=Paper=
{{Paper
|id=Vol-2727/paper12
|storemode=property
|title=Analysis of Aluminium Electrolysis Data in the Context of Extreme Values of Technological Parameters
|pdfUrl=https://ceur-ws.org/Vol-2727/paper12.pdf
|volume=Vol-2727
|authors=Anna Metus,Tatiana Penkova,Anton Mikhalev,Nina Lugovaya,Tatiana Penkova,Anna Molyavko,Evgenia Karepova,Mikhail Sadovsky,Vladimir Shaidurov,Igor Borovikov,Roman Morozov,Margarita Favorskaya,Ivan Perevalov,Tatiana Vitova,Valery Nicheporchuk,Tatiana Penkova,Maria Senashova,Aleksey Korobko,Yulia Ponomareva,Anna Korobko,Anna Vlasenko,Natalia Zhilina,Dmitry Zhuchkov
}}
==Analysis of Aluminium Electrolysis Data in the Context of Extreme Values of Technological Parameters==
https://ceur-ws.org/Vol-2727/paper12.pdf
92
Analysis of Aluminium Electrolysis Data
in the Context of Extreme Values
of Technological Parameters*
Anna Metus[0000-0003-0547-5999] and Tatiana Penkova[0000-0002-0057-0535]
Institute of Computational Modelling of the Siberian Branch
of the Russian Academy of Sciences, 50/44 Akademgorodok, Krasnoyarsk, 660036, Russia
metus@icm.krasn.ru
Abstract. This paper presents the results of monitoring data analysis for
experimental areas producing primary aluminum based on the RA-300 and
Soderberg technologies. The authors considered the events of technological
disorders, such as anode effects and formations on anode face, carried out the
statistical analysis of technological parameters and process disruptions in
relation to the extreme values of the parameters. The results of analysis allowed
us to obtain new knowledge about technological disorders and features of
abnormal working states of aluminum reduction cells, detect the technological
patterns, conditions and causes for the occurrence of technological disorders.
Keywords: Aluminium Electrolysis, Extreme Values, Technological Disorders,
Statistical Analysis.
1 Introduction
Quality control of the aluminum smelting process is based on monitoring the
technological parameters of aluminum reduction cells [1]. Controlled parameters
include measurements of the chemical composition of the melt, physical
characteristics of the melt, energy balance, and other working variables. The most
common technological disorders in aluminum production include anode effects and
distortions of the anode geometry formed during electrolysis [1-2].
Anode effect is a phenomenon characterized by decreasing in the alumina
dissolution and a significant increase in the cell voltage. Distortions of the anode
geometry are classified into formations on the anode face (“spikes”, “laggings”,
“chunks”), and anode destruction (“corner shedding”). “Spike” is a formation of a
cylindrical or conical shape [3]. “Lagging” is the formation of a rectangular cross-
section or unevenness, occupying up to 50–60% of the anode bottom [3]. “Chunk” is
a special term, adopted at Bratsk aluminum smelter, for formations of any form
weighing up to several hundred kilograms on the Soderberg type of carbon anodes.
* Copyright c 2020 for this paper by its authors. Use permitted under Creative Commons
License Attribution 4.0 International (CC BY 4.0).
93
“Corner shedding” is a physical loss of large carbon pieces from the anode surface
[4]. Statistical analysis of the monitoring data allows us to obtain new knowledge
about technological disorders and features of abnormal working states of aluminum
reduction cells.
This paper presents the results of the monitoring data analysis in the context of
extreme values of technological parameters in order to explore technological patterns
and detect the technical conditions and causes for the occurrence of technological
disorders.
2 Analysis of the Monitoring Data in the Context of
Extreme Values of Technological Parameters
The analysis was carried out on monitoring data for three experimental areas: Khakas
aluminum smelter (KhAZ), Boguchansky aluminum smelter (BoAZ), that use the
RUSAL’s proprietary RA-300 technology, and Bratsk aluminum smelter (BrAZ) with
Soderberg technology. Technology determines the design features of aluminum
reduction cells and plants and, as a result, the scope of controlled technological
parameters and types of process disruptions [5, 6]. In the experimental area of KhAZ,
there were 6784 cases of “spikes” and 8123 anode effects over the observation period
of 2014-2019 years. In the experimental area of BoAZ, there were 3872 cases of
“laggings”, 349 cases of “spikes” and 4689 anode effects over the 2019 year. In the
experimental area of BrAZ, there were 30989 cases of “corner shedding”, 2986 cases
of “chunks” and 73412 anode effects over the observation period of 2015-2019 years.
To determine the extreme parameter values, the variability of a set of parameter
values was estimated for each cell, and the ranges of reliable values were found as
μ ± σ, where μ is the mean value of the data series, σ is the standard deviation.
Parameter values outside this range are considered as extreme values. The result of
the analysis of the statistical characteristics showed that the cells, in the context of the
same parameters, differ significantly both in the average values of the parameters and
in their deviation intervals. For each experimental area, the parameters with the
largest percent of anomalies over entire observation period were determined, among
them: for KhAZ – Back EMF, Dose of alumina, Coefficient of anode-cathode
distance; for BoAZ – Cryolite ratio, Coefficient of anode-cathode distance, CaF2
concentration, for BrAZ – AlF3 dose, Minimum distance, MgF2 concentration.
In order to explore the events of technological disorders in relation to the extreme
values of the parameters, we considered the dates of disorders detection (in cases of
anode effects) and the five-day period preceding the registration of the disorder (in
cases of any formations). As a result, for each type of disorder, the distribution of
cases accompanied by extreme values of the parameters was obtained (Tables 1, 2).
94
Table 2. The part of anode formations accompanied by
extreme values of technological parameters (fragment).
Corner
Spikes Spikes Laggings Chunks
Parameter sheddings
KhAZ BoAZ BoAZ BrAZ
BrAZ
AlF3 dose (kg) - 0.46 0.58 0.79 0.84
Duration of pouring (sec) 0.60 0.45 0.47 - -
Dose of alumina (kg) 0.30 0.28 0.31 - -
Number of 0.72 0.46 0.51 0.41 0.33
Alumina doses (pcs)
Cryolite ratio 0.51 0.37 0.38 0.35 0.33
Min. distance (cm) - - - 0.99 0.99
Cell voltage (V) 0.24 0.32 0.42 0.26 0.27
Back EMF (V) 0.41 0.25 0.25 - -
Velocity ratio (kg/cm) 0.52 - - - -
Anode void (cm) - - - 0.66 0.60
Coefficient of anode-cathode 0.76 0.73 0.74 - -
distance (mV/s)
Anode consumption
rate (cm/day) - - - 0.51 0.40
CaF2 concentration (%) 0.33 0.37 0.35 0.46 0.46
MgF2 concentration (%) 0.36 - - 0.29 0.38
CPC temperature (C0) - - - 0.51 0.45
Electrolyte temperature (C0) 0.87 0.61 0.66 0.62 0.60
Aluminium level (cm) 0.51 0.56 0.56 0.33 0.37
Electrolyte level (cm) 0.44 0.44 0.53 0.67 0.63
In the table, highlighted parameters are most specific to a particular type of disorder
in the experimental area. Most of the “spikes” occurred at KhAZ while the values of
Electrolyte temperature or Coefficient of anode-cathode distance or Number of AlF3
doses were extremes. At BoAZ most of the “spikes” were accompanied by extreme
values of Coefficient of anode-cathode distance or Electrolyte temperature or
Aluminium level. Most of the “laggings” occurred at BoAZ while Coefficient of
anode-cathode distance or Electrolyte temperature or AlF3 dose were extremes. Most
of both the “corner sheddings” and the “chunks” occurred at BrAZ while Min.
distance or AlF3 dose or Electrolyte level were extremes.
Table 3. The part of anode effects
accompanied by extreme values of parameters (fragment).
Parameter KhAZ BoAZ BrAZ
AlF3 dose (kg) - 0.08 0.5
95
Duration of pouring (sec) 0.22 0.06 -
Dose of alumina (kg) 0.35 0.09 -
Number of Alumina doses (pcs) 0.25 0.08 0.17
Cryolite ratio 0.1 0.05 0.09
Min. distance (cm) - - 0.4
Cell voltage (V) 0.15 0.07 0.11
Back EMF (V) 0.34 0.15 -
Velocity ratio (kg/cm) 0.19 - -
Anode void (cm) - - 0.3
Coefficient of anode-cathode distance (mV/s) 0.29 0.15 -
Anode consumption rate (cm/day) - - 0.25
CaF2 concentration (%) 0.09 0.05 0.1
MgF2 concentration (%) 0.09 - 0.08
CPC temperature (C0 ) - - 0.38
Electrolyte temperature (C0 ) 0.3 0.11 0.25
Aluminium level (cm) 0.21 0.1 0.3
Electrolyte level (cm) 0.25 0.09 0.24
Most of the anode effects occurred at KhAZ while Dose of alumina or Back EMF or
Electrolyte temperature were extremes, at BoAZ most of the anode effects were
accompanied by extreme values of Counter EMF or Coefficient of anode-cathode
distance or Aluminium level, at BrAZ most of the anode effects were accompanied by
extreme values of AlF3 dose or Min. distance or CPC temperature.
In most cases, technological disorders occur when several parameters have extreme
values at the same time. The distribution of disorders events in the experimental areas
by the number of parameters with extreme values is shown below in Figures 1, 2, 3.
2000
1500 Anode effects
Case count
Spikes
1000
500
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Number of extreme parameters
Fig. 1. Distribution of disorders events by the number of parameters with extreme values
at KhAZ.
96
1400
Anode effects
1200
1000 Spikes
Case count
800 Laggings
600
400
200
0
1 2 3 4 5 6 7 8 9 10 11 12 13
Number of extreme parameters
Fig. 2. Distribution of disorders events by the number of parameters with extreme values
at BoAZ.
12000
Anode effects
10000
8000 Chunks
Case count
6000 Corner sheddings
4000
2000
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
Number of extreme parameters
Fig. 3. Distribution of disorders events by the number of parameters with extreme values at
BrAZ.
It can be seen from the diagrams that during the period of occurrence of disorders, as
a rule, outliers of values are observed for several parameters, but there are cases when
technological disorders occurred when the values of the parameters were within the
statistical norm. At the same time, the number and composition of parameters are
different for different types of disorders.
Moreover, regardless of the aluminum production technology, in the case of anode
effects, the number of parameters with extreme values is less than in the case of
formations on the anodes. So, KhAZ is characterized by extreme values of 2-4
parameters for the anode effect and 6-8 parameters for “spikes”; BoAZ is
characterized by extreme values of 1-2 parameters for the anode effect, 5-7
parameters for “lagging” and 4-6 parameters for “spikes”; BrAZ is characterized by
extreme values of 4-6 parameters in case of anode effects, 9-11 parameters in cases of
“corner shedding” and “chunks”.
Also, this research included the correlation analysis of parameters in part of
extreme values. The result demonstrated a quite strong relationship between the
following parameters: for KhAZ: Duration of pouring and Velocity ratio with
correlation coefficient -0.69, Amperage and Bus voltage with correlation coefficient
97
0.74; for BoAZ: Electrolyte temperature and Cryolite ratio with correlation
coefficient 0.91, Cell Voltage and Electrolyte level with correlation coefficient 0.82,
Cell Voltage and Number of Alumina doses with correlation coefficient -0.86; for
BrAZ: Distance from anode face and Anode void with correlation coefficient 0.96,
Number of Alumina doses and Time of Alumina doses with correlation coefficient 0.9,
Aluminium level and Electrolyte level with correlation coefficient -0.84.
The distribution of the number of parameters with extreme values and the number
of technological disorders for each cell showed that in most cases (75%-95%
depending on the disorder type) a large number of disorders are accompanied by a
large number of extreme values of technological parameters. Additionally, it was
possible to identify periods of everyday disorders with varying duration and to rank
the cells of the experimental areas by the number of technological disorders.
The analysis of detailed data made it possible to detect the dependence of the
number of technological disorders on the location of the anodes in the cells (Figures
4, 5). For instance, at KhAZ we can observe significantly more "spikes" on the anodes
of the front side of the cells – in its central part (anodes No. 9 and No. 10) and along
the edges (anodes No. 1 and No. 18). At BoAZ, in contrast, we can observe more
“spikes” and “laggings” on the anodes of the backside of the cells – mostly around the
edges (anodes No. 22, 23 and 33, 34).
500
400
300
200
100
0
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
Anod number
Fig. 4. Distribution of “spikes” cases by the cell anodes at KhAZ.
500
Spikes
400
Laggings
300
200
100
0
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
Anod number
Fig. 5. Distribution of “spikes” and “laggings” cases by the cell anodes at BoAZ.
98
Thus, as a result of a detailed analysis of the monitoring data in the context of extreme
values of the controlled technological parameters, the authors determined the
characteristic dependencies and features of the functioning of individual units of the
aluminum production complex in atypical operating modes.
3 Conclusion
In this paper, the authors carried out the analysis of the aluminum production process
disruptions in terms of the extreme values of the technological parameters for three
experimental areas: KhAZ, BoAZ and BrAZ.
The analysis of the statistical characteristics showed that the cells, in the context
of the same parameters, differ significantly both in the average values of the
parameters and in their deviation intervals. Within the study, for each experimental
area, the parameters with the largest percent of anomalies were determined. The
distribution of disorders events (in the cases of anode effects and formations on the
anode face) by the number of parameters with extreme values was obtained. The
results showed that in most cases (75%-95%) a large number of disorders are
accompanied by a large number of extreme values of technological parameters. The
analysis of detailed data revealed the dependence of the number of technological
disorders on the location of the anodes in the cells.
Thus, the results of research allowed us to obtain new knowledge about
technological disorders and features of abnormal working states of aluminum
reduction cells, detect the technological patterns, conditions and causes for the
occurrence of technological disorders.
References
1. Maurits A.A.: Methods for analytical control in non-ferrous metallurgy: Leadership.
Metallurgiya, Moscow (1980)
2. Baimakov Yu.V., Kontorovich Ya.E. (eds.): Metallurgist’s Handbook of Nonferrous
Metals. Production of Aluminium. Metallurgiya, Moscow (1971)
3. Mikhalev Yu.G., Polyakov P.V., Yasinsky A.S, Polyakov A.A.: Spikes generation on
anode of aluminium reduction cell. Tsvetnye Metally 9, 43–48 (2018). doi:
10.17580/tsm.2018.09.06
4. Sadler B.A.: Critical issues in anode production and quality to avoid anode performance
problems. Journal of Siberian Federal University. Engineering & Technologies 5(8), 546–
568 (2015). doi: 0.17516/1999-494X-2015-8-5-546-568
5. Mikhalev Yu.G., Polyakov P.V., Yasinsky A.S., Shakhrai S.G., Bezrukikh A.I., Zavadyak
A.V.: Causes of technological disturbances during anode processes. Review of research by
Russian and foreign experimenters. Journal of Siberian Federal University. Technics and
technology 10(5), 593–606 (2017). doi: 10.17516/1999-494X-2017-10-5-593-606
6. Zavadyak, A.V., Puzanov, I.I., Tretyakov, Ya.A., Morozov, M.M., Makeev, A.V.,
Pianykh, A.A.: Mathematical modeling of the impact of anode bottom problems of the
anode current distribution high current electrolyzer. J. Sib. Fed. Univ. Eng. technol. 10(7).
862–873 (2017). doi: 10.17516/1999-494X-2017-10-7-862-873