This paper presents a comprehensive analysis of the technological parameters of aluminum production for three test sites by applying data mining techniques. Based on the principal component analysis and cluster analysis, structural features of the multidimensional monitoring data space were investigated, regularities in the aluminum production complex operation in its basic operating modes were detected, and typical conditions leading to technological disorders were determined. New knowledge and "analytical portraits" of the operation of an aluminum production complex can be used to develop algorithms for the prevention of technological disorders.
High technical and economic indicators in the aluminum industry are largely
determined by the quality of technology and timely assessment of the technological
state of an aluminum production complex and its separate units: reduction cells,
potrooms and series [
This paper presents a study of the features and patterns in the operation of the
aluminum production complex in its basic operating modes based on the data mining
techniques – principal component analysis and cluster analysis – applied to the
monitoring data of the process control system. Data mining techniques provide an
effective tool for discovering previously unknown, nontrivial, useful in practice, and
interpreted knowledge indispensable for decision-making [
The paper describes the results of a comprehensive multidimensional analysis of
technological parameters in aluminum production for three experimental areas:
Khakas aluminum smelter (KhAS) with RA-300 technology (potrooms No. 9 and 10,
336 retention cells) for the period from 2014 to 2019; Boguchansky aluminum
smelter (BoAS) with RA-300 technology (potrooms No. 1 and 2, 336 retention cells)
for 2019; Bratsk aluminum smelter (BrAS) with Soderberg technology (potroom No,
8, 90 retention cells) for the period from 2015 to 2019. For each experimental area,
the authors investigated the structural features of the multidimensional monitoring
data space, detected regularities in the operation and typical conditions leading to
technological disorders. The analysis results for individual years showed that the data
structure and the nature of the behavior in the studied facilities were the same in all
the periods. Within this research, the analysis and visualization of multidimensional
data were performed using the Pyton and ViDaExpert tools [
According to the theory of multidimensional data analysis, the original data are represented as a set of objects and a set of attributes. The set of objects contains moments in the operation of the retention cells. The set of attributes contains technological parameters registered by the Automated Process Control System. The composition of the attributes is determined by the features of the technology and typical disorders. The key data attributes are listed in Table 1.
The most common technological disorders in the aluminum production process
include the anode effect and distortion of the anode surface relief [
To prepare the data for analysis, they were subjected to preliminary processing. The original formats were converted, and data were merged by time. For the indicators of the chemical composition, gaps in the data were filled using interpolation with algebraic polynomials. Additional parameters were calculated, including statistical indicators of technological disorders (Number of "rolled anodes", Number of anode effects, Number of "spikes", Number of “lagging”, etc.) and generalized indicators (Consumption of alumina and Consumption of aluminum fluoride). Finally, entries with the empty parameter values were excluded (appr. 3040% of the entries).
Velocity ratio, kg/cm Electrolyte temperature, С Electrolyte level, cm Cryolite ratio CaF2 concentration, % MgF2 concentration, % Fe concentration, % Se concentration, % Alumina dose, kg Number of alumina doses, pcs.
Aluminum fluoride dose, kg Number of aluminum fluoride doses, pcs.
Coefficient of the anode-cathode distance, mV/s Cell voltage, V Back EMF, V 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
Service life, month Anode consumption rate, cm/day Number of pins not on the horizon, pcs.
Distance from the anode base, cm Sintering cone, cm Leg size, cm CPC level, cm CPC temperature, С Number of "cracks", pcs.
Number of anode effects, pcs.
Number of "corner sheddings", pcs.
Number of "chunks", pcs.
Number of "rolled anodes", pcs.
Number of "spikes", pcs.
Number of "laggings", pcs.
Additionally, the original data set was subjected to a correlation analysis. The result demonstrated quite a strong relationship between the following parameters: for KhAZ and BoAZ: Dose of alumina and Number of alumina doses with r=-0.88, Electrolyte temperature and Cryolite ratio with r=0.7, Duration of pouring and Velocity ratio with r=-0.65; for BrAZ: Alumina feed time in automatic mode and Alumina feed time in manual mode with r=-1.00; Distance from the anode base and Hollow of the anode with r=0.94, Metal level and Service life with r=0.76, Metal level and Cell voltage with r=0.71, Composite level and Sintering cone with r=-0.69. The established dependences are largely explained by the features of technology and nature of physical processes, which makes it possible to understand the general regularities of the aluminum production complex. 3
Multidimensional Analysis of Monitoring Data
In order to apply the multidimensional data analysis, each experimental area was
attributed a dataset: KhAZ dataset contains 200,434 objects and 15 attributes; BoAZ
dataset contains 95,497 objects and 15 attributes; BrAZ dataset contains 103,207
objects and 25 attributes. To reduce the dimension of the data space and identify
patterns in the data structure, it required the principal component analysis (PCA) to be
implemented [
To identify the structure and reveal patterns within the data, the cluster analysis
was performed using the density-based spatial clustering algorithm [
The results of clustering on the PCA plot and in the internal coordinates of the elastic map, obtained from the data of KhAZ for 2018, are presented in Fig. 1. The analyzed objects are divided into four clusters: Cluster 1 (blue) – 93% of the objects, Cluster 2 (red) – 4% of the objects, Cluster 3 (green) – 2% of the objects and Cluster 4 (yellow) – 1% of the objects. Distinctive features and the nature of each cluster are determined by the average values of the key parameters of the cluster objects (Table 2). Cluster 1 describes the basic operating mode of the complex when the specified technical conditions are maintained, and the parameters have standard values. This cluster is characterized by high productivity and minimal risk of technological disorders. Clusters 2, 3 and 4 stand out from Cluster 1 in terms of changes in the technical conditions. Cluster 2 is characterized by a more frequent occurrence of the “anode effect” and an increase in the Consumption of alumina while maintaining a good level of metal production. Cluster 3 is characterized by the technical conditions with a decrease in the Amperage and Electrolyte level which can correspond to the expected outages of the cells or occurrence of emergency situations. Cluster 4 is characterized by the presence of more serious technological disorders – formation of “spikes” which is accompanied by an increase in the Electrolyte temperature and a decrease in the Consumption of alumina. It should also be noted that the Number of “rolled anodes” increases significantly. As a consequence of the occurring disorders, we observed a decrease in the Metal level.
In addition, the cluster analysis in the context of the parameters Alumina dose and Number of alumina doses, allowed us to identify the features associated with the consumption of alumina in the cells: one part receives alumina less often but in large doses, another part, on the contrary, receives alumina more often but in small doses. The study of the occurrence of technological disorders showed that in the second case, the “spikes” formation occurred less frequently than in the first case. This suggests that the Consumption of alumina is one of the key factors affecting the occurrence of this type of disorder.
The results of clustering on the PCA plot and in the internal coordinates of the elastic map, obtained from the data of BoAZ for 2019, are presented in Fig. 2. The distribution of the average values of the key parameters by the clusters is presented in Table 3.
The analysed objects are divided into three clusters: Cluster 1 (blue) – 93% of the objects, Cluster 2 (red) – 4% of the objects and Cluster 3 (green) – 3% of the objects. Cluster 1 describes the basic operating mode of the complex with high productivity and minimal risk of technological disorders. Clusters 2 and 3 are characterized by a more frequent occurrence of technological disorders. Cluster 2 corresponds to the formation of "lagging", which is accompanied by a decrease in the Electrolyte temperature and an increase in the Coefficient of the anode-cathode distance. There is also an increase in the Number of “rolled anodes” and a decrease in the Metal pouring interval. Cluster 3 corresponds to the conditions with the frequent occurrence of the "anode effect". At the same time, we observe an increase in the Electrolyte temperature, a decrease in the Consumption of alumina and a decrease in the Coefficient of the anode-cathode distance. Technological disorders associated with the "spike" formation are observed quite rarely and most of them fall into Сluster 3.
Also, the results of the cluster analysis revealed the features associated with the age distribution of the cells. The cells are divided into two groups: group 1 – cells numbered 1001-1084 have a service life of 40-50 months, group 2 – cells numbered 1085-1168 have a service life of 1-9 months. The study of these groups showed that the age did not significantly affect the occurrence of technological disorders and the level of productivity. The results of clustering on the PCA plot and in the internal coordinates of the elastic map, obtained from the data of BrAZ for 2019, are presented in Fig. 3. The distribution of the average values of the key parameters by the clusters is shown in Table 4.
The analyzed objects are divided into four clusters: Cluster 1 (blue) – 88% of the objects, Cluster 2 (red) – 11% of the objects, Cluster 3 (yellow) – 0.8% of the objects and Cluster 4 (green) – 0.2% of the objects. Clusters 1, 2, and 3 are located along the same axis and differ significantly from Сluster 4. Moving from Cluster 1 to Cluster 3, there is an increase in the Service life, an increase in the Electrolyte temperature, a significant decrease in the Consumption of Alumina and a significant increase in the Metal level. At the same time, Cluster 1 covers most of the objects and presents the main mode of the complex operation, Cluster 3 covers a small percentage of the objects with a special mode of operation.
Metal level Electrolyte level Electrolyte temperature Fe concentration Si concentration Number of alumina doses Consumption of aluminum fluoride Service life Distance from the anode base Sintering cone Leg size CPC level CPC temperature Number of “cracks” Number of anode effects Number of “corner sheddings” Number of “chunks”
Cluster 1 (blue) 37.334 17.755 953.071 0.238 0.047 2217.663 44.841 27.445 0.00 134.651 18.410 36.381 136.506 3.159 0.754 0.198 Cluster 4 corresponds to events with atypical conditions in the technological process. The objects of this cluster are characterized by the average values of the Service life and Consumption of aluminum fluoride and low values of the Metal level. Apart from this, there is an increase in the Consumption of aluminum fluoride and a significant increase in the values of the following parameters: Sintering cone, Leg size and Distance from the anode base. Technological disorders do not form clear-cut clusters with certain conditions, however, the average values show that Cluster 3 is characterized by a more frequent occurrence of the Number of "cracks", whereas Cluster 3 and Cluster 4 – by the occurrence of the "anode effects", and Cluster 1 – by the occurrence of the "corner sheddings".
A detailed analysis of the events with technological disorders confirmed the cluster analysis results, suggesting that the conditions and nature of the occurring disorders are different. In the case of "laggings" type disorders, the Electrolyte temperature decreases, the Consumption of alumina increases, while in "spike" type disorders, on the contrary, the Electrolyte temperature goes up, Consumption of alumina drops. The formation of "chunks" is usually accompanied by an increase in the Electrolyte Temperature and Consumption of Aluminum Fluoride, as well as a decrease in the Consumption of Alumina and Average voltage of anode effect.
Thus, the study of the structural features of the multidimensional monitoring data space made it possible to obtain new knowledge on the operation of the complex and basic regularities, as well as to determine the characteristic conditions for the occurrence of technological disorders. 4
This paper presents a study of features inherent to the aluminum production complex based on applying multidimensional analysis methods to the monitoring data for three experimental areas. The preliminary correlation analysis allowed us to establish relations between the key parameters, determine the strength of their influence on each other, and figure out the general characteristic patterns. The principal component analysis and Cluster analysis allowed us to reveal the structural features and dependences in the multidimensional space of monitoring data.
The clustering of the KhAZ objects revealed certain technological features associated with the consumption of alumina in a group of cells, identified conditions when the energy balance was violated in the post-start-up period and emergency operation. Also, it determined conditions for the occurrence of typical technological disorders – the anode effect and formation of "spikes". The clustering of the BoAZ objects revealed certain technological features associated with the age distribution of the cells. In addition, it determined conditions for the occurrence of typical technological disorders in the process – the anode effect and formation of "laggings". The clustering of the BrAZ objects elucidated the age characteristics of the cells and the effect of the service life on productivity.
The results of this study made it possible to confirm many hypotheses of engineers and to create the so-called "analytical portraits" of how separate units operate in the aluminum production complex, which can, in turn, serve as a basis for algorithms to prevent the occurrence and development of technological disorders.