utilization of an equilibrium calculational program for ...

UTILIZATION OF AN EQUILIBRIUM CALCULATIONAL PROGRAM FOR TEACHING HYDROMETALLURGY H.H. Huang, C.A. Young, L.G. Twidwell Department of Metallurgical Engineering Montana Tech of the University of Montana Butte, MT 59701 Abstract Equilibrium calculational software programs can be effectively utilized to illustrate the importance of activity or concentration, solubility, temperature, solution pH, solution potential, and ligand complexation to students studying hydrometallurgy. The calculational program STABCAL will be demonstrated. Illustrative examples for the creation of diagrams such as EH-pH, activity or concentration-pH and activity or concentration-EH will be presented to demonstrate the fundamentals of real hydrometallurgical systems. Speciation, titration and mixing will be used to simulate or predict operational processes. Applications will include treatment of Acid Mine Drainage (Berkeley Pitlake water and sediments), mass balance potential/pH diagrams, leaching and selective separation of metals from electroplating sludge, metal adsorption at ferrihydrite surfaces, and flowsheet development for treating mercury sulfide K106 sludge.

Introduction Equilibrium calculational software programs can be effectively utilized to illustrate the importance of activity or concentration, solubility, temperature, solution pH, solution potential, and ligand complexation to students studying hydrometallurgy. The objective of the present paper is to provide illustrative examples of real hydrometallurgical systems to demonstrate the use of calculational programs for designing new treatment processes and to understand presently available processes. The program utilized throughout this presentation is STABCAL (stability calculation for aqueous systems). STABCAL STABCAL is an integrated Windows program that is designed to perform comprehensive equilibrium calculations for complex aqueous systems and can be used for academic, research and industrial applications in hydrometallgical and environmental engineering, geochemistry and chemistry. The program is used to calculate various types of stability diagrams from thermodynamic data to show the domains of specie predominance: e.g., potential-pH diagrams and solubility and activity diagrams such as log[activity or concentration]-pH and log [activity or concentration]-log[activity or concentration]. The program can also be used to perform speciation calculations (for single solutions or mixtures of solutions) and titration simulations (in single or multi-steps). The program accepts five types of species (solid, aqueous, liquid, gas and adsorbed species), treats reduction-oxidation couples (individual couples or all couples) and allows gas to be treated as a primary (external) specie or as a secondary specie. Results can be printed as a text file that can be manipulated in spreadsheet programs (such as Excel), copy-and-pasted into any word processing program, or saved as a graphic file that can be opened in any word processing program. STABCAL includes five thermodynamic databases: NBS [1], MINTEQA2 [2], Naumov [3] , Critical and Uncritical Constants[4, 5] for 25oC. Three of the databases can be expanded to other temperatures. Personalized databases can also be created. Predominance Diagrams STABCAL can be used to construct predominance area diagrams using various two axial functions. Normally, each area in the diagram shows the presence of the predominate specie. Additionally, the EH-pH mass balance diagram can show the areas dominated by more than one specie (described later). Programs and their two axial functions are listed Table 1. Table 1. Predominance area programs Program X and Y Axial Program Functions EH-pH Potential and pH O2-pH EH-pH(mass) Potential and pH Cpx-Cpx (mass balanced) Cpx-pH [ligand] and pH [ ]=activity or concentration (when corrected for ionic strength)

X and Y Axial Functions PO2 or pH2 and pH. [ligand] and [ligand].

Distribution diagrams STABCAL can display the results in various ways. The user can choose the y-axis to be: % distribution, activity or concentration of aqueous species, solubility of stable solids, or amount of solids precipitated. All of these options are referred to here as distribution and are abbreviated “Dist” in Table 2 which also list the four different x-axes that can be chosen.

Table 2. Selection of x-axis functions for distribution diagrams Program X-axial function Program X-axial function Dist-EH EH Dist-pH pH Dist-Cpx [ligand] Dist-T Temperature (oC). 3D and 2D contour Solubility Diagrams Calculational options are also available for constructing solubility diagrams in 2 or 3 dimensions. Contour lines are used for 2D-plots to portray the third dimension. Each contour line represents one solubility value, typically an exponential increment that is user determined. The two different available x-y axial functions are presented in Table 3. Table 3. Selection of x and y axes for 3D solubility or 2D contour diagrams Program X and Y axial functions Program X and Y axial functions 3D-Cpx-pH [ligand] and pH 3D-EH-pH EH and pH Speciation calculation Speciation calculations are used to compute activities or concentrations of all species present for each of the following selections: measured potential, dissolved oxygen, oxygen partial pressure, and reduction-oxidation couples, each with or without consideration of solid precipitation. The imbalanced charge for each case is also registered. If solid precipitation is considered, the amount of solids present are computed. If no precipitation is allowed, then the saturation index for each solid is reported. Titration simulation With titration calculations, increments of titrant are added or removed. The titrant can be an ideal reagent (solid, aqueous or gas) or a charge imbalanced solution involving a chemical, a mixture of chemicals, an oxidant, or a reductant. At any given amount of titrant, the result can be branched as a stepwise process. The branched material can be aqueous, solid or slurry in any specified solid/aqueous ratio. Mixing modeling The mixing subprogram performs equilibrium calculations after several sources of water are combined. Each source of water has its own EH, pH, chemical activity and composition, imbalanced charge and whether or not solids are initially present, can dissolve, or can be precipitated. Just as in the titration simulation, the mixing modeling can be used to predict these conditions at equilibrium. Mass Balanced EH-pH Diagrams Background Huang and Young [6] have reported that in most prior investigations, E H-pH diagrams have been calculated by first determining conditions where dissolved species were predominantly stable. The stability regions of the predominant ions were then used to determine the stability regions for the solid species. Robins [7] referred to this method of calculation as the "predominant ion" method and noted that it yields accurate diagrams but only for simple binary metal-water systems. Robins [7] further noted that, in other systems where at least one additional metal was considered, the predominant-ion method for calculating EH-pH diagrams or any other predominance area diagram could "lead to gross errors." To overcome these errors, two methods have been successfully demonstrated and both are based on free-energy minimization routines similar to those discussed by several authors [813]. The first approach is the "all-species" method presented by Robins [7] for the latest

version of DIASTAB, a computer program which performs a "predominant-ion" calculation but simultaneously considers the equilibrium effects of all other dissolved species. The second approach is the "mass-balanced" method which constrains the calculation to a constant mass for each element considered. Mass balances have been incorporated into several computer programs including SOLGASWATER [8], SYSTEM-CHEMIX [9], and MINTEQ [1] among others [10]. In this regard, the versatile computer program STABCAL has been modified [11, 12] to perform mass-balanced calculations of EH-pH diagrams. The modification did not affect its previous capability to (1) generate all of the types of predominance area diagrams, (2) simulate titration experiments, (3) catalog thermodynamic data as a library, and (4) manipulate thermodynamic data from various sources to make the data consistent. Resulting massbalanced EH-pH diagrams are presented later in this paper for the Cu-Fe-S-C-H2O and Cu-SH2O systems. A comparison of the STABCAL mass-balanced results to diagrams available in the literature [13-18] shows excellent agreement. The Newton-Raphson iterative method is used for searching for a best fit between equilibrium concentrations and stable solid masses by minimizing the difference between the given mass and the computed mass, rather than minimizing free energy directly. Initially in the calculations, no solids are assumed to be present, equilibrium calculations are performed, and then saturation indices are determined for each solid. Saturation indices (SI) are calculated from the difference of logarithms of IAP and Ksp where IAP refers to the ion activity product and Ksp is the solubility product [18]. A positive saturation index for a solid indicates supersaturation and therefore suggests that the particular solid could be stable. If solids are found to be stable, their masses are estimated and the N mass balance equations are appropriately modified. This process is repeated until a minimum is found. At the minimum, a solid will be stable if its saturation index is 0 or unstable if it is negative. Solids are systematically removed from or added to consideration depending on their saturation indices. If no solids are found to be present, the calculation is relatively simple yielding an answer within 20 iterations. In order to reduce the number of equations and thus ease the Newton-Raphson search, master aqueous species are selected for each component and substituted in the equilibrium constant expression for all remaining species [20]. The choice of the master species is often critical because the master species may attain extremely low concentrations and thereby cause underflow and overflow errors. Other errors are avoided in the Newton-Raphson iteration by using logarithmic masses and concentrations as opposed to absolute masses and concentrations and controlling the step size of these logarithmic variables. In addition, safeguards are employed to insure that Gibb's phase rule is obeyed and that inferior solids are not considered. For example, goethite (FeOOH) is metastable and is therefore inferior to the stable hematite (Fe2O3). When a calculation is complete, either the EH or pH is incremented and another calculation is performed; however, in this case, the results from a neighboring (not necessarily a previous) calculation serve as the best estimation for the answer. The EH and pH values are incremented depending on how many calculations are desired for constructing the diagram. Essentially, the more calculations there is, the higher the resolution will become and therefore the more accurate the diagram will be. Resulting EH-pH diagrams will be referred to as having a resolution of, for example, 800x400 meaning that the EH was incremented 800 times for each of the 400 increments of pH resulting in 321,201 individual data points to construct the EH-pH diagram. To construct the diagram, each of the data points is labeled with the predominant species that are present. The next task is to isolate the areas that each species occupies and to draw

boundary lines between the areas (i.e., the equilibrium lines between predominant species). Start and end points are located half way between data points. Clearly, the accuracy of drawing the boundary lines and locating the start and end points is dependent on the selected resolution. Finally, in case of jagged lines, a four-points average method is used to smooth the line without over-damping or under-damping the line. Complete details on STABCAL can be found elsewhere [11, 12]. Cu-Fe-S-C-H2O System The Cu-Fe-S-H2O system is important for hydrometallurgical processes and also for geological interpretation. Figure 1 illustrates an idealized vertical section through a porphyry copper ore forming a supergene deposit [21]. Primary sulfide minerals are at depth but due to weathering are oxidized and leached near the surface. In between these two zones is the enrichment zone where extra copper from the leach zone precipitates under reducing conditions.

E Figure 1. Idealized profile of a porphyry copper deposit illustrating the change in mineralogy with depth [26].

Figure 2. The EH-pH diagram for the CuFe-S-C-H2O system in the presence of CO2 (P=10-3.47 atm)

1

The EH-pH diagram, Figure 2, for the Cu-Fe-S-C-H2O system constructed by STABCAL reveals the same pattern shown in Figure 1. Predominant areas indicate oxidation at high E H with leaching at low pH below approximately 5. On the contrary, lower E H reducing potentials stabilize sulfide minerals at all pH conditions explaining how native copper and simple copper sulfides (covellite, CuS; chalcocite, Cu2S) are formed and enriched. The more complex sulfides like chalcopyrite (CuFeS2) are present in the deposit at even lower potentials [21].

)

Figure 2 was constructed by STABCAL using the traditional method of the most dominant species [12]. A more realistic diagram can be constructed using the mass balance approach described previously. Huang and Young [6] constructed the diagram in Figure 3 using the mass balance procedure and the same conditions as used by Woods, Yoon, and Young [15]. The two calculational techniques result in essentially the same diagram. The importance of using mass balance procedures is that two-phase fields can be clearly delineated. Note the presence of two large binary phase areas (Cu2S + Cu1.96S and Cu2S + Cu) and several smaller regions (Cu1.96S + Cu1.75S, Cu1.75S + CuS, Cu1.96S + CuO, Cu1.75S + CuO, and CuS + CuO). Also, be aware that this type of diagram changes dramatically depending not only on total concentrations of Cu and S but also their ratio and the oxidation state considered for sulfur.

0.5

Figure 3. The EH-pH mass-balanced diagram for the Cu-S-H2O system in the absence of CO2 with [S] = 0.059 and [Cu] = 0.118 mole/L.

1

Summary EH-pH diagrams are generally constructed using one of three methods: the predominant-ion, the all-species, or the mass-balanced method. Diagrams constructed by the predominant-ion method are not precise unless applied to simple binaries such as metal-water systems. On the other hand, the calculation of EH-pH diagrams from the other methods can be so laborious that a computer program must be used. In this regard, STABCAL has the capacity to perform massbalanced calculations and has been used to construct EH-pH diagrams for various mineral-water systems. The importance of using mass balance calculational procedures is that two or more phase fields can be clearly delineated which do not appear in simple predominance area diagrams. Chemistry of Berkeley Pitlake Background The Berkeley Pitlake is an abandoned mine located on the northeastern edge of Butte, MT. The pit produced mainly copper and other metal sulfide ores. Since the shut down of mining operations in 1982, the mine floods with acid rock drainage (ARD) at a rate about 5 to 8 million gallons per day. The pit presently contains over 30 billion gallons of highly contaminated water. If left uncontrolled, the pit is expected to fill by the year 2015 and water will eventually mix with surface and ground waters thereby polluting wells and nearby Silver Bow Creek which is one of the headwaters of the Clark Fork River, a tributary of the Columbia River.

0.5

Cu2

CuS

(volts)

In terms of volume and levels of pollutants, Berkeley Pitlake water is probably the most contaminated ARD site in the United States. Comparatively, it is also one of the most studied and monitored sites. Davis [22] has conducted a comprehensive study on water chemistry in early 1987. Samples were taken at the surface and at 3, 10, 50, 100, 200, 300, and 400 ft below the surface. The samples were analyzed for cations, anions, pH and EH. These data are tabulated in Table 4. Table 4. Berkeley Pitlake water (October 1987): All elements (ppm) except As and Pb (ppb) Depth, ft 0 3 10 50 100 200 300 400 Al3+ 101 103 152 165 182 193 193 196 3+ As 1 1 0.5 0.5 42 72 87 101 5+ As 4 5 0.3 1.3 251 598 768 807 Cd2+ 1.0 1.1 1.3 1.7 1.7 1.7 1.9 1.9 2+ Ca 429 433 462 474 451 479 482 492 Cu2+ 130 133 156 207 194 202 203 202 2+ Fe 0.25 60 262 622 900 938 958 962 3+ Fe 196 142 14 28 10 0 14 24 K+ 11.1 10.4 9.6 11.8 21.9 18.7 18.7 18.7 2+ Pb 112 112 149 273 562 668 522 665

Cu1.7

Cu1.75S 0 + CuS

Depth, ft Zn2+ ClSO42pH EH (mV)

0 206 9.9 4190 2.8 820

3 212 9.8 4850 2.7 720

10 280 9.1 5740 2.8 643

50 387 9.2 5960 3.0 570

100 451 20 7060 3.1 500

200 494 22 6940 3.1 457

300 497 22 6760 3.1 468

400 512 28 11600 3.1 463

Iron chemistry for Berkeley Pitlake water As seen in Table 4, the Fe(II) composition varies with depth down to approximately 100 ft and then becomes relatively constant. Below 100 ft, Fe(II) becomes the major cation at approximately 1000 ppm; sulfate (SO42-) is the major anion at about 7000 ppm. The EH-pH diagram (Figure 4) for the Fe-S-H2O system was constructed using STABCAL. Composition data collected over the period 1984-1992 including that of Davis [22] are plotted on the diagram. Solution EH is an important factor because it controls the Fe(II)/Fe(III) and As(III)/As(V) distribution. Clearly surface water to about 3 ft (open circles on the plot) is oxidized, i.e., the data are in the ferric predominance regions where FeSO4+ and Fe8O8(OH)6SO4 (Schwertmannite) are stable. This occurrence is, of course, to be expected. Deep water data (x points on the plot), however, fall within the ferrous stability region (FeSO4(aq)) which predominates at depths below about 100 ft as suggested earlier. It is important to note that 7556 ppm SO42- is equivalent to 2522 ppm S. Another way to view concentration data is to utilize the STABCAL distribution calculation and to observe what solid phase controls the concentration. Figure 5 shows that Fe (III) concentration data falls directly on the predicted solubility curve for Fe8O8(OH)6SO4 (Schwertmannite) which therefore controls the Fe(III) concentration with depth.

1.5

Figure 4. The EH-pH diagram for the Fe-SH2O system, [Fe]=1000 ppm, [S]=2522 ppm (Symbols: o surface water, * deep water ).

Figure 5. Distribution diagram for the Fe-SH2O system (Symbols: o surface water, * deep water).

With the exception of iron and arsenic, most constituents in the Berkeley pit water are divalent under oxidizing conditions, e.g., copper. The EH-pH diagram for the Cu-S-H2O system is presented in Figure 6. The data from field samples are also presented in the diagram which shows that copper is undersaturated with respect to oxide and sulfide solids and that the species responsible for the copper concentration is cupric sulfate, CuSO4(aqueous). This is better illustrated by the solubility diagram in Figure 7. As can be seen, CuSO 4 concentration is far from the solubility line indicating it is undersaturated. Similar diagrams for other divalent cations Fe(II), Zn(II), Mn(II), Cd(II), Mg(II) show that they are also undersaturated in Berkeley Pitlake water. However, Ca(II), Pb(II), As(III), As(V), Si(VI) and K(I) are oversaturated and

100

therefore controlled by the presence of a solid. Davis (22) suggest that Al(III) is oversaturated with respect the mineral jubanite but the present data suggests that is not the case.

1.5

Figure 7. Solubility-pH diagram for copper Figure 6. The EH-pH diagram for the Cu-SH2O system (Symbols: o surface water , * deep (Symbols: o surface water, * deep water). water).

300

Lime precipitation is the EPA Best Demonstrated Available Technology (BDAT) for treating Berkeley Pitlake water. The technology combines aeration to oxidize Fe(II) to Fe(III) with neutralization to precipitate the toxic metal constituents as hydroxides. Solubility plots shown in Figure 8 indicate the optimal pH to precipitate each contaminant is quite different. Because of this, the technology requires two stages to remove a majority of the metals at pH 7 and the remainder at pH 11. Filtering is needed between stages to prevent redissolution of some metals, particularly Al. Figure 8 also accounts for adsorption of As(V) on ferrihydrite beginning at about pH 3.5.

100 Cu2+

1

L) (volts)

Figure 8. Solubility-pH diagram for lime treatment of Berkeley Pitlake water including arsenic adsorption on ferrihydrite.

Berkeley Pit Sediment and Pore Water Characterization Background The objectives of the deep water sediment/pore water characterization and interaction study included: collection of sub-surface sediment/pore water samples (Three sediment core samples were collected during a sampling campaign in April 1998 from a water depth of 890 feet); characterization and speciation of sediment solids and sub-surface pore water (The sediment solids and pore waters have been chemically characterized as a function of sediment depth); Modeling the system to understand the controlling sediment formation reactions (Compounds have been identified that likely are controlling the concentrations for aluminum, arsenic, calcium, ferric iron, potassium, and silicon) [23].

u2,ppm

-2 0.5

Modeling Concentrations of individual elements in the pore water can be explained by modeling the solubility of various compounds known to be present in the sediment. The results of equilibrium modeling the sediment/pore water system verify that the following compounds are

30 10

CuS

responsible for controlling the deep water and sediment pore water elemental concentrations: Aluminum-The compound responsible for controlling the aluminum concentration in the sediment pore water is muscovite (KAl3Si3O10(OH)2). Muscovite solubility, however, does not appear to explain the aluminum concentration in the deep water, i.e., the deep water appears to be undersaturated with respect to aluminum (Figure 9). Arsenic-The compound responsible for controlling the arsenic concentration in the sediment pore water is ferrous arsenate (Fe3(AsO4)2). See Figure 10. Arsenic Solubility

Aluminum Solubility

Ferrous Arsenate, Core One

Muscov ite 1 10000

KAl3Si3 O 10(OH) 2(M)

Al3,ppm

wv z wv w z zz wwz

z z zz zzz z z

0.1

As5,ppm

xxxx 100

Fe3 (AsO4) 2

0.32

1

0.032 Core One top conditions 0.01

0.0032

0.01

0.001

0

1

2

3

4

5

0

1

2

pH

3

4

5

pH

Figure 9. Aluminum concentration in deep water (x symbol) and sediment pore water (all other symbols)

Figure 10. Arsenic concentration in sediment pore water.

Iron-The ferric iron concentration in both the deep and sediment pore water is greater than would be predicted by jarosite formation (diagram not shown). The ferric concentration in both the deep and sediment pore waters is likely controlled by the formation of schwertmannite, Fe8O8(OH)6SO4. This effect is illustrated in Figure 11 for the deep and sediment pore waters. There is some question as to whether the thermodynamic free energy of formation for schwertmannite is well defined [24]. A rigorous redetermination of the free energy of formation of schwertmannite should be conducted. The experimental data also fit the ferric concentrations predicted by modeling of the solubility of ferrihydrite (diagram not shown). However, the EH-pH diagram (not shown) suggests that ferrihydrite does not form at Berkeley Pit pH acidities. Also, a study by Levy et al. [25] of the Spenceville Pit in Nevada (a pit that has a similar acidity and elemental content) concluded that ferrihydrite was not formed. Potassium-The compound responsible for controlling the potassium concentration in the deep and sediment pore water is potassium jarosite, KFe3(SO4)2(OH)6. See Figure 12. Ferric Iron Solubility in Pore Water Schwertzmannite 100

Jarosite A KFe3(SO4) 2(OH)6 B KAl2AlSi3 O 10 (OH)2

Fe8 O 8(OH)6SO 4

10

A + B

A

1000

A

K1,ppm

Fe3,ppm

Potassium Solubility

zwvw zwvw vzz z z

1

0.1

xxx xx x

10

A + B

z z zv z w w zz z ww v w

0.1

0.01 0.001

0.001

0

1

2

3

4

5

pH

Figure 11. Iron concentration in deep water (x symbol) and sediment pore water (all other symbols).

0

1

2

3

4

5

pH

Figure 12. Potassium concentration in deep water (x symbol) and sediment pore water (all other symbols).

Silicon-The compound responsible for controlling the silicon concentration in the deep water and the sediment pore water is silica, SiO2. See Figure 12. Silicon Solubility (x=water colum n; z,v,w=pore waters) SiO 2 + KAl 2 AlSi3 O 10 (OH)2

10000 SiO 2

KAl 2 AlSi3 O 10 (OH)2

Si4,ppm

100

xxxx

v zz wz w wv w z z

Muscovite

Figure 12. Silicon concentration in deep water (x symbol) and sediment pore water (all other symbols

1

0.01

0

1

2

3

4

5

pH

Summary This illustration is an example of the use of equilibrium modeling to confirm the speciation of solids based on the solution specie concentration; i.e., the use of concentration/pH (solubility) diagrams are utillized to identify what solids control the deep water and pore water chemisty in Berkeley Pitlake sediments. Treatment Study for Berkeley Pit Water Background Haung et al have conducted numerous studies (26-29) to develop an appropriate technology for the treatment of Berkeley Pit water. Only a portion of the studies, neutralization and oxidation is presented in this paper to illustrate further the power of STABCAL Berkeley Pitlake water contains about 18 components that are present at relatively high concentrations. This pitlake water can easily contain over 300 species including over 170 solid species. Any chemical reagent introduced to the water may promote many interrelated reactions. Without a reliable and powerful calculational program technology development would be exceedingly difficult. STABCAL allows the user to define which variables are important and what limits should be placed on them. An example illustration of the use of the titration program will be presented and the results will be compared to experimentally collected data. The Berkeley Pitlake water utilized in this study was collected from the 200 ft depth in October 1992. The concentrations are tabulated along with other system parameters in Table 5. The simulation included adsorption of arsenic and heavy metals onto ferrihydrite surfaces. Data for surface adsorption reactions were taken from MINTEQA2 [1] utilizing their model for two surface-sites, Xa and Xb and a surface potential of Xc. A complete listing of all aqueous and solid species used in the present calculation are presented elsewhere [27]. Table 5. Berkeley Pit characteristics (1992) Summary information System data # Components Temp oC pH System Eh (volts) Dissolved O2 dG(kcal) OHdG(kcal) H2O

Element 17 25 2.96 0.621

-37.5777 -56.675

S Si As Cd Ca Al Fe

Components Information Valence Unit Concen#Species tration 6 P 2522 2 4 P 49.2 4 5 P 1.04 4 2 P 2.18 9 2 P 378.72 7 3 P 272.93 8 3 P 113.1 10

Components 1 2 3 4 5 6 7

Summary information System data

Components Information Element Valence Unit Concen#Species tration dG(kcal)O2(a) 3.92 Fe 2 P 957.5 6 Diele. Constant 78.54 Mg 2 P 474.24 4 O2 (atm) Mn 2 P 190.46 5 Cal. Accuracy 1.0E-5 K 1 P 23.69 2 # External Gases 0 Na 1 P 99.33 2 # Total Gases 0 Zn 2 P 565.26 8 delta EH (volts) 0.1 Cu 2 P 196.35 8 dG Database Minteq Xa 1 M 0.005 13 Adsorbent Fe(OH)3 Xb 1 M 0.2 13 Surface Area, m2 53400 Xc 0 1 1 P=ppm, M=mole/L; Xa, Xb=adsorption species, Xc=surface potential

Components 8 9 10 11 12 13 14 15 16 17

One-stage Neutralization Simulation The experimental study and the simulation utilized two neutralizing reagents, e.g., caustic (NaOH) and lime (CaO). The equilibrium calculation included adsorption and original imbalanced charge, but no additional oxidation. Two separate tests were performed. The measured and calculated pHs are presented in Figures 13 and 14, for caustic and lime, respectively. The experimental titration results are in general agreement with the calculated results.

Figure 13. Measured and simulated results for titration of Berkeley Pitlake water with caustic addition (laboratory test results: * slow, o fast agitation).

14

Figure 14. Measured and simulated results for titration of Berkeley Pitlake water with lime addition (laboratory test results: * slow , o fast agitation).

Figure 15 illustrates the simulated solubility curves for heavy metals precipitation as a function of caustic addition. As(V), Al(III), Fe(III) and Cu(II) precipitate at relatively low pH levels (i.e. low caustic additions) and Fe(II), Mn(II), Zn(II) and Cd(II) precipitate at higher pH (i.e. high caustic addition). This illustrates the need for two stage precipitation processes to adequately treat Berkeley Pitlake water.

12

14 12 10

Figure 15. Results for caustic titration simulation applied to Berkeley Pitlake water. Note the pH scale on the right hand axis.

Mixing Simulation for the SPJV Macdonald Project Background and Results A mine water assessment was conducted to characterize the filling and the chemistry of the proposed MacDonald mine near Lincoln, Montana [30]. The six sources of inflow water at the site were examined: (1) Mine wall runoff, (2) Precipitation (i.e. rain and snow), (3) Area runoff, (4) Tuff groundwater at pH 6, (5) Tuff groundwater at pH 4, and (6) Upper groundwater. Flow rate data and chemical composition for each source are presented in Table 6.

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Table 6. McDonald Project: data and simulation results for mixing six sources of water (all concentrations in ppm) Precipitation

ents,log(m/L)

Flow g/m Vol Ratio pH EH(volts) Ca Mg K Na Cl C S F Ba Al As Sb Fe Zn Mn Si

Mine Wall Runoff 90 0.469 6.2 0.48 2.6 1 8.2 2.7 0.8 4.6 1.7 0.6 0.14 0.48 0.03 0.008 0.21 0.052 0.037 3.711

325 1.693 4.9 0.56 0.075 0.027 0.072 0.22 0.2 0.4

0.0004 0.0438

Area Runoff 113 0.598 7.4 0.41 17 4 2 3 1 12.1 1.3 0.1 0.2 0.7 0.003 0.005 0.55 0.04 0.07 10.90

Tuff GW pH 6

Tuff GW pH4

110.6 0.576 6 0 62 2 5 5 1 13.0 76.1 0.14 0.007 0.001 0.090 0 0 0.22 0.17 1.58

3.4 0.018 4 0.10 85 2 5 5 1 4.6 172.2 0.14 0.005 0.097 1.003 0.000 2.6 72.5 0.17 1.58

-4

Upper GW 192 1 6.9 -0.30 15 4 3 5 1 15.1 2.0 0.15 0.1 0.1 0.005 0.004 0.51 0.016 0.027 18.40

Fe

Results in 1 L of Mixed Water Solids

Aqueous 4.35 6.61 -0.18 14.63 1.85 2.56 2.62 0.59 7.44 11.74 0.14 0.010 1.56E-5 1.16E-4 2.56E-3 0.125 0.022 0.044 6.033

Al3 0.056 0.17 0.020 0.11 0.31

0.31

As5

The mixing simulation in STABCAL uses the principles employed in the titration program. Upper GW was chosen as the host, and calculations and results are expressed in terms of per liter of this solution. Amounts of the other sources were based on a volume ratio to the host. The simulation involved 20 elements and 200 species. Fe(II)/Fe(III) was the only redox couple considered. Results of simulation are presented in the above table. The final pH was 6.61 and EH was -0.179 V. The volume changes from 1 liter to 4.35 liters. Ba(II) Al(III), As(V), Fe(III), Zn(II) and Si(IV) should precipitate.

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Treatment of Electroplating Sludge Waste Background Metal-bearing hydroxide sludge wastes are generated by the electroplating (EP) industries throughout the world. These wastes are classified as hazardous materials in the US and have traditionally been disposed of in hazardous landfill sites. EP sludges are mixtures of metal hydroxides, e.g., Fe, Cr, Ni, and Zn, that are effectively leached in a sulfuric acid at pHs< 4. Treatment of EP sludges provides an illustrative example of process design for leaching and selectively separating trivalent metal species from divalent species. STABCAL diagrams were used to model leaching sludges with sulfuric acid and selective separation of iron from the other metals. Montana Tech graduate students [31-33] investigated the recovery of metal values from EP sludges using phosphate chemistry and their data are presented in the following illustrations. Leaching and Selective Separations The distribution diagram presented in Figure 16 illustrates that Zn and Ni should be selectively leached (at pH 5-7) from the sludge materials using sulfuric acid thereby leaving the Fe and Cr in the leach residue. However, Dahnke [31] demonstrated that a neutral leach is ineffective. An alternative treatment suggested by the distribution diagram (Figure 16) is solubilizing all the metals and then selectively precipitating trivalent hydroxide solids. An effective pH to accomplish the dissolution is two. This treatment is effective except adsorption of Zn and Ni onto the hydroxides contaminate the leach residue when the precipitation is conducted at pH 57. The adsorption effect can also be demonstrated to the students by utilizing the STABCAL program; results are presented in Figure 17. Note that several percent Ni and Zn is incorporated in the ferric hydroxide residue at pHs 6-7.

10000

Figure 16. Solubility of metal hydroxides

Figure 17. Solubility of metal hydroxides including adsorption (Initial Ni, Zn = 10,000 ppm. pH 6: Ni =9510, Zn=8540; pH 7: Ni=8430, Zn=6320)

10000

Konda [32] and Rapkoch [33] investigated ways to overcome adsorption contamination and poor filterability problems by utilizing phosphate chemistry. Phosphate precipitates are crystalline and show very little adsorption capacity for metal cations. Konda‟s [32] results are presented in Figure 18. Experimentally it was demonstrated that ferric phosphate could be selectively precipitated from the mixed metal solution at pHs of 3-3.5 without contamination by Ni or Zn. Figure 18 illustrates an important feature that needs to be brought to students attention. Note that it appears that chromium phosphate should also precipitate along with the ferric phosphate. However, kinetically it required extremely long residence times for that to occur. Chromium remained in solution at ambient temperatures for several weeks. Eventually it precipitated it accordance with the calculated solubility diagram. All the concentration data presented in Figure 18 was collected after a holding time of one-half hour.

1000

m

1000

The final flowsheet developed for treating the EP sludge included the following steps : sulfuric acid leaching to solubilize all the metals; selective removal of iron as ferric phosphate; selective removal of chromium as chromium phosphate at 60oC; solvent extraction of nickel; and solvent extraction of zinc. Summary This illustration is an example of the use of equilibrium modeling to evaluate potential unit operations for use in designing process flowsheets. Note that experimental data closely follow the anticipated results displaced by modeling with STABCAL. This example illustrates the use

Figure 18. Solubility of metal phosphates.

x

xx w w x x x

of two types of diagrams, e.g., solubility/pH diagrams to determine what ligand is appropriate (hydroxide or phosphate) for selectively precipitating trivalent metal solids, and solubility/adsorption/pH diagrams to determine whether adsorption on ferric hydroxide precipitates would produce a contaminated residue product.

10000

w

Treatment of Mercury K106 Sludge Background Universal Dynamics, Ltd and Montana Tech have developed a hydrometallurgical process for the recovery and recycle of mercury from caustic plant wastewater sludge materials (US EPA hazardous waste classification K106) [33-35]. The process is also applicable for the treatment of mercury contaminated soils and other mercury bearing waste (EPA hazardous waste classification D009) and is capable of lowering the mercury content in K106 solids from 10% to < 50 mg/kg Hg. The treated solids pass the EPA Toxicity Characterization Leach Procedure, TCLP, (Hg < 25 g/liter, ppb) which means that the solids are deemed nonhazardous. This hydrometallurgical process has been accepted by the EPA as an alternative Best Demonstrated Available Technology (BDAT). Universal Dynamics has commercialized the process under the name REMERC. The REMERC process has been installed at three U.S. Chlor-Alkali plants (Georgia-Pacific, Bellingham, Washington; Westlake C&D Corporation, Calvert City, Kentucky; and Pioneer Chlor-Alkali, St. Gabriel, Louisiana).

nent,ppm

1000

x

100

x

K106 sludges are produced at Chlor-alkali plants during sulfide precipitation clean up of process wastewater. Resulting sludges contain 3-10% mercury. The remainder of the sludge is silica and silicates. The treatment process is based on a two stage hypochlorite leach followed by mercury recovery by iron cementation. Leach The dissolution of mercury or mercury sulfide is not effective without the presence of the chloride ligand (Figure 19) except under oxidizing/acidic conditions. Figure 19 illustrates that the EH/pH conditions for sulfuric acid leaching to solubilize mercury must be chosen to lie within the region marked HgSO4(a). Note in Figure 20 that the dissolution of mercury and mercury solids is thermodynamically favorable over a wide range of solution potentials and pH values in a chloride environment. An example of the influence of solution potential on mercury

10

x

solubility at pH 6 (first stage leach for the REMERC process) is presented in Figure 21. Note the relatively high mercury solubility at EH potentials 500-1000 millivolts. The leach reactions for dissolution of mercury sulfide and mercury in a chloride environment are presented below: HgS + 4 NaOCl + 2 Cl- = HgCl42 - + Na2SO4 + 2 NaCl [1] 2Hg + H2SO4 + NaOCl + NaCl +2 Cl = HgCl4 + Na2SO4 +H2O [2]

2 Figure 19. EH/pH diagram for the Hg/S/H2O system.

Figure 20. EH/pH diagram for the Hg/Cl/S/ H2O system. Shaded area depicts conditions appropriate for dissolution of mercury.

2

Figure 21. Solubility/EH diagram at pH 6 for the Hg/Cl/S/Water system. Diagram illustrates dissolution of mercury to several gpl at EH values from approximately 0.5-1v.

HgSO4 1.5 H

The leach is conducted in two stages. Typical conditions utilized in commercial operations for leaching are Stage One: pH=6, 5-27% NaCl, EH ~1100 mV, ToC=20-100, 30 minutes to 1 hour; Stage Two: pH=2, 5-27% NaCl, EH~1100 mV, T=20-100oC, 15 minutes. Typical leach results achieved at operating commercial facilities are: Georgia-Pacific (60,000 ppm to 150 ppm); Westlake Chemicals (110,000 ppm to 220 ppm); Pioneer Chlor-Alkali (55,000 ppm to 50 ppm).

1.5 1000

lts)

olts)

Mercury Recovery-Mercury recovery from the leach solution can be accomplished by one of two approaches: Cementation (displacement from solution by metallic iron) or electrolytic recovery by recycling the leach solutions to the chlor-alkali cells.

The basic chemistry for cementation of mercury by an iron substrate is electrochemical deposition. Elemental iron lowers the solution potential so mercuric chloride is reduced to elemental mercury, Figure 21. The reaction that controls the formation of mercury is: HgCl4-2 + Feo = Hgo + FeCl2 +2 Cl-1 [3] The cementation reaction can be performed in a simple mix tank or rotating mill. A three-phase system is present in the reactor, i.e. a lower mercury pool, an aqueous phase containing the mercuric chloride and a coarse iron particulate phase. Iron scrap or iron powder floats on a

100

mercury pool that is maintained in the bottom of the reactor. Iron is always in contact with liquid mercury and with the mercury bearing solution phase. Globules of mercury form on the iron as the cementation reaction occurs. The globules of mercury coalesce into the mercury pool. It is therefore possible to remove solution from the reactor containing ferrous chloride but essentially no mercury. Elemental mercury can be extracted from the bottom of the vessel. The mercury product is equivalent in purity to triple distilled mercury. The mercury free ferrous chloride solution is treated by hypochlorite oxidation to produce a ferric hydroxide product and the cleaned brine solution is recycled to the chlor-alkali process plant water.

Figure 4. Eh/pH diagram for the Fe/Hg/S/Cl/H2O system.

2

Summary Mercury waste treatment exemplifies the use of equilibrium modeling to select an appropriate ligand for the solubilization of mercury and for evaluating the possibility of utilizing iron cementation as a final mercury recovery operation. This example illustrates the use of two types of diagrams, i.e. EH-pH diagrams to deside what ligand is appropriate for solubilizing mercury or mercury compounds and for determining that iron would be an appropriate cementing metal for the recovery of mercury from solution; and a concentration/EH diagram at a fixed pH to illustrate what potential is necessary to dissolve mercury and mercury compounds.

Hg2+

Hg

1.5 HgCl+

(volts)

References 1. J.D. Allison, “MINTEQA2/PRODEFA2 A Geochemical Assessment Model for Environmental System”, Version 3, Environmental Research Laboratory, EPA (1990). 2. G.B. Naumov, et. al., “Handbook of Thermodynamic Data, Translated by G.H. Soeimani, USGS, NTIS PB 226 722, (1974), 328 p. 3. D.D. Wagman, W.H. Evans, V.B. Parker, R.H. Schumm, I. Halow, S.M. Bailey, K.L. Churney, R.L. Nuttall, “The NBS Tables of Chemical Thermodynamic Properties, J. of Physical and Chemical References Data, 11, Sup. 2, (1982), 392 p. and Errata, 18, (1989), 1807-1812. 4. R. M. Smith and A.E. Martell, “Critical Stability Constants,” (1974-1985) Inorganic Compounds and NIST Software for Inorganic Compounds, Plenum Press, NY (1995). 5. T.L Woods and R.M. Garrels, “Thermodynamic Values at Low Temperature for Natural Inorganic Material - An Uncritical Summary,” Oxford University Press, London (1987). 6. H. H. Huang and C.A. Young, “Mass-balanced Calculation of EH-pH diagrams using STABCAL”, in Electrochemistry in Mineral and Metal Processing IV, eds. R. Woods, F. M. Doyle and P. Richardson, The Electrochemical Society Proceedings 96-6, (1996), 227. 7. R.G. Robins, in “Hydrometallurgy Fundamentals, Technology and Innovation”, eds. J.B. Hiskey and G.W. Warren, SME, Littleton, CO (1993), 143. 8. G. Eriksson, Anal. Chim. Acta, 112, 375 (1979). 9. A.G. Turnbull and M.W. Wadsley, CSIRO-Monash Thermochemistry System (1992). 10. A.E. Morris, 4th Biennial Conference and Workshop on Computer Software for Chemical and Extractive Metallurgy Calculations, University of Missouri-Rolla, MO (1992). 11. H.H. Huang, “Method for Constructing an EH-pH Mass Balanced Diagram”, Internal Report, Montana Tech of Univeristy of Montana, Butte, MT (1996). 12. H.H. Huang, “Construction of EH-pH and Other Stability Diagrams of Uranium in a Multicomponent System with a Microcomputer-II Distribution Diagrams”, Canadian Met. Quarterly, 28, No. 3, (1989). 13. M.D. Pritzker and R.H. Yoon, Int. J. Miner. Proc., 12, 95 (1984).

FeSO4+ 1

0.5

Hg

Fe(SO

14. M.D. Pritzker and R.H. Yoon, in Electrochemistry in Mineral and Metal Processing I, 8410, eds., P.E. Richardson, S. Srinivasan and R. Woods, The Electrochemical Society, (1984), 126. 15. R. Woods, R. H. Yoon and C. A. Young, “EH-pH Diagrams for Stable and Metastable Phases in The Copper–Sulfur–Water System”, International J. Mineral Processing, 20, (1987), 109. 16. C.A. Young, R. Woods and R.H. Yoon, in Electrochemistry in Mineral and Metal Processing II, 88-21, eds., P.E. Richardson and R. Woods, The Electrochemical Society, (1988), 1. 17. C.A. Young, “Nonstoichiometry of Chalcocite in Water-Xanthate Systems”, MS Thesis, VPI&SU, Blacksburg, VA (1987). 18. K.S.E. Forssberg, B.M. Antti, and B.I. Palsson, in Reagents in the Minerals Industry, eds., M.J. Jones and R. Oblatt, IMM, London, (1984), 251. 19. D..K. Nordstrom, J.L. Munoz, Geochemical Thermodynamics, Blackwell Scientific Publications (1986), 264. 20. B. Carnahan, H.A. Luther and J.O Wilkes, Applied Numerical Methods, John Wiley & Sons, (1969), 321. 21. R. W. Bartlett, “Solution Mining – Leaching and Fluid Recovery of Materials”, Gordon and Breach Science Publishers, (1992), 78. 22. A. Davis, Report to EPA, “Factors Affecting the Geochemistry of the Berkeley pit, Butte, Montana”, EPA Work Assignment no. 373-8L22, Camp Dresser & Mckee, May 2, 1988. 23. L.G. Twidwell, R. Berg, R. Ziolkowski, „Deep Water Sediment/Pore Water Characterization and Interactions‟, Mine Waste Technology Program, Activity IV, Project 9, Final Report to MSE, Butte, MT, (1998), 68 p. 24. H.H. Huang, Personal communications between H.H. Huang (Metallurgical Engineering Department, Montana Tech of the University of Montana, Butte MT) and L.G. Twidwell (Metallurgical Engineering Department, Montana Tech of University of Montana, Butte, MT 59701), September 1996. 25. D.B. Levy, K.H. Custis, W.H. Casey, P.A. Rock, “The Aqueous Geochemistry of the Abandoned Spenceville Copper Pit, Nevada County, California”, J. Environmental Quality, 26, No. 1, 1995, 233-243. 26. Y.C. Tai, “Selective Removal of Metal Values from Acid Mine Waters”, MSc Thesis, Montana Tech of the University of Montana, Butte, MT (1997). 27. H.Y. Gu, “Chemical Treatability of Acid Mine Drainage at the Berkeley Pit”, MSc Thesis, Montana Tech of the University of Montana, Butte, MT (1993). 28. H.H. Huang and Q. Liu, in Hazardous Waste Management V, eds., D.W. Todder and F.G. Pohland, ACS (1995). 29. ARCO, “Treatability Sampling and Bench-Scale Testing Report, Butte Mine Flooding Operable Unit, Butte, Montana”, Canonie Environmental, Bench scale tests performed by Montana Tech of University of Montana, Butte, MT, May (1993). 30. L. B. Kirk, et al, “Mine Lake Geochemical Prediction for the SPJV MacDonald Project, Planning, Rehabilitation and Treatment of Disturbed Lands” Billing Symposium, (1996), 393. 31. D.R. Dahnke, “Removal of Iron from Process Solutions”, MSc Thesis, Montana College of Mineral Science and Technology, Butte, MT, May 1985. 32. E.A. Konda, “Study of Ferric Phosphate Precipitation as a Means of Iron Removal from Zinc Bearing Acidic Aqueous Solutions”, MSc Thesis, Montana College of Mineral Science and Technology, Butte, MT, May 1986. 33. J.M. Rapkoch, “The Effects on Metal Phosphate Precipitation from Complex Solutions by Substituting Sodium Hydroxide, the Titrant, with Ammonium Hydroxide”, MSc Thesis, Montana College of Mineral Science and Technology, Butte, MT, May 1988. 34. M. Rockandel, L.G. Twidwell, “The Recovery and Recycle of Mercury from Chlor-alkali Plant Wastewater Sludge”, Proceedings Third International Conference on Materials Engineering for Resources, ICMR‟98, Akita, Japan, (1998), 258-265. 35. L.G. Twidwell,, J. Selby, “The Recovery and Recycle of Mercury from Chlor-Alkai Plant Wastewater Sludge”, Proceedings REWAS‟99, eds. I. Gaballah, J. Hager, R. Solozaral, Global Symposium on Recycling, Waste, Treatment and Clean Technology, San Sabastian, Spain, (1999), 1765-73.