Alkali Circulation in the Blast Furnace - Process Correlations and Counter Measures

Joel Carlsson

Sustainable Process Engineering, master's level 2018

Luleå University of Technology Department of Civil, Environmental and Natural Resources Engineering Acknowledgements

This master thesis was performed at Swerea MEFOS in Lule˚aduring the spring semester of 2018 and was the last step in my master’s degree at Lule˚aUniversity of Technology in Sustainable Process Engineering. This thesis would not have been possible without the support I got and there are several people that I would like to thank. Amanda and Lena my supervisors at Swerea MEFOS and Anton my supervisor from LTU. For putting up with all my questions and helping me with the report. MEFOS and everyone there for helping and allowing me to perform my master thesis at their facilities. Hesham and Britta for helping with all my TGA experiments. My family for just being there for me through all these years and Linnea for all her support during the thesis. All the friends that I gotten to known through all my years at LTU and that I believe I will know for many years to come. A final thanks to Gelbe for being our dark, cold and dingy basement through many years of study. Hopefully I will never see you again. Lule˚a,August 2018 Joel Carlsson

I Abstract

In blast furnace ironmaking one major challenge is to control and measure the alkalis circulating and accumulating in the blast furnace (BF). Alkali enter the BF with the primary raw material and will form a cycle where it is first reduced to metal at the lower parts forming gas. Alkali then follows the gas flow up where it oxidizes and solidifies as the oxide form has a higher melting and volatilization temperature. Condensation then occurs on burden material and in their pores and by that it is following the burden downwards. The circular nature of the reactions leads to a build-up of alkali in the form of potassium in the BF that is hard to control or measure. Condensation of alkali compounds can also occur on the BF walls functioning like a glue to which particles attach, forming scaffolds that can rapidly increase and disturb the burden descent. The increased alkali catalyzes gasification of coke with CO2 that increases coke consumption and leads to disintegration of coke. A common method today to control alkali is by varying the basicity in the BF. As lower basicity increases the amount alkali removed through slag while at the same time reducing the amount of sulfur that can be removed with the slag. This project was divided into two parts. The first part was a continuation of a previous study performed at Swerea MEFOS. Where to control the effect of alkali on coke gasification a method was tested using coke ash modification to inhibit the catalyzing properties of alkali bound on coke. The method has previously shown that alkalis are bound in the desired form but the added amount was not sufficient for inhibition of all picked-up alkalis. In this study, additional trials with higher additions of kaolin was performed. 2 wt% kaolin was added to the coal blend for producing coke that was then added to LKAB’s experimental blast furnace (EBF) as basket samples in the end of a campaign. The excavated samples were analyzed using XRF, XRD, SEM-EDS and TGA to find if the alkali was bound in aluminum silicates in the coke ash, if the addition was sufficient for binding all alkalis and if the catalytic effect in coke gasification had been achieved. The second part was a novel approach with a statistical process analysis using SIMCA to connect top gas composition of SSAB Oxel¨osund’sBF No. 4 to alkali content using process data. The approach investigated the correlation between NH3(g) and HCN(g) in the top gas to alkali content. Expanding on the possibility to measure alkali content quickly for the operators using top gas measurements. Top gas composition was measured using a mass spectrometer (MS) and where complimented with process and tap data provided by SSAB. Data was analyzed using the multivariate analysis tool SIMCA 15 to find possible correlations. Results from the first part showed that the alkali that was found was present as alkali aluminum silicates independent of kaolin addition after the EBF. As temperature along gas composition was the main factors behind alkali uptake in coke. Main differences in alkali uptake and development of coke properties in the BF was linked to the temperature and gas composition profile during tests campaigns compared. Results from TGA showed that the reaction rate of coke with CO2 increases with increasing K2O and that start of reaction was lower with increasing alkali. The results from the second approach did not find a correlation between HCN(g) and K2O in slag. Positive correlation could be seen between HCN(g) and increased SiO2 in slag and that H2O(g) would affect HCN(g) negatively.

II Sammanfattning

En av de st¨orreutmaningarna vid j¨arnproduktion i en masugn ¨aratt kontrollera och m¨ataalkali som cirkulerar och ackumuleras i masugnen. Alkali f¨oljermed det prim¨arar˚amaterialetin i masugnen d¨ardet cirkulerar enligt en process som startar med att alkali reduceras till metallform och f¨orgasas.Alkaligasen f¨oljersedan gasfl¨odetupp i masugnen d¨arden oxiderar och ¨overg˚artill fast form eftersom oxiden har h¨ogre sm¨alt-och f¨or˚angingstemperatur. Den fasta fasen kondenserar p˚abeskickningen och i dess porer vilket g¨oratt alkali kan f¨oljamed beskickningen ner i masugnen igen. Vilket skapar den cirkul¨araprocessen som g¨oratt alkalim¨andgeni masugnen byggs upp ¨over tid fr¨amstmed avseende p˚akalium. Kondensation av alkali p˚amasugnens v¨agarkan ocks˚aske och bilda p˚akladdningarvilka hindrar beskickningen fr˚an att f¨ardas korrekt genom masugnen. Alkali katalyserar ¨aven f¨orgasningenav koks vilket leder till ¨okad kokskonsumption och ¨okad nedbrytning av koks. Dagens metod f¨oratt hantera alkali i masugnen ¨aratt s¨anka basiciteten i slaggen vilket ¨okar m¨angdenalkali som kan transporteras ut med slaggen. D¨aremot minskar m¨angdensvavel som samtidigt kan tas bort i fr˚anr˚aj¨arnetgenom slaggen. Projektet ¨aruppdelat i tv˚adelar. Den f¨orstadelen ¨aren forts¨attningav en tidigare studie fr˚anSwerea MEFOS i vilken en metod testades f¨oratt minska den katalyserande effekten alkali har p˚a koksf¨orgasningengenom att modifiera koksaskan s˚aatt effekten inhiberades f¨oralkali bundet till koksen. Metoden har tidigare visat att alkali ˚aterfinnsi den ¨onskade formen i askan men att m¨angdenav tillsats varit f¨orliten f¨oratt inhibera all alkali som hittats i proven. I den h¨arstudien utf¨ordesytterligare f¨ors¨ok med h¨ogrehalter av kaolin ¨anvad som testades tidigare och resultaten j¨amf¨ordes. 2 viktprocent av kaolin tillsattes till kolmixen som anv¨andesf¨oratt producera koksen, koksen tillsattes sedan till LKAB:s experimentmasugn (EBF) som korgprover i slutet av en f¨ors¨okskampanj. Efter avslutad kampanj kyldes EBF:n med kv¨ave och gr¨avdesut varvid proverna togs ut. Korgproverna analyserades med XRF, XRD, SEM-EDS och TGA f¨oratt fastst¨allaom alkali var bundet till aluminiumsilikater i koksaskan, om m¨angden tillsatt kaolinit var tillr¨acklig f¨oratt binda all alkali och om den katalyserande effekten p˚akoksf¨orgasningen hade p˚averkats. Den andra delen i projektet utforskade en ny metod som utgick fr˚anatt med hj¨alpav en statistisk processanalys erh˚allaindikationer p˚am¨angdenalkali som cirkulerar i SSAB Oxel¨osunds masugn. Metoden unders¨oktem¨ojlighetenatt korrelera m¨angdenNH3(g) och HCN(g) i toppgasen mot alkalihalten i masugnen, den senare baserat p˚aprocessdata fr˚anmasugnen. En s˚adanmetod skulle ¨oka m¨ojlighetenf¨or processoperat¨oreratt enkelt och snabbt kunna uppskata m¨angdenalkali och vidta n¨odv¨andiga˚atg¨arder. Toppgassammans¨attningenm¨attesmed en masspektrometer, vilket kompletterades med process- och tappningsdata. Data analyserades med flervariabelanalys verktyget SIMCA 15 med avsikt att kunna identifiera m¨ojliga korrelationer i data. Resultaten fr˚anf¨orstadelen av projektet visade att alkalin som hittades ˚aterfannssom alkalialumini- umsilikater i alla prov efter att de varit i EBF:n. Huvudfaktorerna som p˚averkar alkalihalten i koks ¨artemperaturen och gassammans¨attningeni masugnen. Vilket gjorde att skillnaderna som kunde ses mellan korgproverna fr˚ande tv˚akampanjerna var l¨ankad till temperatur- och gassammans¨attningsprofilen i EBF:n. TGA-resultaten visade att reaktionshastigheten f¨orkoks i CO2(g) ¨okade med ¨okande alkalihalt och att starttemperaturen f¨orreaktionen minskade. Resultaten fr˚anden andra delen visade att det inte gick att hitta en signifikant korrelation mellan HCN(g) och halten kaliumoxid i slaggen. Det kunde ses en positiv korrelation mellan HCN(g) och ¨okad halt SiO2 i slaggen och att H2O(g) kom att p˚averka halten HCN(g) negativt.

III Table of Contents

1 Introduction 1 1.1 Background ...... 1 1.2 Objective ...... 2 1.3 Scope ...... 2

2 Theory 3 2.1 Blast Furnace Operation ...... 3 2.1.1 Basic Reactions of Iron and Coke ...... 3 2.1.2 The Alkali Cycle in the Blast Furnace ...... 4 2.1.3 Alkali Effect and Removal ...... 6 2.1.4 Ammonia and Hydrogen Cyanide Formation in the Blast Furnace ...... 6 2.2 Multivariate Analysis ...... 8 2.2.1 Principal Component Analysis ...... 8 2.2.2 Partial Least Squares Projections to Latent Structure ...... 10 2.3 Coke Characterization ...... 11 2.3.1 Coke Graphitisation ...... 11 2.3.2 Reaction Rate and Activation Energy ...... 12

3 Method 13 3.1 Basket Samples in EBF ...... 13 3.1.1 Preparation of Coated Coke and Coke With Modified Ash ...... 13 3.1.2 Basket Samples in the EBF ...... 13 3.1.3 Characterization of Samples ...... 14 3.1.3.1 XRF ...... 14 3.1.3.2 SEM-EDS ...... 14 3.1.3.3 XRD ...... 14 3.1.3.4 TGA ...... 14 3.2 Correlation of Ammonia and Cyanide in the Top Gas ...... 14 3.2.1 Top Gas Measurements ...... 14 3.2.2 SIMCA ...... 15

4 Results 16 4.1 Basket Samples From EBF ...... 16 4.1.1 Basket Location ...... 16 4.1.2 XRF ...... 16 4.1.3 SEM ...... 18 4.1.4 XRD ...... 20 4.1.5 Coke Graphitisation ...... 22 4.1.6 TGA ...... 23 4.2 Correlation of Ammonia and Cyanide in Top Gas ...... 26 4.2.1 Process Data Excel ...... 26 4.2.2 SIMCA ...... 28 4.2.2.1 MS Data ...... 28 4.2.2.2 MS Data + Process Data ...... 32 4.2.2.3 MS Data + Process Data + Tap Data ...... 35

5 Discussion 40 5.1 Coke Analysis ...... 40 5.1.1 Alkali Uptake Depending on Position and Coke ...... 40 5.1.2 SEM Results ...... 40 5.1.3 XRD for analyses and determination of LC ...... 41 5.1.4 Coke Reactivity and Reaction Rate ...... 41 5.1.5 Final Coke Discussion ...... 41 5.2 Process Data Analysis ...... 41 5.2.1 Data handling and SIMCA ...... 41 5.2.2 Final Process Data Discussion ...... 42

IV 6 Conclusions 44 6.1 Coke Analysis ...... 44 6.2 SIMCA Analysis ...... 44

7 Future Work 45

References 47

8 Appendix 48 8.1 Appendix A ...... 48 8.2 Appendix B SEM images ...... 52

V Abbreviations and Chemical Formulas

Al2O3 Alumina BF Blast furnace C Carbon CO Carbon monoxide

CO2 Carbon dioxide EBF Experimental blast furnace EDS Energy dispersive X-ray spectroscopy Fe Iron FeO W¨ustite

Fe2O3 Hematite

Fe3O4 Magnetite HCN Hydrogen cyanide

H2O Water K Potassium

K2O Potassium oxide (K,Na)CN (Potassium or Sodium) cyanide

(K,Na)2CO3 (Potassium or Sodium) carbonate MS Mass spectrometer MVA Multivariate analysis Na Sodium

NH3 Ammonia PCA Principal component analysis PLS Partial least squares SEM Scanning electron microscopy TGA Thermal gravimetric analysis tHM Ton hot metal X(g) Component X in gas form X(s) Component X in solid form X(l) Component X in liquid form XRD X-ray diffraction XRF X-ray fluorescence

VI 1 Introduction

The background, objective and scope for the master thesis is presented in this section.

1.1 Background During the production of iron with a blast furnace (BF), the quality of the charged raw material is important to avoid problems in the process caused by unwanted compounds entering the furnace. As the competitiveness between steel mills have increased globally the need to be able to charge lower quality material have increased while still keeping the iron quality high. The main unwanted compound present in the charge that can be troublesome with respect to removal and the performance of the BF is alkali compounds (K, Na) [1, 2]. The problem with alkalis is that they form a circulating load in the BF as previously shown in literature studies and books about the BF [1, 3] as well as in experimental work [2, 4]. Alkali will continuously be cycled in the furnace unless the basicity is low enough [5] and the charge contains silicates as the main form of alkali added to a BF. The circulation can be summarized as: alkali compounds follow the burden to the lower parts where they are reduced and forms alkali vapors at approximately 1600◦C. Those vapors react further in the lower parts with the blast gases of the furnace to form alkali cyanides. The cyanides then rises quickly through the furnace due to the high gas flow condensating when the temperature goes below the boiling point for (K,Na)CN at 1625◦C and forming a liquid phase. Alkali cyanides can also form alkali carbonates through oxidation when the temperature decreases and thus the cycle starts again. Most of the alkali circulates rather than exiting through the top gas [1, 3, 4]. The presence of alkali leads to lowered production and higher coke consumption in the BF, approximately 4.5% and 2.3%, respectively, for each kg/tHM alkali added with the top charge of raw material [5]. Multiple sources report the same reasons behind the decreased production. Alkali decreases the production by: lowering the threshold for the Boudouard reaction, increased coke gas and reduced strength of coke. Gas permeability is decreased due to coke degradation and scaffolding on the walls can happen reducing the volume of the BF [1, 5, 6, 7]. Thus, it would be of interest to develop a process support tool that can help operators to control the operation for optimizing the production depending on alkali load and for the operators to know when it is necessary to bleed more alkali by running it with parameters that decrease the amount of alkali. One possible way to remove alkali is to lower the basicity so that more alkali goes to the slag phase. While at the same time giving a worse iron quality due to increased sulfur levels so a balance would be needed [4, 5].

As measurements on the top gas from the BF can be used to see the amount of NH3 and HCN there. It could allow operators to have more control over alkali as alkali participate in reactions together with CO and H2O producing NH3 and HCN. Using the knowledge of the reactions coupled with measurements of the top gas performed by MEFOS on SSAB Oxel¨osund’s BF, the total alkali could possibly be derived directly from the top gas content. A previous master thesis done at Swerea MEFOS investigated how three different coke additives would change the effect alkali had on coke [8]. With purpose to help bind alkali to the coke stronger so it will not start the alkali cycle as easily. In the present report further work was performed to see how kaolin addition would affect the properties of coke with regards to alkali. Kaolin was either mixed to the coal blend or coated on the coke. A major process data analysis was also done as the second part of the report to connect the top gas composition of the BF to the total alkali present in the BF. The work to characterize, measure and remove alkali present in the BF was part of a larger RFCS-funded project that Swerea MEFOS had a part in called ALCIRC. Alkali is today generally removed by lowering the slag basicity to increase the uptake of alkali or decreasing the amount of recycled material [5, 7]. Knowing when to perform those process changes cannot be known until a tapping is done and the alkali in the slag can be coupled with a mass balance to know the true alkali load in a BF. If the amount of alkali could be derived at real time for the operators, the processes against alkali could be deployed on a more controlled basis. This is important as the methods used to lower alkali can affect iron properties and productivity negatively by e.g. increasing the sulfur included in the hot metal.

1 1.2 Objective The main objective of this master’s thesis was to:

• See how addition of 2 wt% kaolin to coke and a kaolin coating would affect the way alkali react with coke compared to 1 wt% kaolin. • Investigate the different process variables that can affect alkali circulation and top gases in the BF through a literature review.

• Investigate if NH3 and HCN levels in the top gas of Oxel¨osund’sBF No. 4 can be correlated to the alkali load in the BF

1.3 Scope This thesis investigated how the chemical composition of the top gas could be connected to the alkali level in the BF using process data, mass balances and multi-variate analysis. Noise in the data had to be handled, investigating the equilibrium between HCN and NH3 was also necessary and the different parameters that can affect the reactions. A thorough research in to the different measuring methods used and what type of analysis that must be performed also lay in the scope of this thesis. Practical work using previously developed methods at Swerea MEFOS on coke samples from the EBF. The goal was to investigate the difference of using a kaolin coating compared to a 2wt% kaolin addition by using XRF, XRD, TGA and SEM-EDS with respect to the retention of alkali in coke. The results from the coke samples could then be compared with a previous study on this subject by Olofsson [8].

2 2 Theory

The theory covers a short description of the basis of the BF process and give a background on how alkali problem affects it. Further study is done on how alkali cyanides present in the BF will affect the top gas composition. It is important to know the reactions and ratios between them to control the alkali in the BF.

2.1 Blast Furnace Operation The most common base unit used around the world for production of iron from primary raw material is the BF. A BF can generally be described as a counter-current heat exchanger where the inside is buildup of multiple layers of coke and iron burden, where the iron burden generally consists of either sinter or pellets [6].

2.1.1 Basic Reactions of Iron and Coke The iron burden will react, melt and be tapped as hot metal at the bottom of the furnace along with slag. The process is a reduction process where Fe2O3(s) is reduced to Fe(s). The reduction of iron goes through several steps as the burden descends in the BF. The first step is an indirect reduction of hematite into magnetite: 3F e2O3 + CO 2F e3O4 + CO2 (2.1) Followed by indirect reduction of magnetite:

F e3O4 + CO F eO + CO2 (2.2) The final indirect reduction of iron is:

F eO + CO F e + CO2 (2.3) If reaction 2.3 does not completely reduce all w¨ustitea direct reduction with C will happen in the lower part of the BF [9, 10]: F eO + C F e + CO (2.4)

The reactions will happen in the different thermal regions of the BF. They are generally denoted as preheating zone, thermal reserve zone, and melting zone as can be seen in Figure 2.1. In the first two zones reduction is indirect, as shown in reaction 2.1-2.3, while in the last zone reduction is direct [9]. Part of the material added to a BF will be tapped as slag, a phase that consists mostly of the gaunge from the burden that enter the BF as silicates and alumina compounds. Slag has a lower density than iron and floats on top of the hot metal in the bottom of the BF. A small amount of slag formers are added with the charge to get the desired chemical properties of the slag. Common slag fluxes are CaO and MgO depending on what basicity that is desired by the operators [6]. As the iron burden descend, so will coke. Coke in the BF has several important functions. It has to have the physical properties and strength to both hold the iron burden and at the same time be permeable enough to allow gas to flow through it. Down in the melting zone coke is the only material still in solid form until it reaches the raceway where it will be consumed together with injected reducing agents according to, C + O2 CO2 (2.5) and both be the fuel giving heat to the melting of iron, and the chemical reductants in the form of CO(g) needed for the process. The flame temperature in the raceway will be above 2000◦C depending on the ratio of coal/natural gas/oil used in the BF [6]. The CO2(g) produced in reaction 2.5 will further react with unburnt carbon when the temperature is between 900-950◦C through reaction with carbon:

C(s) + CO2(g) 2CO(g) (2.6) Reaction 2.6 has two names, the Boudouard or solution loss reaction and is an endothermic reaction [1, 9, 10, 11]. The reaction is vital for the efficiency of a BF as it will ensure that the CO2(g) produced at higher temperatures will be transformed back into CO that can further reduce iron oxides further up the BF [12]. Without the reaction, reaction 2.1-2.4 would not be as efficient.

3

Figure 2.1 Thermal zones found in a BF [9].

2.1.2 The Alkali Cycle in the Blast Furnace Alkali will inevitable enter the BF with the iron material and with the coke in the form of silicates. Iron producers generally want to limit the amount of alkali to around 1.5-5 kg/tHM [6] and a literature study on typical alkali limits in plants around the world showed that the real limit could vary between 2.5 to 7.5 kg/tHM depending on plant [13]. Of the two alkali substances sodium and potassium, potassium is generally the main compound entering the BF [5, 6]. Most of the alkali will exit with the slag while some will follow the top gas out as dust and gas. Recirculating alkali can either be removed by the slag or the gas. Potassium will follow the top gas to a higher degree as it is more volatile compared to sodium that will follow the slag more [14, 15]. According to Abraham and Staffansson [3] the behavior of alkali can be explained as following. Alkali enters into the BF in the form of silicates which can be simplified as (K,Na)2SiO3. Further text will only speak of potassium as sodium can be assumed to react in a similar way as potassium. Research in the alkali cycle show that the silicates will descent with the burden and the cycle will start with the alkali silicate being reduced by the coke in the melting zone according to:

K2SiO3 + C(s) 2K(g) + SiO2 + CO(g) (2.7) The reactions take place at approximately 1550◦C according to thermodynamic data for the reactions. Any alkali oxides that enter or are formed in the BF react further up in the BF at lower temperatures according to: K2O + CO(g) 2K(g) + CO2(g) (2.8)

4 As they are not stable [3, 16]. K2O can also dissolve into the primary slag [6]. Further the potassium vapors produced at the hearth of the BF shown in reaction 2.7 will react with the coal and nitrogen injected with the hot blast.

2K(g) + 2C(s) + N2(g) 2KCN(g, l) (2.9) The boiling point for KCN is 1625◦C so as the potassium cyanide rises away from the hot blast from the tuyeres, it transforms into a liquid phase when the temperature drops. The time in the tuyere zone is very short due to the high gas flow so the alkali cyanides have time to move up the BF before transforming into liquid phase. Further up in the furnace alkali cyanides will react with carbon dioxide to form more stable carbonates at temperatures below 1100◦C.

2KCN(l) + 4CO2(g) K2CO3 + N2(g) + 5CO(g) (2.10) The carbonates will either follow the top gas out as gas, or be deposited on the burden as they start to condensate below 900◦C. Compared to alkali silicates, alkali cyanides are unstable so any silica present in the hearth part of the furnace can react with the alkali cyanides to again form alkali silicates [3]. The process of alkali silicates reducing into alkali vapor, that ascend in the BF, exit with the top gas, or react with carbon dioxide to form carbonates can be summarized as the alkali cycle. Several authors have done slightly different summarizing of the process differing exactly which reactions that take place. There are doubts whether carbonates actually are formed at all at the top of the BF as carbonates are not found during excavation of BF:s. Nonetheless the main process that alkali cyanides are formed and that alkali circulate in the BF is agreed on [1, 3, 6, 13, 16]. In figure 2.2 an example of the alkali cycle can be seen. The charged material will descend to the high temperature zone before alkali silicates either will decompose to alkali vapors or be absorbed by the primary slag phase in the form of K2O or Na2O. The cycle also indicates approximately when the alkali vapor would react with silicates to again form silicates [7]. The distribution of alkali vapors through the BF depends on the gas flows path and the extent of central gas flow. Gas flow have a great effect on how heat is distributed in a BF. More central flow means more melting in the middle and less in the periphery of the BF [6].

Figure 2.2 Alkali circulation in a BF. Lines that are solid indicates the solid flows and broken lines the gas flows in a BF [7].

5 2.1.3 Alkali Effect and Removal One of the main negative effects of alkali is that it catalyzes the Boudouard reaction, lowering the temperature for the reaction from 900-950◦C down to approximately 750-850◦C and increasing the coke reactivity depending on the coke. It will also affect the coke structure negatively [12, 17]. The lowered threshold for the Boudouard reaction means that more carbon will be consumed in the BF in a strongly endothermic reaction. Thus, increasing the coke addition needed to the BF to keep a stable operation with 2 to 10 kg coke per kg alkali or with 6 to 11 kg depending on sources used [6, 7]. A third effect of alkali is the increased chance of scaffold formation in the shaft as alkali condense on the lining and may bind fine material to it. Which can lead to either erratic burden descent and/or slipping [1]. Removal of alkali is mostly done with the slag and is best performed at lower basicity values. Of the alkali removed over 90% is removed through the slag [5, 13]. Basicity is a concept that has several definitions depending on which compounds used to calculate it. The basis of basicity is wt% basic oxides in the slag divided by wt% acid oxides and basicity is presented as a fraction. Two basicity definitions were used in this thesis and they were B2: CaO B2 = (2.11) SiO2 And Bell’s ratio [18]: CaO + 0.69 ∗ MgO Bell0s ratio = (2.12) 0.93 ∗ SiO2 + 0.18 ∗ Al2O3

Several articles have investigated how the basicity would affect alkali pickup of the slag and the general consensus is that lower slag basicity will increase the amount of alkali in the slag [5, 6, 14, 15]. A problem with too low basicity is that a higher level of sulfur will stay in the iron, as the sulfur can be counter-acted by CaO present in BF slag and CaO will be lower when the basicity is lower. Therefore, an analyze have been performed to determine the limit for lowest possible basicity while keeping the iron quality under control for one plant. The limit is dependent on the BF parameters and raw material used at the specific plant. A basicity value just above or around 1 could be seen as the limit if alkali is to be removed and iron quality kept [5]. Reaction 2.7 indicates that to hinder the gasification of alkali silicates, the partial pressure of CO should be kept high. The high temperature for the reaction at 1550◦C means that a lower flame temperature also could be used to hinder the reduction and gasification and thereby lower the alkali circulation [7]. Removal of alkali would require decreased re-circulation of alkali containing materials to the furnace as alkali otherwise just would be reintroduced to the BF, which has been previously suggested in a study [13]. Lowering the catalyzing effect of alkali on coke gasification could be done by coke ash additions that can bind the existing alkali in more stable forms which have been tried with certain mineral addition before [8]. As alkali diffuse through the coke a coating of the minerals addition could stabilize the alkali at the surface of the coke stopping it from degrading the inner parts of the coke.

2.1.4 Ammonia and Hydrogen Cyanide Formation in the Blast Furnace Work performed by Turkdogan et al [19] lay the foundation for how ammonia is believed to be formed in a BF. Reaction 2.13 and 2.14 show the reactions behind the ammonia and hydrogen cyanide formation in the BF. The basic reaction is:

2(K, Na)CN + 3H2O (K, Na)2CO3 + 2NH3 + C (2.13) A second reaction occur between ammonia and carbon monoxide as follows:

NH3 + CO HCN + H2O (2.14) The ratio between them depends on several parameters: the amount of moisture available, amount of available KCN in the top and temperature during the reactions. The temperature threshold for NH3 was ◦ ◦ around 600 C and the NH3 formation would continuously decrease exponentially until 500 C afterwards it was not detected. Further NH3 that is formed would be oxidized by either Fe2O3 or CO2 and the amount of ammonia formed would decrease. Figure 2.3 show how Fe2O3 or MnO2 would oxidize NH3 depending on temperature. At lower temperature MnO2 is a stronger oxidant and at higher temperature Fe2O3 is the stronger oxidant.

6 Figure 2.3 Amount NH3 oxidized depending on the temperature for different reactor beds [19].

As HCN and NH3 can be found in the top gas the oxidization kinetics for NH3 is not fast enough to remove it completely. Which could be seen from the data from SSAB where both NH3 and HCN is found [20]. The more water found in the top gas the more formation of NH3 can take place according to reaction 2.13 [19]. The formation of ammonia in the BF is complex as several parameters will affect the amount formed:

• Top gas temperature – The temperature will both affect the moisture content, lower temperature could lead to increased solubility of NH3 in water and HCN is miscible in water, so presence of water could decrease its presence in the top gas [21]. – The ratio between endothermic/exothermic reactions in the BF.

• Flame temperature – The flame temperature will have a minor effect on the amount of alkali vapor produced and the total alkali load. As a high temperature is needed to reduce the alkali silicates in to alkali gas according to reaction 2.7 which starts the alkali circulation. Lowered flame temperature thus leads to more alkali exiting the BF through the slag [7]. • Basicity – A lower basicity would lead to higher alkali uptake in the slag, thus lower circulating alkali in the BF and less ammonia produced according to 2.13.

• Moisture content – Less moisture introduced with the charge or through other ways to the BF would give less water for reaction 2.13 to happen.

The multitude of parameters that can affect the BF process make it a hard process to analyze if only a few select parameters and variables are looked at using 2-dimensional graphs or simple tables. A larger picture generally has to be formed to see possible patterns and which parameters that have a large effect on e.g. alkali.

7 2.2 Multivariate Analysis A good tool to be able to sort through possible connections from large data sets affected by several parameters is multivariate analysis. One common methods used to evaluate data graphically in multivariate analysis is Principal Component Analysis (PCA) that is useful for obtaining an overview of the data set by projecting the data on a coordinate system. A second is Partial Least Squares Projections to Latent Structure (PLS) that work in the same way but is more useful to link several variables together to see how predictor variables can give predict response variables. The methods will be described from the work done by Eriksson et al. [22].

2.2.1 Principal Component Analysis PCA is the first step in the process and give a quick overview if the different variables for made observations differ or have relationships among each other. PCA works by inserting the observation, often time points or similar points in a process, and variables like temperature or chemical analysis. Forming a matrix X of observations N and variables K like shown below.

  x11 x12 x13 . . . x1K    x21 x22 x23 . . . x2K    X =  . . . . .   ......    xN1 xN2 xN3 . . . xNK

Modern multivariate analysis is good at handling matrices like above where the observations are followed by a lot of variables. A dataset used with PCA generally must be pre-treated to normalize the dataset, as variables that have a large internal difference would over power variables with low internal difference. Further on they will be denoted as large or small variables. Scaling will fix this problem by shrinking the large variables and increasing the size of small variables. A standard method used is called Unit Variance Scaling (UV). The dataset is normalized by calculating the standard deviation for each K and multiplying each column K with the inverse of the standard deviation. Thus, obtaining variables with the same variance in size for the entire dataset. It is possible to also weight the scaling performed on each variable based on previous knowledge of each variable. The risk is to give variables more weight in a model than they should have, affecting the model negatively. To make the produced model easier to evaluate a second method is generally deployed as pre-treatment in the form of Mean-centering. Mean-centering is simply performed by calculating the average for each variable and then subtracting it for each variable. The effect of normalizing a dataset is graphically represented in figure 2.4 [22].

Figure 2.4 Graphical representation of data normalization performed by SIMCA before PCA [22]. From the matrix X a space is formed where each variable forms a dimension in the space and the

8 observations form the points in the space. As each variable form a dimension the space will have K dimensions where the average vector point of the dataset will be in the middle of all the points. The space will afterwards be called the K-space. By using mean-centering the average vector will be in the origin of the space. Figure 2.5 show how the vector points are relocated after the mean-centering.

Figure 2.5 (left) Non mean-centered variables. (Right) Mean-centered variables with the average vector at origin in the K-space [22]. When all the variables are centered in the K-space it is necessary to transform the projection into one with less dimension. Making it easier to analyze the dataset. From the K-space a first principal component (PC1) is calculated that form a line that will represent the largest variance in the K-space. To further approximate the data a second principal component (PC2) that is orthogonal to the first and have the largest variation possible is calculated. Together the two principal components form a plane of the K-space that can easily be plotted in 2D-space with each observation projected onto it as shown in figure 2.6.

Figure 2.6 The two principal components PC1 and PC2 form the plane that all the obser- vations then can be projected onto [22].

The plot is called a score plot as each observation can be given a score from how it is located in relation to the principal components. Observations grouped together on the score plot will have similar properties. Meaning that observations laying far apart will have less similar properties and observations located in the middle of the score plot will be average observations. A problem with the score plot is that it will not show how and which variables that affect the observations, only the relation between the observations. To see that a loading plot is calculated and used together with the score plot. The loading plot is calculated first from the angles between the variables and PC1 and then between the variables and PC2. The variables are the axis shown in figure 2.6 in the K-space. Similar features in interpreting the loading plot exist compared to the score plot. Variables grouped together will be correlated, variables laying on the edge of the loading plot will affect the calculated model more, and variables on diagonal sides of origin will affect each other inversely. Score plot and loading plot should be examined together as loading position correlate to which observations are affected by which variables and how they are affected.

9 Observations in the score plot will be affected most by the variables closest in the loading plot. The score plot have to be observed to identify any possible outliers, and also identify which outliers that can be discarded easily or which have to be taken into account for a correct model. The program SIMCA have a tool to find outliers that are not immediately visible in the score plot, called DMod, in which observations that are above a calculated critical value can be considered as moderate outliers. Those outliers should as well be further investigated for their effect on the process as they indicate a small shift in the data that could e.g. be due to natural shifting process parameters or invalid data due to measuring errors. The opposite of moderate outliers found using DMod, are strong outliers which are the one found in the score plot. The strong outliers will affect the model. Which moderate outliers will not do, they rather represent brief changes in the process. The problem with strong outliers is that they can affect how the PCA model calculated the principal components and drag the model out unnecessarily in one direction. The model can be evaluated by R2X and Q2X. Where R2X explain the fit of the model to the dataset and Q2X explain how well the model predict variation. When looking at Q2X it should be larger than 0.5 and the difference in R2X and Q2X should maximum be around 0.2. It is hard to get both a good fit and good prediction as R2X will go to 1 as more parameters are used which can be seen in figure 2.7 [22] while Q2X have a maximum that should be aimed for when modeling.

Figure 2.7 Increased fit R2X by increasing the number of parameters in the model have a maximum for Q2X where more variables is not better for the model [22].

2.2.2 Partial Least Squares Projections to Latent Structure PCA is the first step in analyzing a process using multivariate analyze and shows the observations relationships with both each other and with the variables. When monitoring a process, it is of interest to see how measured variables X affect one or more variable Y called response variable in the data set over an observed time scale. PLS can be used for that type of analyze where it is needed for variables X to be able to predict the responses Y. The dataset will be pre-treated the same way as for PCA with unit variance scaling and mean centering. When using PLS however more care should be taken in how the dataset is prepared. If it is known that certain variables carry more weight in a process the dataset should be scaled accordingly as the normalization otherwise can affect the resulting model in a negative way. The PLS model uses two spaces, meaning that all the observations have two points connected to them. Figure 2.8 show how all observations in the space X will have a corresponding observation in the space Y. The observations in the space X will like PCA have a first component calculated that approximates the observations in one direction, difference being that in PLS the component will also have to correspond to the observations in the Y-space. When using two or more responses component one will be calculated for the X-space called t1 and one for the Y-space called u1. When calculating the second component for the X-space it will be orthogonal to the first component while the second component added to the Y-space does not necessarily have to be orthogonal to the first component. The two new scores are called t2 and u2 respectively. The second component is not necessary when using a PLS model, though often used as a single component may not explain the spaces X and Y properly on its own.

10 Figure 2.8 The space X and the corresponding response variable space Y used for PLS and how they look like when using three variables X and three variables Y [22].

From the projections of the observations on to component 1 and 2 score values ta and ua is obtained for both spaces X and Y. A score plot can be produced for either score values ta or ua to see how the observations group look like. Several different components can be calculated for the spaces to see which fit the dataset in term of correlating response variables Y to factors X. To investigate if the correlation is there, a plot of equation 2.15 can be done:

ui1 = ti1 + hi (2.15)

How close the plotted scores follow a diagonal line with slope 1 indicate the degree of correlation between X and Y. The residual distance hi should be low for a better correlation. To see which variables in X that affect the variables in Y the most a tool called Variable Importance for the Projection (VIP) can be used. VIP can be generalized as a calculation of the weights w∗ for the PLS model and finding the sum of squares while also considering how Y varies in the model [22]. Like PCA, PLS also have a maximum between good fit and good predictability as shown in figure 2.7. In PLS R2Y and Q2Y are investigated instead as it is more important to have a good fit and predictability for the Y variable. OPLS stands for Orthogonal PLS. The basis is the same as for PLS with the difference being that the model is separated into two different Y. One that explains Y and several orthogonal rotations that are not explained by Y. The number of rotation will vary depending on how many are deemed necessary by SIMCA to get a fitting model. The resulting explainability and prediction is the same as for PLS with the benefit of OPLS giving more easily interpreted models.

2.3 Coke Characterization As coke moves through the BF its crystalline properties will change and its reactivity increases with temperature. Thus, different methods have to be used that focus on characterizing coke. Methods such as chemical analysis to get the amount of alkali in the coke or TGA to see the apparent reaction rate or mass loss of the coke are used or can be used. One such method is to investigate the increase in the stacking height of the crystal structure of coke which can be calculated as a value, LC [23].The LC value aims mainly to find the relative temperature that the coke has been subjected to. Alternatively, the apparent reaction rate ka of coke can be investigated using TGA [24]. Moreover, changes in chemical composition and ash content are other important variables indicating the properties of coke collected from a BF.

2.3.1 Coke Graphitisation When coke descends in the BF it will undergo graphitisation. Practically the coke will become more crystalline/ordered the more heat it have been exposed to. The reaction will happen gradually showing

11 the thermal history of the coke through its structure. Graphitisation is also dependent on time and if catalysts like iron were present. A way to measure the graphitisation is through calculating the LC value from the XRD data using Scherrer’s equation as shown in equation 2.16. Where K is 0.89 for the coke peak (002) shown in figure 2.9, λ is X-ray wave length and is 1.5409 for copper, β is the full width half maximum value (FWHM), and Θ is the Bragg angle.

K λ L = (2.16) C β cosΘ

FWHM is calculated as the width of the investigated peak (002) at half its maximum amplitude. A typical coke XRD diffractogram as shown in figure 2.9 further illustrate the peak (002) that will be ”sharper” the more heat the coke sample been exposed to. Which in turn would lead to a higher LC value. The figure also shows the SiO2 peaks that will often be present when characterizing coke [1, 23].

16000

SiO2 14000

12000

10000

8000 Counts 6000

4000 (002) 2000

0 10 20 30 40 50 60 70 80 90 Position °2Θ

Figure 2.9 A XRD plot showing the (002) peak and the two SiO2 peaks often located alongside the peak.

2.3.2 Reaction Rate and Activation Energy

The reaction rate of coke with CO2(g) will change depending on the catalyst present in the coke, increases with more catalyst present. The type of catalyst can vary e.g. alkali compounds, iron compounds or calcium compounds [1, 8, 24]. Increased reactivity of coke can be measured as an increase in apparent reaction rate. Apparent reaction rate must be calculated over the temperature range when the gasification start as that is the point when the reaction would be chemically controlled and not diffusion controlled. The apparent reaction rate was expressed as ka and was calculated using equation 2.17: 1 dW k = (2.17) a W dt W is the coke sample weight at time t and reaction rate was expressed unit wise as [g g−1 s−1] as the reaction was assumed to be of first order. To find the activation energy Ea, Arrhenius equation 2.18 can be used along equation 2.17: −Ea ka = Ae RT (2.18) As the plot of the natural logarithm of k against 1/T in kelvin give A at the intercept of the line on the X axis in the form of ln(A) and the slope of the line will be −Ea/R [24].

12 3 Method

The following text describe the methods deployed to obtain the results for this thesis. The methods used for the coke samples preparation was the same as a previous thesis done in ALCIRC so that the results for the coke could be compared directly to data from that thesis that was performed during the 31st and 32nd EBF campaign [8].

3.1 Basket Samples in EBF Coke basket samples had been previously prepared at Swerea MEFOS from steel mesh/wire and added to the 33rd campaign of LKAB’s EBF which was then excavated by LKAB. [25].

3.1.1 Preparation of Coated Coke and Coke With Modified Ash Three types of coke with kaolin was prepared at MEFOS alongside a reference coke. The composition of the coke used in the experiments can be seen in table 3.1. The test and reference coke were produced by DMT Gmbh & Co. KG to match coke produced by SSAB, the coated coke was prepared from the reference coke using the same type of kaolin. Coke samples consisted of a mix of coke sized <19 mm and >22.5 mmCoke samples CC1 and CC2 got a coating in slurry consisting of 23 and 33 wt% of kaolin mixed with water respectively. The slurry was prepared by weighing water and mixing in kaolin to get the correct weight percent in the slurry. After all kaolin had been incorporated in to the slurry, coke was placed between two handheld screens, and lowered into the slurry. The screens ensured that no coke was lost in the slurry, that the slurry could reach the coke properly, and the top screen hindered coke from floating up and not get coated completely. Mesh size on the bottom screen was 16 mm. Care was taken when coating the coke not to pack it too tight in the screen, as contact between coke pieces could hinder the kaolin coating to cover the entirety of the pieces [25]. Table 3.1 Types of coke added to the EBF in campaign 33.

Kaolin

Added wt% Coating wt%

Reference coke RC - - Test coke TC 2 - Coating 1 CC1 - 23 Coating 2 CC2 - 33

3.1.2 Basket Samples in the EBF The steel wire baskets were connected two and two with a divider in the middle of each basket, separating reference and test coke. All basket had RC in the top half and the type of experimental cokes in each basket varied. Basket K1 and K5 contained TC, basket K2 contained CC1, and basket K6 contained CC2. The baskets found during excavation of the EBF is shown in table 3.2 and layer indicate in which coke charge layer that the baskets where added in. Layer 20 samples where found at a depth below charging between 3.6 to 3.7 m and layer 28 samples at 4.25 m below charging. Table 3.2 All baskets and in which layers they were added in the EBF. Baskets found during excavation and which coke types that were in the baskets.

Basket Layer K1 K2 K5 K6 N20 RC/TC - RC/TC RC/- N24 - - - - N28 RC/TC --- N32 - - - -

Basket K6 in layer N20 only had the top part that contained RC left, the rest of the basket had melted of

13 during the campaign. The campaign only gave a total of 7 coke samples from layer N20 and N28. None of the coated coke samples were found during the EBF excavation.

3.1.3 Characterization of Samples Several different methods were used to characterize the samples and to get data comparable to previous coke characterization performed at Swerea MEFOS and at LTU.

3.1.3.1 XRF From the coke baskets samples an approximate of 45 g was prepared by grinding it to a homogeneous powder using a ring mill for 30 seconds or until a powder was formed. 30 g of the samples were sent to SSAB Lule˚afor chemical analysis using XRF, the X-ray source was Rh based. The other part of the coke samples was used in the XRD analysis.

3.1.3.2 SEM-EDS SEM analysis was done at LTU to see how the alkali was distributed inside the coke samples using a Zeiss Gemini Merlin SEM equipped with a X-Max EDS from Oxford Instruments. Samples were prepared by mounting them in resin, polishing them in several steps and finally coating them with carbon.

3.1.3.3 XRD XRD analysis was performed at LTU using a PANalytical Empyrean XRD unit on powdered coke samples to see which phases and crystalline compounds that was present in the samples. The XRD used the following conditions: Cu Kα radiation, electron emission current 40 mA, accelerating ◦ ◦ voltage 45 kV, measurement in the 2Θ range 10-90 and using a step size of 0.0260 . The LC analysis was performed using an in house developed program at MEFOS that would automatically calculate the LC -value from the diffractograms and produced a figure so that it was possible to see that the analysis had been performed correctly by the program. The LC -value was recalculated for Olofsson’s [8] results as a new method was used in this report so that the results would be comparable.

3.1.3.4 TGA The two coke basket samples recovered from the lowest part of the EBF were analyzed with TGA as highest potassium contents was expected on coke further down in the BF. Coke was crushed using a jaw crusher and 1 g of the fraction between 1-2 mm was extracted using a pair of sieves. The sample tested was placed in an alumina crucible and the atmosphere in the TG was CO2. CO2 was continuously supplied at a rate of 300 ml/min the entire time the samples were in the TG. The CO2 was added as the TG trials are designed to see when the Boudouard reaction 2.6 happened and in extension how alkali would affect the reactivity. Excess CO2 was therefore kept in the TG so that the reaction would ◦ ◦ not be hampered by lack of CO2. The TG was first heated up to 600 C at 20 C/min, the heating rate was then lowered to 3◦C/min between 600◦C to 1200◦C, after which the heating was turned off. From TGA apparent reaction rate and activation energy could be calculated using the equations specified in section 2.3.2. Start of reaction was defined as the point where mass started decreasing steadily for the samples and was determined visually from the figures as that was the method used previously [8].

3.2 Correlation of Ammonia and Cyanide in the Top Gas 3.2.1 Top Gas Measurements Sampling of data for the top gas composition was done at SSAB in Oxel¨osund’s BF No.4 and analyzed using a mass spectrometer (MS). Further data was provided by SSAB on process parameters and gas composition like CO, H2 and CO2 collected during that time period. A probe was inserted into one of four exhaust pipes at the top of the BF and the gas was extracted approximately at the same height as the burden feeding system. BF No.4 is a low-pressure BF, with a gauge pressure of 50-100 mBar above atmosphere pressure in the top gas, facilitating the need to use a pump to feed the gas to the MS. The gas was transported from the probe to the MS through heated pipes to avoid condensation in the system as both NH3 and HCN could dissolve in liquid water [21]. The entire system including the pump was heated. Before the gas was fed to the MS it entered a sample preparation unit from which a probe was inserted that could collect the small amount of gas the MS needed. The unused gas then exited on the other side of the sample collection unit as shown in figure 3.1. The box was there to clean the gas from particles that followed with the BF gas probe so that it did not

14 Figure 3.1 The sampling box used to extract gas to the MS from the gas flow and to clean the gas from the particles plug the MS to fast. Periodically a back-blow procedure was performed to clean out the probe collecting the gas from the BF, the interval was gradually increased as the measuring process continued. The back-blow can be seen as dips down towards 0 each time it was performed in the data graphs produced later on. The MS used was a V&F Analyse- und Messtechnik GmbH Airsense Compact (newer models use the name ”Combisense”). The MS uses a combination of ion-molecule reaction ionization (IMR-MS) and electron-ionization mass spectrometer (EI-MS) to measure the gas composition. The benefit of using the IMR-MS part of the unit was that less fragmentation of the measured molecules could be achieved giving less overlapping peaks. Making it easier to separate species with similar molar mass or properties. The samples entering the MS were ionized by first ionizing a carrying gas like Kr+ or Xe+ by bombarding it with electrons. The carrying gas then get transported through an electrical field and the sample gas was introduced and allowed to interact with the carrying gas. Leading to ionization of the sample gases that then could be transported through another electrical field to a Channeltron detector [26].

3.2.2 SIMCA A dataset was prepared from the process data by averaging the data before taping and correlating it to the tap time. PCA and PLS analysis was then used to find correlations between the variables and the observations in the dataset as described in the theory. SIMCA 15 was used.

The data sets were prepared by removing all the observations when the measured MS values for NH3 and HCN were below zero and the observations when the blast flow was below 80 kNm3/h was removed as the BF was then not in active production. Analysis was performed using data sets from MS measurements, minute data and tap data using the parameters above.

15 4 Results

4.1 Basket Samples From EBF 4.1.1 Basket Location Excavation protocols from the EBF was provided by LKAB and showed the positioning of the baskets as they were found in the EBF. In figure 4.1 the positions are visualized. The most central basket was N20K6 and it had no bottom part as it had melted due to high heat where it had been positioned. The basket N28K1 was the sample found furthest away from the center of the EBF. Figure 8.1 in the appendix showed how the temperature was in the EBF and indicated that at the lower probe (approximately 3.3 m below charging depth) the gas flow had been in the middle. The upper shaft probe (approximately 1 m below charging depth) had a temperature profile that spread out and peaked to the sides more. Shaft probe height taken from [1].

N20 N20 N28

Figure 4.1 Location of the four baskets found in the EBF during the excavation.

No basket was found in the left part of the EBF close to F3 in figure 4.1. Meaning that the samples did not represent the entire EBF and its condition. None of the coated samples were found during the excavation so no results could be extracted from them.

4.1.2 XRF The basic chemical analysis of the samples was done using XRF and are presented in table 4.1. Results showed that components P2O5, S, MgO and TiO2 was similar independently of sample and location found. Fe content was similar in all coke samples except for coke in basket 6 sample RC (reference coke), where it was approximately twice as large compared to the other samples. CaO was higher in RC samples compared to in the TC (test coke) samples. Further, SiO2 was lower in RC samples and higher in TC samples. The reference coke that had not been in the EBF differed most from the others by having the lowest K2O content. It was otherwise fairly similar to the other coke samples. The data of specific interest in this study is the alkali content in the samples. Alkali is plotted in figure 4.2 to show how K2O and Na2O varied in the samples.

Figure 4.2 showed that when the two RC samples N20K5 and N28K1 was compared the amount of K2O differed by 0.01 percent units. The same test cokes TC had a difference of 0.13 percent points showing that the TC samples differed more in alkali indicating exposure to different gas compositions. TC had the highest uptake of both K2O and Na2O for all comparable samples. Figure 4.2 also showed that approximately 4-5 times more K2O than Na2O was found in the coke samples.

16 Table 4.1 XRF data for the coke samples retrieved from the EBF and original coke.

Fe CaO SiO2 MnO P2O5 S Al2O3 MgO Na2OK2O TiO2 Sum Basket [%] [%] [%] [%] [%] [%] [%] [%] [%] [%] [%] [%]

Layer 20 (Depth below charging 3.6 to 3.7 m)

Ref. coke 0.31 0.00 6.55 0.02 0.03 0.71 3.17 0.06 0.05 0.15 0.18 11.00 K1 RC 0.23 0.22 6.16 0.04 0.03 0.68 2.97 0.07 0.14 0.71 0.17 11.18 K1 TC 0.21 0.04 7.46 0.05 0.03 0.63 3.93 0.10 0.23 1.14 0.15 13.73 K5 RC 0.29 0.02 6.69 0.05 0.02 0.69 3.16 0.07 0.17 1.01 0.17 12.12 K5 TC 0.23 0.01 7.39 0.07 0.02 0.64 3.84 0.10 0.25 1.32 0.15 13.80 K6 RC 0.52 0.12 6.51 0.11 0.03 0.69 3.14 0.09 0.29 1.49 0.16 13.01

Layer 28 (Depth below charging 4.25 m)

K1 RC 0.29 0.07 6.60 0.14 0.03 0.69 3.10 0.09 0.20 0.99 0.17 12.14 K1 TC 0.24 0.03 7.39 0.18 0.02 0.65 3.83 0.12 0.29 1.15 0.16 13.82

Figure 4.2 XRF results for total alkali found with XRF.

In figure 4.3 the sum of total alkali showed that no clear correlation could be seen between which layer the baskets were found at and total alkali in the coke. The TC samples in basket N28K1 had an alkali level in between the two other TC samples that was found further up the EBF. Two TC samples also had similar alkali levels despite being found at different depths. Basket sample N20K6 RC found closest to the middle of the EBF had the highest total alkali content. The figure shows the previously mentioned trends that coke with kaolin added (TC) had higher alkali content compared to the RC samples. N20K6 had the overall highest alkali content. N28K1 and N20K5 had similar alkali contents even though they were found on different levels in the EBF. Indicating that horizontal positioning mattered more than vertical i.e. the temperature exposure and gas composition of the EBF mattered more than position. The temperature probes indicated that the heat profile had been central above the height where the samples were found and the melting of basket N20K6 found in the

17 middle indicated that the highest gas flow was at the center of the EBF. During the previous campaign 32 the temperature profile in the EBF had been flat [8].

Figure 4.3 Alkali content for the different baskets depending on exact height found.

A comparison with previous results produced at Swerea MEFOS [8] showed more clearly that baskets found further down in the EBF had had higher alkali levels. The results presented here shows that different gas composition and heat exposure had significant effect on the alkali levels in the baskets.

4.1.3 SEM From the SEM results a visual overview on how the different phases was present in the coke samples could be achieved. The SEM pictures were complemented with EDS analysis to get the atomic ratios at points of interest such as particles or in the middle of the coke matrix. The kaolin and reference samples where similar in structure with a porous coke matrix with particle grains spread out in the matrix along occasional larger particles, and larger clusters of particles. The differences that could be seen in figure 4.4 was that the TC sample had slightly more of smaller particles (coke ash) in the coke matrix at the area investigated. Both samples had the presence of larger particles in the size range of 50 microns in all areas investigated.

Figure 4.4 (Left) Coke basket N20K1-T with RC. (Right) Coke basket N20K1-B with TC.

18 Further comparing the N28K1 basket’s top (RC) and bottom (TC) part in figure 4.5 showed the same results in structure as shown in previous figure 4.4. The coke had a porous structure with particle grains spread throughout. The N28K1 TC sample (2 wt% kaolin) in the right part of figure 4.5 showed an example of how larger particles appeared in the sample. The left particle labeled A in the sample was identified as a phase corresponding to KAl3Si3O11, also known as Potassium mica. Which is formed from dehydroxylation of muscovite at approximately 780◦C according to [27]. The right particle labeled B was identified as a phase corresponding to (K,Na)AlSi3O8 which is known as K-feldspar and specifically in the form of Sanidine due to the high heat and that it has been found before in a BF [28, 8].

Figure 4.5 (Left) Coke basket N28K1-T with RC. (Right) Coke basket N28K1-B with TC.

Reference coke that had not been through the EBF was investigated along the other samples to get a reference of how untreated coke’s structure and composition was. The sample was named 1R<19. The results are presented in figure 4.6. The sample was similar in structure when compared to the other samples that had been in the EBF. In figure 4.6 the presence of a larger particle structure in the right part of the RC coke sample was seen that was not present in the 1R<19 sample. The red shape marks the mentioned structure.

Figure 4.6 (Left) Reference coke 1R<19 (Right) Coke basket N28K1-T with RC coke for comparison.

The phases identified in the sample were summarized in table 4.2 and if possible matched with phases with corresponding composition. The results showed that the phase corresponding to Quartz, excluding the coke matrix, could be found in all samples. The particle identified with the composition Al2Si2O7 was found in all samples except for N28K1 TC and was identified in literature to correspond to the phase Metakaolin [29]. Smaller amounts of the composition matching the phase Kalsilite was found in two different coke samples, N20K1 RC and in N28K1 TC [30]. Three potassium containing compositions was found in all the samples that had been in the EBF, those where corresponding to the phases Leucite, Dehydroxylated muscovite, and Sanidine [24, 30, 28]. The most common phases that could be

19 identified by EDS was Dehydroxylated muscovite, followed by Leucite and finally Sanidine. Table 4.2 Identified compounds in the coke samples using EDS.

N20K1-T N20K1-B N28K1-T N28K1-B 1R<19 Compound RC TC RC TC Ref

SiO2 - Quartz XXXXX

Al2Si2O7 - Metakaolin XXXX

(K,Na)AlSiO4 - Kalsilite XX

KAlSi2O6 - Leucite XXXX

(K,Na)AlSi3O8 - Sanidine XXXX

KAl3Si3O11 - Dehydroxylated muscovite XXXX

Fe2(Si,Al)2O6 X The Carbon matrix had no clear phase and differed from place to place investigated. The base composition consisted of a high carbon content with a sulfur content that varied between spectrum points. The carbon matrix in the reference sample 1R<19 had a lower ratio of potassium compared to the four other coke samples, and no sodium was found in the sample. The samples also contained Si and Al in different ratios without having any clear compounds that would show up as white points in the SEM pictures. An average of the data set used for the compositions and phases can be seen in appendix A in table 8.3, 8.4 and 8.5 along all SEM images in appendix B. Comparing the results with the one previously produced in ALCIRC [8], showed similar results both in the structure inside the coke samples and some of the phases that could be found in the coke. The phases K-feldspar(Sanidine), Leucite, Quartz and Kalsilite was found in both this work and in Olofsson’s [8]. No clear connection between the coke samples that had kaolin added could be found. All samples that had been through the EBF had formed some form of potassium alumina silicate.

4.1.4 XRD The XRD diffractograms produced for this thesis followed the typical coke diffractogram generally seen in XRD results [1, 8, 12, 31]. A broad peak present at 26◦ is indicating that the coke was of semi-amorphous structure. Figure 4.7 shows the normalized XRD diffractograms plotted for comparison. The diffractograms are similar, and no major difference could be spotted which was expected as all samples are made from the same coke with the composition only differing by 2 wt% kaolin. Only trend that could be seen was that the samples that had been found further down the EBF had a higher (002) peak. All coke had been sampled from the EBF.

20 Figure 4.7 Normalized XRD diffractogram results for all coke samples. -T=RC and -B=TC

The mineral phases that was identified from the XRD are summarized in table 4.3. The crystal phases found with XRD correlated to some of the possible phases found with EDS. The results were separated into tables, one where the results showed compounds identified with XRD and SEM. The second table shows minerals found only with XRD. Two types of feldspar was found with XRD, ”Feldspar, alkaline” and Albite. The first where most of the alkaline contribution was from potassium, and the later where only sodium is present. The results further illustrated that some type of alumina silicate with different levels of alkali would be found in the coke ash after it has been through the EBF. Table 4.3 XRD identified minerals in the coke samples that were analyzed with SEM-EDS. -T samples where RC coke and -B samples where TC coke

N20K1-T N20K1-B N28K1-T N28K1-B Compound RC TC RC TC C - Graphite XXXX

SiO2 - Quartz XXXX

KAlSi2O6 - Leucite XXX

Al1.69Si1.22O4.85 - Mullite XX

Al2SiO5 - Sillimanite XX

NaAlSi3O8 - Albite X

K0.8Na0.2AlSi3O8 - Feldspar, alkaline X The samples that had not been investigated using SEM-EDS was instead investigated in the XRD. The results were summarized in table 4.4. Two different types of Mullite was found and both Leucite and Feldspar was present in the samples.

21 Table 4.4 XRD results for the three coke samples that were not investigated with SEM-EDS.

N20K5-T N20K5-B N20K6-T Compound RC TC RC C - Graphite XXX

SiO2 - Quartz XX

KAlSi2O6 - Leucite XX

Al1.83Si1.08O4.85 - Mullite XX

Al4.984Si1.016O9.508 - Mullite X

K0.8Na0.2AlSi3O8 - Feldspar, alkaline XX

4.1.5 Coke Graphitisation

The LC values where recalculated using the in-house program and showed a trend where the TC samples had higher LC value compared with their respective RC sample as shown in figure 4.8. The difference indicated that the TC and RC samples had been exposed to different conditions in the EBF and no pattern could be seen that LC was related to alkali in the samples. Sample N28K1-B (TC) had been −9 through the highest degree of graphitization as it had the highest LC value at 3 ∗ 10 , meaning highest heat exposure. Sample N20K6-T (RC) had the highest total alkali and was not the sample with highest LC value.

Figure 4.8 LC values for all coke samples collected from EBF campaign 33, calculated using the in-house program at Swerea MEFOS. -T is reference coke (RC) and -B is 2wt% kaolin (TC).

The recalculated LC results from [8] shown in figure 4.9 showed that the LC values was lower than in the previous test campaign. The LC values was lower for the samples found higher up in the EBF, between 3.81 m (N17) to 4.49 m (N25) below charging level, and on a similar level for the samples between 4.54 m (N29) to 4.66 m (N33) below charging level when compared to the data in figure 4.8. Comparing sample RC 25 and TC 29 showed that the LC value increased as the samples got lower in the EBF. Previous studies showed the same results that increased temperature increases the graphitization of coke [12, 31, 32]. Samples RC 29 and TC 29 had higher values indicating that the distance below charging was not the only factor affecting the LC value. Unlike in figure 4.8 the reference samples RC varied between having higher or lower LC than the test coke TC. The alkali content was lower for TC 21 and RC 21 compared to TC1 25 and RC 25 while the graphitization was similar.

22 Figure 4.9 LC values for samples from EBF campaign 32 [8], values calculated using the in-house program at Swerea MEFOS for comparison with the data produced for this thesis. TC was 1wt% kaolin and RC was reference coke.

Figure 4.10 showed indication that TC coke had higher LC value compared to RC coke samples during campaign 33. The TC and RC samples in turn had higher LC value than RC [8] and TC [8]. A trend showed that to a certain degree that LC was decided by the depth below charging level, which normally would be correlated to higher temperatures. The figure thus indicated that the samples RC/TC had been exposed to a higher temperature in the EBF than samples RC [8]/TC [8] despite being found higher up in the EBF. Showing that the conditions were different between the campaign which was further confirmed as the temperature profile of campaign 32 was flat with lower temperature at the highest probe compared to campaign 33 temperature profile that had higher temperature in the middle of the EBF [8].

TC RC RC_[8] TC_[8] 3.4

3.6

3.8

4

4.2

4.4

4.6

4.8

Dept below charging level [m] level charging below Dept 17 18 19 20 21 22 23 24 25 26 27 28 29 30

LC [Å]

Figure 4.10 Depth depending on LC for TC (2wt% Kaolin), RC (Reference Coke), TC [8] (1wt% Kaolin) and RC [8] (Reference Coke)

4.1.6 TGA

The TGA showed when the start of the reaction occurred for the coke samples in the CO2 environment. Figure 4.11 along the C conversion curve in figure 8.2 in appendix A shows that the reaction started at approximately 800◦C for both samples with sample N28K1-B (TC) starting a bit earlier. The amount of

23 alkali was highest for sample N28K1-B (TC) shown by the orange bar and that the remaining mass was four percent units lower at the final temperature 1200◦C before the furnace was cooled down.

N28K1-T (RC) N28K1-B (TC)

100% 2.0 90% 1.8 80% 1.6

70% 1.4 wt%] 60% 1.2 50% 1.0

Mass [%] Mass 40% 0.8 30% 0.6

20% 0.4 [ Na2O+K2O 10% RC TC 0.2 0% 0.0 650 750 850 950 1050 1150 1250 Temperature [°C]

Figure 4.11 Mass loss depending on temperature for coke samples N28K1 compared to alkali levels in the samples.

From the TGA results shown in figure 4.12, the apparent reaction rate and activation energy could be extracted as described by equations 2.17 and 2.18. The trend line added to the two graphs in figure 4.12 had a bad fit as R2 was very low and varied between 0.2691 and 0.4269. Reason being the varying data in the beginning of the solution loss reaction as the reaction had not stabilized yet giving spread out data points. The lower the activation energy the lower the reaction start was for the two samples. The orange points show the points used for calculating the activation energy and the blue was all data available to calculate it from.

Figure 4.12 Plot of apparent reaction rate ln ka versus 1/T. (Left) Sample N28K1-T (RC) (Right) Sample N28K1-B (TC)

The exact start of the reaction was extracted from the data as described by the method and showed that the lower the activation energy the lower the start of the reaction temperature. Sample N28K1-B (TC) started to lose mass and react at 711◦C which was 25◦C lower than the start of the reaction temperature

24 at 736◦C for N28K1-T (RC). Either the higher alkali level in the TC sample or the addition of 2 wt% kaolin affected the sample as they otherwise were identical. Table 4.5 Activation energy from the interval 723-824◦C and reaction start for coke samples N28K1-T (RC) and N28K1-B (TC).

−1 ◦ Coke Sample EA [kJ mol ] Reaction Start [ C] N28K1-T (RC) 161.3 736 N28K1-B (TC) 133.7 711

Reaction rate for the samples increases with increased K2O content as seen in figure 4.13. The increase was similar for all samples and showed that the kaolin addition did not change the reaction rate and that it was alkali that was the main reason behind the increase. The increased reaction rate could be fitted to an exponential curve well with a R2 of 0.97. 30.0 Campaign 32 R² = 0.9718 25.0 = Reference coke = 1 wt% Kaolin

= 2 wt% Kaolin

1] 1] 20.0

-

1 1 s -

15.0 Campaign 33

[µ [µ g g

a

k 10.0 µ

Original coke 5.0

0.0 0.0 0.5 1.0 1.5 2.0

wt% K2O

◦ Figure 4.13 Plot of apparent reaction rate depending on K2O at 900 C for campaign 32 data from [8] and from campaign 33 data.

Using data from [8] a plot over the start of the reaction compared to the K2O content in the samples was made as is shown in figure 4.14. Samples containing 2 w% kaolin came from this project. It shows a trend where increased K2O decreases the start of reaction temperature. The start of reaction temperature lowers and flattens out when alkali was present.

The activation energy in the right part of the figure showed the general trend that increased K2O in the samples lead to decreased activation energy. The spread between samples is larger compared to the left figure and no clear pattern could be seen.

25 950 260

240 900 y = -45.005x + 238.2

y = -131.16x + 910.68 ] R² = 0.4923 1

- 220

R² = 0.8796 C]

◦ 850 200

800 0 wt% 180 0 wt% 1 wt% 1 wt% 160

750 2 wt% 2 wt% Start of reaction [ reaction of Start

140 Activation energy [kj mol energy Activation 700 120

650 100 0.00 0.50 1.00 1.50 2.00 0.00 0.50 1.00 1.50 2.00

wt% K2O wt% K2O

Figure 4.14 (Left) Reaction start of the coke reaction and content of K2O. (Right) Activation energy of the coke samples and content of K2O. For samples with 0, 1 and 2 wt% kaolin added.

4.2 Correlation of Ammonia and Cyanide in Top Gas All data was collected from SSAB Oxel¨osund’sBF No. 4. During the test different parameters where changed in the blast furnace e.g. blast flow, flame temperature and basicity of slag which could be seen in the data collected. The process data evaluation was split in to two parts: the multivariate analysis using SIMCA and a simpler plotting of the excel data to see possible connections. Explanation for all parameters is shown in table 8.1 in appendix A. The analysis was performed to find any correlations between NH3 and HCN measured in the top gas and alkali in the BF. Data was averaged out to one tenth before analyzing as the amount of data otherwise would have been too large to handle. The data set was then in the size order of 10 000 - 25 000 data point per variable which was deemed sufficiently large to not lose any important data while not being too large to handle. The data set provided by SSAB stretched between 2017-10-16 to 2017-12-31 while the MS measurements stretched between 2017-11-08 to 2017-11-22.

4.2.1 Process Data Excel Several plots were made from the data set. The first was to investigate how the basicity would affect the ratio of K2O in the slag. Both basicity definition B2 that was pre-calculated in the process data and Bell’s ratio, that was calculated from the known oxides in the slag according to Sikstr¨omet al [18] was used as shown in equation 2.12.

In Figure 4.15 the basicity can be seen to follow the K2O ratio in the slag inversely as previously discussed in section 2.1.3 about alkali removal through decreased basicity. The K2O content was more inconsistent and had a large variance through the measuring period.

26 Figure 4.15 Basicity in the slag from SSAB Oxel¨osund’sBF

To further investigate the relationship in the BF between basicity and K2O in the slag, a plot was done to study the relationship between the two. Figure 4.16 shows a slight negative logarithmic correlation 2 between Bell’s ratio and K2O as shown previously in figure 4.15. The R was low for the trend line at 0.2613. Still a trend could be seen from the data that a decreased slag basicity would lead to increased K2O in the slag and thus a decreased alkali load in the BF, which also was expected from theory. There was one point that was a strong outlier in the figure which was removed before the curve fitting.

Figure 4.16 K2O plotted against Bell’s ratio.

Change in NH3,H2O and HCN could be plotted along the blast flow to see how they changed with each other. Blast flow is indicative on when the BF was not active, a drop in blast flow meant that less hot gas was added through the tuyeres. The figure further showed that when HCN increased the amount of H2O and NH3 decreased. The points when the grey blast flow line drops showed that there where periods

27 when the BF was stopped during the measuring period which affected the top gas composition.

Figure 4.17 K2O, H2O and NH3 plotted against time alongside the blast flow

There was problem to get consistent measurements during the first week of measuring which can be seen between 2017-11-10 to approximately 2017-11-14.

4.2.2 SIMCA The data was prepared according to the method in section 3.2.2. The SIMCA analysis was separated into three parts to look on the data from different perspectives. For each OPLS analysis several different variables were used as Y and the strongest model was then presented in the thesis. A model was evaluated after parameters such as R2Y, Q2Y and/or number of outliers present. An explanation of all parameters used in each model can be seen in appendix A in table 8.1.

4.2.2.1 MS Data The data used stretched between the period 2017-11-14 10:45 to 2017-11-22 14:30 as during the time period before there had been problem to measure the gas composition in a stable way as could be seen in figure 4.17. The first model over the MS data showed a fit of the model R2X at 0.833 and a prediction of variation R2Y at 0.419. The model was named MS. The model could explain the given observations well while the predicted variation was bad as the difference was larger than 0.3 between R2X and Q2X. Figure 4.18 showed several strong outliers in the PCA model. The noisy data combined with the complicated process makes it difficult to separate the outliers that could be removed from real data points that have to be taken into consideration. The data could be ordered into three groups.

28 Figure 4.18 PCA score scatter plot over the basic MS data collected from Oxel¨osund.

The loading plot over the basic MS data in figure 4.19 showed that the variables were spread out and the clearest grouping was between C3H6 and Benzene. H2O and NH3 was correlated with each other and connected to the lower-right group. The relationship for them with HCN was low and HCN was correlated with the top group.

Figure 4.19 PCA loading scatter plot over the basic MS data collected from Oxel¨osund.

Further investigating the possible presence of moderate outliers in model MS showed that the data set contained several points when observations where above the Dcrit(0.05) line as seen in figure 4.20. At 14:00 on the 21 November the curve spikes up, which from figure 4.17, happened at the same time as the blast flow dipped down.

29 Figure 4.20 DModX plot over the MS data that shows when moderate outliers are present in the data.

To investigate how the model was explained for each variable an X/Y overview was made. Figure 4.21 shows that all variables were fit well in the model as the R2X was high, while there was a large difference in the predictability for four variables that had low Q2X. The analyze of the MS data showed that the data was noisy with outliers that was hard to separate from the data inside the confidence interval, so no data was removed from the data set for the PLS analysis.

Figure 4.21 Overview how well each variable was explained by the PCA model

An OPLS analysis showed how well observations of the X-variables could predict one or more Y-variable in the MS data set. The model was named MS. The data set from the PCA was cleaned from the strongest outliers and the best OPLS models were presented in the thesis. The best model was when the MS data 2 2 was used to explain the single Y variable NH3. With an R Y at 0.886 and Q Y at 0.886. The score plot showed two major groupings on each side of the vertical axis as shown in figure 4.22. The left group was observations between November the 19th and the 22th. The right group was the observations before the 19th.

30 Figure 4.22 Score scatter plot for the OPLS model for Y=NH3 using MS data.

The loading plot shows that NH3 and water was positively correlated with each other. HCN could be found weakly correlated to NH3 and more correlated to H2O, the correlation was negative for both. The two groupings of observations seen in figure 4.22 correlated partly to HCN and to NH3. The hydrocarbons were in the top right quadrant and was not related to the components HCN, H2O and NH3.

Figure 4.23 Loading scatter plot for the OPLS model for Y=NH3 for the first of three components.

The variable importance for the projection (VIP) plot in figure 4.24 shows that all variables had importance to the model except SO2 which had a VIP value below 0.5. None of the variables had a confidence interval that included zero, which would be the other reason for a variable to not have significance for a model.

31 Figure 4.24 Variable Importance for the Projection for the MS data model when Y=NH3

4.2.2.2 MS Data + Process Data The data combination used for the analysis was shortened to 2017-11-14 12:20 to 2017-11-22 15:16 for the same reason as for only MS data. The final amount of observations used for the model was 1086 and the model was an OPLS named MSP where Y=NH3. Combining the MS data with the process data showed how the process parameter in the BF affected the gas produced. Figure 4.25 showed that three groups could be seen in the data. The model had a R2Y at 0.920 and Q2Y at 0.918 meaning that the model explained and could predict future NH3 well.

Figure 4.25 Score scatter plot for the OPLS model for Y=NH3 using both process and MS data.

Grouping was corresponding to three different times periods as shown in figure 4.26. The non-marked observations in the top left of the score plot was the earliest observations. The left part of figure 4.26 show which period it correlates to.

32 Figure 4.26 Normalized variable variation over time for selected variables.

Figure 4.27 shows similar results as was seen from just the MS data. The model indicated that HCN, flame temperature (FLAMTEMPERATUR C), (ETA CO 2) and percent CO2 in top gas (PCT CO2 I TOPPGAS) correlate negatively to NH3. While H2O was positively correlated to NH3 in the model. Eta CO2 used in the model is defined as:

CO2 Eta CO2 = (4.1) CO + CO2

The definition with CO is also seen as a variable and is defined the same way with CO as numerator instead of CO2.

Figure 4.27 Loading scatter plot for process and MS data with Y=NH3.

VIP analysis was used to see the effect on NH3 from the variables used in the model. The plot showed that H2O along SO3 was the variables having the largest effect overall for model MSP and that the spread was low for the confidence interval. From the process parameters the total amount of cooling in the BF (KYLEFFEKT TOT) and the moisture in the blast flow (BLASTERFUKT GNMP3) was the most important. The three last variables affected the model the least as they were at or below 0.5.

33 Figure 4.28 VIP plot for the OPLS model where Y=NH3.

Model MSP had significant correlation with the problem that the data was noisy and contained several moderate outliers, as shown by the DMod plot in figure 4.29. During 2017-11-21 a longer period could be identified were several observations was over the 0.05 limit. Indicating a change in the BF process that happened for a longer time period. As the BF process is complicated no moderate outlier were removed as it could be a natural or planned variation in the process that should be considered for the model. Using a t[1]/u[1] plot to investigate the correlation between X and Y showed a linear scatter of the observation as could be seen in figure 4.30. Some points in the bottom left part followed a different linear pattern. Those observations were from the first day 2017-11-14 and the large dip that can be seen in figure 4.26 on 2017-11-21.

Figure 4.29 DMod plot that shows the moderate outliers for the OPLS model.

As only two points differed extensively from the diagonal line at the top and because the plot was linear the correlation between X and Y could be seen as strong. There was a tendency of grouping on each side of the vertical axis.

34 Figure 4.30 OPLS score plot over t[1]/u[1] showing the correlation between X and Y.

4.2.2.3 MS Data + Process Data + Tap Data The final data to take into consideration for a model was the tap data. The inclusion of tap data reduced the amount of observations down to 50 after the data had been processed according to the method. Of the models developed, the one that gave the best explanation of the data and prediction of further observations was Y=HCN with R2Y at 0.961 and Q2Y at 0.882 using 6 components. The model was named MSPT. The score plot showed as for previous models MS and MSP a grouping tendency for the observations, see figure 4.31. One strong outlier was present in the lower right of the score plot and represented the major peak down at 2017-11-21 14:00 seen in figure 4.32.

Figure 4.31 Score scatter plot for the final OPLS model where all the data available from the BF was used, Y=HCN.

The time plot in figure 4.32 shows that the first group is corresponding to the time period until 2017-11-19 and the second group to the time period after that date. The later period in blue was found to have higher and more stable flame temperature compared to the earlier red period. The averaging of the data affected the model so less noise was seen in the figure compared to models MS and MSP.

35 Figure 4.32 Normalized observations over time for the data used in the model MSPT, reduced number of variables shown.

From the loading plot it was possible to see how the variables would depend on HCN. Variables flame temperature (FLAMTEMPERATUR C) and SiO2 in slag (SL SIO2) was correlated positively to HCN. While basicity B2 of slag (SL BAS2) was negatively correlated to HCN.

Figure 4.33 Loading plot over the OPLS model over process, tap and MS data when Y=HCN.

A coefficient plot shows how change in variable X would affect the variable Y HCN. The negative correlation between NH3 and HCN was not significant in model MSPT as the confidence interval included 0, as seen in the coefficient plot presented in figure 4.34. Water had a negative correlation with HCN. Increased CH4 was correlated with increase HCN along top pressure in the EBF (TOPPTRYCK MBAR) and SiO2. K2O was not a significant variable for HCN as the confidence interval included zero. The large confidence interval indicates that the data was noisy.

36 Figure 4.34 Coefficient plot over the OPLS model over process, tap and MS data when Y=HCN.

The number of moderate outliers was lower for this data set as shown in figure 4.35 where only four outliers could be seen. The reason being that the data set was averaged out during the correlation process between the process/MS data with tap data. Noise in the data was thus lowered and only major process differences are seen in the figure.

Figure 4.35 DModX used to identified moderate outliers in the process, tap and MS data.

The VIP analysis for the model shown in figure 4.36 highlighted how most of the variables were important for the model MSPT with only SO2 being deemed insignificant to the model as it had a VIP confidence interval below 0. The most important variables was the top five as they had narrow confidence interval and high VIP values. The variables in the middle was less important for the total model. The top gas variable best correlating to the HCN content in the gas would be H2O and the best slag component would be SiO2 when comparing VIP results with the coefficient results.

37 A difference compared to earlier VIP analysis in figure 4.24 for model MS and figure 4.28 for model MSP was that model MSPT had larger confidence intervals. Several parameters like top pressure (TOPPTRYCK MBAR) or TiO2 in slag (SL TiO2) varied widely between values as high as 1.5 down to VIP values close to 0.3. All variables with a confidence interval including zero was not significant for the model.

Figure 4.36 VIP plot over the variables included in the OPLS model for Y=HCN.

The t[1]/u[1] plot in figure 4.37 shows that correlation was good between X and Y in the model. Thus, the model can model Y well from the given observations X. The regression line had a R2 of 0.9615 with only small tendencies of grouping around the point (4,4) and close to the origin. No tendency of curving was present in the figure.

Figure 4.37 OPLS score plot over t[1]/u[1] showing the correlation between X and Y for the OPLS model MSPT.

Figure 4.38 shows how the grouping seen during the modeling was connected to the basicity of the process using normalized data. There was a connection on how top gas varied depending on the basicity of the BF. Indicating further the importance of basicity for alkali removal and that the amount of alkali in the furnace could be connected to basicity. Basicity varied in three periods and went from a medium, up to high and then down to low relative to itself as shown in the figure below as the red line. Flame temperature was also unstable and lower during the first period until 2017-11-19 after which it increased to normal levels around 2200◦C.

38 Figure 4.38 Plot of average data over time that illustrated how basicity changed over time along MS data for NH3 H2O and HCN. All data normalized.

39 5 Discussion

5.1 Coke Analysis This thesis set out with the objective to compare differences from using different additions of kaolin in coke. So, a continuous discussion about the differences between the results from this work and the previous thesis performed at Swerea MEFOS by Olofsson [8] was done. The focus was on differences present in the results.

5.1.1 Alkali Uptake Depending on Position and Coke Three baskets with 2 wt% kaolin (TC) and four baskets with reference coke (RC) were retrieved from the EBF from different depths and positions in the furnace. In the basket samples with 2 wt% kaolin (TC) it was found that the alkalis formed alkali aluminum silicates indicating that alkalis reacted with kaolin however the total alkali uptake was related to the internal conditions of the EBF in terms of temperature and gas composition. The route of the basket from the top to the point where it was found could also have an impact on alkali uptake. The XRF results as shown in figure 4.2 indicated that a larger amount of potassium than sodium was circulating with the gas. As potassium compounds are more volatile in BF conditions as seen from the literature review and follows the gas up through the BF and sodium follows the slag more. It would also be natural for the baskets closest to the main gas flow to have the highest K2O content as they would be exposed to more gas. However, at some point the temperature and reducing power of the gas is high enough for reduction and volatilization of reduced alkali vapor. If the baskets would sustain to such a position the alkali content in the coke will be reduced. The coke sample that had the highest total alkali was the most center laying basket according to figure 4.1 and those results indicated that the gas flow during the EBF campaign had been central in the furnace. The heat profile in appendix A figure 8.1 show that the highest temperature was in the middle when measured with the lower probe. However, highest temperature was just to the side of the center further up the furnace. The XRF results from both basket charging campaigns indicated that the composition of ash has no impact on the amount of alkalis collected, it depends more on the temperature and gas composition profile in the BF. Comparing the results from this work with earlier work by Olofsson [8] showed that the 1 wt% kaolin samples did not have a clear higher alkali content compared to the reference coke. Rather, in half of [8] kaolin samples the reference coke had higher total alkali and vice versa. The different EBF campaigns had different temperature and gas composition profiles, both vertically and horizontally, and thus it would affect the alkali uptake differently. Therefore, samples that has collected the similar amount of alkalis and has about the sane thermal history as indicated by the LC value could preferably be compared. Time spent in the EBF could also affect the LC values but it can still give an indication.

5.1.2 SEM Results The SEM analysis helped with clearing up if there was a difference between the compounds in the samples by examining the inner structure of the EBF coke samples. No major difference could first be discerned between TC and RC. The coke matrix was porous with white particles spread throughout with occasional collections of larger particles. The particles in the coke samples consisted of different type of alkali alumina silicates with potassium as the major alkali in the samples and there were alumina silicate combinations found equally in both the RC and TC samples. The only sample investigated that did not contain any alkali alumina silicates was sample 1R<19 that was reference coke that had not been in the EBF. Thus, the alkali seemed to bind to alumina silicates in the coke. There was difference between some of the identified compositions and which compounds they could belong to between this work and the work by Olofsson [8]. The differences were small with the same main alkali alumina silicate combinations being found both in this thesis and by Olofsson [8]. The small difference could be explained if temperature and gas composition profiles had been different in the different campaign as these parameters affects which combination of compounds that are created in the coke. A better investigation into the temperature needed to form the different alkali alumina silicate compounds could be performed to better understand which conditions coke samples had been exposed to.

40 5.1.3 XRD for analyses and determination of LC Results presented in figure 4.7 over the diffractograms were hard to visually analyze even though a variation could be seen. In section 2.3 it was discussed how the peak height at (002) for coke would vary depending on temperature, thus all difference in samples should depend mostly on temperature in the EBF. Result in figure 4.10 showed that for Olofsson’s sample there was a correlation with deeper samples having a higher LC value that did not change for samples found furthest down in the EBF. For samples from this work the correlation was not as clear, meaning that samples RC/TC most likely had been exposed to higher temperature higher up in the EBF increasing the LC . The outlier in the figure was basket N20K6 with RC coke in it, that sample had been found in the middle of the EBF and the results from the LC further indicated the gas flow had been central at the point that the basket had been found at and thereby leading to the higher temperature. The phases were hard to find from the diffractogram as the material inspected contains both amorphous and crystalline phases which produces noisy data with hard to interpreted peaks. The combination of data from the chemical analysis and EDS helped with narrowing down the possible candidates for phases that could be present in the coke samples. The general trend for phases found in the coke was the expected Graphite and Quartz. Followed by phases like Leucite, Mullite and Sillimanite. Further confirming that alkaline would be found in the presence with alumina silicates. No trend could be seen for which samples the different phases were found in. Combination of XRD results and deeper study of which temperature the different phases are formed would be beneficial. XRD could then be used as a tool to both investigate graphitisation of the samples and which temperature each sample had been exposed to. Giving a clue which sample had been exposed to higher temperature and how this can be linked to the type of alkali alumina silicates formed.

5.1.4 Coke Reactivity and Reaction Rate The TGA results was performed on the samples that had been found deepest down in the EBF as previous results had shown that alkali content would increase in coke the further down it traveled through the EBF [12]. The XRF results later showed that samples in basket N20K5 had the highest total alkali and could have been a good choice as well. The mass loss results showed that sample N28K1-T (RC) that had lowest alkali content of the two samples had lost less mass and that the reaction started later than sample N28K1-B (TC). The activation energy EA seen in table 4.5 was in the same range as the activation energies for coke samples as seen in literature [24]. The reaction rate is taken at low temperature to get the chemical and not the diffusion controlled reaction rate. The low reaction rate at the start of the reaction give a scatter of the data points in the beginning as shown in figure 4.12. Including reaction rate data from the previous campaign and original coke data then plotting apparent reaction rate against amount of K2O in the samples. Showed a clear trend that an increase of K2O increased the reaction rate. Showing that the alkali content was the main reason for differing reaction rate between campaigns. Further, the higher reaction rate and earlier reaction start for N28K1-B (TC) could be the reason for the slightly larger mass loss.

5.1.5 Final Coke Discussion The final results showed that temperature exposure and gas composition in the EBF would affect the coke samples alkali content. To be able to compare samples between campaigns care would have to be taken to select samples with similar LC values for comparison as they show the thermal history of the coke samples. Alkali was found in the form of alkali alumina silicates in the coke and the amount of alkali would depend on the furnaces internal conditions. The kaolin addition did not affect reaction rate or activation energy of coke samples. Instead it was dependent on alkali level of the coke ash and an increase in alkali led to higher apparent reaction rate.

5.2 Process Data Analysis 5.2.1 Data handling and SIMCA The data used was gathered during an industrial trial where parameters where varied to see their effect on the process. Parameters changed included slag composition, flame temperature by changing oxygen enrichment, and the stockline level. During the trial MS data on top gas composition was collected along process and tap data provided from the BF ordinary systems used during full operation to collect data.

41 The MSP model that included MS and process data was able to explain correlation more clearly between top gas composition and process data. The modeling also showed that using MS data only could not explain the content variation measured in the top gas and more variables had to be included. Further, the model showed how there had been three different main grouping of data points during the measuring period. Showing that the conditions in the BF had been differed during the measuring period.

In both model MS and MSP NH3 was chosen as Y for the OPLS as it had the highest combination of R2Y and Q2Y. What was interesting in the MSP model was how a clearer correlation could be seen between two of the major gases of interest H2O and NH3. Where H2O was clearly positively correlated to NH3 in the model. The VIP analysis of model MSP also indicated that H2O was the most important variable for the model. VIP further showed that most of the variables were important for the overall model and that it would be difficult to discard any of the variables without having a negative effect on the models degree of explanation/prediction. Something that could turn into a cost problem as measuring equipment could be expensive to keep and operate. The second model while good at explaining the process did not include any of the data with the K2O. The need to include amount of K2O in the BF as a variable was clear and therefore the third model was created using the tap data that included K2O in slag. The inclusion of tap data to a third model MSPT was necessary while problematic as it reduced the number of observations from 1086 down to 50. Meaning that the data was more averaged out, a benefit of the averaging was the removal of strong outliers from the data set. The negative part was that the averaging would remove process changes on shorter term than on one hour that could give important information on the variation of top gas composition on short term as well, e.g. on minute basis. Process and MS data was correlated to tap data so that it was an average of data up until a taping which meant that the process data rather represented the time before a tap. The method gave a third model that could be summarized as having high correlation. The earlier seen grouping was still there in the same way as for the other models with the difference being a different loading plot with different correlations between the variables. The variable chosen to be Y was HCN as it gave the highest correlation according to the parameters set out in the method. The model followed known factors such as increased basicity leading to decreased K2O in slag and vice versa according to the loading plot. Unfortunately model MSPT indicated that change in K2O was not correlated to how HCN changed as the coefficient plot showed K2O as insignificant. The large spread of the confident interval in the coefficient plot affected the results negatively and showed that the averaging of data most likely had removed too many observations in the data set. The reason for still seeing correlation in figure 4.38 could be that HCN was connected more to NH3 and H2O that in turn was connected to basicity. Or that the coefficient plot was true just that the averaging had affected the spread of data to give the large confidence intervals. The gases that according to the MSPT model had the largest effect on HCN was CH4 and H2O. Where CH4 was positively correlated to HCN while H2O was negatively correlated to HCN. HCN was also closely positively correlated to the amount of SiO2 in the slag according to the loading and coefficient plot. The coefficient plot had large confidence interval showing that the data was noisy for the MSPT. The noise was not seen in the other models and indicated again that the averaging of data to correlate MS and process data to tap data was not an optimal method and other methods should be investigated. A result that was interesting to see was how clearly basicity gave different periods in the data set as seen in figure 4.38 and the grouping that was seen in all models. Meaning that a way to correlate K2O in slag could be through basicity and then to HCN. The correlation between HCN and basicity could then be useful to get a picture of the alkali situation in the BF, or at least a hint if alkali currently was removed or not. Testing using periods with e.g. similar top temperature coupled with a changing basicity could help discern the correlation using SIMCA. Or using other combinations of parameters where some are kept as stable as possible and others allowed to change in order to reach the objective of having a way to correlate top gas measurements to alkali in the BF. The control of alkali would help reduce the amount of coke used in the BF therefore lowering the carbon foot print of the BF.

5.2.2 Final Process Data Discussion The SIMCA models MS, MSP and MSPT all showed a grouping where at least two different groups could be seen. The grouping depended in turn on when the MS data had been measured during the data period and inclusion of tap data showed that basicity along flame temperature where important factors for the grouping. The periods differentiated in basicity with the later period having lower basicity, higher flame temperature and an increased HCN production. That combined with the loading plots showed that to increase the K2O in slag the basicity can be changed, something that already was known. The results

42 also indicate that the top gases NH3,H2O and HCN could be affected by changing basicity as shown in figure 4.38 which could not be confirmed with model MSPT that included basicity B2 as a variable. The top gases could not be connected directly to K2O in slag in model MSPT.

43 6 Conclusions

6.1 Coke Analysis The conclusions that could be drawn from the coke part was:

• Alkali was present in all coke samples that had been in the EBF as alkali aluminum silicates independent of kaolin addition. Kaolin could then be a good choice as alkali binds to aluminum silicates naturally. • The temperature profile in the EBF between campaign 32 and 33 was different making it hard to compare coke samples as they had been exposed to different conditions during the experiment as evident by the LC values. Campaign 32 had a flat temperature profile in the furnace compared to campaign 33. • The effect of temperature and gas composition distribution in the EBF were important parameters for overall alkali uptake. Thus, further alkali experiments should consider the process parameters used in the BF to make results more comparable to each other.

• In order to bind all alkali present in the samples the amount of added kaolin would not have been sufficient.

6.2 SIMCA Analysis From the process analysis a couple of conclusions could be made:

• The process analysis in SIMCA did not show a correlation between HCN(g) and K2O in the slag.

• It was possible to see a positive correlation between HCN(g) and increased SiO2 in the slag.

• H2O was the gas that was had the largest effect on the model and it was negatively correlated to HCN(g). • Observations would group up in all SIMCA models depending on which period they had been measured in. The periods differed by basicity, which went from high to low during the measured period.

44 7 Future Work

A laboratory experiment in a small scale furnace to see how temperature and gas composition affect alkali uptake in coke samples when kaolin is present in different percentage, also with sufficient added amount binding the amounts of alkali found in the excavation/basket samples. Could also be used to investigate how and if temperature affect which alkali alumina silicate that can be found in coke ash. Work is needed to further correlate the top gas measurements to alkali by including raceway measurement of alkali in future SIMCA models. To get a more direct alkali measurement compared to tap data, that was delayed, to see if correlation to the top gases could be found there. More work could be performed on a mass balance over alkali content in the BF to see when it is removed from the BF. That can in turn be combined to process data to see how the top gases changes depends on the alkali content. Production of SIMCA models that do not average data would better include short term process changes and should be investigated. Investigate the possibility to use more parameters to be used in the process analysis that can be linked to alkali in the BF, e.g. Bell’s ratio. Further, see which parameters are viable to use so that the model only include relevant variables. FactSage calculations to see the thermodynamic relationships between the gases in the top gas of the BF and the reactions connected to alkali circulation. Widening the understanding of the gas productions connection to alkali in the BF.

45 References

[1] M. Lundgren, Development of coke properties during the descent in the blast furnace. Lule˚aUniversity of Technology, 2013. [2] R. Lin, U. Jahnsen, and S. Widner, “Investigations of chlorine and alkali behaviour in the blast furnace and optimisation of blast furnace slag with respect to alkali retention capacity,” 2003. [3] K. P. Abraham and L. I. Staffansson, “ALKALI PROBLEM IN THE BLAST FURNACE.,” Scandinavian Journal of Metallurgy, vol. 4, no. 5, pp. 193–204, 1975. [4] O. Ivanov, L. Savov, and D. Janke, “Experimental Studies of the Alkali Behaviour in Blast Furnace Type Slags,” steel research international, vol. 75, pp. 442–448, jul 2004. [5] I. F. Kurunov, V. N. Titov, V. L. Emel’yanov, S. A. Lysenko, and A. N. Arzamastsev, “Analysis of the behavior of alkalis in a blast furnace,” Metallurgist, vol. 53, pp. 533–542, sep 2009. [6] M. Geerdes, R. Chaigneau, I. Kurunov, O. Lingiardi, and J. Ricketts, Modern Blast Furnace Ironmaking: An Introduction. Amsterdam: IOS Press BV, third ed., 2015. [7] Y. D. Yang, A. McLean, I. D. Sommerville, and J. J. Poveromo, “The correlation of alkali capacity with optical basicity of blast furnace slags,” Iron and Steelmaker, vol. 27, pp. 103–111, oct 2000. [8] J. Olofsson, Alkali Control in the Blast Furnace – Influence of Modified Ash Composition and Charging Practice. Master, Lule˚aUniversity of Technology, 2017. [9] A. K. Biswas, Principles of blast furnace ironmaking : theory and practice. Brisbane: Cootha, 1981. [10] U. Leimalm, Pellet Reduction Properties under Different Blast Furnace Operating Conditions. Licentiate thesis, Lule˚atekniska universitet, 2006. [11] M. Alam and T. Debroy, “The effects of CO and CO2 on the rate of Na2CO3 catalyzed boudouard reaction,” Metallurgical Transactions B, vol. 15, pp. 400–403, jun 1984. [12] T. Hilding, Evolution of coke properties while descending through a blast furnace. Licentiate thesis, Lule˚atekniska universitet, 2005. [13] V. P. Gridasov, G. N. Logachev, S. N. Pishnograev, A. V. Pavlov, V. A. Gostenin, and A. V. Chevychelov, “Behavior of Alkalis in Blast Furnaces,” Metallurgist, vol. 59, pp. 761–765, jan 2016. [14] N. N. Chernov, T. V. Demidenko, B. F. Marder, I. E. Pochekailo, and V. V. Taranovskii, “Distribution of alkali compunds in a large blast furnace,” Metallurgist, vol. 27, pp. 153–156, may 1983. [15] O. Ivanov, L. Savov, and D. Janke, “Alkali Capacity and Physical Properties of Blast Furnace Type Slags,” steel research international, vol. 75, pp. 433–441, jul 2004. [16] A. Pichler, F. Hauzenberger, J. Schenk, H. Stocker, and C. Thaler, “Analysis of the Alkali Flow in Ironmaking Reactors by a Thermochemical Approach: Blast Furnace,” steel research international, vol. 89, p. 1700303, feb 2018. [17] M. Lundgren, U. Leimalm, G. Hyllander, L. S. Okvist,¨ and B. BjOrkman,¨ “Off-gas Dust in an Experimental Blast Furnace,” ISIJ International, vol. 50, pp. 1570–1580, nov 2010. [18] P. Sikstr¨om,L. S. Okvist,¨ and J. O. Wikstr¨om,“Injection of BOF slag through blast furnace tuyeres - Trials in an experimental blast furnace,” in 61st Ironmaking Conference, (Nashville, TN; United states), pp. 257–266, Elsevier B.V., 2002. [19] E. Turkdogan and P. Josephic, “Ammonia and Hydrogen Cyanide Formation in Blast Furnace Stack,” 1984. [20] SSAB, “Mass spectrometry data sampling,” 2018. [21] N.-C. for Biotechnology-Information, “Chemical Compounds.” [22] L. Eriksson, Multi- and megavariate data analysis. P. 1, Basic principles and applications. Ume˚a: Umetrics Academy, 2., rev. a ed., 2006.

46 [23] L. Lu, V. Sahajwalla, C. Kong, and D. Harris, “Quantitative X-ray diffraction analysis and its application to various coals,” Carbon, vol. 39, pp. 1821–1833, oct 2001. [24] M. Grigore, R. Sakurovs, D. French, and V. Sahajwalla, “Influence of Mineral Matter on Coke Reactivity with Carbon Dioxide,” ISIJ International, vol. 46, no. 4, pp. 503–512, 2006. [25] A. Gullberg, “Discussion with supervisor at Swerea Mefos about the coke preparations,” 2018. [26] V&F Analyse- und Messtechnik GmbH, “Technical Description CombiSense,” 2013. [27] L. Heller-Kallai and I. Lapides, “Dehydroxylation of muscovite: study of quenched samples,” Physics and Chemistry of Minerals, vol. 42, pp. 835–845, nov 2015. [28] S. Gupta, Z. Ye, B.-c. Kim, O. Kerkkonen, R. Kanniala, and V. Sahajwalla, “Mineralogy and reactivity of cokes in a working blast furnace,” Fuel Processing Technology, vol. 117, pp. 30–37, jan 2014.

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47 8 Appendix

8.1 Appendix A

Figure 8.1 Temperature profile in the EBF at the moment of cool down provided by LKAB for campaign 33.

N28K1-T (RC) N28K1-B (TC) 120

100

1] 1]

- 1 s 1

- 80

g g g μ 60

40

1/W*(dw/dt) [ 1/W*(dw/dt) 20

0 600 700 800 900 1000 1100 1200 Temperature [°C]

Figure 8.2 C conversion curve for the two TGA samples depending on temperature

48 Figure 8.3 Data for mineral phases identified in reference coke 1R<19.

Figure 8.4 Data for mineral phases identified in coke basket N20K1

49 Figure 8.5 Data for mineral phases identified in coke basket N28K1

50 Table 8.1 Explanation of all the variables used in the SIMCA models. MS model have the notation Average10 before each variable.

Model MS Model MSP Model MSPT Explanation

NH3 ppm NH3 [ppm] MS NH3 ppm Top gas content

H2S ppm H2S [ppm] H2S ppm Top gas content

C2H4 ppm C2H4 [ppm] C2H4 ppm Top gas content

CH4 ppm CH4 [ppm] CH4 ppm Top gas content

C3H6 ppm C3H6 [ppm] C3H6 ppm Top gas content Bensen ppm Bensen [ppm] Bensen ppm Top gas content

O2 vol% O2 [%] O2 vol% Top gas content

SO2 ppm SO2 [ppm] SO2 ppm Top gas content

SO3 ppm SO3 [ppm] SO3 ppm Top gas content CN ppm HCN [ppm] MS HCN ppm Top gas content

H2O ppm H2O [ppm] MS H2O ppm Top gas content BLASTERFLODE KNM3PH BLASTERFLODE KNM3PH Blast flow eta CO eta CO etaCO BLASTERFUKT GPNM3 BLASTERFUKT GPNM3 Blast moisture FLAMTEMPERATUR C FLAMTEMPERATUR C Flame temperature BLASTERTEMPERATUR C BLASTERTEMPERATUR C Blast temperature TOPPGASTEMP C TOPPGASTEMP C Top gas temperature PCT CO I TOPPGAS PCT CO I TOPPGAS vol% CO in top gas

PCT CO2 I TOPPGAS PCT CO2 I TOPPGAS vol% CO2 in top gas

ETA CO2 ETA CO2 etaCO2 TOPPTRYCK MBAR TOPPTRYCK MBAR Gauge pressure in top KYLEFFEKT TOT KYLEFFEKT TOT Total cooling effect HM TEMP Hot metal temperature HM C % C in hot metal HM SI % Si in hot metal HM S % S in hot metal HM P % P in hot metal HM MN % Mn in hot metal HM NI % Ni in hot metal HM CR % Cr in hot metal

SL AL2O3 % AL2O3 in slag SL CAO % CaO in slag

SL SIO2 % SiO2 in slag

SL TIO2 % TiO2 in slag SL MNO % MnO in slag SL MGO % MgO in slag SL BAS2 Basicity B2 of slag

SL K2O%K2O in slag HM TI % Ti in hot metal

51 8.2 Appendix B SEM images N20K1-T

52

53 N20K1-B

54

55 N28K1-T

56

57 N28K1-B

58

59 Reference Coke 1R<19

60

61