Multivariate statistical modeling of an anode backing furnace : Modélisation statistique multivariée du four à cuisson des anodes utilisées dans la fabrication d'aluminium primaire

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Multivariate statistical modeling of an anode baking

furnace

Modélisation statistique multivariée du four à cuisson des anodes

utilisées dans la fabrication d’aluminium primaire

Mémoire

Amélie Dufour

Maîtrise en génie chimique

Maître ès sciences (M. Sc.)

Québec, Canada

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Multivariate statistical modeling of an anode baking

furnace

Modélisation statistique multivariée du four à cuisson des anodes

utilisées dans la fabrication d’aluminium primaire

Mémoire

Amélie Dufour

Sous la direction de :

Carl Duchesne, directeur de recherche

Louis Gosselin, codirecteur de recherche

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Résumé

La stratégie actuelle de contrôle de la qualité de l’anode est inadéquate pour détecter les anodes défectueuses avant qu’elles ne soient installées dans les cuves d’électrolyse. Des travaux antérieurs ont porté sur la modélisation du procédé de fabrication des anodes afin de prédire leurs propriétés directement après la cuisson en utilisant des méthodes statistiques multivariées. La stratégie de carottage des anodes utilisée à l’usine partenaire fait en sorte que ce modèle ne peut être utilisé que pour prédire les propriétés des anodes cuites aux positions les plus chaudes et les plus froides du four à cuire. Le travail actuel propose une stratégie pour considérer l’histoire thermique des anodes cuites à n’importe quelle position et permettre de prédire leurs propriétés. Il est montré qu’en combinant des variables binaires pour définir l’alvéole et la position de cuisson avec les données routinières mesurées sur le four à cuire, les profils de température des anodes cuites à différentes positions peuvent être prédits. Également, ces données ont été incluses dans le modèle pour la prédiction des propriétés des anodes. Les résultats de prédiction ont été validés en effectuant du carottage supplémentaire et les performances du modèle sont concluantes pour la densité apparente et réelle, la force de compression, la réactivité à l’air et le Lc et ce peu importe la position de cuisson.

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Abstract

The aluminum manufacturing process is highly influenced by the anode quality. Several factors affect the anode quality and the actual quality control strategy is inadequate to detect faulty anodes before setting them in the electrolytic cells. A soft-sensor model developed from historical carbon plant data and multivariate statistical methods was proposed in past work to obtain quick predictions of individual anode properties right after baking for quality control purposes. It could only be used for anodes baked at the coldest and hottest positions within the furnace due to the core sampling strategy used at the partner’s plant. To complement the soft-sensor, this work proposes a method for taking into account the thermal history of anodes baked at eventually any position and to allowing for the prediction of properties for all anodes. It is shown that combining categorical variables for pit and baking positions and routinely available firing equipment data is sufficient for predicting the temperature profiles of anodes baked in different positions (measured during pit surveys) and account for its impact on anode properties. Prediction results were validated using core sampling and good performance was obtained for LC, apparent and real density, compressive strength and air reactivity.

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Table list

Table 1 - Raw material properties included in matrix Z1 ... 49

Table 2 - Paste plant process variables in matrix Z2 ... 51

Table 3 - Examples of baking positions as defined in the models... 52

Table 4 - Flue gas temperature variables in matrix X2 (alignment Method 1) ... 58

Table 5- Flue gas temperature variables in matrix X2 (alignment Method 2) ... 58

Table 6 - Pit temperature variables in matrix X3 (alignment Method 1) ... 60

Table 7 - Pit temperature variables in matrix X3 (alignment Method 2) ... 60

Table 8 - Pressure variables in matrix X4 ... 60

Table 9 - Other baking information variables in matrix X5 ... 61

Table 10 - Anode properties variables in matrix YP... 62

Table 11 – Variance of the YP-space explained and predicted (cross-validation) by each components of the PLS-DA model... 64

Table 12 - Variance of the YP-space explained and predicted (cross-validation) by each component of the PCA model ... 67

Table 13 – Summary statistics of the two MB-PLS models built for predicting anode temperature profiles based on anode position indicators and flue gas temperatures aligned using Method 1 ... 72

Table 14 – Variance of the YT-space explained and predicted by the MB-PLS model (Method 1) ... 72

Table 15 - Summary statistics the two MB-PLS models built for predicting anode temperature profiles based on anode position indicators and flue gas temperatures aligned using Method 2 ... 83

Table 16 – Explained and predicted variance for the YT-space by the MB-PLS_YT model (Method 2) ... 84

Table 17 – Summary of data matrices used for modelling of anode properties ... 92

Table 18 – Summary of the MB-PLS_YPmodel built using the two trajectory alignment methods ... 92

Table 19 – Variance explained and predicted of each anode property for the training and validation set. The cross-validated Q2_{are also provided for the training set... 93}

Table 20 – Variance of YP explained and predicted by the MB-PLS_YPmodel (alignment Method 1) ... 93

Table 21 – Variance of the the YP-space explained and predicted by the MB-PLS_YPmodel (Method 2) ... 110

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Figure list

Figure 1 – Configuration of a Hall-Héroult electrolytic cell (side view) (Chaire de recherche industrielle sur la modélisation avancée des cuves d'électrolyse et efficacité énergétique,

2015) ... 2

Figure 2 - Green anodes process (Keller & Sulger, 2008) ... 8

Figure 3 - Simplified flowsheet for coke calciniation process with delayed coking (adapted from Mannweiler, 1994) ... 9

Figure 4 - Relationships between pitch properties (Golubic et al., 2010) ... 12

Figure 5 - Characteristics of primary and secondary QI molecules (Baron et al., 2009) ... 13

Figure 6 - Visual aspects of coke particles coated with pitch from underpitched to overpitched (Hulse, 2000) ... 17

Figure 7 - Illustration of the baking furnace (Grégoire et al., 2013) ... 22

Figure 8 - Behavior of the gases flow from the cooling zone to the heating zone (Keller & Sulger, 2008) ... 23

Figure 9 - Flue gas and anode temperatures measurement for one pit of the baking furnace . 24 Figure 10 - Top view of a section of the baking furnace and side view of a pit within this section ... 25

Figure 11 - Observations plotted in the 3D space (1), 2 first principal components obtained from PCA method (2) and projection of an observation in the 2D latent space (3) (Adapted from Dunn, 2015) ... 28

Figure 12 – NIPALS algorithm for PCA ... 30

Figure 13 - Example of predictions calculated with cross-validation ... 32

Figure 14 - NIPALS for PLS (Westerhuis et al., 1998). ... 35

Figure 15 - Matrices obtained from a PLS model... 35

Figure 16 – Score plot for the calculation of contributions (Dunn, 2015) ... 37

Figure 17 – Obtaining the MB-PLS model based on PLS regression (Westerhuis et al., 1998) ... 39

Figure 18 - Data structure for predicting the temperature profiles of anodes baked in different positions ... 41

Figure 19 – Data structure for predicting the properties of anodes baked in different positions ... 41

Figure 20 - Locations of the thermocouples during a pit survey... 43

Figure 21 – Batch-wise unfolding of the anode temperature array into a matrix ... 45

Figure 22 - Anode temperature trajectories unaligned (raw measurements) ... 46

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Figure 24 - Anode core and ID locations on a baked anode (Courtesy of ALCOA) ... 47 Figure 25 - Time lags applied for synchronizing paste plant data (Adapted from Lauzon-Gauthier (2011)) ... 50 Figure 26 – Batch-wise unfolding of the flue gas temperature array into a matrix X2 ... 53 Figure 27 - Flue 1 front burner valve opening % (left) and the corresponding cumulative natural gas consumption (right) ... 55 Figure 28 – Flue gas temperature profiles a) unaligned trajectories when burners are installed on the section, b) aligned trajectories with Method 1 and c) aligned trajectories with Method 2 ... 57 Figure 29 – Pit temperature profiles a) unaligned, b) aligned with Method 1 and c) aligned with Method 2 ... 59 Figure 30 - Scaled anodes temperatures trajectories during the pit survey of July 2014 performed in furnace 1, section 23 and in pit 3. Colors were assigned according to baking line (left) and baking column (right) ... 63 Figure 31 – PLS-DA model results: a) score plot (t1-t2) and b) weights of YP-space and classes ... 65 Figure 32 – PCA model for anode properties (Yp): a) t1-t2 score plot, and b) p1-p2 loading plot 69 Figure 33 – Score plot (t1-t2) for the MB-PLS_YT model (Method 1). The colors were assigned based on the dates the pit surveys were conducted. The pit number is also indicated in the plot. ... 74 Figure 34 – Weights of the first component for X1 and X2 matrices of the MB-PLS_YT model (Method 1) ... 74 Figure 35 – MB-PLS_YT model weights of the YT matrix in the first component (Method 1) ... 75 Figure 36 – MB-PLS_YT model weights for X1 and X2 in the second component (Method 1) ... 76 Figure 37 – MB-PLS_YT model weights of the YT space in the second component (Method 1) 77 Figure 38 – Score plot (t1-t4.) for the MB-PLS_YT model (Method 1) Colors were assigned according to baking column (left plot) and baking line (right plot) ... 78 Figure 39 - MB-PLS_YT model weights for the X1 and X2 matrices in the fourth component (Method 1) ... 79 Figure 40 – MB-PLS_YT model loadings of the YT matrix in the fourth component (Method 1) .. 80 Figure 41 – Anode temperature trajectories measured (solid line) and predicted (dashed line) for July 2014 pit survey in pit 1. Prediction obtained with the MB-PLS_YT model (Method 1) .... 81 Figure 42 – Anode temperature trajectories measured (solid line) and predicted (dashed line) for June 2011 pit survey in pit 3. Prediction obtained with the MB-PLS_YT model (Method 1) ... 82 Figure 43– Score plot (t1-t2) for the MB-PLS_YT model (Method 2) ... 84

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Figure 44- MB-PLS_YT model weights for the X1 and X2 matrices in the second component

(Method 2) ... 85

Figure 45 – MB-PLS_YT model loadings of the YT matrix in the second component (Method 2) 86 Figure 46 – Score plot (t1-t3) for the MB-PLS_YT model (Method 2). Colors were assigned according to baking column (left plot) and baking line (right plot) ... 87

Figure 47 - MB-PLS_YT model weights for the X1 and X2 matrices in the third component (Method 2) ... 87

Figure 48 – MB-PLS_YT model loadings of the YT matrix in the third component (Method 2) .... 88

Figure 49 – Anode temperature trajectories measured (solid line) and predicted (dashed line) for June 2011 pit survey in pit 3. Prediction obtained with the MB-PLS_YT model (Method 2) ... 89

Figure 50 - Anode temperature trajectories measured (solid line) and predicted (dashed line) for July 2014 pit survey in pit 1. Prediction obtained with the MB-PLS_YT model (Method 2) .... 90

Figure 51 – Data matrices for anode properties predictive models ... 91

Figure 52 – MB-PLS_YPmodel (Method 1): a) score plot (t1-t2), and b) loading plot (c1-c2) ... 94

Figure 53 – X3 weights for first component of MB-PLS_YPmodel (Method 1) ... 95

Figure 54 – Z weights for first component of MB-PLS_YPmodel (Method 1) ... 96

Figure 55 – Z weights for second component of MB-PLS_YPmodel (Method 1) ... 97

Figure 56 – Mixer 1 maximum power for each observation in the model MB-PLS_YP ... 98

Figure 57– X3 weights for second component of MB-PLS_YPmodel (Method 1) ... 99

Figure 58 – MB-PLS_YPmodel (Method 1): a) score plot (t1-t3), and b) loading plot (c1-c3) ... 100

Figure 59- Contributions of each Z and X-variables to the shift in t1-t3 score space from observations in blue dashed circle to observations in black dashed circle (Figure 58 - a) ... 101

Figure 60- Z-variable contributions to the shift in t1-t3 score space from observations in blue dashed circle to observations in black dashed circle (Figure 58 - a) ... 102

Figure 61 – Score plot (t5-t6) for the MB-PLS_YP model (Method 1). Colors were assigned according to baking column (left plot) and baking line (right plot) ... 103

Figure 62 – Weights plot (c5- c6) for MB-PLS_YPmodel (Method 1) ... 103

Figure 63 - Predicted vs observed plots obtained with the MB-PLS_YPmodel (Method 1). Black dots correspond to training data and red dots to validation data. The variance explained (R2₎ and root mean square errors (RMSE) are also provided for both data sets ... 105

Figure 64 - Properties measured and predicted by MB-PLS_YPmodel (Method 1) presented as time series ... 107

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Figure 65 - Variance of each anode property explained by the MB-PLS_YPmodel (Method 2) for different number of components ... 109 Figure 66 - RMSEP of each anode property predicted by the MB-PLS_YPmodel (Method 2) for different number of components ... 109 Figure 67 – MB-PLS_YPmodel (Method 2): a) score plot (t1-t2), and b) loading plot (c1-c2) ... 111 Figure 68 – Weights of the X3-variables in the second component of MB-PLS_YPmodel (Method 2) ... 112 Figure 69– Predicted vs observed plots for MB-PLS_YPmodel (Method 2) ... 113 Figure 70 - Properties measured and predicted by MB-PLS_YPmodel (Method 2) presented as time series ... 114 Figure 71 - Contributions of X-variables to the shift in t1-t2 score space from May 2013 anodes to July 2014 anodes (Figure 33) ... 125 Figure 72 - Anode temperature trajectories measured (solid line) and predicted (dashed line) for June 2011 pit survey in pit 1. Prediction obtained with the MB-PLS_YT model (Method 1) . 126 Figure 73 - Anode temperature trajectories measured (solid line) and predicted (dashed line) for July 2012 pit survey in pit 1. Prediction obtained with the MB-PLS_YT model (Method 1) .. 127 Figure 74 - Anode temperature trajectories measured (solid line) and predicted (dashed line) for July 2012 pit survey in pit 3. Prediction obtained with the MB-PLS_YT model (Method 1) .. 128 Figure 75 - Anode temperature trajectories measured (solid line) and predicted (dashed line) for November 2012 pit survey in pit 3. Prediction obtained with the MB-PLS_YT model (Method 1) ... 129 Figure 76 - Anode temperature trajectories measured (solid line) and predicted (dashed line) for July 2014 pit survey in pit 3. Prediction obtained with the MB-PLS_YT model (Method 1) .. 130 Figure 77 - Anode temperature trajectories measured (solid line) and predicted (dashed line) for May 2013 pit survey in pit 3. Prediction obtained with the MB-PLS_YT model (Method 1) .. 131 Figure 78 - Anode temperature trajectories measured (solid line) and predicted (dashed line) for November 2012 pit survey in pit 1. Prediction obtained with the MB-PLS_YT model (Method 1) ... 132 Figure 79 - Anode temperature trajectories measured (solid line) and predicted (dashed line) for June 2011 pit survey in pit 1. Prediction obtained with the MB-PLS_YT model (Method 2) . 133 Figure 80 - Anode temperature trajectories measured (solid line) and predicted (dashed line) for July 2012 pit survey in pit 1. Prediction obtained with the MB-PLS_YT model (Method 2) .. 134 Figure 81 - Anode temperature trajectories measured (solid line) and predicted (dashed line) for November 2012 pit survey in pit 1. Prediction obtained with the MB-PLS_YT model (Method 2) ... 135

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Figure 82 - Anode temperature trajectories measured (solid line) and predicted (dashed line) for July 2012 pit survey in pit 3. Prediction obtained with the MB-PLS_YT model (Method 2) .. 136 Figure 83 - Anode temperature trajectories measured (solid line) and predicted (dashed line) for November 2012 pit survey in pit 3. Prediction obtained with the MB-PLS_YT model (Method 2) ... 137 Figure 84- Anode temperature trajectories measured (solid line) and predicted (dashed line) for July 2014 pit survey in pit 3. Prediction obtained with the MB-PLS_YT model (Method 2) ... 138 Figure 85 - Anode temperature trajectories measured (solid line) and predicted (dashed line) for May 2013 pit survey in pit 3. Prediction obtained with the MB-PLS_YT model (Method 2) .. 139

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Chapter 1 Introduction

Aluminum exportation generated 6.9 billion dollars for the province of Quebec in 2014, making this sector the second most important after aerospace (Gouvernement du Québec, 2015). There are ten aluminum smelters in Canada and nine of them are located in the province of Quebec (Association de l'aluminium du Canada, 2016). According to the Quebec Ministery of «Économie, Innovation et Exportations» the prospects for aluminium demand in the coming years are promising. In this context, they recently developed the «Stratégie québécoise de développement de l’aluminium 2015-2025» to promote the growth of the aluminum industry in the Province of Quebec. The high availability and the relatively low cost of electricity in the Province of Quebec makes it a prime location for operating aluminum smelters. Indeed, the aluminum manufacturing process requires approximately 13 kWh/ton of aluminum (Keller & Sulger, 2008) and therefore, aluminum smelters are important consumers of electrical energy. With its interesting properties (lightness, corrosion resistance, malleability, etc.), aluminum use in transport industry and infrastructure is expected to increase in the next years. (Ministère de l'économie, innovation et exportations, 2015)

Aluminum is manufactured by the electrolysis of alumina according to the following chemical reaction (Eq 1).

→

2 3 2

2A l O + 3 C 4 A l + 3 C O (1) This electrochemical reaction takes place inside a typically large number of reduction cells (i.e., reactors) electrically connected in series. Figure 1 presents a side view of a Hall-Héroult reduction cell.

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Figure 1 – Configuration of a Hall-Héroult electrolytic cell (side view) (Chaire de recherche industrielle sur la modélisation avancée des cuves d'électrolyse et efficacité énergétique,

2015)

The anodes, which provide the source of carbon for the reaction, are suspended in the cells and are submerged in the electrolytic bath (i.e., the electrolyte in Figure 1) consisting primarily of cryolite. The electrical current flows through the anodes and in the electrolytic bath where the dissolved alumina (Al2O3) is reduced to aluminum which settles at the bottom of the cell. After passing through the molten aluminum layer, the current exits the cell by the cathode block and finally to the steel collector bar and aluminum bus bar assembly that connects the cells together. The temperature of the electrolytic bath is approximately 960°C (Hulse, 2000). The electrical current amperage is very high (100-600 kA) and the voltage, low (~ 4 volts). The CO2 gas released by the reduction reaction is mostly captured by the gas treatment system. The process described in this paragraph is called the Hall-Héroult process. Nowadays, aluminum smelters use this process to manufacture primary aluminum.

In aluminum smelters, about 16 to 40 anodes are suspended within each cell. As illustrated in Figure 1, the anodes are slowly consumed by the reaction and need to be changed every 20 to 30 days approximately. The range for anode consumption in the industry is between 395 to 480 kgC/ton of aluminum (Hulse, 2000). Anodes are not fully consumed during the process and the remaining part is called the anode butt. The butts are recycled in the anode manufacturing process. The anodes are made of three types of raw materials: petroleum coke, coal tar pitch and anode recycled butts. Petroleum coke and coal tar pitch are by-products of petroleum refineries and metallurgical coke production, respectively. Currently, the anode plants are facing a decreasing quality and increased prices of coke and pitch (McClung & Ross, 2000). This situation forces them to make frequent changes of suppliers and to blend

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cokes from different sources in order to achieve the desired raw material specifications and to reduce production costs. This leads to increased variability in the anode manufacturing process and makes it harder to produce anodes with consistent quality.

The traditional and still in use method for controlling anode quality in the industry based on collecting core samples from baked anodes and characterizing them in the laboratory is inadequate for mitigating the increasing variability of raw material properties for two main reasons. First, the cores are sampled from a very small proportion of the anode production (e.g., less than 1 %) because it requires time consuming testing and is also labor intensive. Second, the laboratory analysis results are typically available after several weeks while the anodes are already set in the electrolysis cell for producing aluminum. Chances of detecting faulty anodes in a timely fashion to apply corrective actions on the manufacturing process are therefore very small. Thus several weeks can pass without really knowing that the anodes manufactured present defects until they affect the performance of the reduction cells. Therefore, there is an important need for anode manufacturers to develop more rapid and non-destructive techniques for baked anodes quality control.

Some journal articles focus on the development of different approaches to improve quality control. Several authors worked on developing non-destructive measurement techniques to control and monitor the quality of raw materials, anode paste and green and/or baked anodes. Coke imaging analysis has been studied by Bogoya-Forero et al. (2015) to obtain the aggregate size and vibrated bulk density. Lauzon-Gauthier et al. (2014) focused their work on paste imaging to monitor the changes in the visual aspect of the paste depending on the pitch demand and size distribution of the dry aggregate. Léonard et al. (2014) developed on-line measurement equipment to measure baked anode electrical resistivity. Kocaefe et al. (2015) worked on developping a technique to measure electrical resistivity of green anodes in multiple locations on the anode surface. All these non-destructive techniques would provide direct measurement at the different steps of anode manufacturing process providing useful information on process behavior and product quality. However, the development of these methods is time consuming and their implementation in the plant usually requires important capital investment.

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Empirical modeling, (e.g. soft-sensors), is another interesting avenue for developing anode quality control strategies based on the available data from a given process. Some authors have worked on building models for predicting net carbon consumption in the cells using raw materials and/or anode properties (Keller & Fischer, 1982 and McClung & Ross, 2000). Sarkar et al. (2014), used multiple linear regression analysis to determine the contact angle between coke and pitch from chemical composition of these raw materials. These methods are advantageous because they are rapid to develop and generally are low capital cost solutions. On the other hand, the limitations of these methods are that their performance is often limited by the quantity and quality of the measurements available to build the models and the fact that the models would not necessarily be robust if the new data input to the models is outside the range used to calibrate them (i.e., these models require maintenance and updating). These two options for improving anode quality control are of course complementary and should ideally be jointly developped. The chosen approach for the work presented in this thesis is the empirical modelling of the anode manufacturing process using multivariate statistical methods due to the high amount of measurement available which are still under-exploited, the simplicity of these methods and because this approach complements other efforts made in the CRD-σ research group to develop new hard sensor devices.

An empirical soft-sensor model was recently proposed by Lauzon-Gauthier et al. (2012) to predict anode core properties right after the baking cycle is completed. It uses all the available plant data (raw material properties, paste plant, baking furnace operation and anode core properties). Good predictions were obtained for most of the measured properties, but only for anodes baked at two specific positions within the furnace: the hottest and the coldest due to data availability. Note that collecting and characterizing core samples from all the anodes manufactured at a given site is not possible because the tests are time consuming and labor intensive. Consequently, all smelters use some sampling strategy to measure the anode quality. Hence, the problem investigated in this thesis is not specific to the ADQ plant and is commonly encountered throughout the aluminum smelting industry.

The ultimate goal for developing such a soft-sensor is to predict the properties of anodes baked in any position within the furnace. Hence, the main objective of this thesis is to augment the empirical model proposed by Lauzon-Gauthier et al. (2012) in a way to take into account

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the position in which the anodes have been baked, and to demonstrate the performance of the modified model for predicting the properties of anodes baked in a variety of positions.

The main question to be addressed in this work is how to take into account the changes in the thermal history of anodes when baked in different positions considering that anode temperatures generally are not measured on a routine basis during baking cycles because of operational issues and high costs. In fact, there are approximately 3240 anodes being baked at the same time within each furnace at ADQ. Buying this number of thermocouples would represent a significant investment (in the order of several hundred thousand dollars) also considering that they are generally only used for two baking cycles because of the harsh conditions in which they are used. Moreover, it would be extremely time-consuming to set thermocouples on 108 anodes each time a section is loaded. It is well known in the field that the spatial distribution of temperatures within a pit is not uniform during baking due to the furnace design (e.g. hot flue gas flow patterns in the combustion chambers separating the pits), and this affects the anode properties as discussed in Fischer & Keller (1993), Zhang, et al. (2004), Piffer et al. (2007) and Akhtar et al. (2012). In the model proposed by Lauzon-Gauthier et al. (2012), the difference in thermal history between the coldest and hottest anodes was accounted for by using an indicator variable (also called categorical or dummy variables in the statistical literature) as a regressor in the empirical model. Basically, a binary number was assigned to anodes baked in the coldest and hottest positions and this was found sufficient for capturing the systematic shift in the anode properties due to the difference in thermal history experienced by these anodes. However, such a simple approach can only describe differences between two positions and requires adaptation to allow the model to interpolate between the two extreme positions.

The following hypothesis was tested in this research work: Does using the flue gas temperature profiles, on both sides of a pit, combined with a set of anode position indicator variables, can adequately describe the anode temperature profile during baking, in different positions and its impact on anode properties? The rationale behind this hypothesis is now explained. On one hand, the flue gas temperature is a major component of the heat input through the pit and the shape of the gas temperature profile during a baking cycle is typically very similar to that of the anode temperatures. In addition, flue gas temperatures are routinely measured in every baking cycle as opposed to anode temperatures which are not. Hence, the

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flue gas temperatures serve as an overall energy input indicator in a given pit. On the other hand, the set of binary indicator variables describing the anode position (i.e., pit and location within a pit) represents the net effect of 2-dimensions spatial variations in flue gas flow rate (i.e., convective heat transfer coefficient) and gas temperatures across the pit wall surfaces caused by flue channel design. Therefore, these indicator variables modulate the difference in thermal history experienced by the anodes baked in different positions.

Historical data from different sources were used to test the hypothesis. First, the data collected during so-called pit surveys conducted at the partner site (ADQ) were retrieved in order to 1) verify that baked anode properties vary according to baking position, and then 2) to demonstrate that anode temperature profiles during baking can effectively be predicted by using the combination of flue gas temperatures and anode position indicator variables. Pit surveys are conducted a few times per year at ADQ in order to verify the physical condition of the pit wall materials and the overall performance of the pits during baking conditions. Basically, some anodes in two pits are instrumented with thermocouples allowing to measure the anode temperature profiles. In addition, at the end of the baking cycle, all the instrumented anodes are sampled and the cores are characterized in the laboratory to obtain the baked anode properties. Second, the furnace data routinely collected during each baking cycle were also retrieved for the above mentioned pit surveys. These include the flue gas temperature profiles necessary for testing the hypothesis. Third, a special sampling campaign was conducted in order to collect core samples from anodes baked in different positions within the furnace (other than the coldest and hottest positions), and these samples were characterized. This additional set of data was used to augment the number of samples for model building and validation. Finally, routinely measured paste plant data (raw material properties, formulation, mixing and forming data) were also retrieved to account for variations affecting anode properties caused by other sources unrelated to baking.

Multivariate latent variable methods such as Principal Component Analysis (PCA) and Partial Least Squares (PLS) regression were used for analyzing the large and highly collinear data sets and for estimating the models required to meet the different objectives of this research. It was shown that 1) anode properties indeed vary according to baking position within a pit, 2) the proposed approach using flue gas temperatures and anode position indicator variables yields sufficiently accurate predictions of anode temperature profiles, and 3) combining the

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proposed approach and paste plant data provides good prediction results for anode properties baked in different positions, in the sense that prediction accuracies were similar to those obtained by Lauzon-Gauthier et al. (2012).

An alternative hybrid modeling approach was considered in case the fully empirical modeling approach would not provide satisfactory results. The main idea was to use a simplified mechanistic model to describe within pit variations in anode thermal history (instead of binary indicator variables) and to couple this model with an empirical latent variable model similar as the one proposed by Lauzon-Gauthier et al. (2012). Indeed, detailed mechanistic models are already available in the literature such as those proposed by Leisenberg (2001), Grégoire et al. (2013) and Kocaefe et al. (2013). However, this alternative approach was not pursued further in this thesis because satisfactory results were obtained using only the empirical modeling approach. Note that these phenomenological models of the baking furnace could hardly be used to obtain immediate prediction of anode temperature because they require long computing time.

The thesis is organized as follows. Chapter 2 presents the anodes manufacturing process as well as the effect of the variations in raw materials properties and process conditions on anode properties. The multivariate statistical methods used for building the models are described in chapter 3. The methodology used to collect and organize the datasets is detailed in chapter 4. Chapter 5 presents the modeling results and a discussion. Finally, some conclusions and recommendations for future work are drawn in chapter 6.

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Chapter 2 Anodes manufacturing

Anodes are manufactured through a separate process before being used in aluminum reduction cells. This process is complex and the numerous parameters involved need to be kept in control to achieve the desired anode quality consistently. The flowsheet shown in Figure 2 illustrates the different steps of the anode manufacturing process.

Figure 2 - Green anodes process (Keller & Sulger, 2008)

This section describes the desired characteristics for each of the material used in the anode recipe and the impact of coke and butt size distributions on anode quality. Then, the effect of the proportion of each constituent in the anode paste and the pre-heating, mixing and forming process parameters are discussed.

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2.1 Raw materials

Anodes are made from three types of raw materials: petroleum coke, coal tar pitch and recycled anode butts. Petroleum coke and coal tar pitch are purchased from different suppliers and process conditions need to be adjusted based on the properties of the materials received. This section describes the three materials and the effect of changes in their properties on anode quality.

2.1.1 Petroleum coke

Petroleum coke or ‘green coke’ is a by-product from petroleum refineries. It is produced from the heavy fractions of crude oil distillation by delayed or fluidized coking. The distillation process is optimized in order to produce lighter fractions which are the desired products for the refineries. Heavier fractions are considered as waste products and therefore, petroleum refineries have little incentive to increase their yield and improve their quality (Mannweiler, 1994). Figure 3 illustrates the simplified refinery process of crude oil combined with delayed coking and calcination.

Distillation Vacuum distillation All other processes Coker Calciner 100 Vol % Light Ends Medium Oils 20-30 Vol % 4-6% by volume 2% by value 3-5% by volume 3% by value Gasoline & Light Fuels 94-96% by volume 97% by value Heavy Oil Crude Oil PET coke

Figure 3 - Simplified flowsheet for coke calciniation process with delayed coking (adapted from Mannweiler, 1994)

Delayed coking is usually the method used for producing anode grade petroleum coke (Mannweiler, 1994). Therefore, it is the only method that will be described here. The crude oil

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residual fractions are pumped in a furnace heater and introduced from the bottom in a large coke drum in which cracking and polymerization reactions occur. The process is semi-continuous and takes place in two drums at a time. One day is typically required to fill one drum after which the feed is switched to the other drum (coke mode) while calcined coke is removed from the first one (decoke mode). The coking process requires a temperature of around 450°C and a pressure of several atmospheres. One meter of coke is produced per hour. The drum’s sizes are up to 30 meters high and 8 meters wide. The most important process parameters are reaction time, temperature, pressure and recycle ratio (Hulse, 2000). The coke is then heat treated through a calcination process at around 1250°C in order to remove water and volatiles. The presence of volatiles (CH4, H and tar) in the coke increases the risk of anode cracking due to possible shrinkage during baking caused by degassing of volatiles. Calcination is done using either a rotary kiln or a rotary hearth. The heat-up rate, temperature and residence time influence coke quality. Coke real density will depend on calcination residence time and temperature. These parameters also have an impact on coke degree of crystallinity, while heat-up rate influences porosity (Hulse, 2000).

It has been established by Fischer & Perruchoud (1985) that three main coke properties (purity, structure and porosity) affect anode quality. Since the anodes are made of approximately 65% petroleum coke, it is therefore important to monitoring coke properties to avoid negative impact on anode quality.

Coke bulk density is generally used as a rough indicator of coke porosity. A coke having a higher bulk density has less porosity which yields to a higher anode apparent density and lower air permeability (Hulse, 2000). Coke porosity also influences coke grain stability which indicates the percentage of coke grain remaining intact after coke is ground in a swinging ball mill. Higher grain stability leads to higher coke strength and thus better mechanical properties. The procedure for testing coke properties is well described by Perruchoud & Fischer (1992). The coke macrostructure also has an important impact on anode quality. Macrostructure is typically quantified by coke pore geometrical features. The pore axial ratio is the measurement of the pore breadth over the longest diameter where the breadth is the widest segment perpendicular to the longest diameter. Its median value on a frequency distribution curve can

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indicate the coke macrostructure. A higher median pore axial ratio corresponds to an isotropic macrostructure (fine round mosaics) and a lower one to an anisotropic macrostructure (elongated). The type of macrostructure influences anode elasticity modulus and coefficient of thermal expansion (CTE) in the same way. Anodes made using isotropic coke have a higher CTE and elasticity modulus. The anode crystalline structure, measured by its crystallite size or LC (height in Angström) is influenced by coke crystallinity. The latter is strongly correlated with the calcining temperature but for a coke calcined under a similar temperature, the macrostructure can be different. The LC alone cannot be used to differentiate two cokes according to their macrostructure (Hulse, 2000).

Anode reactivity to air and CO2 are two very important properties to take in account in anode manufacturing in order to minimize carbon overconsumption in the electrolysis cell. The following chemical reactions are responsible for the excess carbon consumption:

Carboxy attack:CO +C2 →2CO (2)

Air burn: O +C₂ →CO₂ (3)

Coke purity plays an important role in air and CO2 reactivity. For air reactivity, the impurities that have the most impact are vanadium, sodium and sulphur. Vanadium is the principal catalyst for the air burn reaction. Sodium also acts has a catalyst but mainly for low sulphur coke (Hume et al., 1993). Sulfur interacts with sodium in a way that sodium sticks to the coke and inhibits the catalytic action of sodium for reaction (2) (Hume S. M., 1993). Coke porosity also influences air reactivity, the higher the porosity the more likely the anode is to be consumed in contact with air.

The carboxy reaction is also mainly catalyzed by sodium (Hume, 1993) and also by calcium, which effect is even more important when sulfur is low (Hume et al., 1993 and Edwards, 2014). Coke structure is also known to influence CO2 reactivity. A higher crystallite size reduces CO2 reactivity losses. As for air burn, a more porous coke also promotes CO2 reactivity (Fischer & Perruchoud, 1985).

Tran et al. (2009) related the effect of baking conditions on the anode’s reactivity to air and CO2. For air reactivity, it decreases when a hotter heat treatment is performed on the anodes. They also confirmed that high coke sulfur content increases air reactivity. In addition, they

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concluded that low-sulfur anodes heat treated to higher temperatures are more reactive to CO2 compared with high-sulfur anodes.

2.1.2 Coal tar pitch

Coal tar pitch is used as the binder for the coke aggregate in the anode formulation. It is produced from coal tar which is a by-product of metallurgical coke production. Metallurgical coke is used to produce steel. Pitch is obtained by the distillation of coal tar in two steps. First, tar is heated and then flashed to remove volatiles from the pitch fractions. The heavier fraction from the first column is then sent to another distillation column to remove more volatiles. There are two different methods to complete the last step of the process, either using a vacuum distillation process or a heat-treatment process (Hulse, 2000). The vacuum distillation is the most commonly used process. However, the heat treatment process is widely used in China which will probably become the major source of coal tar pitch supply in the next years (Baron et al., 2009). The two methods produce pitch with significantly different properties. Several properties are used to define pitch quality and each of them has an important impact on anode quality. Pitch properties are also highly interrelated to one another as it can be seen in Figure 4.

Figure 4 - Relationships between pitch properties (Golubic et al., 2010)

The pitch softening point is determined by distillation conditions: temperature and/or pressure. By increasing temperature and/or reducing pressure the distillation is more severe (more volatiles are extracted from the pitch, the distillation process yields heavier pitch fraction) and

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the pitch softening point is higher (Golubic et al., 2010). The softening point indicates the temperature at which the transition from solid to liquid state occurs. As it is shown in Figure 4 increasing its value has an impact on all other pitch properties.

The coking value is defined as the weight fraction that will remain as pitch coke after baking. This property increases with softening point. A higher distillation temperature removes a greater proportion of low molecular weight compounds from the pitch (i.e. the pitch is heavier) which leads to a higher softening point and coking value. A pitch having these characteristics is less likely to release volatile components when anode is baked.

The pitch composition is typically characterized by quinoline and toluene insoluble molecules. Two types of quinoline insoluble molecules (QI) can be present in coal tar pitch. They are listed in Figure 5 (Baron et al., 2009) and general characteristics for each of them are also presented.

Figure 5 - Characteristics of primary and secondary QI molecules (Baron et al., 2009) The QI molecules formed in coke ovens are divided in two types, Normal or Primary QI and Carry-Over QI. Primary QI is produced by thermal cracking of volatiles and has a very high carbon to hydrogen ratio (C/H). The source of Carry-Over QI is the solid particles that are entrained by gaseous coal volatiles. When a subsequent heat treatment is applied to the pitch a mesophase or secondary QI is produced that has a smaller C/H ratio and contains larger particles than normal QI. The QIs are not present in the distillate. Hence, when the pitch softening point is higher, more QI molecules are present in the pitch(Golubic et al., 2010).

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Toluene insoluble (TI) molecules are quantified in order to calculate the beta resins which is the difference between the content in TI and the content in QI (TI-QI). High beta resins content improves the binding ability of the pitch (Hulse, 2000).

Ash content in pitch indicates the amount of total impurities. Pitch density is directly associated with carbon to hydrogen ratio (C/H). A pitch with higher C/H ratio contains more aromatic molecules, is denser, and thus will produce denser anodes (Hulse, 2000).

Regarding the effects of pitch properties on anode quality, increasing softening point increases coking value. This is good for the anodes because it increases apparent density and decreases losses during baking (Hulse, 2000).

A higher primary QI content seems to have a favorable effect on most of anode properties at laboratory scale (Sakai et al., 2012). Real density is increased and consequently mechanical properties are improved. Higher amount of QI molecules also lowers electrical resistivity. Anode reactivity to CO2 and air are not correlated with QI content. The mesophase or secondary QI has a negative impact on anode quality. The presence of mesophase in the pitch causes problems with anode paste mixing, as it increases paste viscosity and lowers the wettability of the pitch. Anodes containing pitch with secondary QI are more reactive to air and CO2, have a lower apparent density and coking value (Wombles et al., 2009). The test used at the partner plant to measure QI content does not allow quantifying the proportion of primary and secondary QI molecules. The pitch used is not supposed to have undergone any heat treatment so for the work presented here it will be assumed that no secondary QI is contained in the pitch.

2.1.3 Anode butts

Anodes are slowly consumed during the aluminum electrolysis process and to avoid metal contaminations with cast iron (used to seal the anode to the stubs) the anodes are removed from the cell before the stubs are exposed to the electrolytic bath. This remaining part of the anode (called the butt) contains a sufficient amount of carbon to make it worth recycling it back in the anode manufacturing process. Crushed butts account for approximately 20% in the formulation of fresh anodes.

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Anode butts are covered with and sometimes impregnated by electrolytic bath (Na3AlF6) when removed from the cell. Therefore, they are cleaned before being used in a new anode recipe as sodium is an important anode impurity (see section 2.1.1). Anode butts can be classified in two types; hard and soft butts. Soft butts are obtained when an anode is subjected to excessive carbon or air consumption during its life cycle within the cell or when it remained in the cell for a too long period. Hard butts have similar properties to coke particles and should be privileged in the anode recipe. In fact, soft butt produced anode with lower apparent density and flexural strength and also enhance dusting problems in the cell (Hulse, 2000).

Fischer and Perruchoud (1991) showed that increasing the amount of hard butts in anode formulation has a positive effect on apparent density, air permeability and flexural strength. They also concluded that air reactivity residue is improved for anodes manufactured with hard butts.

2.2 Dry aggregate recipe and binder demand

Coke, pitch and butts are stored separately in different type of storage equipment. The coke particles stored in silos are first crushed and separated in different size fractions prior to be used in the recipe. At the partner plant, three coke classification circuits are used to obtain coarse, intermediate, and fine coke fractions. The proportion of the different coke fractions and anode butts to be introduced in the paste is based on a pre-established recipe. The blend of coke particles from different fractions and anode butts is referred to as the dry aggregate mix. After it is preheated to a given temperature, the dry aggregates are mixed with an appropriate amount of liquid pitch to form the anode paste. This section presents the theory behind the choice of the dry aggregate recipe and the amount of pitch to be added to the mix in order to maximize anode quality.

2.2.1 Preparation of the dry aggregate

Butts and coke particles in the coarse, intermediate and fine fractions are separated in 4 different silos. Each of these fractions is weighted by continuous scales and fed in a mixing screw conveyor. Each fraction has its role in the dry aggregate recipe and has an influence on anode quality in different ways.

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The coarse fraction of the dry aggregate should be mainly composed of anode butts. Anode butts help achieve better anode mechanical properties. Indeed, butts contain fewer open pores compared to coke particles and therefore more butts in the coarse fraction should reduce anode porosity and enhance its mechanical properties (Hulse, 2000). Moreover, constraining the anode butts to the coarse fraction prevents butts impurities (mainly sodium) to contaminate the binder matrix which would be detrimental to the anode reactivity (Mannweiler, 1994). Frequently, anode butts can be submitted to bath impregnation which increases sodium content within the butts (Fischer & Perruchoud, 1991). Consequently, lower butt’s particles size would create more surface area and promote both CO2 and air reactivity.

The binder matrix consists of the fine fraction (containing dust) combined with the pitch. Coke dust are the smallest particle size in the aggregate matrix and result from the grinding of coke particles in the ball mills. Achieving a consistent dust fineness is very important because this parameter mostly determines the quantity of pitch to add to the dry aggregate mix in the formulation of the anode paste. A good control of the ball mill operation is therefore important and segregation needs to be avoided in the storage of the fine coke fraction. The proportion of dust in the dry aggregate is also critical for anode properties. In fact, there exists an optimal proportion of dust with respect to anode properties. On one hand, the dust particles are not porous because these are crushed to a very small size. Hence, a higher quantity of dust in the dry aggregate increases the anode apparent and real density. However, on the other hand, pitch demand increases proportionally with the quantity of dust in the dry aggregate which increases costs (more pitch required for manufacturing the anodes) and at some point it becomes difficult to process the anode paste formulated with a high fine particles content. Too high a dust proportion may also result in a weaker anode structure. The limit of dust content not to exceed depends upon its fineness (Hulse, 2000).

The optimum dry aggregate recipe contains a high quantity of coarse particles composed essentially of anode butts to ensure good anode mechanical properties, and a sufficient dust content and fineness so that anode density is high without affecting the structure of the anode. When dust content is low, it is recommended to increase dust fineness because it generally improves anode quality. This beneficial effect is less important when dust content is higher (Hulse, 2000).

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17 2.2.3 Binder demand

As mentioned in section 2.2.2, the mix of pitch and dust forms the binder matrix. The amount of pitch added to the anode paste is of major importance for anode quality and varies according to different parameters. Pitch is needed to glue the dry aggregate particles together in order to be able to form the anode block. There exists an optimum quantity of pitch allowing to achieve the best anode properties for a given dry aggregate mix. This optimum amount of pitch is called pitch demand or binder demand and operators of the anode paste plants are constantly trying to track it (when the dry aggregate mix is changed) and to achieve it. Figure 6 shows that the optimum amount of pitch is the one allowing to coat the aggregate particles entirely leaving only a small void to allow the pitch to expand and fill this void during anode baking.

Figure 6 - Visual aspects of coke particles coated with pitch from underpitched to overpitched (Hulse, 2000)

Optimizing pitch content is not only beneficial for anode quality, but it also helps minimizing the anode manufacturing costs because pitch is more expensive than coke. The raw materials properties, the anode recipe and the process conditions all influence the optimum amount of pitch in the anode paste.

The coke wettability depends mainly on its porosity. A more porous coke requires more pitch. Coke bulk density can be used to estimate its porosity. Hulse (2000) determined that when bulk density increases by 0.1 kg/dm3_{pitch demand decreases by 1.25%.}

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According to Hulse (2000), the ability of the pitch to wet the coke surface and to fill the pores in the coke particles depends on the following pitch properties:

surface tension angle of contact viscosity

primary QI content secondary QI content

Surface tension is a function of pitch density. A high density pitch generally has poor wetting capacity.

Contact angle is temperature dependent. The angle of contact needs to be less than 90° for an efficient wetting. The angle of contact decreases with increasing temperature when temperature is higher than pitch softening point. The pitch does not adequately wet the coke surface when its temperature is too low prior to mixing with the dry aggregate.

Pitch viscosity is also a function of temperature. A lower softening point pitch is less viscous at a given temperature and thus has better wettability.

Higher primary QI content provides better anode properties given the fact that the pitch coking value and viscosity increase with higher QI content. However, the wettability of the pitch is lower when its QI content is very high as shown by Fischer and Perruchoud (1993). Therefore, the optimum amount of pitch to add to the anode paste increases with a higher QI content but to a certain limit.

Dust content and fineness and butts content are the most important aggregate recipe parameters affecting pitch demand. An aggregate with a higher dust content and fineness has a higher surface area. Thus more surfaces need to be coated by the binder and consequently a greater amount of pitch is required in the anode paste formulation. A high level of butts generally reduces pitch demand in the paste (Hulse, 2000).

Preheating and mixing parameters can compensate for low pitch quantity in the paste. Increased temperature during preheating and mixing can help improving anode quality for a lower pitch anode paste. This is mainly due to the paste viscosity that decreases allowing the binder to better adsorb on the aggregate surface creating a more homogenous paste mix.

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Increasing the mixing time may have a negative impact on anode quality producing a dry paste. A longer residence time in the mixer may cause the particles attrition which would increase the dry surface available. Therefore, anode paste mixed during a long time will need a higher pitch content to obtain a good quality anode (Hulse, 2000).

Pitch content can also affect the development of anode properties during the baking of green anodes Overpitched anodes release more volatiles during the degassing stage of the baking cycle. This may create more voids and cracks within the anode block and deteriorate its final properties (e.g., electrical resistivity and mechanical properties). Other consequences of overpitching include the sticking of packing materials on the anode surfaces. This causes problems for the handling of the baked anodes and operational issues when they are set in the reduction cells. Overpitched anode may also suffer from deformation during baking (Hulse ,2000).

Optimizing pitch content is a real challenge for anode plant operators. Many studies showed that adding more pitch in the paste formulation improves anode properties to an optimum but unfortunately this optimal pitch content is not the same for all anode properties. The method used to optimize pitch content should take in account all the desired anode characteristics, the limitations of the different operation units (ball mill, preheater, mixer, vibrocompactor) and the raw materials properties (Hulse ,2000).

2.3 Green anode processing

Manufacturing the green anode from the anode paste material involves two steps: the preparation of the anode paste through preheating, mixing and cooling and the manufacturing of the green anode block itself by vibroforming and cooling.

2.3.1 Preheating and mixing of the anode paste

The dry aggregate is preheated in a continuous heater at a temperature of 50 to 60°C above the pitch softening point to avoid pitch solidification on the surface of the aggregates when these are put into contact. The equipment used for this operation is a preheating screw mixer in which the screw is continually heated by circulating hot oil in the screw flights. The preheater is also equipped with a heating jacket. Important parameters to consider during preheating depend on the equipment efficiency. The temperature at the screw exit needs to be above the

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pitch softening point and the paste temperature should be as homogeneous as possible. A higher preheating temperature helps reaching a sufficiently high paste temperature during mixing with pitch (Hulse, 2000).

A second mixing step of the dry aggregate and the liquid pitch is performed in continuous kneaders. The main objectives of this step are: 1) to achieve a homogeneous mix of the dry aggregate mix and the pitch so that the pitch wets coke particles surfaces evenly, and 2) to impregnates coke pores and fill the open pores (Hulse, 2000). Again, mixing temperature must be maintained high enough to avoid pitch solidification but below the temperature at which the volatiles contained in the pitch start degassing. The devolatilisation in continuous kneaders is less damaging for anode quality because most of the volatiles released are reabsorbed in the paste as the equipment is nearly a closed system. Increasing mixing temperature improves anode quality most probably because it lowers the paste viscosity. In fact, when the pitch has a lower viscosity, the binder adsorbs more easily on the coke surfaces and so the pitch spreads more uniformly in the paste (Hulse, 2000). The energy provided to the paste during mixing also influence anode quality. The energy transferred by the mixer is proportional to its power consumption which is controlled by the rotational speed of the screw, the mixing time and the flap-gate opening. The specific mixing energy is described by the following equation.

Power(KW)

Specific mixing energy=

Throughput(tonne / h)

(4)

Hulse (2000) presents test results comparing different specific mixing energy in order to quantify its effects on anode quality. The mixing energy can be modified by varying the power level of the kneader through the flap-gates opening at the exit. According to this test, increasing specific mixing energy improved all 4 properties tested: baked apparent density, specific electrical resistance, compressive strength and air permeability.

The paste needs to be cooled prior to forming in order to control forming temperature independently from mixing temperature. The equipment used is a mixer-cooler. The desired paste temperature is obtained by adjusting the cooling water flowrate.

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21 2.3.2 Anode forming and cooling

Forming the anode block can be made through hydraulic pressing or vibrocompaction. As vibrocompaction is the method used at the partner plant (ADQ) to manufacture the anode blocks this is the only method that will be described here.

The forming process must maintain as much as possible the original structure of the solid particle in the paste and should allow the pitch to penetrate fine pores (Hulse, 2000). To avoid density gradients within the anode block an attention should be paid to evenly distribute the paste during mold filling. Uniform temperature through the paste is also critical and depends on the paste cooling process. The paste temperature when fed in the mold should be higher for vibrated anodes to reduce paste viscosity and allow the agglomerates to rearrange in order to obtain dense green anode blocks. Vibrated anodes are very sensitive to forming temperature and practically all anodes properties significantly increase with a higher forming temperature. Applying a vacuum during forming also leads to higher anode quality. It decreases porosity as the binder matrix (pitch and dust) is distributed more uniformly around coke particles thus preventing voids formation (Tkac et al., 2007). Paste viscosity is the major parameter that influences the anodes formed by vibrocompaction, and vibroformer frequency is one of the parameter that can be changed to modify paste viscosity (Hulse, 2000). An increase in frequency level generally improves anode quality. The frequency changes the shear rate within the anode block. A higher frequency increases shear rate which, in turn, reduces the paste viscosity due to frictional heat dissipation which leads to similar advantages for the anode properties as described in the previous section.

Right after forming, the green anode blocks are cooled in a water bath. A too high cooling rate can be detrimental to the anodes during their baking. If the anodes are cooled too rapidly or too intensely a lot of stresses can be induced in the anodes making them more likely to crack while heating-up within the baking furnace (Hulse, 2000). Therefore, the anode should be cooled just enough for handling the anodes and to ensure mechanical stability.

2.4 Anode baking

Anodes are baked in an open top ring furnace. The anodes are set vertically in the pits and heated by the hot gases flowing in the flue channels separating the pits. Anodes stay motionless during all the baking cycle and a series of equipment bridges (or ramps) (called a

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fire) are moved from one section to the next at each fire cycle time (~20-24 hours). Figure 7 illustrates the configuration of the baking furnace as it is designed at the partner plant (ADQ).

Figure 7 - Illustration of the baking furnace (Grégoire et al., 2013)

The furnace consists of 34 sections, each composed of 6 pits and 7 flue channels. Two series of equipment bridges (fires) circulate around the furnace. The series of ramps include the following equipment: the exhaust manifold ramp, the underpressure ramp, three burner ramps and two cooling ramps. The anodes need to be baked very slowly in order to avoid cracking. Typically, the heat-up rate should not be above 15°C/h (Keller & Sulger, 2008). The anode is required to reach at least 1100°C and to stay above this temperature for a certain amount of time, called the soaking time, in order to achieve the desired properties.

The exhaust manifold applies suction at one end of the flue channels in order to draw the hot gases out of the furnace. A lower pressure (hence the name of underpressure bridge) is maintained in the furnace from the beginning of the cooling zone to the exhaust manifold to ensure that the gases circulate through the flue channels. The suction is maintained by a fan that directs the gas to the stack. Figure 8 shows how the gases flow through the flue channels. The blue arrows indicate the air entering the furnace to cool the anode and the yellow arrows indicate the direction of the hot gases either escaping the furnace or sucked in the heating zone.

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Figure 8 - Behavior of the gases flow from the cooling zone to the heating zone (Keller & Sulger, 2008)

The energy input to the furnace is provided by combustion of natural gas at the three burner ramps and by the pitch volatiles degassing during the anode heat-up and burning in the flue channels. The volatiles contained in pitch escape the anode when its temperature is between 200 and 600°C (Fischer & Keller, 1993). There are small verticals openings between the flue walls bricks to allow the pitch volatiles to enter the flue channels where they burn and release heat.

The baking step can cause significant damage to the anode and can deteriorate baked anode properties even for a green anode manufactured properly. Tight control of the baking process is therefore very important. One major point to consider is the temperature distribution within the pits. Anode temperature is not measured in real-time due to the high cost of purchasing, installing and handling thermocouples in all anodes. Temperature uniformity needs to be maintained in the pits to obtain anodes that are all baked to the same level. This is particularly difficult to achieve due to the design of the furnace itself and inevitably the anodes set in different positions within a pit have been baked under a different thermal history. Thus the baking position influences the anode properties. The deformation of the flue walls, the wear of the material structure of the furnace, the flue design (baffles and tie bricks configuration) and the control strategy of furnace operation are additional causes for the non-uniform temperature distribution.

Each ramp is equipped with different measuring instruments to provide data to the control system throughout the baking cycle. While the exhaust manifold is set on the section, the gas temperature is measured in each flue channel. The underpressure ramp also measures the gas temperature within each flue channel and the pressure within them. When the burner

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ramps are in used on a section the gas temperature within each flue channel is also measured and the burner front and back opening percentage are controlled to maintain the desired gas temperature. Burner ramps are also equipped with thermocouples to measured pit temperature within two pits (pit 1 and 3 in this case). Burner opening percentage can be used to estimate the natural gas flow rate supplied to the furnace during each fire cycle. Figure 9 presents typical flue gas temperature profiles (top plot) and the anode temperature profiles (bottom plot) for 9 anodes in pit 1 of a baking furnace located at the partner site (measured during a pit survey, see section 4.1.1 for more details). Note the similarity of the gas and anode temperature profiles, except for the apparent discontinuity between the end of the exhaust manifold and the beginning of the underpressure ramp which will be explained later.

Figure 9 - Flue gas and anode temperatures measurement for one pit of the baking furnace No data is shown for the gas temperatures in the 3rd_{step of the baking cycle because no} measurement is collected, between the underpressure bridge and the first burner bridge at the partner site. Figure 7 shows that no equipment is present on the section between these two bridges. The apparent temperature drop between the end of the exhaust manifold operation and the beginning of the underpressure ramp operation is not real. It is simply due to the positioning of the temperature measurement ramp for the section where the exhaust manifold