Chemical Modeling of Ammoniacal Solutions in Ni/Co ......Chemical modeling, a powerful tool for...

Chemical Modeling of Ammoniacal Solutions in Ni/Co Hydrometallurgy

by

Sam Roshdi

A thesis submitted in conformity with the requirements for the degree of Master of Applied Science

Department of Chemical Engineering and Applied Chemistry University of Toronto

© Copyright by Sam Roshdi 2011

ii

Chemical Modeling of Ammoniacal Solutions

in Ni/Co Hydrometallurgy

Sam Roshdi

Master of Applied Science

Department of Chemical Engineering and Applied Chemistry University of Toronto

2011

ABSTRACT

Chemical modeling has become an important subject of research in applied thermodynamics for

designing, developing, optimizing and controlling of different industrial processes. In this work,

a new database for successful modeling of solid and aqueous phase equilibria in specific

hydrometallurgical processes was developed using the Mixed Solvent Electrolyte (MSE(H3O+))

model of the OLI Systems software. The ionic interaction parameters between dominant species

in the solution were determined by fitting available binary and ternary experimental data such as

mean activity, heat capacity and solubility data and then these interaction parameters were

validated in multi-component systems.

A chemical model was also developed to predict the phase behaviour in ammoniacal solutions

containing cobalt, nickel, copper, and zinc. The developed model was shown to accurately

predict the chemistry of the Copper Boil process in ammonia leach process. As it is not practical

to measure solubility data under all possible conditions, because of the large number of

components involved, chemical modeling becomes a valuable tool for assessing the process

chemistry for a wide variety of complex aqueous processing streams.

iii

Also, ammonium nickel sulphate hexa hydrate double salt solubility was measured within its

stability temperature range and used as new sets of data to validate the accuracy of developed

model. Using HSC 6.1 software, the copper boil processes was simulated. This simulation was

linked with thermodynamic results of MSE model to successfully predict the chemistry of the

Copper Boil process as well as provide some practical recommendations for the optimum process

operation.

iv

Acknowledgments

I would like to express my sincere gratitude to all those who have helped make this thesis possible. Many thanks are due to my supervisor, Professor Vladimiros G. Papangelakis for his support, guidance and supervision. Also, special and sincere thanks go to my Supervisory Committee, Professor Donald Kirk, and Professor Honghi Tran for their advice, feedback and commitments. In addition, I would like to acknowledge and thank the OLI Systems Inc., Sherritt International Corp., Ontario Graduate Scholarships in Science and Technology, and CONNAUGHT scholarship for their contributions, and the financial support provided for this project. Many thanks to the Aqueous Process Engineering and Chemistry group members during my study especially, Ghazal Azimi, Ilya Perederiy, Matthew Jones, Ming Huang, Haixia Liu, Srinath Garg ,Samouel Peters, Ramapal Seini and Dr. Li. I wish to pay a very special thank to my family, in particular, my very best friend and my wife, Azadeh, for her continuous support, motivation, inspiration and love as well as my father for his endless encouragement and belief in me, without whom none was possible.

v

Table of Contents

Acknowledgments .......................................................................................................................... iv

Table of Contents ............................................................................................................................ v

List of Tables ................................................................................................................................ vii

List of Figures .............................................................................................................................. viii

List of Appendices .......................................................................................................................... x

1 INTRODUCTION...................................................................................................................... 1

1.1 Objectives ........................................................................................................................... 2

1.2 The Chemistry of the Copper Boil ....................................................................................... 3

2 METHODOLOGY ..................................................................................................................... 8

2.1 Chemical Modeling Methodology ....................................................................................... 8

2.1.1 Chemical Equilibria and Thermodynamic Framework ........................................... 8

2.1.2 Model Parameter Evaluation ................................................................................. 10

2.2 Experimental Methodology ................................................................................................ 11

3 RESULTS AND DISCUSSION ................................................................................................ 15

3.1. Binary Systems.................................................................................................................. 15

3.2. Ternary Systems ................................................................................................................ 24

3.3. Nickel-Ammonia Complexation ....................................................................................... 29

3.4. Experiment Results. .......................................................................................................... 32

4 SIMULATION OF THE COPPER BOIL PROCESS USING THE HSC 6.1 SOFTWARE .. 35

4.1 Prediction of ammonia mole fraction in the vapor phase measured by Sherritt ................ 37

5 CONCLUSIONS ........................................................................................................................ 40

6 REFERENCES .......................................................................................................................... 42

APPENDICES .............................................................................................................................. 45

Appendix 1: Density Tables .......................................................................................................... 45

vi

Appendix 2: HSC results .............................................................................................................. 47

vii

List of Tables

Table 1. Experimental data used for model parameter estimation

Table 2. MSE middle range ion interactions parameters

Table 3. The dissociation reaction, standard state Gibbs free energy and entropy of formation of

the solid

Table 4. The stability constant of nickel and cobalt ammine complexes

Table 5. The Comparison of outflow compositions of the Copper Boil pots calculated by the

HSC Chemistry simulation with available Industrial data. Concentrations are in molality [mol/kg

water]. (H2O= 55.51 mol/kg water)

Table 6. The composition of different pots in the Copper Boil process of Sherritt

viii

List of Figures

Fig 1. The Sherritt nickel/cobalt refinery process

Fig 2. The Copper Boil process flowsheet

Fig 3. Schematic view of the reaction vessel for solubility measurements

Fig 4. NH3-H2O equilibrium curves: Mole fraction of ammonia in both vapour phase and liquid

phase at equilibrium state. using the MSE (H3O+) default database prediction ;experimental data

from [22]

Fig 5. The solubility of ammonium sulphate in water, experimental data from Terres and

Schmidt [31], Ishikawa and Murooka [32], and Linke and Siedel [22] and MSE (H3O+) default

data base are used. Solid phase is ammonium sulphate

Fig 6. The solubility of the CoSO4 in water. Experimental data are from [22, 32, 34]. The curve

is the fitted model.

Fig 7. The Cp of the CoSO4 in water vs. temperature. Data points are from [26]. The curve is the

fitted model.

Fig 8. The mean activity coefficient of the CoSO4 in water. Data points are from [26]. The curve

is the fitted model.

Fig 9. The solubility of ZnSO4 in Water Experimental data are from [22, 36]. The curve is the

fitted model. Solid phase at low temperature is ZnSO4.7H2O and after 42°C it is ZnSO4.6H2O

and at high temperatures ZnSO4.H2O forms a solid phase.

Fig 10. The solubility of the NiSO4 in water. Experimental data are from [22, 23]. The curve is

the fitted model. Solid phase at low temperature is NiSO4.7H2O and after 31.4°C it is

NiSO4.6H2O and at temperatures higher than 100.1°C NiSO4.H2O forms a solid phase.

Fig 11. The solubility of (NH4)2SO4 in NiSO4- H2O, The curve is the fitted model and

experimental data from [22]. T=25 °C.

Fig 12. NiSO4 solubility vs. CoSO4 concentration. Data points are from [22,30]. Lines are fitted

model results.

Fig 13. (NH4)2Ni(SO4)2.6H2O solubility in water. Data points are from [37,38]. Lines are fitted

model results.

Fig 14. Solubility of Ni(OH)2 in ammonia solution. Lines are model predictions and

experimental data are from [ 43-36].

ix

Fig 15. Total pressure vs. ammonia molality in solution of 2 molal ammonium sulphate. Solid

lines are fitted model result where dotted line shows the default model prediction. Experimental

data are from [38, 39]

Fig 16. Total pressure vs. ammonia molality in solution of 4 molal ammonium sulphate. Solid

lines are fitted model result where dotted line shows the default model prediction. Experimental

data are from [38, 39]

Fig 17. Speciation of nickel ammine complexes in NiSO4-(NH4)2SO4-NH3 solution

Fig 18. Experimental set up for solubility measurements. A. Jacketed reactor, B. Temperature

controller, C. Temperature-controlled circulating heater, D. Sampling line, E. stirring speed

controller, F. Stirrer

Fig 19. Ammonium Nickel Sulphate Hexa Hydrate solubility in water. Diamond and dash dots

show this works experiments. Star dots show literature values. Solid line is a model prediction.

Fig 20. The Copper Boil process Flowsheet from the HSC Simulation

Fig 21. Ammonia mole percentage in the vapor phase vs. “ammonia:metal” mole ratio. Dots are

MSE model calculated results; the curve is lab test results from the Sherritt plant.

Fig 22. The Comparison of total pressure in the Copper Boil pots obtained from the MSE model

with that from plant data

x

List of Appendices

Appendix 1: Density Tables

Appendix 2: HSC Results

1

1 INTRODUCTION

Chemical modeling, a powerful tool for predicting and understanding the behaviour of complex

aqueous processing systems, is used to study the ammoniacal solutions existing in Ni/Co

hydrometallurgy. Electrolyte thermodynamics plays an important role in the simulation and

optimization of hydrometallurgical processes where multicomponent electrolyte solutions exist

under diverse operating conditions. The Copper Boil process in the Sherritt Refinery (Corefco)

is an ammonia distillation process for nickel and cobalt recovery. The presence of metal ions,

ammonium sulphate and ammonia in the process solution affects the solution properties in a non-

straightforward manner. This is due to the formation of metal ammonium complexes which

makes the speciation in the solution more sophisticated. The presence of mixed electrolytes in

aqueous solutions leads to the deviation of solution properties and strong nonideality. Therefore,

developing an appropriate methodology to predict solid-liquid-vapour equilibria has become an

important field of research.

In this work, the Mixed Sovent Electrolyte (MSE) model of the OLI software (OLI ESP 8.2) is

used. It is proposed to correlate ion interaction parameters of the MSE model with temperature

and solution composition from literature data such as mean activity, heat capacity and solubility

of binary and ternary systems containing metal sulphates and ammonia. Calculating the model

parameters makes it possible to predict the thermodynamic properties of the systems for which

data are not available in the literature or cannot be measured experimentally. The results

obtained in this study can be applied in different industrial fields including hydrometallurgical

processes.

Developing a chemical model which can predict the solution chemistry in the Copper Boil

process is one of the main goals of this work. For developing such a model, first the

thermodynamic properties of binary and ternary solutions containing the electrolytes of interest

such as NiSO4, CoSO4, (NH4)2SO4, etc. should be used to regress the model parameters. Then,

the parameters obtained in the fitting step can be applied to predict the phase behaviour of mixed

multicomponent solutions in the ammonia recovery process. By using this model, the effect of

changing the feed composition on the amount of ammonia to be recovered can be determined.

Available experimental data for electrolyte systems containing NiSO4, NH3, (NH4)2SO4, CoSO4,

etc. was used to estimate the model parameter through the OLI built-in regression facility. Since

2

complexation has a substantial effect on the behaviour of electrolytes in the ammoniacal nickel

sulphate solutions. The concentration of the free nickel ion in solution is significantly reduced by

the formation of nickel ammine complexes, Ni(NH3)n2+, (n=1-6). Similar to nickel in

ammoniacal solutions, both Co(II) and Co(III) form complexes in the presences of NH3 ligands.

Therefore, Ni(NH3)n2+-H2O system, Co(NH3)n

2+-H2O system, and Co(NH3)63+-H2O system are

effectively developed into the OLI database,(n=1-6).

To develop a comprehensive model with the ability to predict multicomponent solutions under

various conditions in ammoniacal solutions, more experimental data are needed which are either

not available or available in a narrow range of conditions. The solubility data for ammonium

nickel sulphate hexahydrate is available in literature only from 20 to 60 ºC which does not cover

the industrial temperature range; this salt is stable up to 96 ºC. Therefore, solubility of this

double salt alongside with other double salts of picromerite series needed to be measured with

dissolution procedure. Results of these measurements are used to fit interaction parameters of

these salts into the database.

Furthermore, thermodynamic equilibrium data from the established chemical model is used to

modify the simulation database. Using the modified database, HSC Chemistry Software (HSC

6.1) is used to simulate the Copper Boil process. This makes it possible to find the best operating

conditions for the ammonia recovery process. It also provides insight regarding sulphuric acid

and steam consumption of the Copper Boil process.

1.1 Objectives

One of the objectives of this work is to develop a reliable database for chemical modeling of the

ammoniacal hydrometallurgical systems using the OLI software. Using this chemical model

makes it possible to predict the solution chemistry in the ammonia recovery stage of the Copper

Boil process. This can be done by using the MSE model implemented in the OLI software and fit

the MSE model interaction parameters in the systems for which experimental data are available

in the literature. The result of this work would be a dedicated thermodynamic database to predict

the chemistry in this process.

One of the main advantages of the ammonia pressure leach process is that a large amount of

ammonia is recovered from the vapour phase in the circuit to be used again. It is evident that any

3

significant increase in the capacity of the refinery leads to redesign of the leaching stage as well

as the nickel-cobalt separation stage. The ammonia recovery system will also be affected.

Having a model to predict the influence of these changes on the percent of ammonia to be

recovered would be very advantageous. Using such a model makes it possible to discover

whether the current capacity of the ammonia recovery circuit can handle the changes in feed

composition and flow rate.

Another objective of this study is to measure and produce new experimental data which are

useful for validating the chemical model developed. As well, the new data will be measured

under relevant industrial conditions, so they can be used in different research fields or by

different industries, since it is not practical to do these measurements under all different

conditions. Three different types of measurements of interest in this work are to study the Solid-

Liquid Equilibria (SLE) which is to study the solubility of a solid in a solution; to study the

Solid-Vapour-Liquid Equilibria (SLVE) which is to study the solubility of a gas and solid in a

solution; to study the ammonia complexation and speciation in a nickel sulphate solutions.

1.2 The Chemistry of the Copper Boil

The Sherritt refining process has been in operation since 1954. The original process was

described in details by Boldt and Queneau (1967) [1]. There were only minor changes to the

original process f1owsheet from 1954 to 1991. After 1991, some modifications were made in the

refinery according to the feed change [2-6].

The nickel/cobalt sulphides containing 55% Ni, 5% Co, 0.8% Fe, 35% S and 10% moisture are

produced from limonitic laterite ores by pressure acid leaching (PAL) in Cuba (by Moa Nickel

S.A.) and are shipped to the Corefco nickel-cobalt refinery, located in Fort Saskatchewan,

Alberta, as the feed [6-8].

The Sherritt process is an excellent classical approach for treating nickel concentrate. This

process is based on an ammoniacal oxygen pressure leach and subsequent recovery of nickel by

hydrogen reduction. A schematic diagram of the process is shown in Figure 1. The ammoniacal

oxygen pressure leach is very efficient at rejecting most impurities to the leach residue mainly

iron as hematite. Then cobalt enriched solution separated is fed to the cobalt refining circuit. The

cobalt-depleted nickel ammine solution is configured to produce sufficient partially reduced

4

sulphur species to precipitate the copper as copper sulphide in the Copper Boil where free

ammonia is removed, causing the copper to precipitate. Nickel is preferentially reduced during

hydrogen reduction until the “nickel:cobalt” ratio reaches approximately one. The remaining

nickel and cobalt are then precipitated as a mixed sulphide by H2S gas [9].

Fig 1. The Sherritt nickel/cobalt refinery process

The filtered leach liquor from leaching stage is treated with anhydrous ammonia to precipitate a

large portion of cobalt as cobalt(III)-nickel(II) hexamine complexes, which forms the feed to the

5

cobalt refining circuit. On the other hand, the cobalt-depleted nickel ammine solution is fed to

the Copper Boil process to recover most of the ammonia, while concentrating nickel solution and

quantitatively precipitating copper as copper sulphide, by reacting with sulphur dioxide and

elemental sulphur. The ammoniacal copper-free nickel solution is adjusted to give the

“ammonia:nickel” mole ratio of around 2, which is required by the nickel reduction process.

The Copper Boil Process is an ammonia distillation process. In this process, some of the

ammonia that is not complexed with metal ions in the solution is stripped in the Boil Feed

Stripper. The rest is distilled in the Nickel Boil with steam, and the ammonia that is complexed

with metal ions is removed by boiling in the Copper Boil pots and reacted with sulphuric acid in

the reboiler. Besides, adding elemental sulphur and sulphur dioxide to the second pot forces the

copper to precipitate as copper sulphide. Therefore ammonia can be recovered to be used in the

leach circuits and the copper sulphide precipitate is shipped to copper refineries [7].

The ammonia and copper removal circuit consists of three stages. The first stage is an air

stripping column, in which the compressed air and the pre-heated high ammonia content feed

solution enter counter-currently. The second stage is a packed distillation column with a steam

heated thermosyphone reboiler, known as the ‘nickel boil’, which reduces the “ammonia:metal”

mole ratio from about 8:1 to 5:1. The third stage is the original copper boil distillation train,

consisting of four pots and one reboiler. These four pots arranged in cascade, and feed the

solution by gravity into the steam heated reboiler. The vapour phase consists of steam which

flows counter-currently to the liquid phase and striped the ammonia from the solution in each

stage. Copper precipitates in the form of copper sulphide as the ammonia concentration

decreases. Copper reacts with unsaturated sulphur species such as thiosulphate and dithionate to

form insoluble copper sulphide. Sulphuric acid is added into the reboiler to neutralize part of the

residual ammonia in order to adjust the “ammonia:metal” mole ratio of about 2:1. The following

three reactions take place in the Copper Boil pots and the forth one occurs in the reboiler:

3223

263 4)()( NHNHNiNHNi +→

++ (1)

3323

363 4)()( NHNHCoNHCo +→

++ (2)

322

43 4)( NHCuNHCu +→++ (3)

6

heatSONHSOHNH +→+ 424423 )(2 (4)

Figure 2 shows the process flowsheet of the Copper Boil stage. The feed solution to this stage in

the original process contained 130 to 140 g/L NH3, corresponding to a mole ratio in the range

from 6:1 to 9:1. With the introduction of the new cobalt separation process, the feed solution

increased in ammonia content to the range 175 to 185 g/L, corresponding to mole ratios in the

range 10:1 to 14:1. However current average mole ratio is in range of 9:1 to 11:1. The existing

copper boil circuit proved to have insufficient distillation and condensing capacity to handle the

increased ammonia load [6, 7].

Copper sulphide precipitation occurs only in the copper boil circuit following the addition of

elemental sulphur and sulphur dioxide in the second pot. Sulphuric acid is still required to adjust

the ammonia: metal mole ratio to 2:1 in the reboiler.

( ) OHOSNHOHNHSOS 2322442 2 +→++° (5)

( ) ( ) ( ) 424423224443 3 SONHCuSSOHOSNHSONHCu +→++ (6)

( ) ( ) ( ) 42442342443 SONHSONHNiSOHSONHNi +→+ (7)

The copper sulphide precipitate, which includes a significant amount of unreacted elemental

sulphur, is thickened in a lamella thickener. A portion of the underflow is recycled to the

leaching circuits to provide soluble copper, while the balance as precipitated copper sulphide is

filtered on a conventional vacuum drum filter [9]. The copper free overflow solution is filtered to

be sent to the high temperature oxydrolysis step.

In addition to removing the excess ammonia from the nickel solution, the three ammonia

removal steps also remove a substantial amount of water, resulting in a significant increase in

metal and ammonium sulphate concentrations. The overall solution volume reduction

corresponds to 15 to 20%, and as a result, the nickel concentration increases by about 10 g/L e.g.

from 55 to 65 g/L. The copper-free nickel diammine liquor, outflow of the Copper Boil Process,

typically contains 65 to 70 g/L Ni, 3 g/L Co, less than 1 mg/L Cu, 35 to 40 g/L NH3 and 320 to

340 g/L (NH4)2SO4 [6].

7

Fig 2. The Copper Boil process flowsheet

According to the chemistry of the Copper Boil process, there are different electrolytes in the

solution which are important for chemical modeling of the process. Among them, (NH4)2SO4,

NiSO4, CoSO4, CuSO4, ZnSO4 , Na2SO4, H2SO4, nickel ammine complexes, and cobalt ammine

complexes are important to be studied. Cupric and cuprous sulphides exist as precipitate in the

solution. There is also ammonia and water in the vapour phase.

BoilFeed

Stripper

~ 2.8 psi

~ 4.9 psi

~ 4.4 psi

~ 4.0 psi

~ 6.0 psi

ThickenerFilter

NiBoil

NH3

Reboiler

Nickel Pregnant Solution

NH3, Steam, Air

Steam

H2SO4

Solidscontaining CuS & Cu 2S

Copper free solutioncontaining mainly NiSO 4

To Oxydrolysis

toDrum filter

Containing(NH4)2SO4

Cu2S & CuSNiSO4

trace CoSO 4

Steam

SO2

Sulphur

Steam

Condensate

Co/NiSeparation

MR=9-11

Compressed Air(P=120 psi)

MR=6-8

MR=5-5.5

MR=2

T=85 oC

T=82 oC

T=96 oC

T=99 oC

T=112 oC

NH3 vapour

8

2 METHODOLOGY

2.1 Chemical Modeling Methodology

2.1.1 Chemical Equilibria and Thermodynamic Framework

To obtain the equilibrium constant of a reaction at temperature T and pressure P, the standard

state chemical potentials of products and reactants should be determined. The HKF model,

developed by Helgeson and coworkers [10], is embedded in the OLI to calculate standard state

thermodynamic properties at high temperatures and pressures up to 1000°C and 5 kbar.

For a general chemical reaction that may involve solid, aqueous species, gas or vapour species,

the following equation can show the equilibrium:

....... ++↔++ dDcCbBaA (8)

The equilibrium constant of the reaction at temperature T and pressure P is defined as:

......

......

...

..., b

BaA

bB

aA

dD

cC

dD

cC

bB

aA

dD

cC

PT mm

mm

aa

aaK

γγγγ

== (9)

where a is the activity, m is the molality concentration of an aqueous species, and γ is the activity

coefficient. For pure and crystalline solid phases, a is equal to 8. The value of the equilibrium

constant KT,P is determined from following equation:

RT

GK PTPT

0,

,ln∆

= (10)

With ∑=∆i

iiPTG00

, µν (11)

where ∆G° is the Gibbs free energy of the reaction, µi0 is the standard-state chemical potential of

species i, νi is the stoichiometric coefficient of species i in the above reaction , and R is the gas

constant (8.314 J K-1 mol-1) [11-13].

To obtain the equilibrium constant of a reaction at temperature T and pressure P, the standard

state chemical potentials of products and reactants should be determined. The HKF model,

9

developed by Helgeson and coworkers [10], is embedded in the OLI to calculate standard state

thermodynamic properties at high temperatures and pressures up to 1000°C and 5 kbar. The

general equation is as follows:

( )ω,,,,,,,, 2143210, ccaaaaPTXX PT = (12)

where X denotes a thermodynamic function such as chemical potential (µ), partial molal enthalpy

( H ), entropy (S ), heat capacity ( pC ), and volume (V ); a1, a2, a3, a4, c1, c2, ω are HKF

parameters.

The most important parameter to account for the non-ideality of electrolyte solutions is the

activity coefficient, which results from the relation with the excess Gibbs free energy of the

solution:

( )ijnPT

i

E

i nRT

G

≠

∂

∂=

,,

lnγ (13)

where ni is the mole number of species i, and j is any other species (excluding i). The pursuit of

an expression for GE to calculate γ has been ongoing for decades. Numerous models have been

proposed and some of them have also been incorporated into commercial software and applied in

industry [14].

The recently developed Mixed Solvent Electrolyte (MSE) model [11-13] is capable of describing

systems of electrolyte solutions by means of calculating the thermodynamic properties of

electrolyte solutions from infinite dilution to the fused salt state, as well as electrolytes in mixed

solvents. Capability and accuracy of this model in predicting multicomponent solutions in

hydrometallurgy has been proven where the MSE model in the OLI software was applied into

complex solutions [14-17].

In this work, the MSE model is used in the OLI software to calculate activity coefficients in

order to establish a chemical model to investigate chemistry and equilibria of ammonia recovery

system. In the MSE model, the excess Gibbs energy consists of three terms [13]:

10

RT

G

RT

G

RT

G

RT

G ESR

EMR

ELR

E

++= (14)

where ELRG represents the long-range electrostatic interactions which is expressed by the Pitzer-

Debye-Hückel formula, ESRG is the short-range interaction term counting primarily for

interactions between neutral molecules and is calculated by the UNIQUAC model, and EMRG

accounts for the middle-range ionic interactions (i.e., ion-ion, ion-molecule) that are not included

in the long-range term. It is a second virial coefficient-type term for the remaining ionic

interactions and is given by:

( )∑∑∑

−=i j

xijjii

i

EMR IBxxn

RT

G (15)

where x is the mole fraction of species, and Bij is a binary interaction parameters between species

i and j (ion or molecule), and is also a function of ionic strength as following:

( ) ( ) ( )jiijxijijxij BBIcbIB =+−+= 01.0exp. (16)

TBMDTBMDT

BMDTBMDBMDbij ln43

210 2 ×+×++×+= (17)

TCMDTCMDT

CMDTCMDCMDcij ln43

210 2 ×+×++×+= (18)

where BMDk and CMDk (k =0,…,4) are adjustable parameters between species i and j that can be

obtained by fitting available experimental data such as mean activity coefficient, activity of

water, heat capacity, solubility, etc.[14,15].

2.1.2 Model Parameter Evaluation

Regression of experimental data ensures the thermodynamic consistency among those data. It is

essential for reliable process modelling and simulation to ensure consistency with experimental

observations. The pursuit for model reliability results in the generation of new thermodynamic

data and parameters, or the modification of existing data. A successful regression involves

11

selecting the right model and appropriate parameters, and regressing the data with using as few

adjustable parameters as possible.

The OLI-Systems Software needs well tuning of its available default database to manage the

modeling of complex multicomponent systems of our interest. This software provides an

efficient built-in regression facility as well as a databank which facilitates to obtain equilibrium

constants.

The validation of model parameters is a necessary final step in process modelling and simulation

to ensure that the regressed parameters produce results consistent with experimental data that

were not used in the regression step. This is accomplished by comparing the model results with

experimental data beyond the range of available data used for parameterization, i.e., at different

temperatures and in more complicated multi-component systems, or even in real

hydrometallurgical or chemical process solutions. This prevents excessive fitting that has no

chemical foundation. Process modelling without parameter validation can produce serious

deviation from experimentally known chemical behaviour.

2.2 Experimental Methodology

The new measurements are meant to produce new experimental data to be used either for

validating the chemical model developed so far or for regressing interaction parameters in the

chemical model. As well, the new data are measured under relevant industrial conditions, so they

will be useful in different research fields or by different industries, since it is not practical to do

these measurements under all different conditions. The solubility of solid in solution and

ammonia measurements will be discussed further.

Relevant industrial conditions are in the following ranges. The feed solution to the Copper Boil

process contained 175 to 185 g/L, corresponding to mole ratios in the range 10:1 to 14:1[18, 19].

After removing the excess ammonia from the nickel solution, the three ammonia removal steps

also remove a substantial amount of water, resulting in a significant increase in metal and

ammonium sulphate concentrations. The overall solution volume reduction corresponds to 15 to

20%, and as a result, the nickel concentration increases by about 10 g/L e.g. from 55 to 65 g/L.

The copper-free nickel diammine liquor, outflow of the Copper Boil Process, typically contains

12

65 to 70 g/L Ni, 3 g/L Co, less than 1 mg/L Cu, 35 to 40 g/L NH3 and 320 to 340 g/L (NH4)2SO4

[18]. Temperature is varying from 85 to 115 ºC and pressure is in the range of 14.7 to 20 psia.

Since nickel and ammonia are two dominant species in the Copper Boil process, the prediction of

their chemistry is crucial to predict the whole solution chemistry. To obtain the interaction

parameters between Ni2+ and NH4+ ions, the solubility data of the double salt of ammonia nickel

sulphate hexahydrate, (NH4)2SO4.NiSO4.6H2O, in NiSO4-(NH4)2SO4-H2O system is required.

The solubility of this double salt in water has been measured by Sue et al. [20] up to 60 ºC. Their

data were used in this work to regress the Gibbs free energy and entropy of the double salt and

the new data were added into the OLI database. But it is necessary to measure the solubility of

this double salt in water up to the double salt stable temperature of 96.6 ºC to make sure the

fitting can cover the condition of the Copper Boil process.

Also, there are some other solid phases for which solubility data are either missing or are at

lower temperature ranges up to 50 ºC and it is necessary to measure their solubilities. Cobalt

ammonium sulphate hexahydrate double salt ((NH4)2SO4.CoSO4.6H2O), and Cobalt(III)-nickel

hexammine sulphate complex salts are examples for which solubilities need to be determined.

The latter is the salt which precipitates in the nickel leach solution and dissolves in water to set

nickel free. This is the key factor for nickel and cobalt separation process.

There are two methods for measuring the solubility of solid phases: dissolution method and

precipitation method. Reliability and feasibility of the dissolution method is higher, since the

complicated formation of intermediate phases is avoided [21]. However, in some studies,

solubility of a known solid was measured during heating and subsequent cooling. If the results

obtained from heating are similar to those obtained from cooling, it shows that there was no

intermediate metastable phase formation.

To study the solubility of a solid phase, a 2L flanged jacketed glass reaction vessel (ACE Glass)

with stirrer is used (Figure 3). A temperature-controlled circulating heater with oil is used to heat

the solution to ±0.2 ºC of the desired temperature. Also, a calibrated thermometer, conductivity

meter, condenser, and sampling line are positioned in the vessel. The reaction vessel is filled

with water or a solution of known composition with an excess amount of solid phase for example

ammonium nickel sulphate hexahydrate double salt (ANSH) is added in excess beyond its

solubility at highest temperature.

13

The charged reactor is heated to a known temperature while stirring takes place. Then, the

solution reaches to equilibrium over time; equilibrium is reached when conductivity and

solubility reaches a constant value. This equilibrium time is established for lowest temperature

where kinetic is slow to ensure that at higher temperatures equilibrium is reachable. Using

preheated syringe, samples are taken at the temperature of the experiment and prepared for

analysis. Inductively coupled plasma (ICP-EOS) analysis is used for the concentration of Ni, Co,

Zn, Cu, and S in the solution. X-ray diffraction (XRD), scanning electron microscopy (SEM),

and thermo gravimetric analysis (TGA) can be used for solid phase sample analysis to analyze

the morphology of solid particles, solid phase characterization and amount of hydrate,

respectively.

This experiment is repeated at different temperature intervals for instant from 20 ºC up to 120 ºC

with 10 ºC intervals. Highest temperature is limited for some solids due to the dehydration

temperature or instability of that solid for instance ANSH is only stable up to 96.6 ºC.

Furthermore, to investigate the possibility of the formation of any intermediate metastable solid

phase, precipitation method is conducted. Same experiment procedure is repeated starting from

the highest temperature to the lowest one with the same temperature intervals. Consistency

between the results obtained from ICP and XRD analysis confirms the solubility of the same

solid phase is measured through both dissolution and precipitation method. However, in case of

discrepancy in results of heating and cooling method, further investigation is due to analyze the

nature of new meta-stable solid phase and related solubility mechanism.

14

Fig 3. Schematic view of the reaction vessel for solubility measurements

Sampling line

Temperature controller

Stirring speed

controller

Stirrer

2L Flanged jacketed reactor

Jacket heater

2L Glass reaction vessel Flange

Pressure gauge

15

3 RESULTS AND DISCUSSION

3.1. Binary Systems

There are some experimental data available for electrolyte systems containing NiSO4, NH3,

(NH4)2SO4, CoSO4, etc. This work focuses on binary and ternary systems of the mentioned

electrolytes. The model parameter estimation was performed through the OLI built-in regression

facility from collected experimental data. A list of experimental data sources used for model

parameter estimation is presented in Table 1. Literature data type includes various

thermodynamics properties including but not limited to solubility data, mean activity coefficient,

heat capacity, activity coefficient of water, and stability constant of complex. BMDs and CMDs

are the MSE ion interaction parameters between dominant species in studied systems. Table 2

shows these ion interaction parameters and the database that is developed with these new ion

interaction parameters.

Table 1. Experimental data used for model parameter estimation

System Data type Temperature range (°C) Reference

CoSO4-H2O Solubility, γ±, Cp 0-200 [22-26]

Ni(NH3)n2+-H2O

*βn, (n=1,…,6) 25 [27]

Co(NH3)n2+-H2O

*βn, (n=1,…,6) 25 [27]

Co(NH3)63+-H2O

*βn, (n=5,6) 25 [27]

(NH4)2Ni(SO4)2-H2O Solubility 20-60 [22,28, 29]

NiSO4-CoSO4-H2O Solubility 17-90 [22,30]

*βn=stability constant of the complex

16

Table 2. MSE middle range ion interactions parameters

Systems

Species

i

Species

j BMD0 BMD1 BMD2 CMD0 CMD1 CMD2

Temp.

range(°°°°C) Database

CoSO4-H2O Co2+ SO4

2- -76.43047 -0.0381 20551.6 -49.3487 0.44399 - 0-200 Niboil

NiSO4-CoSO4-

H2O

Co2+ Ni2+ 266.2736 -0.8961 -235328.9 978.051 0.23133 - 17-90 Niboil

NiSO4-H2O Ni2+ SO4

2- -62.81981 0.131417 -110727.55 29.09487 0.0107966 32862.21 0-300 Niboil

NiSO4-H2O Ni2+ HSO4

- -228.2051 0.505456 - 151.6307 -0.227221 - 0-300 Niboil

NiSO4-H2O Ni2+ SO4

2- -257.629 0.335022 31865.56 301.1285 -0.239186 -31179.97 0-300 MSE

NiSO4-H2O Ni2+ HSO4

- -123.997 0.452732 -14193.98 267.8592 0.870478 - 0-300 MSE

(NH4)2SO4-H2O NH4+ SO4

2- 526.427 -1.38466 -69253 -1151.25 2.99106 153609 MSE

(NH4)2SO4--

NH3-H2O

NH4+ NH3 -2.17 0.01472 -1501 29.99 -0.05427 -4751 0-160 Niboil

The standard state Gibbs free energy (∆Gfº) and entropy of formation (Sfº) should be adjusted

since the reaction equilibrium constant is the exponent function of the Gibbs free energy of

formation; the reaction equilibrium constant directly affects the solubility calculations.

Therefore, the standard state Gibbs free energy (∆Gfº) and entropy of formation (Sfº) of several

solids of studied systems were adjusted by fitting the experimental solubility data over the entire

range of temperature to obtain the optimum solubility product of those solids. Table 3 shows the

regressed values of the standard state Gibbs free energy (∆Gfº) and entropy of formation (Sfº) of

the studied solids. First column of Table 3 shows the dissociation of these solids in water and the

dissociation reaction is defined in the database. It should be noted that formation reaction which

is the formation of one mole of a compound from its elements is different from dissociation

reaction.

17

Table 3. The dissociation reaction, standard state Gibbs free energy and entropy of formation of the solid

Dissociation reaction ∆Gf

°

(kcal/mol)

Sf°

(cal/K/mol) Temperature range, °C

CoSO4.7H2O=Co2++SO4

2-+7H2O -591.2 106.9 0-43.3

CoSO4.6H2O=Co2++SO4

2-+6H2O -534.5 89.1 43.3-64.2

CoSO4.1H2O=Co2++SO4

2-+1H2O -250.4 12.8 64.2-200

CoSO4=Co2++SO4

2- -191.3 -55.4 0-200

(NH4)2Ni(SO4)2.6H2O=2NH4++Ni2++SO4

2-+6H2O -752.8 117.9 20-60

NiSO4.7H2O=Ni2++SO4

2-+7H2O -588.5 95.8 0-31.4

NiSO4.6H2O=Ni2++SO4

2-+6H2O -531.8 80.7 31.4-100.1

NiSO4.1H2O=Ni2++SO4

2-+1H2O -244.8 30.9 100.1-250

NiSO4=Ni2++SO4

2- -185.5 22.1 0-250

Figure 4 shows how well the MSE model can predict the thermodynamic properties. Mainly,

solubility of vapor or solid is shown which is of interest. Ammonia and water equilibria is

predicted well by default model as shown in Figure 4. Mole fraction of ammonia in ammonia-

water mixture is predicted very well in both liquid and vapour phase over a wide range of

temperature and pressure. This prediction is very important in the Copper Boil process since the

majority of vapour phase is consist of ammonia and water vapour, however due to the existence

of other electrolytes in solution, prediction of vapour pressure over multi-component solution of

the studied process is complex.

Absolute average relative deviation is used as a statistical measure to show the deviation of

experiment or literature value from calculated value from the modeling results.

∑=

−=

N

n value

valuecalculatedvalue

NAARD

1 exp_

_exp_1

18

The solubility of ammonium sulphate in water was studied by Terres and Schmidt [31], Ishikawa

and Murooka [32], and compiled by Linke and Siediel [22]. The OLI default database is capable

of predicting ammonium sulphate solubility in water very well using MSE model. As can be seen

in Figure 5, the solubility is high. This graph also shows the well prediction with AARD of 2.7%

over the temperature range of interest in the studied process. No additional fitting was carried out

in these two systems.

Fig 4. NH3-H2O equilibrium curves: Mole fraction of ammonia in both vapour phase and liquid phase at

equilibrium state. using the MSE (H3O+) default database prediction ;experimental data from [22]. Lower lines

and square dots illustrated the mole fraction of ammonia in liquid phase and upper lines and circles show the

mole fraction of ammonia in vapour phase.

-100 -50 0 50 100 150 200-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Exp (Linke & Siedel) x NH3

Exp (Linke & Siedel) y NH3

x NH

3 an

d y

NH

3

Temperature ( °C)

Patm

=0.2 0.5 1 2 4 6 8 10

OLI (MSE) xNH3

OLI (MSE) yNH3

19

5

6

7

8

0 20 40 60 80 100

Temperature (°C)

(NH

4) 2

SO

4 ,m

ol/k

g w

ater

OLI ( default databas-MSE)

Exp data (Linke and Siedel)

Exp data (Ishikawa and Murooka)

Exp dara (Terres and Schmidt)

Fig 5. The solubility of ammonium sulphate in water, experimental data from Terres and Schmidt [31],

Ishikawa and Murooka [32], and Linke and Siedel [22] and MSE (H3O+) default data base are used. Solid phase

is ammonium sulphate.

The solubility of CoSO4 in water has been measured by Bruhn et al. [33], Bursa [34] and was

sited by Linke and Seidell [22]. There are also experimental data available on the mean activity

coefficient and the heat capacity of CoSO4 in water [26,35] and activity of water. These

experimental were used in this work to fit the MSE middle-range interaction parameters between

Co2+ and SO42-

ions, as well as to adjust the standard state Gibbs free energy and entropy of

formation of the solids, CoSO4.7H2O, CoSO4.6H2O, and CoSO4.1H2O, as a function of

temperature. Therefore, Cobalt sulphate and its solid phases, namely CoSO4.7H2O,

CoSO4.6H2O and CoSO4.1H2O, were added in the Niboil database of the OLI software.

Solubility of CoSO4 in water is shown over a wide range of temperature in Figure 6 where solid

phase change temperature is shown and well fitted model is shown by solid line compared to

experimental points as dots in the figure.

AARD%=2.7

20

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0 50 100 150 200

Temperature, oC

CoS

O 4, m

ol/k

g w

ater

Linke & Seidell (1958)

Bruhn (1965)

Bursa (1980)

Fitted model results

Fig 6. The solubility of the CoSO4 in water. Experimental data

are from [22, 32, 34]. The curve is the fitted model.

The fitted parameters and the adjusted values for the Gibbs free energy and entropy of formation

are presented in the Tables 2 and 3, respectively. Figures 6 to 8 show the fitted results for the

solubility, γ± and Cp values compared with experimental data and as can be seen the fit is very

good. Absolute average relative deviation of 5.2% is a collective measure for all

thermodynamics properties used in cobalt sulphate and water system.

CoSO4.7H2O

CoSO4.6H2O

CoSO4.1H2O T=43.3 oC

T=64.2 oC

AARD%=5.2

21

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

0 0.5 1 1.5 2

CoSO4, mol/ kg water

Cp

, J/k

g/K

Asseyev (1996), T=25 C

Fitted Model results, T=25 C

Asseyev (1996), T=95 C

Fitted Model results, T=95 C

Fig 7. The heat capacity (Cp ) of the CoSO4 in water vs. temperature. Data points are from [26]. The curve is the fitted model. AARD%=5.2

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0 0.2 0.4 0.6 0.8 1.0

CoSO4, mol/kg water

Mea

n ac

tivity

coe

ffici

ent

Francesco (1999)


Fig 8. The mean activity coefficient of the CoSO4 in water. Data points are from [26].

The curve is the fitted model. AARD%=5.2

22

Fig 9. The solubility of ZnSO4 in Water, Experimental data are from [22, 36]. The curve is the fitted model. Solid phase at low temperature is ZnSO4.7H2O and after 42°C it is ZnSO4.6H2O and at high temperatures

ZnSO4.H2O forms a solid phase.

The solubility of ZnSO4 in water is predicted using default database and compared by measured

data from Rudolph [36], and Link and Siedel [22] as shown in Figure 9. The OLI default

database was tested using the mean activity coefficient, activity of water, and the solubility of

aqueous solution of zinc sulphate that was shown to be adequate to describe this system

compared with experimental data. There is only 4.3 % deviation which is in an acceptable range.

Results obtained from the OLI default database for the solubility of NiSO4-H2O is shown in

Figures 10. Within the studied range of temperature, both Ni2+and NiSO4-aq are dominant

species. The fitted parameters and the adjusted values for the Gibbs free energy and entropy of

formation are presented in the Tables 2 and 3, respectively. Figures 10 shows the fitted results of

this work for the solubility compared with experimental dada and as can be seen the fit is very

good with AARD of 3.9%. It should be noted that previously adjusted parameters and recent

parameters in the updated database, both predicting the NiSO4 behavior very well. Therefore, it

AARD%=4.3

ZnS

O4,

mol

/kg

wat

er

23

was decided to be consistent with the new version of software and use the updated new version

of database. Nickel ion is the most dominant species in electrolyte solution in the Copper Boil

process, so predicting the behavior of nickel sulphate and its speciation in water and multi-

component solution is of great importance. It can be noted from this figure that nickel sulphate

hexa hydrate could be the possible precipitation within the process temperature range providing

that it reaches its solubility limits.

Fig 10. The solubility of the NiSO4 in water. Experimental data are from [22, 23]. The curve is the fitted model.

Solid phase at low temperature is NiSO4.7H2O and after 31.4°C it is NiSO4.6H2O and at temperatures higher than

100.1°C NiSO4.H2O forms a solid phase.

AARD%=3.9 0

1

2

3

4

5

6

7

-10 40 90 140 190 240

Temperature ( oC)

NiS

O4

(mol

/kg

wat

er)/

Exp Data, Linke & Siedel 1965,Brune 1965

Fitted model result

NiSO4.7H2O

NiSO4.6H2O

NiSO4.1H2O

Ice

NiS

O4,

mol

/kg

wat

er

AARD%=3.9

24

3.2. Ternary Systems

Since most of binary systems were predicted well over the condition range of interest, ternary

systems were investigated. The solubility of NiSO4 in ammonium sulphate solution at 25°C was

measured and data collected by Linke and Siedel [22]. The OLI default database can predict the

solubility behavior of this system very accurately at 25°C. However there is a lack of

experimental data for this important system that requires measurements. Figure 11 shows the

solubility of (NH4)2SO4 in NiSO4 where a double salt NiSO4.(NH4)2SO4.6H2O forms as a solid

phase.

0

1

2

3

4

5

6

0 0.5 1 1.5 2 2.5NiSO 4 molality

(NH

4)2S

O4

mol

ality

Exp. data ( Linke & Siedel)

OLI (Default database-MSE)

Fig 11. The solubility of (NH4)2SO4 in NiSO4- H2O, where (NH4)2SO4.NiSO4.6H2O is forming as a solid phase. The

curve is the fitted model and experimental data from [22]. T=25 °C.

AARD%=1.2 (NH4)2SO4.NiSO4.6H2O

(NH

4)2S

O4,

mol

/kg

wat

er

Ni SO4, mol/kg water

25

The solubility of NiSO4 in CoSO4 was measured from 17 to 90°C by Benarth [30] and also was

sited by Linke and Seidell [22]. These data were used again in this work to adjust the interaction

parameters between Ni2+ and Co2+ ions. Figure 12 shows the experimental solubility of NiSO4 in

CoSO4 solution and the fitted model results at different temperatures. As it is clear, the fitting is

very good. The error between the experimental data and the fitted results is 2 % very close to

previous work. The parameters between Co2+ and SO42- are those obtained in this work. The

system of NiSO4 in water is decided to use the updated default database rather than previously

regressed parameters. Since, cobalt ion is the second dominant metal ion in the Copper Boil

process solution, its interaction with nickel ion plays an important role for understanding of the

chemistry of electrolyte solution in the process. In general, these interactions are also crucial to

analyze the behavior of ammonia pressure leach stage and nickel-cobalt separation process.

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

0 5 10 15 20 25 30

CoSO4, mol/Kg H2O

NiS

O4,

mol

/Kg

H2O

Benrath (1934), 17.5 C

Benrath (1934), 30 C




Model, 17.5 C

Model, 30 C

Model, 50 C

Model, 70 C

Model, 90 C

Fig 12. NiSO4 solubility vs. CoSO4 concentration. Data points are from [22,30]. Lines are fitted model results.

Ammonium nickel sulphate hexahydrate (ANSH) is one of the picromerite series of double salts.

The picromerite series of double salts includes a large number of compounds formed from

NiSO4.6H2O

NiSO4.7H2O

NiSO4.1H2O

AARD%=2

NiS

O4,

mol

/kg

wat

er

CoSO4, mol/kg water

26

sulphates of divalent metals such as Mg, Ni, Co and Zn with alkali metals such as K, Rb and

NH4. These crystallize from water as hexahydrates and show resemblances in many properties.

In ammoniacal nickel sulphate media, the double salt of (NH4)2SO4.NiSO4.6H2O or

(NH4)2Ni(SO4).6H2O precipitates as the solid phase. So in order to model this system, it was

necessary to add this solid to the OLI database. There are some data available for the solubility

of ANSH in water in the literature [28, 29]. These experimental data were used to fit the Gibbs

free energy and the entropy of formation of this solid. The fitted values are reported in Table 3.

Figure 13 shows the fitted model results compared with experimental data. As it is clear the fit

was very good with a relative error of 2.75%.

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

10 20 30 40 50 60 70

Temperature, oC

AN

SH

sol

ubili

ty, m

ol/ k

g w

ater

Su (2002), Hill (1940) Exp data


Fig 13. (NH4)2Ni(SO4)2.6H2O solubility in water. Data points are from [37,38]. Lines are fitted model results.

The solubility of nickel hydroxide in ammonia solution was measured by Arkhipov [37],

Bonsdorff[38], Paris [39], Ziemniak[40] at different concentrations and temperatures in the range

of 16-60ºC. Model can predict the solubility of Ni(OH)2 in ammonia solution well as shown in

AARD%=2.7

Solid: (NH4)2Ni(SO4)2.6H2O

27

Figure 14. However there is space for further investigation to narrow the error range and obtain

better agreement by manipulating interaction parameters of Ni2+ ion and NH3(aq).

0

0.1

0.2

0.3

0.4

0.5

0.6

0 5 10 15 20 25

NH3, mol/kg water

Ni(O

H)2

, mol

/kg

wat

er

MSE, T=16C

MSE,T=20C

MSE, T=25C

MSE, T=40C

MSE, T=60C

Arkhipov et al. 1950, T=20C

Arkhipov et al. 1950,T=20CdilutionBonsdorff 1904, T=25C

Bonsdorff 1904, T=25C

Muendel et al. 1961, T=40C

Muendel et al. 1961, T=60C

Paris 1951, T=16C

Ziemniak and Goyette 2004

Fig 14. Solubility of Ni(OH)2 in ammonia solution. Lines are model predictions and experimental data are from [ 37-40]

The behaviour of ammonia over an aqueous solution containing studied species is an important

issue to understand very well and investigate. Rumpf and Maurer [41, 42] measured solubility of

ammonia in aqueous solutions of sodium and ammonium sulphate at temperatures from 60 to

160 °C and pressures up to 3 MPa. Figures 15 and 16 present total ammonia pressure versus

ammonia molality in solution while the molality of ammonium sulphate is fixed at two levels of

2 and 4 molal. The solubility of ammonia in an aqueous solution of ammonium sulphate is not

predicted well by using the OLI default database therefore interaction parameters between

dominant species which are ammonia and ammonium ion are regressed and reported in Table 3.

Solid lines in both figures represent the fitted model results showing a good agreement between

28

the fitted model and experimental data at both 2 and 4 molal ammonium sulphate solution.

Deviation of fitted model result from the experimental data is 2.1 and 3.3 % for 2 and 4 molal

ammonium sulphate solution, respectively.

Since ammonium sulphate molality is changing from 1.3 to 2.9 within the Copper Boil process

and temperature range of 85 to 115 °C, these two figures provide a rough estimate about total

pressure above solution. For instance, ammonia molality is changing between 2 and 7 molal

(Table 5). In reboiler where molality of both ammonia and ammonium sulphate is around 2

molal, total pressure can be read from figure 15 to be around 1 atm which is close to actual plant

data. However, in this example the effect of other metal sulphate electrolytes were neglected

which creates an error into final calculation.

Furthermore, dashed lines in figures 15 and 16 show the prediction of MSE model with default

database without the contributions of this work to clearly illustrate the benefits and accuracy of

fitted model resulted from this work.

(NH4)2SO4= 2 mol/kg water

0

5

10

15

20

25

30

35

0 5 10 15 20 25 30NH3, mol/kg water

Tot

al P

ress

ure,

atm

T=60 C, regression

T=60 C, exp data

T=160 C, regression

T=160 C, exp data

T=60 C, default database


T=120 C, regression

T=120 C, exp data


Fig 15. Total pressure vs. ammonia molality in solution of 2 molal ammonium sulphate. Solid lines are

fitted model result where dotted line shows the default model prediction. Experimental data are from [38,

39]

T=160 C

T=120 C

T=60 C

AARD%= 2.1

29

(NH4)2SO4=4 mol/kg water

0

5

10

15

20

25

30

0 5 10 15 20 25 30 35

NH3 mol/kg water

Tot

al P

ress

ure,

atm






T=60 C, regression

T=80 C, regression

T=120 C, regression

T=140 C, regression

T=160 C, regression

T=60 C,exp data

T=80 C,exp data

T=120 C,exp data

T=140 C,exp data

T=160 C,exp data

Fig 16. Total pressure vs. ammonia molality in solution of 4 molal ammonium sulphate. Solid lines are

fitted model result where dotted line shows the default model prediction. Experimental data are from [38,

39]

3.3. Nickel-Ammonia Complexation

Complexation has a substantial effect on the behaviour of electrolytes in the ammoniacal nickel

sulphate solutions. The concentration of the free nickel ion in solution is significantly reduced by

the formation of nickel ammine complexes, Ni(NH3)n2+, (n=1-6).

The role of ammonia is to increase the solubility of the nickel in the reduction solution by

forming complexes and on the other hand to neutralize the acid which forms during the reduction

[43].

++ =+ 23)(32 )( naqaq NHNinNHNi (19)

OHNHNHOH aq 24)(33 +=+++ (20)

T=140 C

T=120 C

T=80 C

T=60 C

T=160 C

AARD%= 3.3

30

The stability constants of complexes for reaction 19 were collected from the literature [27] and

entered into the OLI database. These data are presented in Table 4. There is no data available on

the entropy or enthalpy of formation of these complexes.

The amount of each complex can be measured using different technologies such as Raman

spectroscopy. This is beyond the scope of this work however it is of interest to check whether the

speciation based on the new complexation data added to the OLI meet the real world or not. If

not, those data to be measured can be used to introduce the complexes more properly to the OLI

database. In other words, there is lack of literature data for the speciation of complextion to

validate the accuracy of model prediction except the model overall performance.

Figure 17 shows the speciation of different nickel amine complexes versus NiSO4 concentration

in a solution containing 3.5 m ammonium sulphate and 3.5 m ammonia at 112°C which is near

the condition in the Copper Boil process.

0 1 2 3 4 50

20

40

60

80

100

Ni S

peci

atio

n, %

NiSO4, mol/kg water

Ni2+

Ni(NH3)2+

Ni(NH3)2

2+

Ni(NH3)3

2+

T=112 oC[NH

3]=3.5 molal

[(NH4)2SO

4]=3.5 molal

Ni(NH3)4

2+

Ni(NH3)5

2+

Ni(NH3)6

2+

Fig 17. Speciation of nickel ammine complexes in NiSO4-(NH4)2SO4-NH3-H2O solution

31

Similar to nickel in ammoniacal solutions, both Co(II) and Co(III) form complexes in the

presences of NH3 ligands. So, stability constants of these complexes were found in the literature

[27] and were added into the OLI database. These data are presented in Table 4. There is no data

in the literature to shows the speciation of these complexes versus temperature or composition.

Table 4. The stability constant of nickel and cobalt ammine complexes

Reaction Log βn T (ºC), Medium Reference

Ni2++NH3=NiNH32+ 2.67 30, Dilute [27]

Ni2++2NH3=Ni(NH3)22+ 4.79 30, Dilute [27]

Ni2++3NH3=Ni(NH3)32+ 6.4 30, Dilute [27]

Ni2++4NH3=Ni(NH3)42+ 7.47 30, Dilute [27]

Ni2++5NH3=Ni(NH3)52+ 8.1 30, Dilute [27]

Ni2++6NH3=Ni(NH3)62+ 8.01 30, Dilute [27]

Co2++NH3=CoNH32+ 1.99 30, Dilute [27,44]

Co2++2NH3=Co(NH3)22+ 3.5 30, Dilute [27,44]

Co2++3NH3=Co(NH3)32+ 4.43 30, Dilute [27,44]

Co2++4NH3=Co(NH3)42+ 5.07 30, Dilute [27,44]

Co2++5NH3=Co(NH3)52+ 5.13 30, Dilute [27,44]

Co2++6NH3=Co(NH3)62+ 4.39 30, Dilute [27,44]

Co3++5NH3=Co(NH3)53+ 29.51 25, Dilute [44]

Co3++6NH3=Co(NH3)63+ 33.66 25, Dilute [18]

*βn=stability constant of the comple

32

3.4. Experiment Results.

As discussed in the introduction, the solubility of ammonium nickel sulphate hexa hydrate in

water is of importance due to its role in the Cupper Boil chemistry. Solubility of picromerite salts

are described in detail in the experiment methodology. Ammonium nickel sulphate hexa hydrate

was used as reagent chemical grade from Fisher with 99.9% purity. A solution of known

composition with an excess amount of ANSH solid beyond its solubility at highest temperature is

prepared. To ensure the existence of only one solid phase for solubility measurement, both

dissolution and precipitation methods were used. To study the solubility of a solid phase, a 2L

flanged jacketed glass reaction vessel from ACE Glass with magnetic controllable stirrer was

used. A temperature-controlled circulating heater with oil is used to heat the solution to ±0.2 ºC

of the desired temperature. Also, a calibrated thermometer, conductivity meter, condenser, and

sampling line are positioned in the vessel. The reaction vessel is filled with the prepared solution.

Figure 18 shows the actual experiment set-up for solid-liquid equilibria measurements.

The charged reactor was heated to a known temperature starting at room temperature while

stirring took place. Then, the solution reached to equilibrium over time; equilibrium was reached

when conductivity and solubility reached a constant value (plateau). This equilibrium time was

established for the lowest temperature where kinetic is slow to ensure that at higher temperatures

equilibrium is reachable within that time. For sampling, a syringe was preheated to the

experiment temperature. Samples were withdrawn by syringe at the temperature of the

experiment through a filter to prevent any solid withdrawal into the solution. Also, tight sealed

were used to prevent solution evaporation and maintain the same solution concentration.

Withdrawn solution samples were diluted with 5% HNO3 and stored in sealed plastic test tubes at

room temperature prepared for analysis. The concentration of nickel was measured by

inductively coupled plasma ICP-EOS analysis using the 231.604, 221.648, and 232.008 nm

emission lines. The X-ray diffraction (XRD), scanning electron microscopy (SEM), and thermo

gravimetric analysis (TGA) can be used for solid phase sample analysis to analyze the

morphology of solid particles, solid phase characterization and amount of hydrate, respectively.

This experiment is repeated at different temperature intervals for instant 10 ºC up to 96 ºC which

is limited due to the dehydration temperature and instability of ANSH. After each temperature

increase, 0.5 h was allocated to reach thermal equilibrium. Time allowed to reach a chemical

33

equilibrium depends on the dissolution rate of the solid phase at the experiment conditions. For

ANSH solubility measurement, around 12 h was the equilibrium time at lowest temperature.

However this time is shorter at higher temperatures due to faster dissolution rate at higher

temperatures, the same 12 h was allowed for each repeated experiment to satisfy the chemical

equilibrium.

Then precipitation method is conducted by cooling from the highest temperature to the lowest

one with the same temperature intervals to investigate the possibility of the formation of any

intermediate metastable solid phase. The consistency between the results obtained from ICP

analysis of both methods confirmed that the solubility of same solid phase is measured through

both dissolution and precipitation method.

Fig 18. Experimental set up for solubility measurements.

A. Jacketed reactor, B. Temperature controller, C. Temperature-controlled circulating heater, D. Sampling line, E. stirring speed controller, F. Stirrer

Figure 19 shows the results of experiments carried out in this work extending to ANSH stability

temperature of 96.6 °C beyond available literature experimental data. Diamond dots show the

results carried out in dissolution (heating) method and dash dots shows the results obtained in

A

B

A

A

D

C

E F

34

precipitation (cooling) method. Star dots presenting limited available literature data. Also using

enhanced database in OLI software, ammonium nickel sulphate solubility is well predicted over

a wide range of temperature with absolute average relative deviation of 4.8 percent. Appendix 1

shows a density table of ANSH which can be used to convert g/100 ml to mol/ kg water.

0

5

10

15

20

25

30

35

40

0 20 40 60 80 100

Temperature (C)

AN

SH

(g/1

00 m

l)

Dissolution method results

Precipitation method results

Sue et al data

MSE model prediction

Fig 19. Ammonium Nickel Sulphate Hexa Hydrate solubility in water. Diamond and dash dots show this works

experiments. Star dots show literature values. Solid line is a model prediction.

35

4 SIMULATION OF THE COPPER BOIL PROCESS USING

THE HSC 6.1 SOFTWARE

To understand the behavior of the process chemistry, it’s important to simulate the Copper Boil

process with simulation software to use the industrial data and connect thermodynamic modeling

to the real world. The HSC Chemistry 6.1 is simulation software designed based on independent

chemical reactions and process units including metal-refining unit operations. The newly added

HSC-Sim module consisting of flowsheet and spreadsheet type processes increases the ability of

the software to be applied to a real process. The HSC Add-In functions are used to turn these

independent calculation units into small HSC engines for thermodynamic applications. The

HSC-Sim module is used to create a simple but still powerful simulation tool to simulate the

Cupper Boil process. This simulation is based on known chemical reactions, known inflows, and

conditions to determine the amount of chemical compounds all around the process.

The industrial data are categorized into two groups: stream and unit operation conditions such as

flow rates, temperature, pressure, etc., and stream composition data. The former is available from

plant data all over the process like Table 6, but the latter data is sparse. Therefore another goal of

using simulation is to find these data. Calculated composition data can be used to validate the

chemical model against industrial data. Therefore, the data from the HSC Chemistry model can

be used in the model developed to simulate the chemistry of the process. As well,

thermodynamic equilibrium model from the OLI provides more detailed and concise

thermodynamic relationship between species which can be used in HSC simulation. This back

and forth approach continues until both the process simulation and the thermodynamic model

become consistent regarding to experimental data and industrial data validation.

The flowsheet of the Copper Boil process is shown in Figure 20. All streams and unit operation

data were gathered during the visit of the Sherritt plant (Corefco) in Fort Saskatchewan, Alberta.

Data used in the simulation are the average of daily values From May-1-2007 to October-11-

2007. Then species that are not included in HSC database such as nickel ammine sulphates and

cobalt ammine sulphates were added to the database using their enthalpy, entropy and heat

capacity values. Furthermore, controllers are used to adjust the mole ratio of ammonia to metals

to the desired amounts by changing the extent of reactions occurring in the process. The most

important mole ratio to be adjusted is 2:1 in the outflow of the reboiler.

A new equilibrium function was built to correlate ammonium ion concentration to H2SO4

concentration in the reboiler. This correlation is confirmed by thermodynamic modeling as well.

36

Feed from

Co/Ni Separation

NH3

Gas

Boil

Feed

Stripper

Ni

Boiler

NH3

Gas

Reboiler

S

SO2

Steam H2SO4 Solids containing

Cu(I) & Cu(II)

Containing NH4SO4Cu2S & CuS NiSO4

coppper boiler

pot #1 ~2.8psi

copper boiler

pot #2 ~4.0psi

copper boiler

pot #3 ~4.4psi

copper boiler

pot #4 ~4.9psi

SteamCompressed

air

Steam

Steam

Steam

Steam

NH3 gas output

ThickenerFilter

To Oxydrolysis

Fig 20. The Copper Boil process Flowsheet from the HSC Simulation

Using the composition of pot #1 as an inflow to pots and other available flow conditions from

the plant, the Copper Boil pots were simulated and the outflow of four pots and the reboiler were

obtained. Table 5 shows the composition of different species in molality obtained from the HSC

compared to industrial data. Calculated data from HSC are very close to the actual data from

37

Sherritt plant. It should be noted that measured data were in g/L and using density of solution,

they were converted to molality as shown in the following table (*).

Table 5. The Comparison of outflow compositions of the Copper Boil pots calculated by the HSC Chemistry simulation with available Industrial data. Concentrations are in molality [mol/kg water]. (H2O= 55.51 mol/kg water)

Species Measured Data from Sherritt Plant * Simulated Data by HSC Chemistry

Pot #1 Pot #2 Pot #3 Pot #4 Reboiler Pot #1 Pot #2 Pot #3 Pot #4 Reboiler

NH3 7.062 5.595 4.913 4.176 2.051 6.302 2.768 1.746 1.719 1.698

(NH4)2SO4 1.314 1.302 1.320 1.325 2.905 1.333 1.328 1.328 1.848 1.838

NiSO4 1.068 1.018 1.019 1.038 1.175 1.059 1.059 1.059 1.063 1.059

CoSO4 0.0475 0.0451 0.0163 0.0464 0.0525 0.0386 0.0386 0.0386 0.00387 0.0386

CuSO4 0.0203 0.0157 0.0364 0.0147 0.00 0.0098 0.0096 0.0094 0.00094 0.0059

ZnSO4 0.0383 0.0452 0.0452 0.0371 0.0415 0.0342 0.0342 0.0342 0.0342 0.0342

4.1 Prediction of ammonia mole fraction in the vapor phase measured

by Sherritt

The model developed in this work based on the new parameters that were added to the OLI

database, was checked against the data provided by Sherritt company to see whether the results

from the model is comparable to the test results obtained in the plant. Figure 21 shows the mole

percentage of ammonia in the vapour phase versus different “ammonia:metal” mole ratio.

38

Table 6. The composition of different pots in the Copper Boil process of Sherritt

Solution Analysis unit Pot #1 Pot # 2 Pot #3 Pot #4 Reboiler

Ni g/L 62.67 59.74 59.84 60.90 68.95

Co g/L 2.80 2.66 2.67 2.74 3.10

Cu g/L 1.29 1.00 1.03 0.94 0.00

Zn g/L 2.50 2.37 2.38 2.42 2.72

S g/L 80.50 77.46 77.72 78.86 133.74

(NH4)2SO4 g/L 173.73 172.08 172.84 175.09 383.89

NH3 (titratable) g/L 120.33 95.33 83.72 71.17 34.94

NH3:TM* Molar Ratio 5.73 5.03 4.41 3.69 1.62

Operating Conditions

Pressure atm 1.19 1.27 1.31 1.35 1.36

Temperature ºC 77.92 93.60 97.19 100.91 113.60

*TM= Total Metals, MR=Molar Ratio of titratable ammonia to total metals

In order to obtain this figure, the pot composition presented in Table 6 was used. These data

were measured in the Sherritt plant under real process conditions. The line in Figure 21 presents

the lab test work conducted in Sherritt which is the distillation of ammonia over a solution with

similar composition to the one in pot no. 1. Using measured data from Sherritt plant presented in

Table 6, the mole per cent of ammonia vs. “NH3:metal” molar ratio was calculated by using the

new model. These results are shown as dots in the figure 21. Figure 22 also shows the calculated

total pressure in pots compared to the measured total pressure in pots in the Copper Boil process.

Since the condition in pot #2 and pot #3 is closer to lab test condition, better predictions were

observed.

39

0

10

20

30

40

50

1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5

NH3: Metal Mole Ratio

% N

H3

in v

apou

r ph

ase

Lab test results from Sherritt

Model result prediction

Fig 21. Ammonia mole percentage in the vapor phase vs. “ammonia:metal” mole ratio.

Dots are MSE model calculated results; the curve is lab test results from the Sherritt plant.

0.00

0.50

1.00

1.50

2.00

2.50

3.00

Pot #1 Pot #2 Pot #3 Pot #4 Pot #5

Tot

al P

ress

ure

(atm

)

Model result prediction

Sherritt plant data

Fig 22. The Comparison of total pressure in the Copper Boil pots obtained from the MSE model

with that from plant data

40

5 CONCLUSIONS

The chemistry of the Copper Boil process was studied in this work, and a chemical model for

predicting the phase behaviour in ammoniacal solutions containing cobalt, nickel, copper, and

zinc was investigated. The OLI software with the MSE (H3O+) model was used, and the ionic

interaction parameters between dominant species in the solution were determined by fitting

available binary and ternary experimental data, and then these interaction parameters were

validated in multicomponent systems. This approach proves the predictability and consistency of

the chemical model. Using the chemical model makes it possible to predict the solution

chemistry in the ammonia recovery stage of the Copper Boil process. Therefore the results of this

work would be a dedicated thermodynamic database to predict the chemistry of the Copper Boil

process and assess the solubility of nickel sulphate in a wide variety of complex aqueous process

units in Ni/Co Hydrometallurgy.

Using experimental data available for electrolyte systems containing NiSO4, NH3, (NH4)2SO4,

CoSO4, etc., the model parameter estimation was performed through the OLI built-in regression

facility. Since complexation has a substantial effect on the behaviour of electrolytes in the

ammoniacal nickel sulphate solutions. The concentration of the free nickel ion in solution is

significantly reduced by the formation of nickel ammine complexes, Ni(NH3)n2+, (n=1-6).

Similar to nickel in ammoniacal solutions, both Co(II) and Co(III) form complexes in the

presences of NH3 ligands. Therefore, Ni(NH3)n2+-H2O system, Co(NH3)n

2+-H2O system, and

Co(NH3)63+-H2O system are added to OLI database,(n=1-6).

To develop a comprehensive model with the ability to predict multi-component solutions under

various conditions in ammoniacal solutions, more experimental data are needed which are either

not available or available in a narrow range of conditions. The available literature data for the

solubility data of ammonium nickel sulphate hexa hydrate is limited and does not cover the

industrial temperature range of interest. A comprehensive solubility measurement of this double

salt was carried out through both dissolution and precipitation method. MSE model with

developed database predict the behaviour of this solid –liquid equilibria very well.

41

Furthermore, thermodynamic equilibrium data from the established chemical model was used to

modify the simulation database. Using the modified database, HSC Chemistry Software (HSC

6.1) was used to simulate the Copper Boil process. This simulation provided some insight on the

ability of the developed model to well predict the chemistry of the Copper boil. Also, it shed

some light on possibility of finding the optimum operating condition for the ammonia recovery

process to minimize the use of sulphuric acid reagent and steam consumption of the Copper Boil

process while maintaining mole ratio of 2 at the end of this process.

42

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