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A mathematical model of the nickel converter Kyllo, Andrew Kevin 1989

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A MATHEMATICAL MODEL OF THE NICKEL CONVERTER i ANDREW KEVIN KYLLO B.A.Sc, The University of British Columbia, 1986 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE in THE FACULTY OF GRADUATE STUDIES METALS AND MATERIALS ENGINEERING We accept this thesis as conforming to the required standard THE UNIVERSITY OF BRITISH COLUMBIA January 1989 ©Andrew Kevin Kyllo, 1989 In presenting this thesis in partial fulfilment of the requirements for an advanced degree at the University of British Columbia, I agree that the Library shall make it freely available for reference and study. I further agree that permission for extensive copying of this thesis for scholarly purposes may be granted by the head of my department or by his or her representatives. It is understood that copying or publication of this thesis for financial gain shall not be allowed without my written permission. Department of The University of British Columbia Vancouver, Canada Date ir:\ m<\ DE-6 (2/88) ABSTRACT A mathematical model of the nickel converter has been developed based on the assumption that the converting reactions pass through a finite series of equilibrium steps. The model predicts the bath temperature and the composition of the three phases present. Detailed data collected during in-plant trials are used to test the validity of the model predictions. The model is found to give relatively accurate predictions for the first blows of a converting charge, but overpredicts both temperature and iron removal during the last blows. The errors in the last blows are expected to be caused by the converting reactions coming under liquid phase mass transport control. An analysis of some of the more important variables in a converter operation indicates that, according to the assumption of equilibrium, there is very little that can be done to chemically improve the converting process. ii TABLE OF CONTENTS Abstract ii Table of Contents iii List of Tables vi List of Figures viii Nomenclature xi Acknowledgement xii 1.0 Introduction 1 2.0 Literature Review 7 2.1 Converter Modelling 7 2.1.1 Converter Operation 7 2.1.2 Impurity Distribution 12 2.2 Thermodynamics of the Condensed Phases .... 15 2.2.1 Matte Thermodynamics 15 2.2.2 Slag Thermodynamics 17 3.0 Objectives 21 4.0 Model Development 23 4.1 Introduction 23 4.2 Heat Balance 25 4.3 Compositional Calculations 35 4.4 Model Operation 45 iii 5.0 Model Validation 49 5.1 General Mass Balance 49 5.2 Modelling of Plant Trials 53 6.0 Discussion 72 6.1 Sensitivity Analysis 72 6.1.1 Model Operating Parameters 76 6.1.2 Converter Operating Parameters 78 6.1.3 Converter Inputs 84 6.1.4 Thermodynamic Data 91 6.1.5 Other Variables 95 6.2 Validity of the Equilibrium Assumption 101 7.0 Analysis of Nickel Converter Operation 104 7.1 Varying Matte Composition 104 7.1.1 Converting High Grade Mattes 104 7.1.2 Converting Sulphur Deficient Mattes 108 7.2 Iron and Sulphur Elimination 112 7.3 Oxygen in Matte 118 7.4 Effect of Carbon Addition 121 7.5 Operating Efficiency 125 8.0 Conclusions and Further Work 126 9.0 Bibliography 129 10.0 Appendix 134 iv 10.1 Plant Trials 134 10.1.1 In] ection Equipment 136 v. LIST O F T A B L E S Table II-I Reported activity coefficients of nickel oxide in 19 slags Table IV-I Converter dimensions and refractory data 27 Table IV-II Integrated values of CP. for use in equation 4.9.(64] 29 Table LV-III Heat of formation values used i n equation 4.13.[64] 31 Table rV-IV Values of constants used to calculate heat capacity. [64] 34 Table IV-V Constituents of the phases in the converter. 36 Table TV-VI Equi l ibr ium equations used in the compositional calculations 38 Table IV-VII Mass balance equations used in the compositional calculations 39 Table IV-VIII Free energy equations for the reactions used in the mass balance model.[9,44,64] 41 Table IV-IX Activity coefficients of the non-gaseous constituents 42 Table IV-X Standard assays (weight %).[66] 44 Table V-I Details of charges modelled 54 Table V-II Details of fitting carried out 56 Table V-III Comparison of model predicted matte compositions with assays taken at the end of the stated blow 64 vi Table V-LV Comparison of model predicted slag compositions with assays taken at the end of the blow, (weight fraction), #3 converter Charge 98, May 1988 65 Table V-V Comparison of model predicted slag compositions with assays taken at the end of the blow, (weight fraction), #3 converter Charge 105, May 1988. ... 65 Table V-VI Comparison of model predicted slag compositions with assays taken at the end of tine blow, (weight fraction), #3 converter Charge 106, May 1988. ... 66 Table V-VII Comparison of model predicted slag compositions with assays taken at the end of the blow, (weight fraction), #3 converter Charge 107, May 1988. ... 67 Table V-VIII Comparison of model predicted slag compositions with assays taken at the end of the blow, (weight fraction), #3 converter Charge 108, May 1988. ... 68 Table V-IX Comparison of model predicted slag compositions with assays taken at the end of the blow, (weight fraction), #3 converter Charge 109, May 1988. ... 68 Table V-X Flux assays for charges 105 to 109, May 1988, (weight fraction) 71 Table VI-I Standard charge used in sensitivity analysis. 73 Table VI-II Variables tested in sensitivity analysis 74 Table VI-III Scrap compositions used in the sensitivity analysis 75 Table VI-IV Units of measurement of converter inputs and outputs with approximate conversion factors used in the model 75 Table VII-I Compositions of mattes used in analysis (weight fraction) 105 vii LIST OF FIGURES Figure 1.1 Schematic flow diagram of nickel smelting routes.11] 2 Figure 4.1 Flow chart of the converter model 47 Figure 5.2 Iron in matte versus matte grade, comparison of model predictions with plant average data 50 Figure 5.3 Schematic showing the limiting converting path in the iron-nickel-sulphur system. [2] 51 Figure 5.4 Comparison of model predicted bath temperature with plant data, #3 converter Charge 98, May 1988 57 Figure 5.5 Comparison of model predicted bath temperature with plant data, #3 converter Charge 105, May 1988 58 Figure 5.6 Comparison of model predicted bath temperature with plant data, #3 converter Charge 106, May 1988 59 Figure 5.7 Comparison of model predicted bath temperature with plant data, #3 converter Charge 107, May 1988 60 Figure 5.8 Comparison of model predicted bath temperature with plant data, #3 converter Charge 108, May 1988 61 Figure 5.9 Comparison of model predicted bath temperature with plant data, #3 converter Charge 109, May 1988 62 Figure 6.1 The effect of time step on model predicted bath temperature and weight fraction iron in matte 77 Figure 6.2 Effect of oxygen efficiency on model predicted bath temperature and weight fraction iron in matte 79 viii Figure 6.3 Effect of bath emissivity on model predicted bath temperature and weight fraction iron in matte. 80 Figure 6.4 Effect of refractory thickness on model predicted bath temperature and weight fraction iron in matte 83 Figure 6.5 Effect of water in flux on model predicted bath temperature and weight fraction iron in matte 85 Figure 6.6 Effect of water in scrap on model predicted bath temperature and weight fraction iron in matte 86 Figure 6.7 Effect of weight of matte charged on model predicted bath temperature and weight fraction iron in matte 88 Figure 6.8 Effect of scrap composition on model predicted bath temperature and weight fraction iron in matte 89 Figure 6.9 Effect of NiS activity coefficient on model predicted bath temperature and weight fraction iron in matte 92 Figure 6.10 Effect of NiS activity coefficient on model predicted weight fractions of nickel and sulphur in matte. 93 Figure 6.11 Effect of initial temperature on model predicted bath temperature and weight fraction iron in matte 96 Figure 6.12 Effect of air rate on model predicted bath temperature and weight fraction iron in matte 97 Figure 6.13 Effect of oxygen enrichment on model predicted bath temperature and weight fraction iron in matte 99 Figure 6.14 Effect of oxygen enrichment on model predicted sulphur dioxide content of the off-gas 100 Figure 6.15 View of the inside back wall of a converter showing colour variation between matte and slag 102 ix Figure 7.1 Effect of using high grade matte on model predicted bath temperature and weight fraction iron in matte 107 Figure 7.2 Effect of using high grade sulphur deficient matte on model predicted bath temperature and weight fraction iron in matte 109 Figure 7.3 Effect of using high grade sulphur deficient matte on model predicted partial pressure of sulphur dioxide I l l Figure 7.4 Effect of initial bath temperature on the relative rates of iron and sulphur elimination 113 Figure 7.5 Effect of oxygen enrichment on the relative rates of iron and sulphur elimination 114 Figure 7.6 Effect of matte type on the relative rates of iron and sulphur elimination 115 Figure 7.7 Variation of iron-to-sulphur ratio with weight percent iron in matte. Regression line from model simulation of plant trials, points from plant data 117 Figure 7.8 Oxygen in matte versus matte grade 120 Figure 7.9 Effect of carbon addition at 15 kg min"1 on model predicted bath temperature and weight fraction iron in matte 122 Figure 7.10 Effect of carbon addition at 15 kg min"1 on model predicted nickel in the slag and partial pressure of sulphur dioxide 123 Figure A l Schematic layout of coke injection system. ..137 x NOMENCLATURE subscripts A Area (m)2 acc Accumulation a,b,c,d Constants B Bath C P Specific heat (Jkg^K1) c Converter ea Heat balance tolerence cb Converter barrel e2 Compositional calculation tolerence ce con Converter endwall Consumption k Thermal conductivity (Wm^K"1) ext External L Length (m) G Gas phase M Moles H Hood m Molecular weight (g mol"1) int Internal q Heat loss (kJ) M Matte phase R Radius (m) m Mouth T Temperature (K) 0 Old t Time (min) P Phase W Weight (kg) rad Radiation w Interaction parameter rea Reaction X Mole fraction ref Refractory X Thickness (m) SI Slag phase AHf Heat of formation To Total y Activity coefficient e Bath emissivity P Density (kgm3) a Stephan-Boltzman constant (Wm 2 K 4 ) xi ACKNOWLEDGEMENT I would like to acknowledge the financial support for this project from Inco Ltd., as well as personal support from the Cy and Emerald Keyes Foundation. For their assistance during the plant trials I would like to thank the staff of the Copper Cliff smelter, in particular Dave Hall, Ron Falcioni, Ahmed Vahed, and Sam Marcusen. I would also like to thank Dr. Greg Richards for guidance, and my wife for proof-reading and patience. xii 1 INTRODUCTION 1 INTRODUCTION There are three different routes used in the pyrometallurgical processing of nickel sulphide concentrates. The converting step is present in each route as the final stage before matte separation or refining. The composition of the feed to the converter will depend on the smelting furnace used, as well as on the original concentrate composition. Each of the processing routes is comprised of three unit operations.il] A schematic layout of each route is given in figure 1.1, although there are operating differences between sites using the same basic route. The main difference between routes is in the choice of the smelting furnace used. If a reverberatory furnace, as at Inco's Copper Cliff operation, or an electric furnace, as at Inco's Thompson operation, is used the first stage is roasting. This is carried out either in a multiple-hearth or fluidized-bed roaster. The roasting stage drives off a large proportion of the sulphur from the concentrate. This is caused by the oxidation of iron sulphide to magnetite. The off-gas from fluid bed roasters is often sent to an acid plant, but the sulphur concentration in the off-gas from multiple hearth roasters is not usually high enough for this. 1 1 INTRODUCTION REVERBERATORY FURNACE e.g. Inco - Copper Cliff Concentrate + Flux calcine Multiple Hearth Roasters c matte 1 Reverberatory Furnace Bessemer matte to further processing Converter ELECTRIC FURNACE e.g. Inco - Thompson Falconbridge .[Concentrate + Flux calcine Fluidized Bed Roasters 1 Electric Furnace 1 Converter Bessemer matte to further processing FLASH FURNACE e.g. Western Mining BCL Dry concentrate + Flux + Fuel matte Rotary Dryer Converter Bessemer matte to further processing Flash Furnace Figure 1.1 Schematic flow diagram of nickel smelting routes.[1] 2 1 INTRODUCTION The roasted concentrate is fed to either the reverberatory furnace or the electric furnace for the smelting stage. In this stage the magnetite formed in roasting is removed in the slag, primarily by the reaction 3Fe304+FeS -> lOFeO +S02 - l - l thus further removing iron sulphide from the matte. The matte produced in the electric furnace usually contains much less sulphur than mattes from either the reverberatory or flash furnaces, due to the more reducing nature of the electric furnace. If a flash furnace is used for smelting concentrates, as, for example, by Western Mining at Kalgoorlie or by B.C.L. in Botswana, the first stage is drying the concentrate. In this stage the concentrate is also mixed with flux and fuel. The more oxidizing conditions prevalent in the flash furnace tend to produce a higher grade matte than the reverberatory or electric furnaces. Therefore, the composition of the matte fed to the converter varies widely between nickel smelters. The converting process is primarily the selective oxidation of iron from the matte. The heat for the process is provided by the reactions involved, and is generally more than is required to make up for heat losses. Cold materials are added to regulate the temperature, and siliceous flux is added to form a fayalite 3 1 INTRODUCTION slag. The end product is a low iron, copper-nickel sulphide matte known as Bessemer matte. The amount of copper in the matte depends on the initial copper level in the concentrate, and in some operations is negligible. Bessemer matte may also contain cobalt and precious metals which can be recovered in the refining stage. The production of nickel metal from Bessemer matte by continued blowing of air, as used for copper production, is only thermodynamically possible at temperatures which are above the safe operating temperature of a standard converter. [2] Nickel converting is a batch operation consisting of a series of charging, blowing and skimming steps. Each complete cycle is known as a 'charge', with each charge being divided into a number of 'blows'. The nature of the process is such that no two charges will be the same. The composition of the input materials changes between charges and throughout each charge. The air rate to the converter also varies during the blow. At the beginning of the charge the converter will usually contain i some semi-solid 'mush'. This is the final slag from the previous charge which was too .viscous to be skimmed. Mush composition varies over a wide range, but generally has a relatively high nickel content. The 4 1 INTRODUCTION amount of mush in the converter is not known, but can be estimated from the weight of flux and scrap added to the last blow of the previous charge. The amount of matte initially charged depends on the size of the converter, and to some extent on matte availability. Often some form of cold scrap is added before the first blow. There is no specific time length for any blow, as in most cases the converter is blown until the temperature reaches a specified value. The converter is then taken off stack. A short settling time is allowed before skimming the slag to remove some of the matte which is entrained in the slag. Following skimming, more matte, and possibly scrap, is charged and blowing is recommenced. Flux is usually added immediately following the start of each blow. The amount of flux added is dependent on the size of the converter, but is roughly calculated to achieve a specified silica content in the slag at the end of the blow. The charge-blow-skim cycle is repeated a variable number of times until the iron content of the matte has been reduced to below a specified value, usually five to ten weight percent. In the last two or three blows matte is not usually added, to allow the iron content to drop sufficiently. In the last blow a large amount of cold charge and flux is often added, which makes up most of the mush. 5 1 INTRODUCTION Nickel converting, then, is a complex process involving many variables. The qualitative effects of most of these variables are relatively well known, but not necessarily understood. The quantitative effects can only be estimated from historical data. In order to aid in the overall understanding of the converting process a mathematical model of the nickel converter has been developed. 6 2.1.1 Converter Operation 2 LITERATURE REVIEW 2.1 Converter Modelling Of the mathematical models published to date which consider the converting process, only two consider the particular chemistry of the nickel converter. The majority of models of converting deal with the distribution of impurities between matte, metal, and slag in the copper converter. Three models attempt to reproduce the overall material balances during the copper converting operation, and of these only one attempts to reproduce the heat balance as well. 2.1.1 Converter Operation One of the models which considers the converting of nickel mattes calculates only the instantaneous oxidation path in the ternary iron-nickel-sulphur system[2] in equilibrium with a fayalite slag with fixed iron-to-silica ratio. As such it cannot properly be considered a model of the converter. However, it can be used to make some predictions about the operation of the converter, and the effect of some of the variables on the oxidation path. In particular this model introduces the concept of the 'limiting converting path'. This is a compositional path which the converter follows after initially correcting for any sulphur excess or deficiency. The path is dependent on the temperature of the bath, as well as some of the other converting variables, especially those relating to the oxygen content of the gas. 7 2.1.1 Converter Operation The second model which considers nickel converting is an empirical model developed at Falconbridge Limited.[3] The compositional variations of the bath are calculated based on curves fitted to plant data. The heat balance is calculated only at the end of each blow. Considering the empirical nature of the model, its predicted results are remarkably accurate. None of the empirical correlations involve a dependence on temperature, suggesting that the converting operation is not particularly sensitive to temperature variations. The model originated by Nagamori and Mackey,[4,5] and further developed by Nagamori and Chaubal,[6-8] concerns the Noranda continuous converting process. This model only calculates the equilibrium composition at a given temperature, partial pressure of sulphur dioxide, magnetite activity, and, in the case of matte-making, matte grade. It does not attempt to model the entire process. Some aspects of the model, however, may be of some interest. The idea of suspension coefficients to allow for mechanical entrapment of matte in slag is introduced.[4] These are numbers which are calculated from assays of matte and slag samples collected from the Noranda Process, and can be used to determine the amount of matte trapped in the slag and vice versa. The suspension coefficients for the nickel converter, however, will be different from those for the Noranda Process, because they are dependent on the slag viscosity, 8 2.1.1 Converter Operation composition, temperature, and the degree of mixing in the converter. A s the first three of these factors vary considerably during a blow this concept would be difficult to apply to the nickel converter. Nickel is an impurity in the Noranda Process, and so is considered as s u c h in the model.[5] The form of nickel sulphide used, Ni 2 S, was introduced in a n earlier discussion of the thermodynamics of continuous converting. [9] This form allows the model[5] to consider the so called 'sulphur deficient' mattes often found in nickel converting. The free energy of formation of N i 2 S is also given, allowing for its use in the present model. The other model which assumes a constant temperature appears to be more a demonstration of a possible use for the Solgasmix program than a bone jide model. [10] A more recent extension of this model us ing a modification of Solgasmix has been written to simulate an injection concentrate smelting technique.! 11] It does not include a heat balance as the process is assumed to be isothermal. Experiments were carried out to determine how good the model was in predicting the behaviour of three different concentrates. For the more complex concentrates the model predictions were poor, but were somewhat better for the less complex sulphides. The third model, developed by Goto, has been gradually improved u p o n over the last fifteen years, most recently being applied to the 9 2.1.1 Converter Operation copper flash smelter. [12-16] The mass balance model was developed first. [12] Assuming that the converter is in thermodynamic equilibrium a set of simultaneous equations was developed and solved using a modified form of the Newton-Raphson technique formulated by Brinkley.[17] The activity coefficients required in these equations were derived from published data and experimental work by the authors. The model considered nine elements and nineteen compounds, to give a representation of the distribution of most of the important minor elements present in copper concentrates in Japanese smelters. The second development was the addition of a heat balance model, to complete the representation of the entire converter. [13] The model applied a heat balance to the converter, using the mass balance calculations to derive a heat generation term. An iterative technique was utilized to calculate the temperature change caused by any net heat production during a given interval. Calculations were carried out over a two minute time step throughout the converting cycle, allowing for charging and skimming. It was claimed that the model was able to predict temperature variations fairly well, [13] but no direct comparison with plant data was published, and ho comparison of matte or slag composition was given. More recent developments of the model have included its extension to cover the copper flash smelter, [15] and the addition of a calculation 10 2.1.1 Converter Operation of oxygen consumption using kinetic considerations.[16] It should be noted that this last modification is only valid when the converter is under gas phase transport control, and so may not be valid towards the end of the matte blow and blister blow where the kinetics are likely to be under mixed or liquid phase control. A model has been written to calculate the heat losses from an empty Pierce-Smith converter while standing idle.[18] This model indicated that there is a rapid initial heat loss, particularly in the region of the mouth. An idle time of thirty minutes is calculated to cause a temperature drop of about 200 K at a central tuyere, and about 170 K at an end tuyere. The heat losses calculated by this model will probably be greater than those normally seen in practice, as a converter is rarely empty. The presence of molten material will increase the total heat capacity of the system and, hence, reduce the rate of temperature drop. Although the total heat capacity of the bath is approximately half that of the refractory, Bustos et al[ 18] also determined that only six to eight centimetres of the refractory were thermally active. This means that the total heat capacity of the thermally active refractory will be about the same as that of the bath, so the bath will have a significant effect on the cooling rate. Also, the thermal profiles in the refractory 11 2.1.2 Impurity Distribution calculated by Bustos et aL[18] indicate that the average temperature drop of the thermally active section of the refractory was about 25 K less than that of the surface after 30 minutes. 2.1.2 Impurity Distribution The more recent papers on modelling of the Noranda Process are primarily concerned with the distribution and volatilization of impurities.[5-8] In particular, a technique to calculate the relative amounts of impurities, including nickel, lost to the off-gas is formulated. From these calculations it has been concluded that approximately one percent of the nickel is lost to the gas during continuous converting. This result is not directly applicable to the Pierce-Smith converter, but does give a rough indication of the amount of dust formed. An attempt to theoretically model the distribution of impurities between matte and bullion in the copper converter using the Temkin model has been carried out. [19] The model gives good results for some of the impurity elements, but is quite poor for predicting nickel and cobalt distributions. Different choices of the sulphide species used for nickel and cobalt in the Temkin model give widely varying values of the distribution coefficient; values which are both higher and lower than those experimentally measured. This tends to indicate the presence of non-stoichiometric nickel and cobalt compounds. 12 2.1.2 Impurity Distribution A different model has been developed to predict the amount of metal loss to the slag in both copper[20] and nickel[9] converting. For copper losses both oxidic and sulphidic dissolution in the slag are considered. During the early stages of converting sulphidic losses to the slag predominate. However, they are not easily quantifiable because it is often difficult to distinguish between entrained and dissolved sulphides. In general, sulphidic losses tend to become more significant when there is over five percent iron in the matte; this covers almost the entire period in conventional converting. Modelling of the continuous converting of copper-nickel mattes indicates that to minimize losses to the slag, the final matte must contain at least five percent iron, otherwise a separate slag cleaning step would be required.[9] It is also expected that the sulphidic loss of nickel would be lower than that of copper. [9] The equilibrium calculations required for the Goto model[12] are quite complex. They include both sulphidic and oxidic copper dissolution, as well as the presence of both wustite and magnetite in the matte. Activity coefficients were available or calculable for most of the constituents of the matte and slag. They were not available, however, for sulphides in the slag, and so were estimated. 13 2.1.2 Impurity Distribution The predictions made by this model indicate that the majority of oxygen dissolves in the matte as magnetite. It also calculated that there was a gradual increase in sulphidic copper losses with increasing copper in the matte, but oxidic dissolution increases rapidly above fifty weight percent of copper in matte. In this composition range it is also predicted that the majority of sulphur in the slag will be associated with copper losses. Unfortunately an indication of the variation of the total sulphur in slag is not given. [12] 14 2.2.1 Matte Thermodynamics 2.2 Thermodynamics of the Condensed Phases 2.2.1 Matte Thermodynamics Extensive experimentation has been carried out on the constituent systems of copper-nickel mattes. Most of this, however, has been directed towards the calculation of phase diagrams, and is of limited use to modelling. Due to the complexity of industrial mattes, thermodynamic experiments on them are of limited usefulness without a fairly extensive knowledge of the subsystems of which they are made up. Thermodynamic work on ternary metal-sulphur systems can be used to find approximate activity coefficients for the less abundant species in the matte. In this way, for example, the activity coefficient of CoS can be approximated as 0.4, while those of metallic Co and Fe are in the ranges 21-27 and 35-57 respectively. The wide range for the last two values is due to a large sensitivity to temperature variations. [21] The effect of 'dissolved' oxygen on matte thermodynamics has been found to be significant. It has been calculated that the thermodynamic effect of the oxygen in the matte, when combined with the effect of sulphur in the slag, is sufficient to account for all of the copper reporting to the slag in copper smelting.[22] This, in turn, implies that entrainment may not be an important means of copper loss. There is 15 2.2.1 Matte Thermodynamics much disagreeament about the amount of entrainment of matte in slag, but it has been reported that about twenty-five percent of the copper losses in reverberatory slag are due to entrainment. [23,24] This result does, however, appear to go against the findings of slag cleaning experiments in which almost sixty percent of the valuable metal was recovered from the slag by settling alone.[25] It has been determined that dissolved copper is precipitated from slags during cooling, [26] implying that tests carried out at room temperature may overestimate the amount of entrainment. The conditions under which settling tests are carried out may also affect the copper solubility in slag, and hence predict entrainment incorrectly. Analyses of industrial mattes indicate that the oxygen in solidified matte is almost entirely in the form of magnetite, but may have some nickel or copper replacing iron in the compound.[27] The theoretical modelling of industrial mattes is not an easy task. The copper-iron-sulphur system has been found to be reasonably described by the Temkin model[28] or the Flood model[29], but mattes involving nickel become more complex. The associated solution model has now been applied extensively to matte systems.[30-43] This model is based on the three-suffix Margules equation 16 2.2.2 Slag Thermodynamics \ n n i = ~ X (yvtj + wJt)Xj + I (WJJ -Zj=l j=l y = l p = l Z ; = l p = l ...2.1 where Wy are interaction parameters accounting for the effect of specie i on specie j . The interaction parameters are functions of temperature of the form w --1+B.. ,J T 1 ...2.2 As is evident from the equations a large number of fitting parameters are required. These have been derived from a collection of thermodynamic studies of the various systems. Unfortunately this model has not been expanded sufficiently to allow its use for modelling industrial mattes. 2.2.2 Slag Thermodynamics The thermodynamics of converter slags have undergone much study, and work up to 1980 has been thoroughly reviewed by Mackey.[44] As far as a heat balance is concerned the most important variable in the slag is the wustite to magnetite ratio. The variation of this ratio with oxygen potential is quite well known from equilibrium considerations. Also of interest is the distribution of the valuable metals between matte and slag. In particular their activities as oxides 17 2.2.2 Slag Thermodynamics and sulphides in slag are quite important. Almost all experimental work published to date on the solubility of nickel in slags has ignored the presence of sulphur in the slag, assuming that the solubility of nickel sulphides is small. [45-58] The work of Grimsey and Biswas[44-49] has shown that there is a small amount of metallic nickel dissolution as well as oxidic dissolution. They have also reported that yNi0 = 2.6 at 1573 K in silica saturated slags, [45] and that the addition of lime to the slag decreases nickel solubility. [47] A more recent analysis of their data indicates that the conclusion of the existence of dissolved metallic nickel may have been due to experimental error. [50] A comparison of the published values of nickel oxide activity coefficients is given in table II-I. It is evident that there is little agreement on the actual value of the nickel oxide activity coefficient, but a range of values can be determined, depending on the slag composition. It is important to note that only one of these studies covered a temperature range common in converting. [52] Converter temperatures rarely rise above about 1530 K. A recent study on copper matte-slag equilibrium has reported that the Herasymenko model of an ionic solution can be successfully applied to both copper matte and fayalite slag. [58] This model is similar to the more well known Temkin model, except that it does not 18 2.2.2 Slag Thermodynamics Temperature (K) Slag Conditions Ref. 2.6 1573 Silica saturated 45 3.0-4.0 1573 CaO in fayalite 46 exp(3980/T-1.62) 1523-1623 Silica saturated 50 1.2-1.4 1623-1773 Silica saturated and unsaturated 51 1.66-2.37 1473 Silica saturated and unsaturated 52 7-11 1573 Fayalite 53 1.59 1573 Alumina saturated 54 .375 1573 Alumina saturated 55 Table II-I Reported activity coefficients of nickel oxide in slags. 19 2.2.2 Slag Thermodynamics differentiate between cations and anions in the solution. Using the Herasymenko model the changes in oxidic and sulphidic dissolution of copper in slag can be explained, as well as the effect of adding lime or alumina to the slag. The activity coefficient of copper oxide is usually given in the form y C u 0 o y and has a widely accepted value of 3.[44,59] A value of y c^ 0 equal to 204Xc™o at 1573 K has also been reported.[60] Values of yCo0 have been reported as 0.66 at 1523 K,[61] and 1.16 at 1573 K.[44] 20 3 OBJECTIVES 3 OBJECTIVES The mathematical simulation of an industrial process is a complicated procedure. The actual nature of the process may not be well known, and what is happening inside a reaction vessel is often not easily observed or measured. This is especially the case in pyrometallurgical operations, which by definition require high temperatures. The modelling process is one of making a number of assumptions, creating an initial model based on them, testing their validity, and then refining them to achieve a final product. This project represents the first two stages of this process. The primary objective, then, is the development of a model of the nickel converter, based on one major assumption regarding its operation: it is assumed that during operation the nickel converter passes through a finite series of equilibrium steps. The model is to be developed to simulate both the temperature and compositional variations of the converter during a full charge. Also, it should be able to handle the full range of input matte compositions in use in industry. The purpose of such a model is to obtain a better understanding of the nickel converting process. As such it may also aid in the improvement of converter operations. 21 3 OBJECTIVES Checking the validity of the primary assumption, as well as any others made, is a secondary objective. Recommendations for modifications to the model should be made. If the model is considered sufficiently valid some discussion of its predictions and their bearing on the operation of the process should also be presented. 22 4.1 Introduction 4 MODEL DEVELOPMENT 4.1 Introduction The main purpose of the computer model is to give a better understanding of the working of the nickel converter. The model should be capable of predicting the effect of changes in operating procedures on the temperature and composition of the contents of the converter. In this way the process can be optimized without carrying out extensive and expensive tests. The model should also provide a basis for comparing what actually happened in any particular test with what would have happened had the test not been carried out. The complexity of industrial processes generally necessitates that a number of assumptions be made in order to simplify the modelling process. The most important assumption which must be made in the case of the converting operation is that the entire system is in both chemical and thermal equilibrium. This assumption has been made in all previous theoretical models of the converting operation, and will be made here also, although with some serious reservations. The assumption that the bath is in equilibrium must be made to allow the compositions of the three phases to be calculated. Ideally kinetic considerations should also be involved, but there is not sufficient data on the kinetics of the matte system to allow this. In addition, the equilibrium model should be tried first because it is the 23 4.1 Introduction simplest case. It might be inefficient to develop a kinetic model if the process runs very close to equilibrium. The validity of this assumption will be discussed later. The model is primarily comprised of two intimately linked parts, the heat balance and the compositional calculations. The heat balance is used to calculate the temperature at which the equilibrium components of the compositional calculations are carried out. The change in composition provides the 'generated heat term' required for the heat balance. An iterative procedure is required to obtain a final composition and temperature. 24 4.2 Heat Balance 4.2 Heat Balance To calculate the temperature variation of the converter with time, a simple heat balance is carried out. The heat balance considers all of the major heat inputs and outputs to and from the converter. The general formulation is: heat accumulation = heat in - heat out + heat generation - heat consumption ...4.1 The net heat accumulation is calculated over one time step and translated into the temperature change over that time step. The only heat input is the sensible heat of input materials, and in most cases this is zero as ambient temperature is used as a baseline. The heat outputs are the heat losses from the converter. These heat losses come from two sources; radiation losses to the hood or atmosphere, and conduction losses through the walls. Radiation losses are calculated as Values for the bath emissivity, eB, and hood temperature, T H have to be estimated. The values used in the model were .8 and 873 K respectively. The sensitivity of the model to these values will be studied below. 25 4.2 Heat Balance The conduction losses are divided into two separate parts; the losses through the end walls, and the losses through the barrel. Assuming a linear temperature profile in the converter walls, losses through the end wall are calculated by dT .»4.3 Q ce — ^ce dx with the thermal conductivity expressed as a linear function of temperature[62] k = ^ (1 +Jt,T) = 1.08(1 +4.5.Jtl(r4r) Wm^K" 1 .4.4 and the end wall area gives Vs-T^+^Wl-Tl) Similarly the heat losses from the barrel are given as <lcb = 2k,Lc In R:. .4.5 .4.6 .4.7 The converter dimensions along with refractory data are given in table IV-I. Equations 4.6 and 4.7 require that there is no temperature variation with position on either the end walls or the barrel. 26 4.2 Heat Balance Converter Length, L c (m) 13.7 Inside Diameter, 2Rtat (m) 3.8 Outside Diameter, 2Rext (m) 4.6 Barrel Refractory Thickness, x<,b (m) .4 Endwall Refractory Thickness, (m) .7 Mouth Area, (m2) 9 Refractory Thermal Conductivity, k[62] (Wm^K1) 1.08(1+4.5(10"4)T) Refractory Density, pref[63] (kgm3) 2930 Refractory Specific Heat, CPJ63] (Jkg^K1) 960 Table IV-I Converter dimensions and refractory data. 27 4.2 Heat Balance These equations also assume that the rate of heat conduction through the walls is equal to the rate of convective and radiative heat loss from the shell, and represents the total heat loss through the converter walls. The effect of this assumption is expected to be very small, as the heat lost through the walls is approximately one percent of the total heat output during blowing. [13J The heat consumption term is made up of the heats required to raise the charged materials and the injected gas to the bath temperature. The heat required to raise the charged materials and gas to the final temperature is calculated as = ? k f ' y ambient ...4.8 Cp{T)dT The values of the integral in equation 4.8 are pre-calculated to give a final equation of the form Icon = X Mi V 4 ...4.9 Values of the constants in equation 4.9 are given in table IV-II. As no values for specific heat or heat of formation are published for Ni2S, they were estimated assuming that and CuJ Ni2S AH f,Cu^S AH, .4.10 CuS AH, NiS f.CuS AH, f.NiS 28 4.2 Heat Balance i jcpm Jmol"1 Notes a, b, qx lO 3 d,xlO' 5 Fe 7841.02 24.476 4.226 -Ni -6529.9 25.104 3.766 C u -141.3 31.38 Co -3940.3 13.807 12.259 FeS -8925.9 51.045 4.979 T<1468K FeS 4664.09 71.128 T>1468K Ni 2S -25182.5 20.318 NiS -12784.5 38.911 13.389 C u 2 S -6958.62 84.935 CoS -13682.7 44.35 5.251 F e 3 0 4 -52247.3 200.832 FeO -32218.8 68.199 NiO -9589.16 46.777 4.226 C u 2 0 -19636.7 62.342 11.924 T<1509K C u a O -38545.1 96.274 .293 -1.925 T>1509K CoO -14205.8 48.283 4.268 -1.674 s2 -12165.7 36.484 .355 3.766 o 2 -9507.5 29.957 2.092 1.674 so2 -15407.7 43.43 5.314 5.941 N 2 -8493.394 27.865 2.134 SiQ 2 -29666.3 58.911 5.021 Table IV-II Integrated values of CP. for use in equation 4.9.(64] 29 4.2 Heat Balance The heat generated by reaction is calculated from a comparison of the results of the mass balance calculation with the bath composition at the end of the previous time step. The reaction heat is given by qrea = mM-Mit0)AHLiiT] ...4.11 i Values of AHfiJ are calculated from AHf • T - AHf,. 2 9 8 + f CP (T)dT + AH. J29S TB ...4.12 Trans,i A s with the heat consumption term, the heat of formation equations are precalculated to give the form d ...4.13 Wf>iiT = ai + biTB + ciT> + -Values of the constants in these equations are given in table IV-III. The heat of mixing is assumed to be negligible. This is considered justifiable as it has been found in previous modelling studies to be of the order of 0.5% of the total heat involved.[13] The heat required to raise the internal surface of the refractory to the new bath temperature is given as A r ...4.14 4ref =  Wrefp^~Y 30 4.2 Heat Balance i Notes b, c,xl03 diXlO" 5 Fe 7841.02 24.476 4.226 Ni -6529.906 25.104 3.766 Cu -141.298 31.38 Co -3940.269 13.807 12.259 FeS -104321.1 51.045 4.979 T<1468K FeS -90731.11 71.128 T>1468K Ni2S -82524.68 20.318 NiS -105669.3 38.911 13.389 Cu2S -88965.02 84.935 CoS -101965.1 44.35 5.251 Fe 30 4 -1168957 200.832 FeO -296647.6 68.199 NiO -250169.1 46.77 4.226 Cu 20 -186996.7 62.342 11.924 T<1509K Cu 20 -205905.1 96.274 .293 -1.925 T>1509K CoO -253112.2 48.283 4.268 -1.674 s2 -12165.72 36.485 .335 3.766 o 2 -9507.5 29.957 2.092 1.674 so2 -312262.5 43.43 5.314 5.941 Table IV-III Heat of formation values used in equation 4.13.[64] 31 4.2 Heat Balance with the weight of the refractory calculated as 2 „ ...4.15 The heat accumulation over the time step is then calculated as <lacc = Irea ~ 4 raM ~ 4 ce^ ~ 4 d,** 1 ...4.16 -<lcon = WBCPAT + -Wre£PrAT Where and CP =2ZMiCP -4.17 WB = XMfml. - 4 - 1 8 The new bath temperature can then be calculated as TB = TBo + ^ B w r +-w r ...4.i9 Values of the heat capacities at converting temperatures are calculated from equations of the form c . ...4.20 CP,i,TB = ^  + biTB+ — 1B 32 4.2 Heat Balance The constants used in these equations are given in table IV-IV. All heat capacities are calculated at the final temperature in each time step. If the absolute value of the temperature change over a single time step is above an arbitrarily set tolerence value, e1( the heat balance is recalculated using the new temperature. This allows corrections to the temperature dependent variables to be made, which may, in turn, effect the overall temperature change. This process is repeated until the temperature difference between iterations becomes less than e^  The affect of the magnitude of ev will be considered in the sensitivity analysis. 33 4.2 Heat i Cpjn Jmol -1 Notes ai bpclO 3 CjXlO- 5 Fe 24.4764 8.452 Ni 25.104 7.531 Cu 31.38 Co 13.807 24.518 FeS 51.045 9.958 T<1468K FeS 71.128 T>1468K Ni2S 76.735 NiS 38.911 26.778 Cu2S 84.935 CoS 44.35 10.502 Fe 30 4 200.832 FeO 48.785 8.368 -2.803 NiO 46.777 8.452 CuaO 62.342 23.849 T<1509K CoO 48.283 8.535 1.674 s2 36.484 .669 -3.766 o2 29.957 4.184 -1.6736 so2 43.43 10.627 -5.941 Table IV-IV Values of constants used to calculate heat capacity. [64] 34 4.3 Compositional Calculations 4.3 Compositional Calculations Compositional calculations are carried out assuming that the converter is in equilibrium. Using the equilibrium assumption the composition of the three phases can be determined from a knowledge of the amount of each element present and the equilibrium temperature. The model considers eight 'elements' (Fe, Ni, Cu, Co, S2, 02, N2, and 'Si02'), and twelve compounds in three phases. Nitrogen and 'silica' are considered as inert, and act only as diluents in their respective phases. The 'silica' includes all other components of the slag, which are also considered inert. The constituents of each phase are given in table IV-V. Two nickel sulphides are used in the matte phase to account for the sulphur excesses and deficiencies found during the converting cycle. The specie Ni2S is chosen because of its high nickel to sulphur ratio. It should be noted that Ni3S2, which is commonly used to represent nickel sulphides, would be made up of a one-to-one mixture of the two nickel sulphide species used here. The system is formed into a set of twenty-three non-linear simultaneous equations: eleven equilibrium equations, six elemental balances, three molar balances (one for each phase), and an equation relating the activity of magnetite in the matte to the activity of magnetite in the slag. These equations are solved using a 35 4.3 Compositional Calculations Phase Matte Slag Gas Elements Fe, Ni, 'sicy N2, 02, Cu, Co s 2 Compounds FeS, FeO, so 2 NiS, Fe304, Ni2S, NiO, Cu2S, Cu20, CoS, CoO Fe 30 4 Table IV-V Constituents of the phases in the converter. 36 4.3 Compositional Calculations "Quasi-Newton" method to give values for the twenty-three unknowns. The equations solved are given in tables IV-VI and IV-VII. Values of the mole fractions, Xj, required for the equilibrium calculations are found from M ...4.21 i,p ~ MpJo The non-linear equation solving routine used was obtained from the program library of the University of British Columbia. [65] It is assumed that all material charged during the time step dissolves immediately. For the equilibrium calculation it is also assumed that all the gas blown or formed during the time step remains in the converter until the end of the time step, when it all exits through the hood. The calculations are carried out at atmospheric pressure. It is necessary that an assumption be made regarding the amount of material lost as dust in the flue, splashing from the mouth and as spillage during pouring. None of these losses can be measured with any accuracy. The amounts however should be sufficiently low so as to be negligible when compared to the overall inaccuracy of the weights of materials charged and removed from the converter. 37 4.3 Compositional Calculations Equation Equilibrium 1 FeO+\s2^>FeS + l-S02 equations 2 NiO+\s2->NiS+\S02 for 3 Cup +\s2 -> Ci^S +\so2 reactions 4 CoO +\s2^> CoS +\S02 5 \s2+o2->so2 6 3FeO+l02-^Fe30A 7 2Ni+\s2^>Ni2S 8 Fe+\S2^>FeS 9 2Cu+\s2^Cu2S 10 Co+\s2^CoS 11 Ni+\S2^>NiS Magnetite equilibrium aFe304 ~aFe304 Table IV-VI Equilibrium equations used in the compositional calculations. 38 4.3 Compositional Calculations Equat ion Elemental MFe,T = MFeS + MFeQ + MFe+3[MF^M + M P ^ balance MNiJo = Mm + MNi0 + MNi + 2MNhs equations Mcujo = MCu + 2(MCU2S + MCU20) MCO,TO = MCoS+MCo0 + MCo Ms j0 = MFeS+Mm + MNij +MCuj + MCoS + MS02 + 2MSi M0,TO = MFe0 + Mmo + MCui0 + MCo0+2(M02 + Ms0) +4{MWu + MF^ Molar balances MM,TO = MFeS + MNiS + MNi2S+MCu2S + MCoS + MF^ +MFe + MNi + MCu + MCo within MSl,To = MFeO + MNiO + MClLp+MCo0 + MPtfi + phases MG,To = M02 + MSi + MSo2 + MN2 Table IV-VII Mass balance equations used in the compositional calculations. 39 4.3 Compositional Calculations The equations for calculation of the free energy of the reactions considered are given in table IV-VIII. The activity coefficients of the non-gaseous constituents are calculated from the empirical relations given in table IV-IX. Although many of these equations are derived from experimental data their accuracy is questionable, as they were not derived for the particular system considered here. The experiments were also not carried out on complex systems. Many of the activity coefficients could potentially be considered to be fitting parameters for the model, however only a minor amount of fitting had to be done. If the values or equations used are valid all charges simulated by the model should show a reasonable fit using the same values for the activity coefficients. The values of some activity coefficients had to be estimated based on the values for similar species in copper mattes. The values of the activity coefficients of nickel and copper oxides in slag were fitted to try to bring the predicted slag compositions closer to measured values. The use of the activity coefficients as fitting parameters will be discussed later. The gases involved are assumed to be ideal. A further problem is the characterization of the composition of all of the inputs, many of which do not have reliable assays. For these materials a 'standard' assay based on historical plant data is used. 40 4.3 Compositional Calculations Reaction No. (Table IV-VI) AG* (J) 1 -53000+15. IT 2 -58300+18.01T 3 -182000+17.93T 4 -73700+13.6T 5 -363000+72.42T 6 -402000+169.8T 7 -172000+72.0IT 8 -105000+24.18T 9 -147000+42.05T 10 -125000+48. IT 11 -139000+67.03T Table IV-VIII Free energy equations for the reactions used in the mass balance model.[9,44,64] 41 4.3 Compositional Calculations Phase i Ji Ref. Matte Fe 40 20 Ni 15 (1) C u 14 12 Co 25 20 FeS exp((^ )ln(.54 + lAXFa]nXpa + .52XFeS)) 15 Ni 2S exp((^)-.2) 9(2) NiS 1 (1) C u 2 S 1 (1) CoS .4 21 F e 3 0 4 exp((^) (4.96 + 9.9 ]n(Xc^ + XNiS +XNhS) +lA3(\n(XClhS+XNiS+XNhS))2 +2.55(ln(XCu^+XNiS+XNi2S)f) 15 Slag FeO exp((^)ln(1.42XF,0-.2)) 15 F e 3 0 4 .69 + 56.8X^ + 5.45*^ 12 NiO 4.1 (2) C u a O .006 (2) CoO .66 61 (1) Estimated. (2) Fitted. Table IV-IX Activity coefficients of the non-gaseous constituents. 42 4.3 Compositional Calculations 'Standard* assays used are given in table IV-X. Assays used for the other charged materials are average values over the charge period, but are not expected to change significantly. 43 4.3 Compositional Calculations Material Cu Ni Co Fe S O Si02 Mush 8.04 16.6 .8 20.2 10.4 5.73 38.22 Washout 13.6 36.9 1.28 17.8 26.4 3.9 .123 Scrap 12.7 27 .94 18.5 16.9 7.31 15.85 Transfer matte 21.4 42.5 1.04 9.9 25.3 .12 -Table IV-X Standard assays (weight %).[66] 44 4.4 Model Operation 4.4 Model Operation The model operates by combining the heat balance and compositional calculations over a set time step. The total amount of each element charged over the entire time step is calculated, together with the oxygen and nitrogen in the air blown over that period. These values are added to the contents of the converter at the end of the previous time step, and the mass balance is calculated. The total change in the amounts of each specie present over the time step is calculated and then reduced by the amount of that specie in the material charged during the time step. The net change is multiplied by the heat of formation of the respective specie at the bath temperature, and the result is summed over all species present to give the heat generated by the reactions (equation 4.11). The heat balance over the time step is then carried out. After the heat balance calculations have converged as detailed above, the overall temperature change over the time step is compared with a tolerance value, e2. If the temperature change is greater than e2 the equilibrium calculations are carried out at the new temperature and the heat balance is repeated. This continues until the temperature change between successive calculations of composition becomes less than e2. The purpose of this is to correct the composition for the change in 45 4.4 Model Operation temperature over the time step, which may also change the generated heat. The final temperature and composition for the time step are printed to a file, the time is incremented, and the process is repeated. If the converter is idle during the time step the generated heat is assumed to be zero. The heat balance is calculated normally, but the composition change is not calculated. The effect on the bath temperature of the heat generated by the change of composition during cooling is corrected for during the time step immediately following the idle period. This is necessary because the bath is not at equilibrium with the surrounding atmosphere during idle periods, so the compositional calculations would not be valid. Slag skimming is carried out while the converter is idle. The removal of slag should not affect the overall composition of the slag or matte, nor the temperature of the phases, and so is considered to be a simple subtraction in the model. If, during the analysis of an industrial charge, there is not sufficient slag in the converter model to skim the indicated amount, the model will note this in the output and skim 95% of the total slag present. A flow chart of the program is shown in figure 4.1. The model was run on the University of British Columbia Amdahl V8 mainframe computer, and on an AST Premium 286 personal computer with an 46 4.4 Model Operation Read Initial Conditions and Operating Conditions Remove Slag Figure 4.1 Flow chart of the converter model. 47 4.4 Model Operation 80287 math co-processor. The running time on both machines varied widely depending on the particular charge. There was little difference between results from the different computers. 48 5.1 General Mass Balance 5 MODEL VALIDATION 5.1 General Mass Balance In order to verify that the results of the mass balance calculations were consistent with reality, six charges were simulated and their results compared with published data. In particular, a comparison of the variation of weight fraction iron in matte with the sum of the weight fractions of copper, nickel, and cobalt (matte grade) was made. A regression line calculated using data from five different operations has been published,[1] and figure 5.1 shows a comparison of the model predictions with the published line. The agreement is quite good, but a deviation from the general linearity is seen at lower iron levels. This deviation is mainly due to the invalidity of the use of the regression line at low iron levels. This is illustrated by the fact that the regression line gives zero iron in matte at a matte grade of .75, whereas in practice higher matte grades will still contain between .005 and .01 weight fraction iron. In the region where the model deviates from the regression line there is an increase in the ratio of sulphur to iron removal. To some extent this is caused by increased nickel and cobalt oxidation, but the matte is also becoming more sulphur deficient. 49 5.1 General Mass Balance 0.4 0.5 0.6 0.7 WT FRACTION Cu+Ni+Co Figure 5.1 Iron in matte versus matte grade, comparison of model predictions with plant average data. 50 5.1 General Mass Balance AT. % Fe Figure 5.2 Schematic showing the limiting converting path in the iron-nickel-sulphur system.[2] 5.1 General Mass Balance Both the linear and non-linear regions of this curve can be explained by Kellogg's limiting oxidation path (figure 5.2).[2] This was calculated as being approximately linear up to about eight atomic percent iron in matte, which corresponds to a weight fraction of iron of approximately .09. At this point it curves to reduce the sulphur content in the matte while changing the iron content slightly. This behaviour is very similar to that predicted by the present model. The model predicts a linear reduction of iron content down to a weight fraction between .08 and .1. At this point the rate of iron removal with respect to matte grade is reduced indicating increased sulphur removal. 52 5.2 Modelling of Plant Trials 5.2 Modelling of Plant Trials The model was run for six charges monitored during plant trials at Inco's Copper Cliff smelter (see appendix). Details of the charges are given in table V-I. The charges covered a wide range of operation conditions and provide a fairly rigorous test of the model. Comparisons of the predicted temperatures with those measured in-plant are given in figures 5.3-5.8. All model runs assumed an oxygen efficiency of 95%[66], and used the air-rates measured in-plant. The conformity of the model predictions to the plant data until the last blows indicates that the heat balance portion of the model is valid over most of the charge. This is a further validation of the mass balance. The deviation at the end of a charge may be caused by the rate of oxygen consumption being mass transfer controlled at low iron levels. The dissolution of mush has been simulated by adding an equivalent mass of scrap at a constant rate during part of the first blow. Details of the fitting carried out are given in table V-II. In all cases the initial mush was also present, but this is not expected to have a significant effect on the overall composition, as the amount of scrap added was small and the actual initial weight of mush present is not known. No fitting was required for charge 105 (figure 5.4), as the 53 5.2 Modelling of Plant Trials Charge 98 105 106 107 108 109 Initial matte 120 130 90 120 60 100 contents mush 30 30 30 30 30 (tonnes) scrap 10 5 15 15 other 10(1) 40(2) Blow 1 Times Blowing 119 103 88 95 102 (min) Idle 46 62 74 40 116 No. idle periods 2 3 2 5 5 Material Flux 2.2 added Matte 22 44 22 50 66 (tonnes) Scrap 10 20 Slag skimmed (tonnes) 88 88 88 88 66 Blow 2 Times Blowing 78 85 120 80 95 68 (min) Idle 50 88 70 34 76 77 No. idle periods 1 4 2 2 2 1 Material Flux 20.3 20.2 20.2 20.3 25 20.2 added Matte 66(2) 88 66 44 44 44 (tonnes) Scrap 10 5 10 11 10 10 Slag skimmed (tonnes) 44 44 66 66 44 66 Blow 3 Times Blowing 146 68 46 83 116 67 (min) Idle 83 108 15 33 91 21 No. idle periods 2 2 1 2 3 2 Material Flux 25.2 20.1 13.2 20 25.3 20.2 added Matte 22 44(2) 44 22 44(2) (tonnes) Scrap 10 10 10 15 5 Slag skimmed (tonnes) 66 66 22 44 66 44 Table V-I Details of charges modelled. 54 5.2 Modelling of Plant Trials Charge 98 105 106 107 108 109 Blow 4 Times Blowing 43 77 99 52 (min) Idle 20 20 23 No. idle periods 1 1 3 0 Material Flux 11.9 20.2 20.2 20.4 added Matte 30(2) 44 44 (tonnes) Scrap 10 17 10 Slag skimmed (tonnes) 22 44 22 44 Blow 5 Times Blowing 86 47 (min) Idle 37 , 27 No. idle periods 3 2 Material Flux 20.2 20 added Matte 44(2) 44(2) (tonnes) Scrap 10 5 Slag skimmed (tonnes) 22 44 Blow 6 Times Blowing 41 32 (min) Idle 24 17 No. idle periods 1 1 Material Flux 20.2 8 added Matte (tonnes) Scrap Slag skimmed (tonnes) 22 44 (1) Slag (2) Matte transferred from another converter. Table V-I Details of charges modelled (continued). 55 5.2 Modelling of Plant Trials Charge Tonnes scrap added to simulate mush dissolution addition rate (tonnes min"1) 98 25.6 .4 105 - -106 25.3 .55 107 20.68 .47 108 15.6 .52 109 12.6 .45 Table V-II Details of fitting carried out. 56 5.2 Modelling of Plant Trials 1600 0 44 84 174 214 304 356 396 442 550 TIME (MINUTES) Figure 5.3 Comparison of model predicted bath temperature with plant data, #3 converter Charge 98, May 1988. 57 5.2 Modelling of Plant Trials Figure 5.4 Comparison of model predicted bath temperature with plant data, #3 converter Charge 105, May 1988. 58 5.2 Modelling of Plant Trials Figure 5.5 Comparison of model predicted bath temperature with plant data, #3 converter Charge 106, May 1988. 59 5.2 Modelling of Plant Trials 1 i 1 1 1 1 1 1 1 1 r 0 42 100 202 2 4 4 316 390 438 486 552 612 TIME (MINUTES) Figure 5.6 Comparison of model predicted bath temperature with plant data, #3 converter Charge 107, May 1988. 60 5.2 Modelling of Plant Trials 1540 1 400 — i n m n II 1111 i i n i | i i ' r i m i i i t i m i i i 11 i n i n n i i i i i u n n ] i i T T T T T T r m H i i i n | m i i i i 1111 n M1111111111111111M1111 n 111111 T r 11111111111111111 n 11 f 1111 M n 11 0 44 84 218 258 382 422 468 542 TIME (MINUTES) Figure 5.7 Comparison of model predicted bath temperature with plant data, #3 converter Charge 108, May 1988. 61 5.2 Modelling of Plant Trials 0 44 138 242 282 402 470 TIME (MINUTES) Figure 5.8 Comparison of model predicted bath temperature with plant data, #3 converter Charge 109, May 1988. 62 5.2 Modelling of Plant Trials starting point for the model was the beginning of the second blow and both the matte and slag assays were available for this point. However, initial weights of matte and slag had to be estimated from the operation of the first blow. To further check the validity of the mass balance, the predicted matte and slag compositions can be compared with the assays obtained during the trials. A comparison of the predicted and all measured matte compositions is given in table V-III, and of the skimmed slags in tables V-rV to V-IX. In the slag compositions 'Si02' includes all of the minor oxides which are not considered in the model. The agreement in the matte compositions is good in most cases, although the predicted sulphur level is too low, possibly due to incorrect activity coefficients of the sulphide species. In particular yNiS and yCu2s are probably not equal to one; however, no measured values are available. The wide variation seen in the amount of oxygen in the assayed matte samples appears to indicate that there is no particular relationship between matte grade and matte oxygen content in nickel converters. The model's almost complete inability to predict the oxygen in matte tends to imply that the oxygen is not in equilibrium. However, more information on the nature of the oxygen in matte is required. 63 5.2 Modelling of Plant Trials Charge Blow Fe Ni Cu Co S O 105 4 Model .1221 .4428 .1626 .0128 .2429 .0168 Assay- .114 .425 .154 .0145 .249 .0397 106 2 Model .0968 .4448 .1915 .0097 .2461 .0108 Assay .0983 .425 .184 .0127 .241 .0364 3 Model .0696 .4710 .2037 .0094 .2380 .0082 Assay .0579 .468 .217 .0103 .225 .0191 107 2 Model .1388 .4397 .1481 .0115 .2393 .0224 Assay .146 .428 .147 .0153 .255 .0066 108 1 Model .1334 .4417 .1522 .0138 .2362 .0226 Assay .125 .455 .148 .0153 .255 .0009 3 Model .1399 .4348 .1491 .0126 .245 .0185 Assay .124 .449 .149 .0139 .252 .0071 109 2 Model .1665 .4185 .1422 .0109 .2375 .0244 Assay .157 .407 .134 .0145 .257 .024 Table V-III Comparison of model predicted matte compositions with assays taken at the end of the stated blow, (weight fraction) 64 5.2 Modelling of Plant Trials Slag Fe Ni Cu Co O 'Si02' 1 Model .5125 .0177 .0017 .0067 .1675 .2935 Assay- .539 .0121 .0081 .0051 .1984 .2372 2 Model .5153 .0187 .0019 .007 .1691 .289 Assay .514 .0095 .0056 .0058 .1904 .2583 3 Model .5021 .0211 .0019 .0077 .1667 .3005 Assay .487 .0194 .0093 .0104 .1889 .2761 4 Model .5171 .04174 .0075 .0138 .1783 .2415 Assay .409 .0664 .0223 .0138 .1766 .3119 Table V-IV Comparison of model predicted slag compositions with assays taken at the end of the blow, (weight fraction), #3 converter Charge 98, May 1988. Slag Fe Ni Cu Co O 'SiCV 2 Model .4778 .0164 .0011 .0073 .1551 .3422 Assay .517 .0077 .0048 .0053 .1763 .2865 3 Model .4885 .0198 .0014 .0085 .1618 .3199 Assay .506 .0102 .0049 .006 .1627 .3101 4 Model .4946 .0228 .002 .009 .164 .3075 Assay .497 .009 .0044 .0067 .1471 .3357 5 Model .4994 .0291 .0032 .0113 .1679 .2889 Assay .486 .0339 .0143 .0114 .1533 .3011 6 Model .4472 .0364 .004 .013 .1528 .3466 Assay .446 .0335 .0107 .0155 .1545 .3398 Table V-V Comparison of model predicted slag compositions with assays taken at the end of the blow, (weight fraction), #3 converter Charge 105, May 1988. 65 5.2 Modelling of Plant Trials Slag Fe Ni Cu Co O *SiCV 1 Model .4297 .012 .0008 .003 .1363 .418 Assay .498 .0228 .011 .0078 .1761 .2842 2 Model .4561 .0162 .0014 .0045 .1465 .3752 Assay .511 .026 .0086 .0137 .1359 .3048 3 Model .4959 .0292 .0038 .0089 .1659 .2962 Assay .556 .0117 .0071 .0051 .174 .246 4 Model .4616 .0415 .0071 .0113 .1596 .3188 Assay .46 .0395 .0123 .0156 .167 .3056 Table V - V I Comparison of model predicted slag compositions with assays taken at the end of the blow, (weight fraction), #3 converter Charge 106, May 1988. 66 5.2 Modelling of Plant Trials Slag Fe Ni Cu Co O 'SiCV 1 Model .4673 .0128 .0006 .0047 .1501 .3643 Assay .552 .0139 .0075 .0054 .1616 .2595 2 Model .5028 .0186 .0013 .007 .1651 .3051 Assay .526 .0099 .0054 .0060 .1628 .2899 3 Model .5104 .0226 .0018 .0082 .1699 .2871 Assay .486 .0437 .0172 .0754 .1496 .296 4 Model .513 .0291 .0324 .0099 .1719 .2726 Assay .48 .0346 .0132 .0078 .153 .3113 5 Model .4822 .0298 .0028 .0095 .1622 .3134 Assay .427 .0479 .0185 .0109 .1426 .3531 6 Model .5143 .0301 .0031 .0106 .174 .2678 Assay .393 .06 .0222 .0136 .1391 .3721 Table V-VII Comparison of model predicted slag compositions with assays taken at the end of the blow, (weight fraction), #3 converter Charge 107, May 1988. 67 5.2 Modelling of Plant Trials Slag Fe Ni Cu Co O •Si02' 1 Model .5191 .024 .0021 .0108 .1736 .2703 Assay .504 .0227 .0095 .0066 .1587 .2985 2 Model .4867 .0213 .0017 .0082 .1598 .3222 Assay .492 .0154 .0063 .0078 .152 .3265 3 Model .4592 .0476 .006 .0153 .1612 .3105 Assay .468 .0282 .0086 .0131 .1479 .3342 Table V-VIII Comparison of model predicted slag compositions with assays taken at the end of the blow, (weight fraction), #3 converter Charge 108, May 1988. Slag Fe Ni Cu Co O 'Si02' 1 Model .5618 .0245 .0016 .0091 .1832 .2198 Assay .557 .0133 .0068 .0049 .1529 .265 2 Model .5811 .0246 .0017 .0092 .1879 .1987 Assay .528 .0089 .0048 .005 .1595 .2937 3 Model .5294 .0246 .0017 .0089 .171 .2643 Assay .495 .0221 .0104 .006 .1528 .3137 4 Model .4467 .0371 .0024 .0099 .1477 .3561 Assay .462 .0148 .006 .0081 .1536 .3554 Table V-IX Comparison of model predicted slag compositions with assays taken at the end of the blow, (weight fraction), #3 converter Charge 109, May 1988. 68 5.2 Modelling of Plant Trials The values of copper, nickel, and cobalt in the slag are not expected to be particularly accurate, as entrainment is not considered in the model. This is because entrainment in converter slags is dependent on a number of parameters which cannot be calculated by the model. Some fitting could be carried out using the activity coefficients of the respective oxide species in the slag, but this approach would not be technically valid. The amount of entrainment is not dependent on thermodynamics, and so cannot be modelled using thermodynamic parameters. Even if a fitted activity coefficient gives a good result for one case it is unlikely to do so for all others. The predicted iron and 'silica' compositions, however, are generally close to the assayed values. The error in the silica content of the slags is usually large for the first blow, reflecting the lack of knowledge of the silica content of the mush. Much of the error in the slag composition can be ascribed to variations in the silica content of the flux. Table V-X shows the assays of the flux taken over five consecutive blows. This indicates that there is a wide variation in the silica content of the flux during any specific charge, and may lead to significant errors in the model predictions. The only major discrepancies in the iron predictions are for the last slag of charge 98 and the last two slags of charge 107. In these cases the predicted iron in the slag is much higher than was measured. This may have been caused by errors in 69 5.2 Modelling of Plant Trials the amount silica in the flux, or iron in the matte charged. Overall, the model appears to give a good representation of both the mass balance and the heat balance for the middle blows. Some fitting is required in the first blow to account for the mush composition, weight, and dissolution. In the final blows the model generally tends to overpredict both the temperature and iron removal. This overprediction is likely to be caused by the converting reactions coming under liquid phase mass transfer control. 7 0 5.2 Modelling of Plant Trials Charge Fe Ni Cu Co S Si02 105 .168 .0192 .0114 .0009 .0101 .630 106 .0954 .0208 .0545 .0006 .0008 .712 107 .167 .0192 .0118 .0007 .0141 .648 108 .204 .0117 .0079 .0008 .0106 .613 109 .129 .0064 .0034 .0005 .0006 .756 Table V-X Flux assays for charges 105 to 109, May 1988, (weight fraction). to 71 6.1 Sensitivity Analysis 6 DISCUSSION 6.1 Sensitivity Analysis A sensitivity analysis of the model is required to calculate the effect of errors or uncertanties in the inputs on the overall results. It can also be used to test the effects of some of the less important assumptions, and to give an indication of what are the most important variables controlling the converter. A 'standard' charge was developed based on plant practice to allow direct comparisons between different values of the same variable. Details of this charge are given in table VI-I. It is not as complex as an actual charge, and has a constant air rate. This is so that the effects of the variable being tested are not obscured by other considerations. The variables tested and the values used are given in tables VI-II and VI-III. Table VI-IV shows the units of measurement of the more important inputs and outputs to and from the converter. The effect of changes on both the temperature and bath composition are of interest. The effect on composition will be shown by the weight fraction of iron in the matte, which gives a good indication of the overall matte composition (see figure 5.1). The variation in the slag composition is not as large as in the matte composition, and to a certain extent depends on the iron in the matte. In cases where there is a large variation in the gas composition this variation will also be shown. 72 6.1 Sensitivity Analysis Initial Contents Matte 110 Tonnes Mush 30 Tonnes Air Rate 633 Nnr'mln'1 Blow Time (min) Idle (min) Matte (Tonnes) Scrap (Tonnes) Flux (Tonnes) 1 120 34 22 18(1) -2 94 20 44 15 20 3 92 18 22 15 20 4 104 28 22 15 11 5 72 30 - - 11 6 38 - - - 11 (1) Mush dissolution simulation. 95% of slag skimmed after blows 1-5. Table VTI Standard charge used in sensitivity analysis. 73 6.1 Sensitivity Analysis Variable low base high Time step (min) 1 2 4 Heat balance tolerence, e! (K) .5 1 2 Composition calculation tolerence, e2(K) 5 10 15 Oxygen efficiency (%) 90 95 100 Emissivity of bath .6 .8 1 Hood temperature (K) 773 873 973 Refractory thickness (m) .3 .4 .5 Shell Temperature (K) - 553 603 Water in scrap (%) - 2 5 Water in flux (%) - 0 5 Tonnes scrap in ladle 12 15 18 Tonnes matte in ladle 20 22 23 yNiS .7 1 1.3 .7 1 1.3 Initial temperature (K) 1300 1350 1450 Air rate (Nm^in1) 536 633 730 Oxygen enrichment (%) 25 21 30 Table VI-II Variables tested in sensitivity analysis. 74 6.1 Sensitivity Analysis Scrap Composition (weight fraction) Fe Ni Cu Co S O 'SiCV Flue dust (base) .185 .27 .127 .009 .164 .073 .159 Concentrate .026 .329 .179 .005 .162 .034 .265 Slag .46 .039 .012 .016 .006 .166 .30 Table VI-III Scrap compositions used in the sensitivity analysis. Input or Output Industrial Unit Unit Equivalent Matte Ladle 22 (103)kg Scrap Ladle (large) 15 (103)kg ti Ladle (small) 10 (103)kg II Tons or belts variable Air Tons/day .48727 Nm3min 1 Oxygen Tons/day .46873 Nm3min 1 Flux Tons or belts variable Slag Ladle 20 (103)kg Bessemer matte Tons .9072 (103)kg Table VI-IV Units of measurement of converter inputs and outputs with approximate conversion factors used in the model. 75 6.1.1 Model Operating Parameters 6.1.1 Model Operating Parameters A number of the model parameters may have some effect on the accuracy of the predictions made. The most important of these is the length of the time step used. In general a shorter time step will give better accuracy, but increases the running time and computing cost. The effect of varying the time step is given in figure 6.1, and is quite small. The change in time step does appear to have a larger effect on the weight fraction of iron in the matte than on the temperature, but the difference is still relatively small. The four minute time step, however, does cause a reduced accuracy in the timing of some inputs which must be at the beginning of a time step. The other two model operating parameters which could have an effect on the model predictions are the heat balance and mass balance temperature tolerences, ex and e2. Smaller values of these are also likely to give better accuracy, but will increase running time. In this case neither of the tolerence values were found to have a significant effect on the model predictions in the range tested. 76 6.1.1 Model Operating Parameters 1580 -i — , 0 84 220 320 428 TIME (MINUTES) Figure 6.1 The effect of time step on model predicted bath temperature and weight fraction iron in matte. 77 6.1.2 Converter Operation Parameters 6.1.2 Converter Operation Parameters There are five primary parameters of converter operation which are assumed to be constant for the purposes of modelling. Of these, three may be expected to have a significant effect on the model predictions; the oxygen efficiency, bath emissivity, and converter hood temperature. The oxygen efficiency of a converter, defined as theoretical oxygen requirement * J Q Q ^ -actual oxygen used is very rarely 100% and is more often around 95%.[66] Figure 6.2 shows the effect of this difference on bath temperature and iron in matte. The change in both of these predictions is significant. Both iron and sulphur removal are increased by a higher oxygen efficiency due to the increased oxygen available for reaction. The increased amount of reaction also causes higher temperatures. This is approximately proportional to the extra amount of oxygen available. The emissivity of the bath may also have a significant effect on the converter temperature, although radiation losses are much smaller than the heat lost to the gas. Figure 6.3 shows that the effect is not as large as that of oxygen efficiency, but is still significant over a full charge. The variation of composition, however, is seen to be very-small. This implies that the composition of the bath is relatively independent of temperature over the range shown in figure 6.3. 78 6.1.2 Converter Operation Parameters 1600 H TIME (MINUTES) Figure 6.2 Effect of oxygen efficiency on model predicted bath temperature and weight fraction iron in matte. 79 6.1.2 Converter Operation Parameters 1600 I ' " I " " I " " " I ' " " " I ' I "I " ' I'" " " ' | " " 0 42 82 158 220 260 320 360 430 504 TIME (MINUTES) Figure 6.3 Effect of bath emissivity on model predicted bath temperature and weight fraction iron in matte. 8 0 6.1.2 Converter Operation Parameters The temperature of the converter hood will also affect the radiation losses from the converter. This effect, however, was found to be insignificant. The two parameters which are expected to have a much smaller effect are the converter shell temperature and the thickness of the refractory lining. Goto determined that heat losses through the converter walls were comparatively small! 13], so factors involving wall losses are not likely to be important. Changing the shell temperature is found to cause little difference in the predictions, but figure 6.4 indicates that, at least initially, the refractory thickness does have some effect. This effect, however, is the opposite of that which would be expected; a thinner refractory lining gives a higher temperature at the end of each blow. A similar result was obtained by Bustos and Sanchez[16] over a single blow in a model of the copper converter. They concluded that there must be increased heat losses through the walls which could not be predicted by the model. A better explanation is that the effect is caused by the increased thermal mass of the refractory at higher wall thicknesses. This requires a larger amount of heat to raise the refractory to the average wall temperature and, hence, will reduce the bath temperature for the same amount of accumulated heat. The rate 81 6.1.2 Converter Operation Parameters of heat loss during idle periods and flux addition is larger for thinner linings, as would be expected. Also, the effect over an entire charge is still small. 82 6.1.2 Converter Operation Parameters 1580 0 42 82 158 220 260 320 360 430 504 TIME (MINUTES) Figure 6.4 Effect of refractory thickness on model predicted bath temperature and weight fraction iron in matte. 83 6.1.3 Converter Inputs 6.1.3 Converter Inputs The composition and amounts of materials charged to the converter cannot always be accurately measured, and the results of variations in these may be significant. Both flux and scrap are added at ambient temperature, and may contain some water. The amount of moisture contained in each may vary, and has not been measured. For modelling it was assumed that there was no water in the flux, and 2% moisture in the scrap. The effect of increasing the water in the flux and scrap to 5% is shown in figures 6.5 and 6.6. The increased moisture in the flux has a larger effect than the moisture in the scrap. This is due to the larger increase in water content in the flux. The change in the composition is small in both cases as neither flux nor scrap are significant contributors to the matte. Therefore, small variations in the amount of either added will not have much effect on the overall composition. For similar reasons an increase or decrease of 20% in the weight of scrap charged causes little change in the matte composition. The effect of the same variation on the temperature is not large for a single scrap addition, but becomes significant over a full charge. It is not likely that the weight of scrap added will be consistently high or low however, so the actual effect of this variation will be small. 84 6.1.3 Converter Inputs 1580 1500 H U J C C cc L U QL 2 U l I-1400 0.38 o cc z o < cc 0.1 W A T E R IN F L U X 0 % 5 % |IIIIIIIIIIIIIIIIIII|IIIIIIIIIIIHIIIIIHIIIII 0 42 82 l l l i i l i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i j i i i i i i i n n i i i i i i i i i i i i i i i i i i u n i i i i i i i i i i u ) ! 158 220 260 TIME (MINUTES) 320 360 430 504 Figure 6.5 Effect of water in flux on model predicted bath temperature and weight fraction iron in matte. 85 6.1.3 Converter Inputs Figure 6.6 Effect of water in scrap on model predicted bath temperature and weight fraction iron in matte. 86 6.1.3 Converter Inputs The main factors which cause variation of the matte ladle volume, spillage and solidification, would, in general, cause consistently lower values than might be expected. A reduced matte addition has little effect on temperature, but a fairly significant effect on matte composition (figure 6.7). This can be explained by the reduction in the amount of iron charged combined with the same amount of air blown, giving an overall decrease in the amount of iron in the matte. The rate of iron removal is not changed significantly, but the difference in iron added due to the reduced amount of matte charged, (approximately 9% less matte), is sufficient to cause a difference of about .03 weight fraction of iron over the entire charge. Perhaps the most variable, and the least well quantified, of the inputs to the converter is the composition of the scrap. For modelling it was assumed that all scrap added was the same composition, approximating the composition of flue dust. Figure 6.8 shows the effect of changing the 'scrap' composition to either a low-iron concentrate or converter slag, both of which are used as scrap. The difference in temperature between the three scrap compositions is small, and can be explained by differences in their heat capacities. The variation in matte composition, however, is large. The reduced final iron content of the matte for both the slag and the low-iron concentrate cases, is caused by the lower amounts of iron sulphide 87 6.1.3 Converter Inputs 1580 ' ' " I " " " " I '""1 1 1 I""' ' " " I " " '"I I"" 0 42 82 158 220' 260 320 360 430 504 TIME (MINUTES) Figure 6.7 Effect of weight of matte charged on model predicted bath temperature and weight fraction iron in matte. 8 8 6.1.3 Converter Inputs I I I1 mini [inn i ni| mn j iii)iiiii|iiu 0 42 82 158 220 260 320 360 430 504 TIME (MINUTES) Figure 6.8 Effect of scrap composition on model predicted bath temperature and weight fraction iron in matte. 89 6.1.3 Converter Inputs added with the scrap. The higher iron in the matte seen for the slag case is due to an increase in its magnetite content. This increase is caused by the extra oxygen added iri the slag raising the oxygen potential and, hence, the magnetite activity. Towards the end of a charge there is a large increase in the activity coefficient of magnetite in the matte, which leads to the sudden drop in both the iron and oxygen contents of the matte. It is at this point that the 'slag' line drops below the 'flue dust' line. 9 0 6.1.4 Thermodynamic Data 6.1.4 Thermodynamic Data Measured data was not available for some of the activity coefficients involved in the model. In particular the activity coefficients of NiS and Cu2S were set at unity. In a previous model of the copper converter! 13] a value of yCu^ equal to one was successfully employed. This assumption was made primarily because Cu2S was the main constituent of the matte. In the present model, however, copper is not the primary component of the matte, and so a significant deviation in activity coefficient may occur due to interactions with other matte components. Variations in the activity coefficient of Cu2S were determined to have no significant effect on the bath temperature or composition. This is because the amount of copper oxide present sis negligible, and is the only form of copper present outside of the matte. For there to be any significant effect on the matte there would have to be a large increase in the amount of CuaO present in the slag, which is not thermodynamically likely at low oxygen potentials. The effect of varying the activity coefficient of NiS was found to have a slight effect on the bath temperature, and a larger effect on the iron in matte (figure 6.9). The cause of the change in matte composition is best seen from the changes in the weight fractions of 91 6.1.4 Thermodynamic Data 1580 Figure 6.9 Effect of NiS activity coefficient on model predicted bath temperature and weight fraction iron in matte. 92 6.1.4 Thermodynamic Data 0.5 o 0.4 H Z o H O X o LU § 0.3 0.29 0.28 H § 0.27 X Q. _ J 5) 0.26 z o O °-25 H 0.24 CD L U 5 0.23 0.22 H 0.21 lT|TIIIIII!M1IIIIIIM[ir NIS ACTIVITY COEFFICIENT .7 i rrr|n Tin ii 1  irrr>Mi i]i turn ii 1  i|f rr rr n f rmn ITII mi MIMJ MI I n i| i rirmiiif r rip n< i r JITI n 40 80 156 218 258 318 358 428 TIME (MINUTES) 502 Figure 6.10 Effect of NiS activity coefficient on model predicted weight fractions of nickel and sulphur in matte. 93 6.1.4 Thermodynamic Data nickel and sulphur present. Figure 6.10 shows that decreasing the activity coefficient of NiS causes the weight fractions of both nickel and sulphur to increase. This is caused by the increased amount of nickel as NiS altering the relative amounts of the two nickel sulphides present. The increased amount of NiS present increases the amount of sulphur required in the matte per mole of nickel. This, in turn, reduces the amount of sulphur removed from the matte and, hence, increases the amount of iron removed per mole of oxygen blown. The effects of the variation of NiS activity coefficient, however, are within the accuracy of the compositional predictions of the model. 94 6.1.5 Other Variables 6.1.5 Other Variables There are three other variables which can have a significant effect I on the operation of the converter. These are all measured with a relatively good accuracy, and so are not considered as possible sources of error in the model. However, they give useful information regarding converter operation and so should be examined. The effect of the initial temperature of the bath is shown in figure 6.11. It is seen that an initial difference in temperature of 100 K is reduced to about 10 K over the period of a complete charge. This will be caused by the increased heat losses at higher temperatures, and the increased amount of heat required to raise the charged materials to the bath temperature. The difference in iron in matte is almost entirely due to the differences in equilibrium compositions with temperature. The overall iron removal is the same regardless of initial temperature. The effect of the air rate is much more significant, as is shown in figure 6.12. It is not surprising that increasing the air rate gives increased iron removal and, hence, increased temperature. More air is being introduced into the bath over the same time period, so a greater amount of iron will be oxidized. The reduction in the rate of temperature increase at the end of the case with the highest air-rate (730 Nm3/min) is due to the large amount of nickel oxidation which occurs at low iron levels. The heat generated by the oxidation of nickel 95 6.1.5 Other Variables rpxTTi r>iiinnrr»n)iii imni| rrrnt | m linrirniii n n i i | i n ir r i i i n i r i r i t T i | n n IIIHITIII frmn 0 42 82 158 220 260 320 360 430 504 TIME (MINUTES) Figure 6.11 Effect of initial temperature on model predicted bath temperature and weight fraction iron in matte. 96 6.1.5 Other Variables Figure 6.12 Effect of air rate on model predicted bath temperature and weight fraction iron in matte. 97 6.1.5 Other Variables is less than that generated by the oxidation of iron. The figure does indicate the increased production rate possible at higher air rates caused by the increased amount of oxygen added in a given time. A similar, but even larger effect is obtained by an increase in the oxygen content of the blown gas (figure 6.13). In fact, the use of oxygen enrichment to 25% throughout an entire charge would lead to the temperature rising well above the maximum temperature allowed in a standard converter (about 1550 K). There is a large increase in the production rate at higher oxygen levels, which is approximately proportional to the additional oxygen available for reaction. The abrupt end of the 30% enrichment case was caused by a convergence failure in the model at very low iron levels in matte (less than 0.1%). A further advantage of oxygen enrichment is shown in figure 6.14. Higher oxygen enrichment increases the fraction of sulphur dioxide in the off-gas, and so increases the feasibility of off-gas cleaning. 98 6.1.5 Other Variables Figure 6.13 Effect of oxygen enrichment on model predicted bath temperature and weight fraction iron in matte. 99 6.1.5 Other Variables Figure 6.14 Effect of oxygen enrichment on model predicted sulphur dioxide content of the off-gas. 100 6.2 Validity of the Equilibrium Assumption 6.2 Validity of the Equilibrium Assumption The assumption of equilibrium is usually considered justifiable because of the high oxygen efficiency generally obtained in the converting process. However, towards the end of the cycle the oxygen efficiency is seen to drop[66], indicating that the process becomes kinetically controlled. Also, some converting operations do not achieve a high oxygen efficiency. In one case an average efficiency as low as 55% for an entire cycle has been reported for a copper converter. [69] Finally, it can be observed that the condensed phases in the converter (matte and slag) are at different temperatures. Figure 6.15 shows the inside back wall of a converter after being rotated off the tuyeres, standing idle for approximately five minutes, and then rotated again to skim the slag. A definite colour difference can be seen between the sections of the wall which were in contact with the matte and the slag, indicating a difference in temperature. Five minutes would not be sufficient time to cause this difference, as the converter cools under 10 K over this period. As can be seen in figure 6.15, a layer of solidified slag is quickly formed on the top of the bath which significantly reduces the rate of heat loss from the bath. This means that the matte and slag cannot be in thermal equilibrium and, therefore, invalidates the standard formulation of chemical equilibrium equations which require a single temperature to calculate the 101 6.2 Validity of the Equilibrium Assumption 102 6.2 Validity of the Equilibrium Assumption equilibrium constant. The reduced efficiency is often ascribed to tuyere leakage, or faulty calculations.[2] Leakage from the tuyeres does occur, but is unlikely to amount to five percent of the air blown, as that would imply over 30 Nm3 per minute of air leaking, which is not observed in practice. A more probable explanation of the reduced oxygen efficiency is that the operation is under gas phase mass transfer control for the majority of the charge.[70] To a certain extent an artificially set oxygen efficiency will account for this in an equilibrium model. However, it will obviously not allow for variations in oxygen efficiency during a charge caused by changing mass transfer conditions. From these considerations it can be seen that the converting operation is not strictly an equilibrium process. However, the use of equilibrium calculations may still be justified, at least for the early stages of converting. The three phases are likely to be at thermal and chemical equilibrium at the reaction site, that is the bubble-liquid interface. The heat and mass transfer from the interface to the bath and gas will probably govern the actual operation of the converter. 103 7.1.1 Converting High Grade Mattes 7 ANALYSIS OF NICKEL CONVERTER OPERATION 7.1 Varying Matte Composition The different types of smelting furnaces used in various operations produce mattes with very different compositions. To determine what happens to these mattes during converting the 'standard' charge (Table VI-I) was run using a high grade matte and a sulphur deficient high grade matte. Table VII-I shows the compositions of the mattes used. As the operating conditions for the 'standard' charge were developed for a low grade matte it is expected that the iron level in the converter will be reduced to below a weight fraction of .01 well before the end of the charge. Results beyond this point will not be reported, as they are not relevent to converting operations. A more realistic charge for a high grade matte would involve shorter blowing times or higher loadings. 7.1.1 Converting High Grade Mattes The converting of high grade mattes is carried out successfully at a number of operations which employ flash smelters.[1] There is essentially very little difference between a high grade flash furnace matte and a partially converted matte, so converting high grade mattes can basically be considered as starting in the middle of a charge. 104 7.1.1 Converting High Grade Mattes Matte Type Fe Ni Cu Co S O Base .402 .2 .09 .01 .281 .017 High Grade .279 .344 .106 .012 .247 .013 Sulphur Deficient .262 .356 .16 .024 .183 .014 Table VII-I Compositions of mattes used in analysis (weight fraction). 105 7.1.1 Converting High Grade Mattes Figure 7.1 shows the results of carrying out the 'standard' charge using a high grade matte. The figure shows that the initial rate of temperature increase is greater for a high grade matte than for a low grade matte. This is caused by the faster rate of iron removal also seen for the high grade matte. While the amount of oxygen reacting is the same in both cases, the difference in the initial compositions appears to cause a more pronounced preferential oxidation of iron over sulphur. As the heat generated per mole of oxygen is greater for the production of FeO than SOa [AHf equal to -785 kJ/mol O a versus -236 kJ/mol 0 2 respectively at 1473 K), the temperature rise is faster. As the charge progresses the rate of iron removal decreases with more sulphur and, eventually, nickel being oxidized. The lower rate of heat generation caused by this leads to the slower rate of temperature increase seen in the figure. The only apparent problem involved with the converting of high grade mattes is the heat balance during the first blow. It is required that the converter contents are at a sufficiently high temperature at the end of the first blow to give a slag which is fluid enough to skim. The initial temperature of the bath at the beginning of the first blow is generally quite low, and so requires the oxidation of a large amount of iron to bring it up to the required skimming temperature. But, to minimize cobalt losses it is also desirable to keep the weight percent of 106 7.1.1 Converting High Grade Mattes 1560 _ 1500 rr < rr LLI u 1400 H 0.4 0.35 H 0.3 0.25 — 0.2 o I— o < rr 0.15 0.1 H 0.05 H "I I" "I" I" 11| I ll inn I ll I ll I ill i(nn III i imi III II • 1  II II II i II i II i inimi " " " '""I I""" I""" I "'"I 0 42 82 158 220 260 TIME (MINUTES) "1 1 'I" I l l 1 l l l l l l l l l l 320 360 430 504 Figure 7.1 Effect of using high grade matte on model predicted bath temperature and weight fraction iron in matte. 107 7.1.2 Converting Sulphur Deficient Mattes iron in the matte above .1, which may not be possible with high matte grades. It should also be noted that the iron level in the first blow of figure 7.1 does not drop below a weight fraction of .1, while the temperature rises above that of the standard matte. The problem, if it occurs, is how to increase the bath temperature sufficiently without oxidizing too much iron. To a large extent the problem may be reduced by covering the converter mouth[18], and reducing the time taken between charges. Cold materials should not be charged to the converter before the first blow, and the amount of mush left after the previous charge should be minimized. All of these procedures will allow the first blow to start at a higher temperature, and so reduce the amount of heat required from the converting reactions. 7.1.2 Converting Sulphur Deficient Mattes Sulphur deficient mattes are generally defined as mattes which do not contain sufficient sulphur to form stoichiometric FeS, Ni3S2, Cu2S, and CoS. Such mattes are commonly produced in the electric furnace smelting of nickel concentrates.! 1] Figure 7.2 shows the results of carrying out the 'standard' charge using a high grade sulphur deficient matte. As with the standard high grade matte the initial temperature rise is very rapid, again because of 108 7.1.2 Converting Sulphur Deficient Mattes 1580 0 42 82 158 220 260 TIME (MINUTES) Figure 7.2 Effect of using high grade sulphur deficient matte on model predicted bath temperature and weight fraction iron in matte. 109 7.1.2 Converting Sulphur Deficient Mattes the higher rate of iron oxidation relative to sulphur oxidation. In the case of a sulphur deficient matte, however, the effect is even more pronounced. The reason for this can be seen from figure 7.3. Over the initial 20 minutes of the first blow the partial pressure of sulphur dioxide is low compared to the standard charge. This indicates that only a very small amount of the sulphur is being oxidized. Over the same time period figure 7.2 shows a rapid rate of iron removal, which decreases with time until it reaches a steady rate. This behaviour is similar to that predicted by Kellogg[2] for sulphur deficient mattes. The initial rapid iron removal with little sulphur removal is the approach to the limiting path. Once the converter has reached the limiting path the process continues as it would for the standard case. 110 7.1.2 Converting Sulphur Deficient Mattes 0.18 0 42 82 158 220 260 TIME (MINUTES) Figure 7.3 Effect of using high grade sulphur deficient matte on model predicted partial pressure of sulphur dioxide. I l l 7.2 Iron and Sulphur Elimination 7.2 Iron and Sulphur Elimination The relative rates of iron and sulphur elimination from the matte are of some importance, both to the efficiency of iron removal and to pollution control. Ideally sulphur elimination should be kept to a minimum to increase the amount of iron removed, and to reduce the amount of sulphur dioxide emitted to the atmosphere. It has been suggested that low temperatures and high oxygen enrichment reduce the rate of sulphur removal relative to iron. [2] To determine if this is predicted by the present model plots were made of the iron-to-sulphur ratio in the matte versus the weight fraction of iron in the matte. The plots were drawn for varying initial bath temperatures and oxygen enrichment levels. These are shown in figures 7.4 and 7.5 respectively. Theoretically, if the rate of iron removal is increased more than the rate of sulphur removal there should be an increased slope of the lines on these figures. It is evident that this is not the case, and in fact there is almost no difference in either situation. A similar plot is also shown for the different matte compositions tested above (figure 7.6). This again shows that there is essentially no difference between operations once the limiting path has been reached. The initially steep slope seen in the sulphur deficient case indicates the rapid removal of iron with little sulphur removal as stated above. 112 7.2 Iron and Sulphur Elimination Figure 7.4 Effect of initial bath temperature on the relative rates of iron and sulphur elimination. 113 7.2 Iron and Sulphur Elimination 1.4 1.3 -0 0.1 0.2 0.3 0.4 WEIGHT FRACTION Fe IN MATTE Figure 7.5 Effect of oxygen enrichment on the relative rates of iron and sulphur elimination. 114 7.2 Iron and Sulphur Elimination 115 7.2 Iron and Sulphur Elimination A regression line of iron-to-sulphur ratio versus weight fraction iron in matte has been calculated from the model simulation of the plant trials, and is plotted along with the values actually measured during the trials in figure 7.7. This figure shows a very good fit of the plant data to the calculated line, and indicates that the relative rates of iron and sulphur removal are close to constant regardless of operating conditions. The results which gave rise to the initial suggestion were based on calculations carried out for matte compositions not on the limiting path. [2] As such they indicate the behaviour of the matte over a short, transient period, and cannot be used to indicate the behaviour over the entire blow. 116 7.2 Iron and Sulphur Elimination 0 - i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i 0 0 . 0 2 5 0 . 0 5 0 . 0 7 5 0.1 0 . 1 2 5 0 . 1 5 0 . 1 7 5 0 . 2 0 . 2 2 5 0 . 2 5 0 . 2 7 5 0 . 3 WT FRACTION Fe IN MATTE Figure 7.7 Variation of iron-to-sulphur ratio with weight percent iron in matte. Regression line from model simulation of plant trials, points from plant data. 117 7.3 Oxygen in Matte 7.3 Oxygen in Matte The amount of oxygen contained in converter mattes is not well known. The effect of it, however, is thought to be significant on the activity of Cu2S in the matte.[22] The form of the oxygen in the matte is also not known, but it is usually assumed to be associated with iron. Goto considered both magnetite and wustite in the matte, but found the wustite content was much less than the magnetite content. [12] In the present model, then, only magnetite in matte was considered for simplicity. A relationship between oxygen in matte and matte grade in nickel mattes has been published,[49] but is not directly applicable to converter mattes. If the converter is considered as being in equilibrium, the oxygen in the matte must be related to the overall oxygen potential as set by the wustite-magnetite ratio in the slag. The matte and slag samples considered in the published study were obtained from either electric furnaces or settling sections of flash and blast furnaces. None of these situations are as oxidized as a converter; in fact, the electric furnace slags are likely to be quite reduced. Also, almost all the slags studied were silica saturated and had relatively high lime and magnesia levels. Lime in the slags tested ranged from 2 to 11% and magnesia levels were between 4 and 8%. Converter slags usually contain under 1% of both. 118 7.3 Oxygen in Matte These factors all tend to reduce the magnetite content in the slag and hence the degree of oxidation. [44] The conditions of collection and the compositions of the slags considered imply a low oxygen potential compared to the converter, and hence a low estimate of oxygen in matte. Figure 7.8 shows a comparison of the oxygen in matte predicted by the model for the standard charge used in the sensitivity analysis, with that of the published correlation. As expected the model predicts a higher oxygen content than obtained from the correlation. This figure also shows the effect of temperature on the oxygen content of the matte, as well as some data points calculated by difference from converter matte assays obtained during the plant trials. Increasing temperature appears to reduce the amount of oxygen in the matte. This effect can be explained by the reduction of the magnetite activity in the slag at higher temperatures.[2] It is worth noting that the model predictions fall within the scatter of plant data. 119 7.3 Oxygen in Matte 120 7.4 Effect of Carbon Addition 7.4 Effect of Carbon Addition A modification to the equilibrium model allowed the prediction of the effects of the addition of carbon into the converter. As stated above carbon addition may have two beneficial effects: heating the bath during the first blow, and reducing valuable metal losses to the slag. Figure 7.9 shows the results of adding carbon at a rate of 15 kg per minute to a converter containing 110 tonnes of matte and 30 tonnes of mush, assuming equilibrium. It can be seen that there is no actual increase in temperature rise with carbon addition. However, the iron level in the matte does not drop as quickly. This indicates that some oxygen is reacting with the carbon rather than the iron. As such carbon injection could be used to solve the possible heat balance problems involved with converting high grade mattes, if the converting reactions reach equilibrium. This would be accomplished by reduced iron removal rather than by increased heat input. The effect of carbon addition on the metal losses to the slag will be due to the reduction of oxides. This will be more prominent towards the end of a charge, when the oxygen potential of the converter starts to rise. Although the model does not predict the metal losses to the slag accurately, the change in the nickel content of the slag will indicate the effect of carbon addition. Figure 7.10 shows that there is a drop in nickel content of the slag caused by carbon addition, 121 7.4 Effect of Carbon Addition 1580 0.341 I 0 10 20 30 40 50 TIME (MINUTES) Figure 7.9 Effect of carbon addition at 15 kg min"1 on model predicted bath temperature and weight fraction iron in matte. 122 7.4 Effect of Carbon Addition Figure 7.10 Effect of carbon addition at 15 kg min"1 on model predicted nickel in the slag and partial pressure of sulphur dioxide. 123 7.4 Effect of Carbon Addition although it is not particularly significant for the amount of carbon added in the run. A larger amount of carbon added is likely to cause a greater reduction of metal losses. It should be noted that the slag cleaning effect of carbon addition may be enhanced if the carbon does not come to equilibrium with the gas and is entrained in the slag. Figure 7.10 also shows that carbon addition causes a reduction in the partial pressure of sulphur dioxide in the gas. This reflects a reduced amount of sulphur oxidation, due to the reduction of the oxygen potential of the system. During the model run the CO/C0 2 ratio dropped from approximately 1.3 to a steady value close to .1, indicating an increase in the oxygen partial pressure similar in form to that shown for sulphur dioxide. The relatively large proportion of carbon monoxide formed initially explains the slower rate of temperature rise seen in figure 7.9 for the carbon addition case, as it indicates a much lower amount of heat generated. 124 7.5 Operating Efficiency 7.5 Operating Efficiency A number of suggestions to improve the efficiency of the converting process can be made based on the equilibrium model and the data collected during the plant trials. In many cases the suggestions involve increased control of the inputs, and an increase in the degree of automation of the process. The main points of these suggestions are summarized below. 1. The use of high pressure air to allow the converter to operate without punching the tuyeres.[70] This will also reduce the number of tuyeres required, and consequently the time required to drill them. This, in turn, should reduce the time lost between charges, which can be over three hours. 2. Increased automation of both converting and smelting. This should include; a link between the converters and the smelter to ensure that sufficient matte is available, control of flux addition by the amount of oxygen blown, and control of scrap addition by bath temperature. This should give better overall control of the process and may reduce metal losses to the slag. 3. Increased air-rate and oxygen enrichment to reduce the time required for a charge. 125 8 CONCLUSIONS AND FURTHER WORK 8 CONCLUSIONS AND FURTHER WORK The results of modelling the plant trials indicate that an equilibrium model is able to predict the converter temperature and bath composition for the majorpart of a converter charge. However, it also indicates that the converting reactions are kinetically controlled. The model gives a good fit over the first blows where gas phase mass transport is rate controlling, but to do this requires artificially setting the oxygen efficiency to 95%. Towards the end of a charge it appears that liquid phase mass transport becomes rate controlling, so the model tends to overpredict both iron removal and temperature. The requirement for fitting the dissolution of the mush during the first blow, along with deviations of the model predictions from the test results after scrap addition, indicates that the assumption that all material charged during a time step is dissolved and reaches the bath temperature during that time step is not valid. While this assumption has very little effect on the predicted temperature and bath composition at the end of a blow, it will affect the predictions shortly after scrap addition. This means that the model will be unable to predict the immediate compositional effects of a scrap or flux addition very accurately. Improvements in the equilibrium model can be made to allow it to give better predictions of the composition during the first blow and 126 8 CONCLUSIONS AND FURTHER WORK following scrap or flux addition. These improvements would be in the form of dissolution rate equations for the mush, scrap, and flux. This would not be difficult for the flux, which is of a relatively well known size range, but would not be as easy for the scrap or mush, as their properties are generally not known and quite variable. To improve the temperature predictions during the first blow would also require a heat transfer calculation between the matte and undissolved mush. At present there is insufficient knowledge of the parameters involved to allow for this calculation. The equilibrium model, then, can be used to predict the bath temperature and composition of a nickel converter during the first blows. However, it is not able to predict the total valuable metal losses to the slag at any time, or the bath temperature and composition towards the end of a charge. As these are important factors in the converting process it would be useful to be able to predict them. To model the metal losses to the slag would require a better knowledge of the amount of entrainment of matte in converter slag. This should include the effect of slag viscosity, which is related to the slag composition and temperature. The prediction of the bath composition and temperature towards the end of a charge would require the formulation of a kinetic model of the converter. Also, a 127 8 CONCLUSIONS AND FURTHER WORK kinetic model would be able to include an improved heat transfer model which would allow differences in temperatures between the three phases. At present, the formulation of a kinetic model is hindered by an extreme shortage of data regarding mass transport in the matte and, to a lesser extent, in the slag. There is also insufficient knowledge regarding the oxygen contained in the matte. Therefore, a large amount of experimental work is required before a full kinetic model can be developed. The analysis of the predictions made by the equilibrium model has indicated that, within the operating range of the Pierce-Smith converter, there is very little that can be done to improve its chemistry, with the possible exception of a slag cleaning process. That is, the converter contents will follow approximately the same compositional path regardless of operating conditions. As such, the only way to improve the production of the converter is by improving operating procedures. This apparent inability to improve, within the operating range, suggests that the converting process is a prime candidate for replacement with a more efficient, clean, and preferably continuous technology. 128 9 BIBLIOGRAPHY 9 BIBLIOGRAPHY 1. Diaz, C.M., Landolt, C.A., Vahed, A., Warner, A.E.M., and Taylor, J.C., Proceedings 117th TMS Annual Meeting, Phoenix, Arizona, 1988 2. Kellogg, H.H., Can. Met. Quart., 26, no. 4, 1987, pp. 285-298 3. Bustos, A.A., lp, S.W., O'Connell, G., Kaiura, G.H., and Toguri, J.M., Extractive Metallurgy of Nickel and Copper, (eds. Tyroller, G., and Landolt, C.), TMS, 1988 4. Nagamori, M., and Mackey, P.J., Met. Trans. B, 9B, 1978, pp. 255-265 5. Nagamori, M., and Mackey, P.J., Met. Trans. B, 9B, 1978, pp. 567-579 6. Nagamori, M., and Chaubal, P.C., Met. Trans. B, 13B, 1982, pp. 319-329 7. Nagamori, M., and Chaubal, P.C., Met. Trans. B, 13B, 1982, pp. 331-338 8. Chaubal, P.C., and Nagamori, M., Met. Trans. B, 13B, 1982, pp. 339-348 9. Nagamori, M., Met Trans., 5, 1974, pp. 569-548 10. Bjorkman, B., and Eriksson, G., Can. Met. Quart., 21, 1982, pp. 329-337 11. Flynn, H.E., and Morris, A.E., Mathematical Modelling of Materials Processing Operations, (eds. Szekelly, J. , et. al), TMS-AIME, 1987, pp. 767-797 12. Goto, S., Copper-Metallurgy - Practice and Theory, IMM, London, 1974, pp. 23-34 13. Goto, S., Copper and Nickel Converters, (ed. Johnson, R.E.), TMS-AIME, 1979, pp. 33-54 14. Shimpo, R., Watanabe, S., Goto, S., and Ogawa, O., Advances in Sulphide Smelting vol. 1, TMS-AIME, 1983, pp. 295-316 129 9 BIBLIOGRAPHY 15. Kemori, N., Kimura, T., Mori, Y . , and Goto, S., Pyrometallurgy '87, IMM, London, 1987, pp. 647-666 16. Bustos, M., and Sanchez, M., Copper 87 vol. 4, (eds. Diaz, C , Landolt, C., and Luraschi, A.A.), 1987, pp. 473-487 17. Brinkley, S.R., J. Chem. Phys., 15, 1947, pp. 107-110 18. Bustos, A.A., Brimacombe, J.K., and Richards, G.G., Met. Trans. B, 17B, 1986, pp. 607-685 19. Taylor, J.R., Advances in Sulphide Smelting vol. 1, TMS-AIME, 1983, pp. 217-229 20. Nagamori, M., Met. Trans., 5, 1974, pp. 531-538 21. Sinha, S.N., and Nagamori, M., Met. Trans. B, 13B, 1982, pp. 461-470 22. Lumsden, J. , Metal-Slag-Gas, Reactions and Processes, (ed. Foroulis, F.A. and Smeltzer, W.W.), The Electrochemical Society, Princeton, New Jersey, 1975, pp. 155-169 23. Wiese, W., Erzmetal 16, No.9, 1963, pp. 452-458 24. Spira, P. and Themelis, N.J., J. Metals, 21, 1969, pp. 35-42 25. Barnett, S.C.C., and Jeffes, J.H.E., Trans. I.M.M., 86, 1877, pp. C155-157 26. Victorovich, G.S., Proceedings 19th Annual Conference of Metallurgists, CIM, 1980 27. Nedvetskii, B.P., Chaikina, N.T., Tsemekhman.L.Sh., Gorbunova.I.E., and Nemoitin, M.A., Tsvetnye Metally, 17, no. 5, 1976, pp. 23-25 28. Bale, C.W., and Toguri, J.M., Can. Met. Quart., 15, no. 4, 1976, pp. 305-318 29. Rosenqvist, T., Advances in Sulphide Smelting vol. 1, TMS-AIME, 1983, pp. 239-255 130 9 BIBLIOGRAPHY 30. Kellogg, H. H., Physical Chemistry in Metallurgy; Proceedings of the Darken Conference, (eds. R. M. Fisher, R. A. Oriani, and E. T. Turkdogan), U.S. Steel Research Lab., Monroevllle, PA., 1976, pp. 49-68 31. Lee, S.L., and Larrain, J.M., Can. Met. Quart., vol. 19, 1980, pp. 183-190 32. Fosnacht, D.R., Goel, R.P., and Larrain, J.M., Met. Trans. B, 11B, pp. 69-71 33. Sharma, R.C., and Chang, Y.A., Met. Trans. B, 10B, 1979, pp. 103- 108 34. Sharma, R.C., and Chang, Y.A., Z. Metallkde., 70, H.2, 1979, pp. 104- 108 35. Sharma, R.C., and Chang, Y.A., Met. Trans. B, 11B, 1980, pp. 139-146 36. Sharma, R.C., and Chang, Y.A., Met. Trans. B, 11B, 1980, pp. 575-583 37. Chuang, Y.-Y., Hsieh, K.-C, and Chang, Y.A., CALPHAD, 5, no. 4, 1981, pp. 277-289 38. Chuang, Y.-Y., and Chang, Y.A., Met. Trans. B, 13B, 1982, pp. 379-385 39. Chuang, Y.-Y., and Chang, Y.A., Proceedings of the First Symposium of Molten Salt Chemistry and Technology, April 20-22, 1988, Kyoto, Japan, pp. 201-208 40. Chuang, Y.-Y., and Chang, Y.A., Second International Symposium on Metallurgical Slags and Fluxes, TMS-AIME, 1984, pp. 73-79 41. Chuang, Y.-Y., Hsieh, K.-C, and Chang, Y.A., Met. Trans. B, 16B, 1985, pp. 277-285 42. Sharma, R.C., Lin, J . -C , and Chang, Y.A., Met. Trans. B, 18B, 1987, pp. 237-244 43. Chang, Y.A., and Hsieh, K.-C, Can. Met. Quart., vol. 26, No. 4, 1987, pp. 311-327 131 9 BIBLIOGRAPHY 44. Mackey, P.J., Can. Met. Quart., 21, 1982 45. Grimsey, E.J., and Biswas, A.K., Extractive Metallurgy Symposium, University of Melbourne, 1975, VI.2 46. Grimsey, E.J., and Biswas, A.K., Trans. I.M.M., 85, 1976, pp. C200-207 47. Grimsey, E.J., and Biswas, A.K., Trans. I.M.M., 86, 1977, pp. Cl-8 48. Grimsey, E.J., and Biswas, A.K., AusJ.M.M. Conference, South Australia, 1975, pp. 299-306 49. Grimsey, E.J., Aus.7.M.M. Conference, Western Australia, 1979, pp. 121-128 50. Grimsey, E.J., Met. Trans. B, 19B, 1988, pp. 243-247 51. Taylor, J.R., and Jeffes, J.H.E., Trans. I.M.M., 84, 1975, pp. C136-148 52. Shaw, R.W., and Willis. G.M., Can. Met. Quart., 20, No. 2, 1981, pp. 153-161 53. Takeda, Y., Kanesaka, S., and Yazawa, A., Proceedings 25th Annual Conference of Metallurgists, C.I.M., 1986, pp. 185-202 54. Sahoo, P., and Reddy, R.G., Second International Symposium on Metallurgical Slags and Fluxes, TMS-AIME, 1984, pp. 533-545 55. Reddy, R.G., and Acholonu, C.C., Met. Trans. B., 15B, 1984, pp. 33-37 56. Wang, S.S., Kurtis, A.J., and Toguri, J.M., Can. Met. Quart., 12, No. 4, 1973, pp. 383-390 57. Wang, S.S., Santander, N.H., and Toguri, J.M., Met Trans., 5, 1974, pp. 261-265 58. Shimpo, R., Goto, S., Ogawa, O., and Asakura, I., Can. Met. Quart., 25, No. 2, 1986, pp. 131-121 59. Yazawa, A., Nakazawa, S., and Youichi, T., Advances in Sulphide Smelting vol. 1, TMS-AIME, 1983, pp. 99-117 132 9 BIBLIOGRAPHY 60. Ruddle, R.W., Taylor, B., and Bates, A.P., Trans. IMM, 75C, 1966, pp. C14-25 61. Celmer, R.S., and Toguri, J.M., Proceedings 25th Annual Conference of Metallurgists, C.I.M., 1986, pp. 147-163 62. Szekely, J . , and Themelis, N.J., Rate Phenomena in Process Metallurgy, Wiley-Interscience, New York, NY, 1971 63. Geiger, G.H., and Poirier, D.R., Transport Phenomena in Metallurgy, Addison-Wesley Pub. Co., Reading, MA, 1973 64. Kubaschewski, O., and Alcock, C.B., Metallurgical Thermochemistry, Pergamon Press, 1979 65. Moore, C , UBC NLE: Zeros of Nonlinear Equations, Computing Centre, University of British Columbia, 1984 66. Inco plant data 67. Richards, G.G., and Brimacombe, J.K., Met. Trans. B, 16B, 1985, pp. 529-540 68. Richards, G.G., and Brimacombe, J.K., Met. Trans. B, 16B, 1985, pp. 541-549 69. Johnson, R.E., Themelis, N.J., and Eltringham, G.A., Copper and Nickel Converters, (ed. Johnson, R.E.), TMS-AIME, 1979, pp. 1-32 70. Brimacombe, J.K., Bustos, A.A., Jorgensen, D., and Richards, G.G., Physical Chemistry of Extractive Metallurgy, (eds. Kudryk, V., and Rao, Y.K.) TMS-AIME, 1985, pp. 327-351 133 10.1 Plant Trials 10 APPENDIX 10.1 Plant Trials In-plant trials of the submerged injection of petroleum coke into a nickel converter were carried out at the Copper Cliff smelter of Inco Ltd., between April 27 and May 26, 1988. There were four primary objectives of the test work: 1) to test the feasibility of submerged injection into a converter, and to determine the most important variables involved, 2) to test the possible use of the submerged combustion of petroleum coke as a means of adding heat to nickel mattes, 3) to test the possible slag cleaning effect of adding a reductant to the converter during normal operation, and 4) to obtain detailed information regarding the variation of temperature and composition in the converter for use in modelling. As far as the injection is concerned, the first of these objectives can be considered to be the most important. Obviously if the equipment installed was unable to inject material into the bath the second and third objectives could not be achieved. It was required that the effect of the variables important to injection be found. This information could then be applied to any other similar material using the same equipment. The initial testing of the injection system also gave the opportunity to see where changes could be made to improve 134 10.1 Plant Trials its performance. Although the addition of heat to the nickel converter is not required in the present operation at Copper Cliff, it may become important in the future. The increased matte grade obtained by replacing the reverberatory furnaces with flash furnaces is expected to cause a heat deficit in the early stages of a converter charge. To overcome this problem some means of adding heat to the bath is required. Submerged combustion probably offers the best heat transfer conditions of any method, and so is considered to be worth investigating. The choice of fuel for the tests was dictated by considerations of safety and availability. Of the fuels considered, the best choice for combustion purposes would have been a pulverized bituminous or sub-bituminous coal. The nature of this fuel however required a completely sealed system, including a pulverizer, which was not available. The only fuels which were readily available were breeze coke and petroleum coke. Breeze coke also requires a completely sealed system, so petroleum coke was used for the tests. As petroleum coke is not a particularly reactive fuel it was not likely that evidence of combustion would be observed, however tests were carried out to see if some form of indirect heating could be obtained. 135 10.1.1 Injection Equipment The idea of cleaning the converter slag during the converting process could be considered somewhat unusual. The converting operation is primarily the oxidation of iron sulphides out of the matte, while the slag cleaning operation is the reduction of oxides out of the slag. It has been found, however, that oxidation and reduction can take place simultaneously in the same reactor[67], and it was thought that this may also be possible in the nickel converter, removing the requirement for some form of secondary slag cleaning step. 10.1.1 Injection Equipment The design of the injection system was carried out by a consulting engineering firm, with the emphasis being placed on the combustion application. The most important factors to be considered were: 1) to keep the tuyere open without mechanical or manual punching, 2) to provide a sufficiently low fuel-to-air ratio to increase the probability of combustion. The system was designed to operate as a high pressure tuyere. In this way the tuyere could be kept open while providing a high volume of air for combustion. The use of high pressure tends to increase the degree of solids entrainment[68] and hence reduces the amount of fuel remaining in the gas stream. This was, however, 136 10.1.1 Injection Equipment COKE STORAGE COKE HOPPER ROTARY VALVE FULLER-KINYON j PUMP COKE INJECTOR 4 CONVERTER I 1 AIR COMPRESSORS COKE LINE AIR LINE Figure A l Schematic layout of coke injection system. 137 10.1.1 Injection Equipment considered to be less important than ensuring that the tuyere would remain open. A schematic layout of the injection equipment is given in figure A l . The injector itself was a length of pipe inserted into a standard two inch tuyere in place of the punching machine. The injector carried the petroleum coke and conveying, dr primary, air. The volume of secondary air blown through the annulus formed by the injector and the tuyere pipe was approximately seven to ten times that of the primary air. The solids feeding system consisted of a 2 m 3 surge hopper which was filled from a storage silo. The solids were fed from the surge hopper into a four inch Fuller-Kinyon pump by a rotary valve. The solids feed rate was controlled by the rotation rate of the rotary valve, which was in turn controlled by a variable speed motor. The Fuller-Kinyon pump fed the solids directly into the primary air flow. The weight of the surge hopper was obtained using three load cells which gave the total weight of solids added during an injection period by difference. The theoretically possible feed rates ranged between 4.5 and 22.5 kg min"1. The primary and secondary air were supplied by two compressors which fed the same main air line. Initially 20 and 7 Nm3 per minute compressors were used, nominally supplying air at 690 kPa. The 7 138 10.1.1 Injection Equipment Nm3 per minute compressor was replaced early in the tests by a second 20 Nm3 per minute compressor, as insufficient air flows and pressures were being obtained. The pressures and air flows in both the primary and secondary lines were measured and recorded on a minute by minute basis by the central smelter data acquisition system. The bath temperature was measured by an Trcon Modline' two-colour pyrometer mounted in the converter hood. The temperatures were recorded in the same manner as the air pressures and flows. 139 

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