediate value of 8*10 12 m²/s. This is larger, but in the same order of magnitude as the best fit obtained by De Windt and Badreddine (2006), i.e. 12 De = 3*10 m²/s. Values in the same order of magnitude have also been reported for Na, K, Ca and Pb (Tiruta Barna et al., 2005) and for Cl (MacCarter et al., 2000) for the same class of materials.
94 CHAPTER 7: DIFFUSION TEST MODELLING
35000
30000
25000
20000
15000
experimental 10000 3*10^-11
cumulative release of Na (mg/m²) Na releaseofcumulative 5000 8*10^-12 3*10^-12 0 0 50 100 150 200 250 time (days)
Figure 7.1: Sensitivity of the cumulative release of sodium for sample B0 with respect to the effective diffusion -11 -12 -12 coefficient: De = 3*10 m²/s, De = 8*10 m²/s and De = 3*10 m²/s.
The cumulative release of potassium is then reported in Figure 7.2. Model predictions approach the experimental results. Sodium and potassium are mainly present in the pore fluid whose stock is progressively exhausted by diffusion, explaining the plateau in the release profiles (Figures 7.1 and 7.2).
Effect of carbonation Experiments revealed that cumulative leaching of sodium and potassium in the diffusion test decrease as the degree of carbonation of the samples progresses (experimental data shown in Figure 7.3). This is assumed to be caused by the decrease of porosity as a result of carbonation (Van Gerven, 2005). As such, the model is run for sample B60 using the lower porosity value measured by Van Gerven (2005) (and given in Chapter 5). The model predictions accurately reflect the observed trend (Figure 7.3), although, in particular for potassium, no quantitative correlation exists between model predictions and experimental data (Figure 7.3b). The leached amount of Na and K as predicted by the model is smaller for the carbonated sample, but still too high. This lack of quantitative match is probably the consequence of using the same value for the effective diffusion coefficient for B0 and B60. As porosity decreases, De will decrease too and thus a smaller value must be used for B60. More information about De is needed in order to obtain better model predictions.
95 CHAPTER 7: DIFFUSION TEST MODELLING
35000
30000
25000
20000
15000
10000
cumulative releasecumulative Kof (mg/m²) 5000 experimental 8*10^-12
0 0 50 100 150 200 250 time (days)
Figure 7.2: Cumulative release of potassium (sample B0).
30000 35000 30000 25000 25000 20000 20000 experimental B60 15000 predicted B60 15000 experimental B0 predicted B0 10000 experimental B0 10000 predicted B0 5000 experimental B60 5000 predicted B60 cumulativerelease (mg/m²) of K
cumulativerelease of(mg/m²) Na 0 0 0 50 100 150 200 250 0 50 100 150 200 250 a time (days) b time (days)
Figure 7.3: Observed and predicted cumulative release of Na (a) and K (b) for samples B0 and B60.
3. Leaching of calcium and lead
In the extraction test, equilibrium is assumed to be obtained and the leaching process is referred to as being solubility controlled. In the diffusion test, leachate percolation occurs at the surface of the solid material, causing molecular diffusion to be the dominant process determining contaminant release from the waste. In this case, the leaching process is said to be diffusion controlled (Sabbas et al., 2003). As such, in a first step only diffusion is included by using our 3D diffusion file and leaving out any geochemical reaction. Therefore, the same approach as used for Na and K is applied, i.e. the maximum leachable concentrations instead of the total concentrations are used as input. To determine the maximum leachable concentration that was used as input value, a NEN7341 test was performed by Van Gerven (2005). In this test the sample is leached for 3 hours at pH 7 and afterwards for 3 hours at pH 4. The
96 CHAPTER 7: DIFFUSION TEST MODELLING obtained concentrations are assumed to be the maximum leachable concentrations and these are used as input. Model predictions and experimental data are shown in Figure 7.4. The predicted calcium release is too high, but the shape of the curve seems realistic. For lead, the predicted cumulative release at the end of the test is the same as the measured value. However, looking back at Chapter 6, the results of the extraction test already showed that for Ca, leaching is one to two orders of magnitude higher at pH 4 and 7 than at pH 13 (i.e. the pH relevant for the fresh sample B0) (Figure 6.27a). As such, using this too high value without including reactions provides a possible explanation for the predicted too high release of Ca (Figure 7.4). For lead however, due to its amphoteric character, leaching at high and low pH values are both elevated 12 , which might explain why in this case using the maximum leachable concentration obtained with the NEN7341 test, gives a fairly good approximation of the cumulative release of lead at high pH. Other reasons for inaccuracy might again lie in the uncertainty on the value used for De.
45000 10 40000 9 35000 8 30000 7 6 25000 5 20000 4 15000 3 10000 2 experimental experimental 5000 1 predicted predicted
0 cumulative release Pb of (mg/m²) 0 cumulative release Ca of (mg/m²) 0 50 100 150 200 250 0 50 100 150 200 250 a time (days) b time (days)
Figure 7.4: Observed and predicted Ca (a) and Pb (b) cumulative release for sample B0 (diffusion only).
Next step is to couple chemical and transport processes. Therefore, the pore water is assumed to be in thermodynamic equilibrium with the minerals included in the extraction test model and the total concentrations are used as input values. However, while using the (high) total concentrations as input did work well for the extraction test, PHREEQC is not able to converge when they are used in combination with the transport part. Therefore the input is defined as solid phases. For sample B0 this means that all Si is assumed to be present as CSH_1.8, all Mg as brucite, all Al as hydrogarnet and Ca as portlandite 13 (these are the phases that were predicted to form at pH 13 for sample B0 by the extraction test model). These solids are then brought into contact with the pore water so that they can (partially) dissolve to reach equilibrium. Model predictions are compared to the experimental data in Figure 7.5. Calcium cumulative release is predicted to within one order of magnitude. This might seem acceptable but, this is worse than the predictions that were obtained by only considering diffusion (Figure 7.4a). This might indicate that there is probably still a problem with the coupling between chemical reactions and transport in the
PHREEQC file. Moreover, the uncertainty on the value used for De remains. Last remark is the
12 Lead leaching at pH 4 is however higher than at pH 13, but leaching at pH 7 is extremely low. It seems that by leaching 3 hours at pH 7 and 3 hours at pH 4, a good approximation for leaching at pH 13 is obtained. 13 By defining all Si as CSH_1.8 and all Al as hydrogarnet, there is already some Ca defined too. The amount of portlandite is thus the total amount of Ca minus the Ca already present in CSH_1.8 and hydrogarnet. 97 CHAPTER 7: DIFFUSION TEST MODELLING observation that carbon dioxide from the atmosphere interfered with leaching of the uncarbonated sample in the diffusion test by a decrease of pH and precipitation of carbonates (Van Gerven, 2005). In the model simulation for sample B0, however, it was assumed that there is no carbonate present. This might also explain some deviations between modelled and experimental data.
1000000 1000000
100000 100000 10000 10000 1000 1000 100 100 10 10 experimental B60 experimental B0 1 1 predicted B0 predicted B60 cumulative release of (mg/m²) Ca
cumulative release of (mg/m²) Ca 0.1 0.1 0 50 100 150 200 250 0 50 100 150 200 250 time (days) time (days) a b Figure 7.5: Observed and predicted Ca cumulative release for sample B0 (a) and B60 (b) (chemical and transport processes included).
4. Conclusion
In this chapter, the diffusion test was modelled. A step wise approach was followed. Sodium and potassium leaching have a significant influence on pH and were therefore modelled first. The effective diffusion coefficient was chosen by performing a sensitivity analysis on the cumulative release of sodium. Good predictions were obtained for the cumulative release of sodium and potassium from the fresh sample. Moreover, the effect of carbonation on sodium and potassium leaching was predicted qualitatively. To obtain a better quantitative match, more information about the effective diffusion coefficient is needed. In a next step, the cumulative release of calcium and lead was considered. For lead, good predictions were obtained by including only diffusion and using the maximum leachable amount as input. Calcium release was overestimated. In a last step, diffusion and chemical processes were coupled. Calcium release is then predicted within one order of magnitude. This is however worse than the predictions that were obtained by only considering diffusion, indicating that there is still place for some further improvement.
98 CHAPTER 8: CONCLUSION AND PERSPECTIVES FOR FUTURE RESEARCH
Ordinary Portland Cement mainly consists of CSH phases, portlandite, sulphate minerals, hydrated calcium aluminates and ferrites, and magnesium phases. During carbonation, portlandite dissolves, the Ca/Si ratio of the CSH gel lowers and carbonate minerals are formed. Cement and municipal solid waste incinerator bottom ash have a lot of minerals in common. Lead can be either precipitated in a pure mineral due to exceeding of the solubility product, included in minerals as a solid solution, or adsorbed to the solid phases by surface complexation. A benchmarking study proved that both the GEM and PHREEQC geochemical modelling code are able to handle solid solutions. In a second benchmarking study, a PHREEQC input file to model three dimensional diffusional transport was constructed and approved for different test cases. In order to allow the modelling of dissolution/precipitation reactions and solid solution formation in PHREEQC, chemical formulas, dissolution reactions and equilibrium constants are required. These were obtained from literature. Surface complexation modelling on the other hand, requires data for following parameters: (i) specific surface area, (ii) concentrations of binding sites, and (iii) available sorbent mineral concentrations. The first two can be found in literature, the latter can be obtained by performing selective chemical extractions. Based on all above information, a model was developed to simulate the extraction test. A general model which is applicable to fresh, partially and fully carbonated cementitious waste samples was constructed. The model is able to predict the leached concentrations of the major elements (Ca, Mg and Al) to within one or two orders of magnitude. These results were achieved by incorporating only equilibrium precipitation/dissolution reactions, which indicates that leaching of major elements is mainly solubility controlled. For each major element, the main mineral phase(s) controlling solubility were identified. The pH dependent leaching of aluminium is adequately described over a large pH range by the solubility behaviour of gibbsite. Magnesium is mainly controlled by brucite. However, magnesium predictions became worse for the completely carbonated sample which suggests (an) additional mineral(s) control(s) solubility. A test case revealed that beside brucite, magnesite is the most probable mineral controlling solubility for the carbonated sample. For calcium, a larger set of minerals is needed to simulate Ca leaching accurately. Portlandite, a CSH phase and calcite are minimally required. For silicon, acceptable results were obtained for the fresh sample by addition of the zeolite wairakite to the model. However, this mineral has not (yet) been observed in bottom ash. Although wairakite is probably not the exact mineral present in reality, the better agreement between the model curve and the data may point to the presence of other zeolites. As such, further research is needed. Calcium leaching decreases as carbonation proceeds. This process was accurately simulated by the model. In order to obtain a further improvement of the model predictions, different steps can be undertaken: (i) including kinetics as 24 h was possibly not sufficient to reach a complete equilibrium state, (ii) setting up an internal completely consistent thermodynamic database, and (iii) search for still unidentified minerals. Further modelling indicated dissolution and precipitation of carbonates and (hydr)oxides are not sufficient to explain pH dependent lead leaching. Taking into account more minerals improved the
99 CHAPTER 8: CONCLUSION AND PERSPECTIVES FOR FUTURE RESEARCH model predictions. However, modelling predictions revealed that leaching of Pb is not controlled by dissolution/precipitation of pure lead containing minerals only. Beside dissolution/precipitation, adsorption and solid solution formation are suggested as controlling mechanisms for lead leachability. Addition of formation of solid solutions (i.e. calcite cerrusite and gibbsite ferrihydrite litharge solid solutions) and surface complexation on hydrous ferric oxides and amorphous aluminium minerals further improved model predictions. Carbonation decreases lead leaching. This effect was correctly predicted by the model. The formation of cerrusite as end member of a calcite cerrusite solid solution was put forward as explanation for the decreased lead leaching from the carbonated sample. Modelling results also indicated that AAM are more important than HFO in the retention of lead. Some suggestions for the further improvement of the model predictions for the lead leaching profile are: (i) measure DOC content in order to make it possible to include Pb DOC complex formation in the model, and (ii) perform a dithionite extraction in order to investigate the influence of surface complexation on crystalline iron oxides. However, model results correctly simulate the amphoteric Pb leaching profile with a solubility minimum between pH 8 and 10 and a significant increase of solubility in both acidic and alkaline conditions ánd they agree relatively well with the whole experimental data set. In a last chapter, the diffusion test was modelled. The effective diffusion coefficient was chosen by performing a sensitivity analysis on the cumulative release of sodium. Good predictions were obtained for the cumulative release of sodium and potassium from the fresh sample. Moreover, the effect of carbonation on sodium and potassium leaching was predicted qualitatively. To obtain a better quantitative match, it is advised to search for more information about the effective diffusion coefficient and to use a different value for each element. For lead, good model predictions were obtained by including only diffusion and using the maximum leachable amount as input. Calcium release was overestimated. If diffusion and chemical processes are coupled, calcium release is predicted within one order of magnitude, which is however worse than the predictions that were obtained by only considering diffusion. As such, it can be concluded that a lot of progress is made in modelling of the diffusional transport. The created file for three dimensional diffusion modelling works well but some more research is needed to refine the coupling between geochemical and transport processes.
100
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Appendices
Appendix 1: Overview of the minerals included in each model Model 1: ‘simple model’ Portlandite, CSH_1.8, hydrogarnet, hydrotalcite
Model 2: model 1 without CSH_1.8 Portlandite, hydrogarnet, hydrotalcite
Model 3: model 1 with amorphous SiO 2 Portlandite, CSH_1.8, hydrogarnet, hydrotalcite, amorphous SiO 2
Model 1* : includes same minerals as model 1, but with Si = 106 mmol/l instead of 917 mmol/l
NOTE: In all subsequent models Si = 106 mmol/l is used.
Model 4: model 1 with illite Portlandite, CSH_1.8, hydrogarnet, hydrotalcite, illite
Model 5: model 1 with brucite Portlandite, CSH_1.8, hydrogarnet, hydrotalcite, brucite
Model 6: model 1 with brucite instead of hydrotalcite Portlandite, CSH_1.8, hydrogarnet, brucite
Model 7: model 6 with gibbsite and amorphous Al(OH)3 Portlandite, CSH_1.8, hydrogarnet, brucite, gibbsite, amorphous Al(OH) 3
Model 8: model 7 with ferrihydrite Portlandite, CSH_1.8, hydrogarnet, brucite, gibbsite, amorphous Al(OH) 3, ferrihydrite
Model 9: model 7 with microcrystalline and amorphous Fe(OH) 3 Portlandite, CSH_1.8, hydrogarnet, brucite, gibbsite, amorphous Al(OH) 3, microcrystalline Fe(OH) 3, amorphous Fe(OH) 3
Model 10: fresh concrete model Portlandite, CSH_1.8, hydrogarnet, brucite, gibbsite, amorphous Al(OH) 3, ferrihydrite, ettringite, gypsum, monosulfo aluminate
Model 11: model 10 with wairakite Portlandite, CSH_1.8, hydrogarnet, brucite, gibbsite, amorphous Al(OH) 3, ferrihydrite, ettringite, gypsum, monosulfo aluminate, wairakite
Model 12: model for carbonated samples = general model Portlandite, CSH_1.8, hydrogarnet, brucite, gibbsite, amorphous Al(OH) 3, ferrihydrite, ettringite, gypsum, monosulfo aluminate, calcite, magnesite, monocarbo aluminate, CSH_0.8, CSH_1.1
Model 13: model 12 without gibbsite Portlandite, CSH_1.8, hydrogarnet, brucite, amorphous Al(OH) 3, ferrihydrite, ettringite, gypsum, monosulfo aluminate, calcite, magnesite, monocarbo aluminate, CSH_0.8, CSH_1.1
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Model 14: model 12 without calcite Portlandite, CSH_1.8, hydrogarnet, brucite, gibbsite, amorphous Al(OH) 3, ferrihydrite, ettringite, gypsum, monosulfo aluminate, magnesite, monocarbo aluminate, CSH_0.8, CSH_1.1
Model 12* : includes same minerals as model 12, but with log K (magnesite) = 0.5
Model 15: general model with CSH solid solutions Portlandite, hydrogarnet, brucite, gibbsite, amorphous Al(OH) 3, ferrihydrite, ettringite, gypsum, monosulfo aluminate, calcite, magnesite, monocarbo aluminate + CSH solid solutions
Model 16: general model with lead minerals used by Jing et al. (2004) Portlandite, CSH_1.8, hydrogarnet, brucite, gibbsite, amorphous Al(OH) 3, ferrihydrite, ettringite, gypsum, monosulfo aluminate, calcite, magnesite, monocarbo aluminate, CSH_0.8, CSH_1.1, cerrusite, hydrocerrusite and lead hydroxide
Model 17: general model with extended list of lead minerals Portlandite, CSH_1.8, hydrogarnet, brucite, gibbsite, amorphous Al(OH) 3, ferrihydrite, ettringite, gypsum, monosulfo aluminate, calcite, magnesite, monocarbo aluminate, CSH_0.8, CSH_1.1, cerrusite, hydrocerrusite, lead hydroxide, Pb 2SiO 4, litharge, laurionite, anglesite, alamosite and blixite.
Model 17*: includes same minerals as model 17, but with log K (lead hydroxide) = 13.6
Model 17**: includes same minerals as model 17, but with log K (lead hydroxide) = 8.15
Model 18: model 17 with (Ca,Pb)CO 3 solid solution Portlandite, CSH_1.8, hydrogarnet, brucite, gibbsite, amorphous Al(OH) 3, ferrihydrite, ettringite, gypsum, monosulfo aluminate, magnesite, monocarbo aluminate, CSH_0.8, CSH_1.1, hydrocerrusite, lead hydroxide, Pb 2SiO 4, litharge, laurionite, anglesite, alamosite, blixite + (Ca,Pb)CO 3 solid solution
Model 19: model 18 with solid solution of gibbsite, ferrihydrite and litharge Portlandite, CSH_1.8, hydrogarnet, brucite, amorphous Al(OH) 3, ettringite, gypsum, monosulfo aluminate, magnesite, monocarbo aluminate, CSH_0.8, CSH_1.1, hydrocerrusite, lead hydroxide, Pb 2SiO 4, laurionite, anglesite, alamosite, blixite + (Ca,Pb)CO 3 solid solution + solid solution of gibbsite, ferrihydrite and litharge
Model 20: model 19 with surface complexation on HFO (log K = 0.3)
Model 20*: model 19 with surface complexation on HFO (log K = 1.7)
Model 21: model 19 with surface complexation on HFO and AAM (log K = 0.3)
Model 21*: model 19* with surface complexation on HFO and AAM (log K = 1.7)
Model 21**: model 21* with surface complexation (all Fe as HFO, Al according to measurement and log K = 1.7)
Model 21***: model 21* with surface complexation (all Fe as HFO, all Al as AAM and log K = 1.7)
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Appendix 2: General model with wairakite
Including wairakite in the general model for simulation of the extraction test (model 12) provides better predictions for the Si concentrations leached from the fresh sample (cf. Chapter 6). However, Si leaching profiles can only be validated for the fresh sample as no experimental data are available for the carbonated samples. Therefore, it was decided not to include wairakite in our general model. However, as a test, the general model (model 12) is extended with the mineral wairakite in this appendix. This extended model predicts wairakite formation at pH 3.5 – 10 for all samples. As a consequence, less gibbsite and no CSH_0.8 are formed. However, model results reveal that this predicted changes in mineralogical composition do not have a significant influence on the predicted leaching curves for Ca, Mg and Al (Figures A 1 to A 4). The carbonation process and related decalcification are also clearly illustrated: initially portlandite, CSH_1.8, CSH_1.1 and wairakite (which has a Ca/Si ratio of 0.25) are present (B0), then portlandite dissolves (B14) and the amount of CSH_1.8 gradually decreases (B14 and B30) until it is completely dissolved too (B60). The Ca ions that are released this way are used to form calcite.
1.0E+06 1.0E+04 1.0E+05 1.0E+03 1.0E+02 1.0E+04 1.0E+01 1.0E+03 1.0E+00 1.0E-01 1.0E+02 experimental experimental general model 1.0E-02 general model
Ca leachedCa out (mg/kg) general model with wairakite
Mgleached out (mg/kg) general model with wairakite 1.0E+01 1.0E-03 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 pH pH a b
0.120 portlandite 1.0E+05 CSH_1.1 CSH_1.8 experimental ferrihydrite 1.0E+04 general model 0.100 1.0E+03 general model with wairakite 0.080 gibbsite 1.0E+02 1.0E+01 0.060 hydrogarnet brucite 1.0E+00 0.040 ettringite 1.0E-01 wairakite 0.020 gypsum 1.0E-02 amounttotal (mol/L) Alleached out (mg/kg) 1.0E-03 0.000 0 2 4 6 8 10 12 14 1 3 5 7 9 11 13 monosulfo- pH pH aluminate c d
Figure A-1: Comparison between predicted Ca (a), Mg (b) and Al (c) leaching profiles using the general model with and without wairakite and experimental results and (d) predicted pure phase assemblage for the general model with wairakite (sample B0).
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1.0E+06 1.0E+04
1.0E+05 1.0E+03 1.0E+02 1.0E+04 1.0E+01
1.0E+03 1.0E+00
experimental 1.0E-01 experimental 1.0E+02 general model 1.0E-02 general model Ca leached out(mg/kg) leachedCa general model with wairakite Mg leached out (mg/kg) general model with wairakite 1.0E+01 1.0E-03 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 pH pH a b 0.140 1.0E+05 experimental calcite 1.0E+04 0.120 general model CSH_1.1 ferrihydrite 1.0E+03 general model with wairakite 0.100 gibbsite 1.0E+02 0.080 1.0E+01 0.060 hydrogarnet 1.0E+00 0.040 brucite 1.0E-01 wairakite
1.0E-02 amounttotal (mol/L) 0.020 gypsum Alleached out (mg/kg) 1.0E-03 0.000 0 2 4 6 8 10 12 14 1 3 5 7 9 11 13 pH pH monosulfo- CSH_1.8 c d ettringite aluminate Figure A-2: Compar ison between predicted Ca (a), Mg (b) and Al (c) leaching profiles using the general model with and without wairakite and experimental results and (d) predicted pure phase assemblage for the general model with wairakite (sample B14).
1.0E+06 1.0E+04
1.0E+05 1.0E+03 1.0E+02 1.0E+04 1.0E+01
1.0E+03 1.0E+00
1.0E-01 experimental 1.0E+02 experimental general model 1.0E-02 general model
Caleached out (mg/kg) general model with wairakite Mg Mg outleached (mg/kg) general model with wairakite 1.0E+01 1.0E-03 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 pH pH a b
1.0E+05 0.160 experimental calcite 1.0E+04 0.140 general model 1.0E+03 0.120 CSH_1.1 ferrihydrite 1.0E+02 0.100 gibbsite 1.0E+01 0.080
1.0E+00 0.060 hydrogarnet 1.0E-01 0.040 brucite wairakite
1.0E-02 amounttotal (mol/L) 0.020 gypsum Al Al leached out (mg/kg) 1.0E-03 0.000 0 2 4 6 8 10 12 14 1 3 5 7 9 11 13 pH monosulfo- CSH_1.8 c pH d ettringite aluminate Figure A-3: Compariso n between predicted Ca (a), Mg (b) and Al (c) leaching profiles using the general model with and without wairakite and experimental results and (d) predicted pure phase assemblage for the general model with wairakite (sample B30).
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1.0E+06 1.0E+04
1.0E+05 1.0E+03 1.0E+02 1.0E+04 1.0E+01 1.0E+03 1.0E+00 experimental 1.0E-01 1.0E+02 experimental general model 1.0E-02 general model Caleached out (mg/kg) general model with wairakite Mg outleached (mg/kg) general model with wairakite 1.0E+01 1.0E-03 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 pH pH a b
1.0E+05 0.250 experimental calcite 1.0E+04 general model general model with wairakite 0.200 1.0E+03 hydrogarnet 1.0E+02 0.150
1.0E+01 CSH_1.1 ferrihydrite 0.100 1.0E+00 gibbsite 1.0E-01 0.050 brucite wairakite total amounttotal (mol/L)
Al leached out leached Al (mg/kg) out 1.0E-02 gypsum 0.000 1.0E-03 0 2 4 6 8 10 12 14 1 3 5 7 9 11 13 monosulfo- pH d pH ettringite aluminate c Figure A-4: Comparison b etween predicted Ca (a), Mg (b) and Al (c) leaching profiles using the general model with and without wairakite and experimental results and (d) predicted pure phase assemblage for the general model with wairakite (sample B60).
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Appendix 3: Lothenbach and Winnefeld model
Lothenbach and Winnefeld (2006) developed a model to simulate the hydration of Portland cement. Their model consists of an extended list of pure mineral phases and solid solutions (Table A 1) and was implemented in GEM (Kulik, 2002) using the standard GEM database. In this appendix, the applicability of this model for the simulation of the extraction test with PHREEQC is investigated. A PHREEQC input file including all the minerals and solid solutions from Table A 1 is therefore created. A PHREEQC version of the GEM database is available online (http://les.web.psi.ch) and was used.
Table A-1: Lothenbach and Winnefeld model. Mineral Reaction log K Carbonate or sulphate minerals + 2+ Calcite CaCO 3 + H = Ca + HCO 3 1.849 + 2+ Magnesite MgCO 3 + H = HCO 3 + Mg 2.041 2+ 2 Anhydrite CaSO 4 = Ca + SO 4 4.41 2+ 2 Gypsum CaSO 4 2H 2O = Ca + SO 4 +2 H 2O 4.60 2 Syngenite K2Ca(SO 4)2 H2O = Ca2+ + 2K+ + 2 SO 4 + H 2O 7.2
C S H solid solutions and portlandite Silica, amorphous SiO 2 + 2 H 2O = Si(OH) 4 2.713 2+ Tobermorite I (Ca(OH) 2)2(SiO 2)2.4 2H 2O + 0.4 H 2O = 2 Ca + 2.4 SiO(OH) 3 + 1.6 OH 18.2 2+ Tobermorite II (Ca(OH) 2)1.5 (SiO 2)1.8 1.5H 2O + 0.3 H 2O = 1.5 Ca + 1.8 SiO(OH) 3 + 1.2 OH 13.65 2+ Jennite (Ca(OH) 2)1.5 (SiO 2)0.9 0.9H 2O = 1.5 Ca + 0.9 SiO(OH) 3 + 2.1 OH 11.85 + 2+ Portlandite Ca(OH) 2 +2 H = Ca + 2 H 2O 22.80
AFt phases 2 2+ Ettringite Ca 6Al 2(SO 4)3(OH) 12 26H 2O = 2 Al(OH) 4 + 3 SO 4 + 6 Ca + 4 OH + 26 H 2O 45.09 2 2+ Fe ettringite Ca 6Fe 2(SO 4)3(OH) 12 26H 2O = 2 Fe(OH) 4 + 3 SO 4 + 6 Ca + 4 OH + 26 H 2O 49.41 2 2+ Tricarboaluminate Ca 6Al 2(CO 3)3(OH) 12 26H 2O = 2 Al(OH) 4 + 3 CO 3 + 6 Ca + 4 OH + 26 H 2O 45.09
AFm phases 2+ C4AH13 (CaO) 4(Al 2O3) 13H 2O = 4 Ca + 2 Al(OH) 4 +6 OH + 6 H 2O 25.56 2+ C4FH13 (CaO) 4(Fe 2O3) 13H 2O = 4 Ca + 2 Fe(OH) 4 +6 OH + 6 H 2O 29.88 2+ C2AH8 (CaO) 2(Al 2O3) 8H 2O = 2 Ca + 2 Al(OH) 4 +2 OH + 3 H 2O 13.56 2+ C2FH8 (CaO) 2(Fe 2O3) 8H 2O = 2 Ca + 2 Fe(OH) 4 +2 OH + 3 H 2O 17.88 2+ C2ASH8 (CaO) 2(Al 2O3)(SiO 2) 8H 2O = 2 Ca + 2 Al(OH) 4 + SiO(OH) 3 + 1 OH + 2 H 2O 20.49 2+ C2FSH8 (CaO) 2(Fe 2O3)(SiO 2) 8H 2O = 2 Ca + 2 Fe(OH) 4 + SiO(OH) 3 + 1 OH + 2 H 2O 24.80 2+ 2 C4ASH12 (CaO) 3(Al 2O3)(CaSO 4) 12H 2O = 4 Ca + 2 Al(OH) 4 + SO 4 + 4 OH + 6 H 2O 27.70 2+ 2 C4FSH12 (CaO) 3(Fe 2O3)(CaSO 4) 12H 2O = 4 Ca + 2 Fe(OH) 4 + SO 4 + 4 OH + 6 H 2O 32.02 + 2+ C4ACH11 (CaO) 3(Al 2O3)(CaCO 3) 11H 2O + H = 4 Ca + 2 Al(OH) 4 + HCO 3 + 4 OH 31.47 + 5 H 2O + 2+ C4FCH11 (CaO) 3(Fe 2O3)(CaCO 3) 11H 2O + H = 4 Ca + 2 Fe(OH) 4 + HCO 3 + 4 OH 35.79 + 5 H 2O + 2+ C4AC0.5H12 (CaO) 3(Al 2O3)(Ca(OH) 2)0.5 (CaCO 3)0.5 11.5H 2O + 0.5 H = 4 Ca + 2 Al(OH) 4 29.75 + 0.5 HCO 3 + 5 OH + 5.5 H 2O + 2+ C4FC0.5H12 (CaO) 3(Fe 2O3)(Ca(OH) 2)0.5 (CaCO 3)0.5 11.5H 2O + 0.5 H = 4 Ca + 2 Fe(OH) 4 34.07 + 0.5 HCO 3 + 5 OH + 5.5 H 2O
Hydrogarnets 2+ C3AH6 (CaO) 3(Al 2O3) 6H 2O = 3 Ca + 2 Al(OH) 4 + 4 OH 22.46 2+ C3FH6 (CaO) 3(Fe 2O3) 6H 2O = 3 Ca + 2 Fe(OH) 4 + 4 OH 26.78 2+ CAH10 (CaO)(Al 2O3) 10H 2O = Ca + 2 Al(OH) 4 + 6 H 2O 7.49
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Aluminum and iron hydroxides Al(OH)3(am) Al(OH) 3 + OH = Al(OH) 4 0.24 + 3+ Gibbsite Al(OH) 3 + 3 H = Al + 3 H 2O 7.760 + + Fe(OH)3(am) Fe(OH) 3 + 3 H = Fe3 + 3 H 2O 5.00 + + Fe(OH)3(microcr) Fe(OH) 3 + 3 H = Fe3 + 3 H 2O 3.00
Magnesium phases + 2+ Brucite Mg(OH) 2 + 2 H = Mg + H 2O 16.84 2+ OH hydrotalcite Mg 4Al 2(OH) 14 3H 2O = 4 Mg + 2 Al(OH) 4 + 6 OH + 3 H 2O 56.02 + 2+ CO3 hydrotalcite Mg 4Al 2(OH) 12 (CO 3) 2H 2O + H = 4 Mg + 2 Al(OH) 4 + HCO 3 + 4 OH + 2 H 2O 51.14
If the entire model is implemented in PHREEQC, no convergence is obtained for the fresh sample (B0). The reason is unclear, but it is observed that by removing the AFM II and AFM III solid solutions from the model and setting the carbonate concentration to 20 mmol/l instead of 0 mmol/l, convergence is obtained for pH 2.5 12.
1.0E+06 1.0E+04 1.0E+05 1.0E+03 1.0E+02 1.0E+04 1.0E+01 1.0E+03 1.0E+00 experimental 1.0E-01 experimental 1.0E+02 general model 1.0E-02 general model
Caleached out (mg/kg) Lothenbach and Winnefeld Lothenbach and Winnefeld Mgleached out (mg/kg) 1.0E+01 1.0E-03 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 pH pH a b
1.0E+05 1.0E+05 experimental 1.0E+04 general model 1.0E+04 Lothenbach and Winnefeld 1.0E+03 1.0E+03 1.0E+02 1.0E+01 1.0E+02
1.0E+00 1.0E+01 1.0E-01 experimental 1.0E+00 general model 1.0E-02 Sileached out (mg/kg)
Alleached out (mg/kg) Lothenbach and Winnefeld 1.0E-03 1.0E-01 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 pH pH c d
Figure A-5: Comparison betw een predicted Ca (a), Mg (b), Al (c) and Si (d) concentrations by the general model (model 12) and the Lothenbach and Winnefeld model without AFM-II and AFM-III solid solutions and with carbonate concentration 20 mmol/l and experimental results for sample B0.
116
0.140 gibbsite C2FSH8 (SS) 0.120 SiO2 (SS) 0.100 Fe(OH)3 (microcr)
0.080
C2ASH8 0.060 (SS)
Tob-I (SS)
totalamount (mol/L) 0.040 Tob-II (SS) calcite Jen (SS) 0.020 gypsum
0.000 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5 11.5 pH CO3-hydrotalcite ettringite (SS) Fe-ettringite (SS)
Figure A-6: Predicted pure-phase assemblage with the Lothenbach and Winnefeld model without AFM-II and AFM-III solid solutions and with carbonate concentration 20 mmol/l for sample B0.
Calcium and aluminium leached concentrations are well predicted, but predicted leaching profiles for magnesium and silicon are not satisfying (Figure A 5). Remember that the general model predicted brucite dissolution control (Chapter 6). The Lothenbach and Winnefeld model, on the other hand, predicts CO 3 hydrotalcite to be more stable than brucite (Figure A 6). Furthermore, the Lothenbach and Winnefeld model includes the same CSH solid solution model as discussed earlier (Chapter 6, paragraph 3.4). An explanation for the disagreement between predicted and experimental results for silicon was already given there.
For the partially carbonated samples, i.e. B14 and B30, the Lothenbach and Winnefeld converges for pH 3.5 – 12 and pH 3 – 12, respectively. Model results are shown in Figures A 7 & A 8. The same remarks as for the fresh sample (B0) can be made concerning leaching profiles and mineral phases.
For the fully carbonated sample (B60), good simulation results are obtained for magnesium, but aluminium predictions are unacceptable at alkaline pH (Figure A 9b and c). If all AFT and AFM solid solutions are removed, then the aluminium leaching profile is well predicted but the magnesium leaching profile becomes worse (Figure A 10b and c). This can be explained as follows: if tricarbo aluminate is not allowed to form, then more calcite and CO 3 hydrotalcite are predicted to form and thus less magnesium can leach out.
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1.0E+06 1.0E+04
1.0E+05 1.0E+03 1.0E+02 1.0E+04 1.0E+01 1.0E+03 1.0E+00
experimental 1.0E-01 1.0E+02 general model experimental 1.0E-02 general model Mg (mg/kg) out leached Mg Caleached out (mg/kg) Lothenbach and Winnefeld 1.0E+01 Lothenbach and Winnefeld 1.0E-03 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 pH a pH b 0.140 1.0E+05 gibbsite experimental 1.0E+04 0.120 calcite general model SiO2 (SS) Fe(OH)3 (microcr) 1.0E+03 Lothenbach and Winnefeld 0.100 1.0E+02 0.080 1.0E+01 Tob-II (SS) 1.0E+00 0.060 Tob-I (SS) 1.0E-01 Tricarboaluminate (SS) 0.040 C4ACH11 (SS) 1.0E-02 C4FCH11 (SS) 0.020 total (mol/L) amount gypsum 1.0E-03
Alleached out (mg/kg) Jen (SS) 0.000 1.0E-04 OH-hydrotalcite 0 2 4 6 8 10 12 14 3.5 5.5 7.5 9.5 11.5 pH pH d Fe-ettringite (SS) c CO3-hydrotalcite ettringite (SS) Figure A-7: Comparison between predicted Ca (a), Mg (b) and Al (c) concentrations by the general model (model 12) and the Lothenbach and Winnefeld model and experimental results for sample B14. (d) Predicted pure-phases assemblage with the Lothenbach and Winnefeld model.
1.0E+06 1.0E+04 1.0E+05 1.0E+03 1.0E+02 1.0E+04 1.0E+01 1.0E+03 1.0E+00 experimental 1.0E-01 1.0E+02 general model experimental 1.0E-02 general model Mg (mg/kg) out leached Mg Caleached out (mg/kg) Lothenbach and Winnefeld 1.0E+01 Lothenbach and Winnefeld 1.0E-03 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 pH a b pH
0.160 1.0E+05 calcite experimental 0.140 gibbsite 1.0E+04 general model 1.0E+03 0.120 Lothenbach and Winnefeld SiO2 (SS) Fe(OH)3 (microcr) 1.0E+02 0.100 1.0E+01 0.080 Tob-II (SS) 1.0E+00 0.060 Tricarboaluminate (SS) 1.0E-01 0.040 1.0E-02 Tob-I (SS) C4FCH11 (SS)
total (mol/L) amount 0.020 gypsum C4ACH11 (SS) 1.0E-03
Alleached out (mg/kg) Jen (SS) 0.000 1.0E-04 OH-hydrotalcite 0 2 4 6 8 10 12 14 3 5 7 9 11 pH c pH d CO3-hydrotalcite ettringite (SS) Fe-ettringite (SS) Figure A-8: Comparison between predicted Ca (a), Mg (b) and Al (c) concentrations by the general model (model 12) and the Lothenbach and Winnefeld model and experimental results for sample B30. (d) Predicted pure-phases assemblage with the Lothenbach and Winnefeld model.
118
1.0E+06 1.0E+04
1.0E+05 1.0E+03 1.0E+02 1.0E+04 1.0E+01
1.0E+03 1.0E+00
experimental 1.0E-01 1.0E+02 general model experimental 1.0E-02 general model Mg (mg/kg) out leached Mg Caleached out (mg/kg) Lothenbach and Winnefeld 1.0E+01 Lothenbach and Winnefeld 1.0E-03 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 pH a b pH
0.250 1.0E+05 calcite 1.0E+04 1.0E+03 0.200 1.0E+02 1.0E+01 0.150 gibbsite 1.0E+00 Fe(OH)3 1.0E-01 SiO2 (SS) (microcr) 1.0E-02 0.100 1.0E-03 1.0E-04 Tricarboaluminate (SS) experimental 0.050 1.0E-05 Tob-I (SS) general model amounttotal (mol/L) gypsum CO3-hydrotalcite Alleached out (mg/kg) 1.0E-06 Lothenbach and Winnefeld 1.0E-07 0.000 0 2 4 6 8 10 12 14 2.5 4.5 6.5 8.5 10.5
c pH d pH C4FCH11 (SS)
Figure A-9: Comparison between predicted Ca (a), Mg (b) and Al (c) concentrations by the general model (model 12) and the Lothenbach and Winnefeld model and experimental results for sample B60. (d) Predicted pure-phases assemblage with the Lothenbach and Winnefeld model.
1.0E+06 1.0E+04 1.0E+03 1.0E+05 1.0E+02 1.0E+04 1.0E+01
1.0E+00 1.0E+03 1.0E-01 experimental experimental general model 1.0E+02 general model 1.0E-02 Lothenbach and Winnefeld Lothenbach and Winnefeld
Mg leached out Mg(mg/kg) leached 1.0E-03 Caleached out (mg/kg) L&W without +AFT&AFM solid solutions 1.0E+01 L&W without AFT&AFM solid solutions 1.0E-04 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 pH pH a b
1.0E+05 0.250 1.0E+04 calcite 1.0E+03 0.200 1.0E+02 1.0E+01 0.150 1.0E+00 gibbsite Fe(OH)3 1.0E-01 SiO2 (SS) (microcr) 0.100 1.0E-02 Jen Tob-II (SS) 1.0E-03 (SS) Tob-I (SS) 1.0E-04 0.050 experimental amounttotal (mol/L) gypsum CO3-hydrotalcite Al leachedAl (mg/kg) out 1.0E-05 general model Lothenbach and Winnefeld 1.0E-06 0.000 L&W without AFT&AFM solid solutions 1.0E-07 2.5 4.5 6.5 8.5 10.5 0 2 4 6 8 10 12 14 pH C3AH6 (SS) c pH d Figure A-10: Comparison between predicted Ca (a), Mg (b) and Al (c) concentrations by the general model (model 12) and the Lothenbach and Winnefeld model without AFT and AFM solid solutions and exper imental results for sample B60. (d) Predicted pure-phases assemblage with the Lothenbach and Winnefeld model without AFT and AFM solid solutions. 119
Overall, the model simulations revealed that the Lothenbach and Winnefeld model is not stable at all, i.e. many iterations and increasing of the tolerance level were necessary to obtain convergence. Moreover, calculation time was 4 to 8 times larger than for the general model and in some cases it was necessary to remove parts of the model (i.e., some of the solid solutions) in order to obtain convergence. No convergence can be obtained for a zero carbonate concentration (the fresh sample). The most obvious reason for the unstability is the extended list of solid solutions. It can thus be concluded that the Lothenbach and Winnefeld model is not stable in PHREEQC and that it does not provide satisfying results for all samples.
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Appendix 4: PHREEQC inputfile for the general model for the extraction test (model 21*)
TITLE Extraction test
SOLUTION 1 pH 0 Ca 476 Na 29 K 17 Al 128 Mg 41 Si 106 Pb 0.49 S(6) 22 as SO4 Cl 45 charge C 218 Fe(+3) 101
PHASES # CSH phases and portlandite CSH_0.8 Ca0.8SiO5H4.4 + 1.6 H+ = 0.8 Ca++ + H4SiO4 + H2O log_k 11.1 CSH_1.1 Ca1.1SiO7H7.8 + 2.2 H+ = 1.1 Ca++ + H4SiO4 + 3 H2O log_k 16.7 CSH_1.8 Ca1.8SiO9H10.4 + 3.6 H+ = 1.8 Ca++ + H4SiO4 + 5 H2O log_k 32.6 Portlandite Ca(OH)2 + 2 H+ = 1 Ca++ +2 H2O log_k 22.80 # Magnesium phases Brucite Mg(OH)2 + 2 H+ = Mg++ + 2 H2O log_k 16.84 Magnesite MgCO3 +1.0000 H+ = + 1.0000 HCO3 + 1.0000 Mg++ log_k 2.2936 # Aluminum and iron hydroxides Al(OH)3(am) Al(OH)3 + OH = Al(OH)4 log_k 0.24 Gibbsite Al(OH)3 +3.0000 H+ = + 1.0000 Al+++ + 3.0000 H2O log_k 7.760 Ferrihydrite Fe(OH)3 + 3H+ = Fe+3 + 3H2O log_k 4.891 # Hydrogarnets Hydrogarnet Ca3Al2O12H12 + 12 H+ = 3 Ca++ + 2 Al+++ + 12 H2O log_k 78 # Sulphate minerals Ettringite Ca6Al2(SO4)3(OH)12:26H2O + 12H+ = 6Ca+2 + 2Al+3 + 3SO4 2 + 38H2O log_k 56.7
121
Monosulfo aluminate Ca4Al2SO10:12H2O + 12 H+ = 4 Ca++ + 2 Al+++ + SO4 + 18 H2O log_k 71.00 # Carbonates Monocarbo aluminate Ca4Al2CO9:10H2O + 13 H+ = 4 Ca++ + 2 Al+++ + HCO3 + 16 H2O log_k 80.33 # Lead minerals Alamosite PbSiO3 +2.0000 H+ + 1.0000 H2O = + 1.0000 Pb++ + 1.0000 H4SiO4 log_k 5.6733 Litharge PbO +2.0000 H+ = + 1.0000 H2O + 1.0000 Pb++ log_k 12.6388 Pb2SiO4 Pb2SiO4 +4.0000 H+ = + 1.0000 H4SiO4 + 2.0000 Pb++ log_k 18.0370 Laurionite PbClOH +1.0000 H+ = + 1.0000 Cl + 1.0000 H2O + 1.0000 Pb++ log_k 0.2035 Hydrocerussite Pb3(CO3)2(OH)2 +4.0000 H+ = + 2.0000 H2O + 2.0000 HCO3 + 3.0000 Pb++ log_k 1.8477 Pb(OH)2 Pb(OH)2 + 2H+ = Pb+2 + 2H2O log_k 11 Pb2(OH)3Cl Pb2(OH)3Cl + 3H+ = 2Pb+2 + 3H2O + Cl log_k 8.793 Cerrusite PbCO3 = Pb+2 + CO3 2 log_k 13.13 Anglesite PbSO4 = Pb+2 + SO4 2 log_k 7.79 # pH Fix_H+ H+ = H+ log_k 0.0
SURFACE_SPECIES # Lead Hfo_sOH + Pb+2 = Hfo_sOPb+ + H+ log_k 4.65
Hfo_wOH + Pb+2 = Hfo_wOPb+ + H+ log_k 1.7#0.3
SOLID_SOLUTIONS 1 (Pb,Ca)CO3 comp Cerrusite 0 comp Calcite 0 (PbO,Al(OH)3,Fe(OH)3) comp Litharge 0 comp Gibbsite 0 comp Ferrihydrite 0 SURFACE 1 equilibrate with solution 1 Hfo_sOH 2.17e 04 600 3.87 122
Hfo_wOH 8.69e 03
EQUILIBRIUM_PHASES 1 Portlandite 0 0 CSH_1.8 0 0 Monocarbo aluminate 0 0 Brucite 0 0 Hydrogarnet 0 0 Al(OH)3(am) 0 0 Magnesite 0 0 CSH_0.8 0 0 CSH_1.1 0 0 Monosulfo aluminate 0 0 Ettringite 0 0 Gypsum 0 0 Pb2SiO4 0 0 Hydrocerussite 0 0 Pb(OH)2 0 0 Laurionite 0 0 Anglesite 0 0 Alamosite 0 0 Pb2(OH)3Cl 0 0 fix_H+ 1 NaOH 10.0
EQUILIBRIUM_PHASES 2 Portlandite 0 0 CSH_1.8 0 0 Monocarbo aluminate 0 0 Brucite 0 0 Hydrogarnet 0 0 Al(OH)3(am) 0 0 Magnesite 0 0 CSH_0.8 0 0 CSH_1.1 0 0 Monosulfo aluminate 0 0 Ettringite 0 0 Gypsum 0 0 Pb2SiO4 0 0 Hydrocerussite 0 0 Pb(OH)2 0 0 Laurionite 0 0 Anglesite 0 0 Alamosite 0 0 Pb2(OH)3Cl 0 0 fix_H+ 1.5 NaOH 10.0
EQUILIBRIUM_PHASES 3 Portlandite 0 0 CSH_1.8 0 0 Monocarbo aluminate 0 0 Brucite 0 0 Hydrogarnet 0 0 Al(OH)3(am) 0 0 Magnesite 0 0 CSH_0.8 0 0 CSH_1.1 0 0 Monosulfo aluminate 0 0 Ettringite 0 0 Gypsum 0 0 123
Pb2SiO4 0 0 Hydrocerussite 0 0 Pb(OH)2 0 0 Laurionite 0 0 Anglesite 0 0 Alamosite 0 0 Pb2(OH)3Cl 0 0 fix_H+ 2 NaOH 10.0
EQUILIBRIUM_PHASES 4 Portlandite 0 0 CSH_1.8 0 0 Monocarbo aluminate 0 0 Brucite 0 0 Hydrogarnet 0 0 Al(OH)3(am) 0 0 Magnesite 0 0 CSH_0.8 0 0 CSH_1.1 0 0 Monosulfo aluminate 0 0 Ettringite 0 0 Gypsum 0 0 Pb2SiO4 0 0 Hydrocerussite 0 0 Pb(OH)2 0 0 Laurionite 0 0 Anglesite 0 0 Alamosite 0 0 Pb2(OH)3Cl 0 0 fix_H+ 2.5 NaOH 10.0
EQUILIBRIUM_PHASES 5 Portlandite 0 0 CSH_1.8 0 0 Monocarbo aluminate 0 0 Brucite 0 0 Hydrogarnet 0 0 Al(OH)3(am) 0 0 Magnesite 0 0 CSH_0.8 0 0 CSH_1.1 0 0 Monosulfo aluminate 0 0 Ettringite 0 0 Gypsum 0 0 Pb2SiO4 0 0 Hydrocerussite 0 0 Pb(OH)2 0 0 Laurionite 0 0 Anglesite 0 0 Alamosite 0 0 Pb2(OH)3Cl 0 0 fix_H+ 3 NaOH 10.0
EQUILIBRIUM_PHASES 6 Portlandite 0 0 CSH_1.8 0 0 Monocarbo aluminate 0 0 Brucite 0 0 Hydrogarnet 0 0 124
Al(OH)3(am) 0 0 Magnesite 0 0 CSH_0.8 0 0 CSH_1.1 0 0 Monosulfo aluminate 0 0 Ettringite 0 0 Gypsum 0 0 Pb2SiO4 0 0 Hydrocerussite 0 0 Pb(OH)2 0 0 Laurionite 0 0 Anglesite 0 0 Alamosite 0 0 Pb2(OH)3Cl 0 0 fix_H+ 3.5 NaOH 10.0
EQUILIBRIUM_PHASES 7 Portlandite 0 0 CSH_1.8 0 0 Monocarbo aluminate 0 0 Brucite 0 0 Hydrogarnet 0 0 Al(OH)3(am) 0 0 Magnesite 0 0 CSH_0.8 0 0 CSH_1.1 0 0 Monosulfo aluminate 0 0 Ettringite 0 0 Gypsum 0 0 Pb2SiO4 0 0 Hydrocerussite 0 0 Pb(OH)2 0 0 Laurionite 0 0 Anglesite 0 0 Alamosite 0 0 Pb2(OH)3Cl 0 0 fix_H+ 4 NaOH 10.0
EQUILIBRIUM_PHASES 8 Portlandite 0 0 CSH_1.8 0 0 Monocarbo aluminate 0 0 Brucite 0 0 Hydrogarnet 0 0 Al(OH)3(am) 0 0 Magnesite 0 0 CSH_0.8 0 0 CSH_1.1 0 0 Monosulfo aluminate 0 0 Ettringite 0 0 Gypsum 0 0 Pb2SiO4 0 0 Hydrocerussite 0 0 Pb(OH)2 0 0 Laurionite 0 0 Anglesite 0 0 Alamosite 0 0 Pb2(OH)3Cl 0 0 fix_H+ 4.5 NaOH 10.0 125
EQUILIBRIUM_PHASES 9 Portlandite 0 0 CSH_1.8 0 0 Monocarbo aluminate 0 0 Brucite 0 0 Hydrogarnet 0 0 Al(OH)3(am) 0 0 Magnesite 0 0 CSH_0.8 0 0 CSH_1.1 0 0 Monosulfo aluminate 0 0 Ettringite 0 0 Gypsum 0 0 Pb2SiO4 0 0 Hydrocerussite 0 0 Pb(OH)2 0 0 Laurionite 0 0 Anglesite 0 0 Alamosite 0 0 Pb2(OH)3Cl 0 0 fix_H+ 5 NaOH 10.0
EQUILIBRIUM_PHASES 10 Portlandite 0 0 CSH_1.8 0 0 Monocarbo aluminate 0 0 Brucite 0 0 Hydrogarnet 0 0 Al(OH)3(am) 0 0 Magnesite 0 0 CSH_0.8 0 0 CSH_1.1 0 0 Monosulfo aluminate 0 0 Ettringite 0 0 Gypsum 0 0 Pb2SiO4 0 0 Hydrocerussite 0 0 Pb(OH)2 0 0 Laurionite 0 0 Anglesite 0 0 Alamosite 0 0 Pb2(OH)3Cl 0 0 fix_H+ 5.5 NaOH 10.0
EQUILIBRIUM_PHASES 11 Portlandite 0 0 CSH_1.8 0 0 Monocarbo aluminate 0 0 Brucite 0 0 Hydrogarnet 0 0 Al(OH)3(am) 0 0 Magnesite 0 0 CSH_0.8 0 0 CSH_1.1 0 0 Monosulfo aluminate 0 0 Ettringite 0 0 Gypsum 0 0 Pb2SiO4 0 0 126
Hydrocerussite 0 0 Pb(OH)2 0 0 Laurionite 0 0 Anglesite 0 0 Alamosite 0 0 Pb2(OH)3Cl 0 0 fix_H+ 6 NaOH 10.0
EQUILIBRIUM_PHASES 12 Portlandite 0 0 CSH_1.8 0 0 Monocarbo aluminate 0 0 Brucite 0 0 Hydrogarnet 0 0 Al(OH)3(am) 0 0 Magnesite 0 0 CSH_0.8 0 0 CSH_1.1 0 0 Monosulfo aluminate 0 0 Ettringite 0 0 Gypsum 0 0 Pb2SiO4 0 0 Hydrocerussite 0 0 Pb(OH)2 0 0 Laurionite 0 0 Anglesite 0 0 Alamosite 0 0 Pb2(OH)3Cl 0 0 fix_H+ 6.5 NaOH 10.0
EQUILIBRIUM_PHASES 13 Portlandite 0 0 CSH_1.8 0 0 Monocarbo aluminate 0 0 Brucite 0 0 Hydrogarnet 0 0 Al(OH)3(am) 0 0 Magnesite 0 0 CSH_0.8 0 0 CSH_1.1 0 0 Monosulfo aluminate 0 0 Ettringite 0 0 Gypsum 0 0 Pb2SiO4 0 0 Hydrocerussite 0 0 Pb(OH)2 0 0 Laurionite 0 0 Anglesite 0 0 Alamosite 0 0 Pb2(OH)3Cl 0 0 fix_H+ 7 NaOH 10.0
EQUILIBRIUM_PHASES 14 Portlandite 0 0 CSH_1.8 0 0 Monocarbo aluminate 0 0 Brucite 0 0 Hydrogarnet 0 0 Al(OH)3(am) 0 0 127
Magnesite 0 0 CSH_0.8 0 0 CSH_1.1 0 0 Monosulfo aluminate 0 0 Ettringite 0 0 Gypsum 0 0 Pb2SiO4 0 0 Hydrocerussite 0 0 Pb(OH)2 0 0 Laurionite 0 0 Anglesite 0 0 Alamosite 0 0 Pb2(OH)3Cl 0 0 fix_H+ 7.5 NaOH 10.0
EQUILIBRIUM_PHASES 15 Portlandite 0 0 CSH_1.8 0 0 Monocarbo aluminate 0 0 Brucite 0 0 Hydrogarnet 0 0 Al(OH)3(am) 0 0 Magnesite 0 0 CSH_0.8 0 0 CSH_1.1 0 0 Monosulfo aluminate 0 0 Ettringite 0 0 Gypsum 0 0 Pb2SiO4 0 0 Hydrocerussite 0 0 Pb(OH)2 0 0 Laurionite 0 0 Anglesite 0 0 Alamosite 0 0 Pb2(OH)3Cl 0 0 fix_H+ 8 NaOH 10.0
EQUILIBRIUM_PHASES 16 Portlandite 0 0 CSH_1.8 0 0 Monocarbo aluminate 0 0 Brucite 0 0 Hydrogarnet 0 0 Al(OH)3(am) 0 0 Magnesite 0 0 CSH_0.8 0 0 CSH_1.1 0 0 Monosulfo aluminate 0 0 Ettringite 0 0 Gypsum 0 0 Pb2SiO4 0 0 Hydrocerussite 0 0 Pb(OH)2 0 0 Laurionite 0 0 Anglesite 0 0 Alamosite 0 0 Pb2(OH)3Cl 0 0 fix_H+ 8.5 NaOH 10.0
128
EQUILIBRIUM_PHASES 17 Portlandite 0 0 CSH_1.8 0 0 Monocarbo aluminate 0 0 Brucite 0 0 Hydrogarnet 0 0 Al(OH)3(am) 0 0 Magnesite 0 0 CSH_0.8 0 0 CSH_1.1 0 0 Monosulfo aluminate 0 0 Ettringite 0 0 Gypsum 0 0 Pb2SiO4 0 0 Hydrocerussite 0 0 Pb(OH)2 0 0 Laurionite 0 0 Anglesite 0 0 Alamosite 0 0 Pb2(OH)3Cl 0 0 fix_H+ 9 NaOH 10.0
EQUILIBRIUM_PHASES 18 Portlandite 0 0 CSH_1.8 0 0 Monocarbo aluminate 0 0 Brucite 0 0 Hydrogarnet 0 0 Al(OH)3(am) 0 0 Magnesite 0 0 CSH_0.8 0 0 CSH_1.1 0 0 Monosulfo aluminate 0 0 Ettringite 0 0 Gypsum 0 0 Pb2SiO4 0 0 Hydrocerussite 0 0 Pb(OH)2 0 0 Laurionite 0 0 Anglesite 0 0 Alamosite 0 0 Pb2(OH)3Cl 0 0 fix_H+ 9.5 NaOH 10.0
EQUILIBRIUM_PHASES 19 Portlandite 0 0 CSH_1.8 0 0 Monocarbo aluminate 0 0 Brucite 0 0 Hydrogarnet 0 0 Al(OH)3(am) 0 0 Magnesite 0 0 CSH_0.8 0 0 CSH_1.1 0 0 Monosulfo aluminate 0 0 Ettringite 0 0 Gypsum 0 0 Pb2SiO4 0 0 Hydrocerussite 0 0 129
Pb(OH)2 0 0 Laurionite 0 0 Anglesite 0 0 Alamosite 0 0 Pb2(OH)3Cl 0 0 fix_H+ 10 NaOH 10.0
EQUILIBRIUM_PHASES 20 Portlandite 0 0 CSH_1.8 0 0 Monocarbo aluminate 0 0 Brucite 0 0 Hydrogarnet 0 0 Al(OH)3(am) 0 0 Magnesite 0 0 CSH_0.8 0 0 CSH_1.1 0 0 Monosulfo aluminate 0 0 Ettringite 0 0 Gypsum 0 0 Pb2SiO4 0 0 Hydrocerussite 0 0 Pb(OH)2 0 0 Laurionite 0 0 Anglesite 0 0 Alamosite 0 0 Pb2(OH)3Cl 0 0 fix_H+ 10.5 NaOH 10.0
EQUILIBRIUM_PHASES 21 Portlandite 0 0 CSH_1.8 0 0 Monocarbo aluminate 0 0 Brucite 0 0 Hydrogarnet 0 0 Al(OH)3(am) 0 0 Magnesite 0 0 CSH_0.8 0 0 CSH_1.1 0 0 Monosulfo aluminate 0 0 Ettringite 0 0 Gypsum 0 0 Pb2SiO4 0 0 Hydrocerussite 0 0 Pb(OH)2 0 0 Laurionite 0 0 Anglesite 0 0 Alamosite 0 0 Pb2(OH)3Cl 0 0 fix_H+ 11 NaOH 10.0
EQUILIBRIUM_PHASES 22 Portlandite 0 0 CSH_1.8 0 0 Monocarbo aluminate 0 0 Brucite 0 0 Hydrogarnet 0 0 Al(OH)3(am) 0 0 Magnesite 0 0 130
CSH_0.8 0 0 CSH_1.1 0 0 Monosulfo aluminate 0 0 Ettringite 0 0 Gypsum 0 0 Pb2SiO4 0 0 Hydrocerussite 0 0 Pb(OH)2 0 0 Laurionite 0 0 Anglesite 0 0 Alamosite 0 0 Pb2(OH)3Cl 0 0 fix_H+ 11.5 NaOH 10.0
EQUILIBRIUM_PHASES 23 Portlandite 0 0 CSH_1.8 0 0 Monocarbo aluminate 0 0 Brucite 0 0 Hydrogarnet 0 0 Al(OH)3(am) 0 0 Magnesite 0 0 CSH_0.8 0 0 CSH_1.1 0 0 Monosulfo aluminate 0 0 Ettringite 0 0 Gypsum 0 0 Pb2SiO4 0 0 Hydrocerussite 0 0 Pb(OH)2 0 0 Laurionite 0 0 Anglesite 0 0 Alamosite 0 0 Pb2(OH)3Cl 0 0 fix_H+ 12 NaOH 10.0
EQUILIBRIUM_PHASES 24 Portlandite 0 0 CSH_1.8 0 0 Monocarbo aluminate 0 0 Brucite 0 0 Hydrogarnet 0 0 Al(OH)3(am) 0 0 Magnesite 0 0 CSH_0.8 0 0 CSH_1.1 0 0 Monosulfo aluminate 0 0 Ettringite 0 0 Gypsum 0 0 Pb2SiO4 0 0 Hydrocerussite 0 0 Pb(OH)2 0 0 Laurionite 0 0 Anglesite 0 0 Alamosite 0 0 Pb2(OH)3Cl 0 0 fix_H+ 12.5 NaOH 10.0
EQUILIBRIUM_PHASES 25 131
Portlandite 0 0 CSH_1.8 0 0 Monocarbo aluminate 0 0 Brucite 0 0 Hydrogarnet 0 0 Al(OH)3(am) 0 0 Magnesite 0 0 CSH_0.8 0 0 CSH_1.1 0 0 Monosulfo aluminate 0 0 Ettringite 0 0 Gypsum 0 0 Pb2SiO4 0 0 Hydrocerussite 0 0 Pb(OH)2 0 0 Laurionite 0 0 Anglesite 0 0 Alamosite 0 0 Pb2(OH)3Cl 0 0 fix_H+ 13 NaOH 10.0
USE solution none USE equilibrium_phases none
END
USER_PUNCH headings Ca_aq Mg_aq Al_aq Si_aq Pb_aq Calc Cer start 10 PUNCH (TOT("Ca"))*1000*40.08*10 20 PUNCH (TOT("Mg"))*1000*24.312*10 30 PUNCH (TOT("Al"))*1000*26.9815*10 40 PUNCH (TOT("Si"))*1000*28.0843*10 50 PUNCH (TOT("Pb"))*1000*207.19*10 60 solid = S_S("Cerrusite") + S_S("Calcite") 70 if solid = 0 then goto 150 80 Calc = S_S("Calcite")*100/solid 90 Cer = S_S("Cerrusite")*100/solid 110 goto 200 150 Calc = 0 160 Cer = 0 200 PUNCH Calc 210 PUNCH Cer end
SELECTED_OUTPUT file extractietest.sel selected_out true reset true ph true pe false reaction false temperature false alkalinity false ionic_strength false water false charge_balance false percent_error false user_punch true 132
equilibrium_phases Portlandite CSH_1.8 Brucite Al(OH)3(am) CSH_1.1 CSH_0.8 Magnesite Hydrogarnet Monocarbo aluminate Monosulfo aluminate Ettringite Gypsum Pb2SiO4 Hydrocerussite Pb(OH)2 Laurionite Anglesite Alamosite Pb2(OH)3Cl saturation_indices Portlandite CSH_1.8 Brucite Al(OH)3(am) CSH_1.1 CSH_0.8 Magnesite Hydrogarnet Monocarbo aluminate Monosulfo aluminate Ettringite Gypsum Pb2SiO4 Hydrocerussite Pb(OH)2 Laurionite Anglesite Alamosite Pb2(OH)3Cl solid_solutions Calcite Cerrusite Litharge Gibbsite Ferrihydrite molalities Pb+2 PbOH+ Pb(OH)2 Pb(OH)3 Pb(OH)4 2 Pb2OH+3 PbCl+ PbCl2 PbCl3 PbCl4 2 PbCO3 Pb(CO3)2 2 PbHCO3+ PbSO4 Pb(SO4)2 2
USE solution 1 USE equilibrium_phases 1 USE surface 1 USE solid_solutions 1 END
USE solution 1 USE surface 1 USE solid_solutions 1 USE equilibrium_phases 2 END
USE solution 1 USE surface 1 USE solid_solutions 1 USE equilibrium_phases 3 END
USE solution 1 USE surface 1 USE solid_solutions 1 USE equilibrium_phases 4 END
USE solution 1 USE surface 1 USE solid_solutions 1 USE equilibrium_phases 5 END
USE solution 1 USE surface 1 USE solid_solutions 1 USE equilibrium_phases 6 END
USE solution 1 USE surface 1 USE solid_solutions 1 USE equilibrium_phases 7 END
USE solution 1 USE surface 1 USE solid_solutions 1 USE equilibrium_phases 8 END
USE solution 1 133
USE surface 1 USE solid_solutions 1 USE equilibrium_phases 9 END
USE solution 1 USE surface 1 USE solid_solutions 1 USE equilibrium_phases 10 END
USE solution 1 USE surface 1 USE solid_solutions 1 USE equilibrium_phases 11 END
USE solution 1 USE surface 1 USE solid_solutions 1 USE equilibrium_phases 12 END
USE solution 1 USE surface 1 USE solid_solutions 1 USE equilibrium_phases 13 END
USE solution 1 USE surface 1 USE solid_solutions 1 USE equilibrium_phases 14 END
USE solution 1 USE surface 1 USE solid_solutions 1 USE equilibrium_phases 15 END
USE solution 1 USE surface 1 USE solid_solutions 1 USE equilibrium_phases 16 END
USE solution 1 USE surface 1 USE solid_solutions 1 USE equilibrium_phases 17 END
USE solution 1 USE surface 1 USE solid_solutions 1 USE equilibrium_phases 18 END
134
USE solution 1 USE surface 1 USE solid_solutions 1 USE equilibrium_phases 19 END
USE solution 1 USE surface 1 USE solid_solutions 1 USE equilibrium_phases 20 END
USE solution 1 USE surface 1 USE solid_solutions 1 USE equilibrium_phases 21 END
USE solution 1 USE surface 1 USE solid_solutions 1 USE equilibrium_phases 22 END
USE solution 1 USE surface 1 USE solid_solutions 1 USE equilibrium_phases 23 END
USE solution 1 USE surface 1 USE solid_solutions 1 USE equilibrium_phases 24 END
USE solution 1 USE surface 1 USE solid_solutions 1 USE equilibrium_phases 25 END
135