To validate the simulator, four different alumina feeding scenarios were generated in the cell to create alumina concentration gradients in the electrolytic bath. For all scenarios, the alumina supplied to the cell was reduced by turning off certain feeders. However, the position of the stopped feeders was changed depending on the scenarios. During these test, individual anode current was monitored continuously with the system provided by the aluminum smelter. Furthermore, the cell’s gas composition was also continuously monitored and bath samples were taken periodically for analysis of the alumina concentration. The four different scenarios investigated are presented in Table II. These scenarios were designed to deplete the overall alumina concentration in order to obtain LVAE in cells, within a reasonable amount of time (approximately 1 hour) while avoiding HVAE for as long as possible. The different locations of the feeders were chosen to assure that the model was consistent for symmetric and asymmetric scenarios. Finally, in the case of scenarios 1 and 4, the tests had to be slightly modified to avoid disrupting the normal operations of the electrolysis process (i.e., anode change schedule).
Following the Alumina Concentration
To investigate the change in alumina concentration within the cells, samples of the electrolytic bath were extracted periodically in six different positions illustrated in Figure 9 using a sampling probe to obtain conical shaped samples. Prior to every test (t = 0), one sample in each of the six locations was extracted to determine the initial conditions for the simulator. Then, the time lapse between each sampling was always 10 minutes or less and three samples were always taken almost simultaneously (± 30 s). The three samples were extracted in order to obtain information on the concentration gradient along the longitudinal direction (x-axis) of the cell. In alternation, the 3 samples along the x-axis of the cell were taken on opposite sides of the y-axis. Analysis of the alumina concentration was performed on site at the Alouette smelter’s laboratory using the ALCAN method.
Gas Composition Monitoring
The gas was extracted using stainless steel sampling probes directly from each of the five inner ducts that route the gas from the different parts of the cell up to the main exhaust duct in order to well represent the overall cell. The flow rate of each probe was regulated to 1 LPM and the total gas flow (5 LPM) was routed to a GASMET™ DX-4000 FTIR (Fourier Transformed Infra-Red) spectrometer using a Peltier-cooled mercury-cadmium-telluride detector (sample cell path: 9.8 m, volume: 0.5 L, resolution: 7.8 cm−1). The gas stream was sent sequentially through the desiccant, activated alumina, a 5-mm filter, and finally a 2-mm filter to remove traces of water, hydrogen fluoride, and dust, respectively, for the protection of the measuring equipment. The gas was preheated to 120 °C before entering the FTIR and concentration measurements were performed at a rate of 10 scans per second. Average values for 20 second periods were recorded. The background spectrum was redefined using high-purity nitrogen prior to every test.
Validation of the Cell Voltage
A first element to be evaluated is the accuracy of the simulator to reproduce the behavior of the overall cell voltage; therefore, a scenario with different feeding periods was observed in the real electrolysis cell and reproduced in the simulator. As illustrated in Figure 10, the long-term tendencies of typical voltage variations in cell voltage provoked during different feeding periods can be adequately represented by the simulator. However, the mathematical model was not designed to represent cell events with a higher frequency such as the movement of the bubbles or cell instabilities caused by the movement of the metal pad or anode incidents (spikes). Additionally, some discrete events like the movement of the crust breaker can also perturb the cell voltage as it locally and randomly increases the gas flow out of the electrolytic bath. Despite neglecting certain events, changes in the global ACD of the cell can be represented well with the simulator. However, the actual movement of the anode beam had to be approximated in this study. More detailed results can be expected if the anode beam movements were monitored and measured precisely in terms of “distance traveled (mm)” instead of “total time of travel (s).”
Validation of the Alumina Distribution
As mentioned previously, four scenarios were planned and the deviations between the simulated results and data series taken from the monitored cell were evaluated. The results of the simulation with the initial hypotheses discussed in the previous section of this paper are presented in Figures 11(a) through (d). For each figure, the data resulting from the simulator are the average alumina concentration predicted for each element of the cell. Hence, the blue line represents the average for the duct end side, the red dashed line is the average for the center region, and the black dotted line is the average concentration on the tapping side. The results demonstrate that the general behavior is well represented by the simulator. However, there is more uniformity across the different regions of the cell in the simulator than there is in the real cell. This lack of agreement can be caused by a too strong coupling (high value of the exchange factor) between the different elementary volumes. This exchange factor is primarily dependent on the bath velocity in the cell. As a matter of fact, the average bath velocity from the simulator was estimated using the results of an external study. Even if the study performed by Hofer had many similarities with the investigated cells, some differences may cause the actual bath velocity to be smaller than predicted numerically. For this reason, an optimization was performed using the data collected during the validation, and as a result, a reduction of 60 pct was imposed on the bath velocity used in the simulator. The corrected results are presented in Figures 11(e) through (h).
The corrected results demonstrate a better agreement with the real measurements, especially in the cases where there was an asymmetry in the alumina feeding (cases 1 and 3). Under these scenarios, the measured alumina concentration shows a higher range of values. Therefore, as the corrected bath velocity reduces the overall cell mixing, it describes better the cell’s inhomogeneity leading to a closer agreement between the simulator and the reality. In its current state, the simulator cannot reproduce the unpredictable fluctuations in the alumina additions coming from random sources such as the effect of anode cover material or the recuperation of alumina from the sludge located below in the aluminum pad. Important instabilities in the cell (e.g., during high-voltage anode effect) may provoke significant reoxidation of the aluminum metal pad for short amount of time, which, in turn may generate significant alumina additions to the electrolytic bath. This phenomenon was previously observed by the authors when the cell’s conditions reach LVAE or HVAE. In the current study, we can observe this phenomenon at the end of scenario #3 where there is a significant increase (half a percent) in the alumina concentration at both extremities of the cell even if no additional alumina feeding was provided by the cell’s feeders.
Validation of the Standard Deviation Among Individual Anode Currents and Validation of PFC Emissions
After optimization of the bath velocity, the efficiency of the simulator to predict LVAE was investigated using the standard deviation among the simulated individual anode currents. The evolution of this parameter for the four validation scenarios is presented in Figure 12, along with the standard deviation of the measured individual anode currents in the real cell. The measured concentration values of the CF4 gas extracted at the duct end of the cell are also shown. The calculated standard deviations were normalized with respect to the average value of these respective parameters under normal behavior. Moreover, the measured standard deviation in the real cell showed an important noise level. For this reason, the results were smoothed using a moving average of 20 seconds. Finally, the vertical arrow in each figure represents the instant where the standard deviation threshold value was reached according to the simulator.
The evolution of the simulated standard deviation correlates strongly with the observed PFC emissions for all the scenarios investigated, demonstrating that this indicator can be used to predict occurrence of low-voltage anode effects. Interestingly, the simulator’s correlation with the CF4 emissions is stronger than the actual measured standard deviation. In all cases, the increase in standard deviation predicted by the simulator is obtained earlier or at the same time as the increase in standard deviation measured by the individual current monitoring tool. This curious behavior is mainly due to some elements discussed previously that are not considered in the simulator like cell instabilities or variations in the local value of ACD. For this reason, the real cell is less sensitive than the simulator to the variations that are caused strictly by different alumina concentrations in the cell. This observation reveals that the simulator is capable to reveal information that cannot even be observed on an operating electrolysis cell, thus making it a very effective and sensitive tool to predict low-voltage anode effects.
Further Improvements and Potential of the Simulator
The simulator satisfies its original goal as to “correctly simulate temporal and spatial variations of the alumina distribution in an electrolysis cell in order to predict low voltage anode effects.” Further improvements can be achieved of the simulator to enhance its performance and provide more detailed results and useful information for improving the electrolysis process. Some of these improvements are as follows:
Improvement of the fidelity of the mass exchange pattern and bath velocity flow based on the specific cell technology.
Introduction of metal pad instabilities that influence the local ACD in time.
Current efficiency should vary in time depending on the actual state of the process, leading to higher rate of metal reoxidation during LVAE or HVAE.
Introducing a sink/source of alumina to represent the formation and dissipation of sludge below the metal pad.
Incorporation of the energy balance in the mathematical model to follow the formation of hot or cold regions leading to different dissolution efficiencies.
However, even in its current state, the simulator can be used efficiently to investigate and improve some elements of the electrolysis process as demonstrated in the next section.
Using the Simulator to Improve the Electrolysis Cell Process
Using the alumina distribution simulator can also be beneficial to increase the understanding of some elements influencing the electrolysis process, which might lead to improvements of the cell stability and increased metal production. Investigations are presented in the next sections of this paper and the described results provide useful information as well as possible refinements on the electrolysis process.
Analysis of the effect of the conductors
Due to the important size of the electrolysis cell, the electrical network carrying the current is similar but not exactly the same depending on the anode positions. Therefore, the anticipated difference in current can be evaluated by the simulator. Figure 13 illustrates the current distribution in a cell with identical anode assemblies, i.e., with the same carbon height. Therefore, only the slight differences of the electrical resistance network prior to the anode rods will influence the current distribution. It is possible to observe a difference of 375 A between the anodes driving the most and less current only due to the differences in the electrical resistance network for an ACD of 25 mm. As the ACD increases to 40 mm, the difference between the different anodes diminishes due to the relatively high resistivity of the electrolyte. Interestingly, the figure demonstrates that when the ACD increases, the current redistributes from the region driving the most current (upstream center) to the regions that were driving the less current (downstream extremities). Consequently, only minimal change in current can be observed in the regions of the cell where the current was already close to the theoretical average current (19.75 kA).
However, the case simulated in Figure 13 is unrealistic due to the continuous anode changes that occur in a cell, leading to different heights in carbon, hence different resistances. Figure 14 is more representative of the predicted current distribution during normal operation due to the different states of carbon consumption of the individual anode assemblies. In this case, a change in ACD will lead to a similar behavior but its effect is amplified. In Figure 13, a 15-mm change in ACD leads to a maximum change in individual current of 75 A. However, a similar change in ACD with different carbon heights leads to a maximum change of 384 A. Therefore, the effect of the ACD on the local anode current can easily reach 1.3 pct/cm. This value appears insignificant for a small change in the global ACD. However, if the ACD is not consistent for the entire cell due to a deformed metal pad, the localized differences can be even more important.
On the other hand, the effect of the carbon erosion is extremely important on the individual anode current. The results demonstrate that the last anode that was changedFootnote 1 in the cell drives less current, due to a higher resistance caused by the carbon, while the next anode to be changed is among the ones driving the most current. In this scenario, the overall difference between the anodes driving the most and less current is 2.9 kA (Figure 14). This difference represents nearly 15 pct of the average anode assembly current. For this reason, a study of the anode change cycle would be beneficial to evaluate if areas of the cell can be more propitious to increased cell current during specific periods, which could lead to higher risk of alumina depletion and ultimately leading to LVAE or HVAE.
Improvement of the feeding strategy
In scenario illustrated in Figure 15, operations under normal behavior were simulated with constant feeding from each feeder with feeding periods similar to those of a real electrolysis cell. It can be observed that there is a constant irregularity in the alumina concentration of the different zones. As expected, the zones closest to the center of the cell are constantly richer in alumina than the extremities. However, the two extremities are also different from each other due to the absence of symmetry in the alumina exchange caused by the cell-scale MHD convective loops. Therefore, at the end of the underfeeding periods, the zones at the duct end of the cell tend to have a very low alumina concentration. Knowing that these zones are not as uniform as the rest of the cell, various measures can be put into place to improve the uniformity of the alumina concentration. A plausible solution to this problem could be to increase the total amount of alumina fed by the specific feeder in this region to lean toward a more uniform distribution. However, considerations, such as changing the point feeder’s action pattern, should also be taken into account to assure that this additional alumina dissolves properly in order to avoid blocking the feeder and generating more problems.
The results also clearly demonstrate that some regions of the cell receive alumina from multiple sources. In the zoomed part of Figure 15, we can see that the alumina concentration of some region varies with the feeding from the adjacent feeder as well as feedings from a distant feeder. It is possible to observe this behavior due to the transfer of undissolved alumina from one region to another. The opposite behavior can also be observed in the corner regions of the cell where a smaller amount of undissolved alumina is distributed, which leads to smaller amplitude in the variation of the alumina concentrations. Consequently, these regions are more likely to be at low level of alumina concentration.
Analysis of the feeder stoppage after an anode change
It is a common practice to stop for a significant period any feeders next to an anode that has undergone a procedure such as an anode change or anode covering. During these events, an important amount of alumina can be injected into the electrolyte by the anode cover material, which is a mix of crushed electrolytic bath and alumina. For this reason, the feeders are generally stopped until this parasitic alumina is dissolved and consumed. However, stopping a feeder too long can have a negative impact on the cell behavior. Using the simulator, different scenarios were investigated to provide information in order to determine the ideal duration of the stoppage depending on the smelter’s specific amount of alumina added during the procedure. The results of this investigation are presented in Figure 16.
The alumina concentrations shown in the figure represent the average alumina concentration of the four zones adjacent to the stopped alumina feeder for different stoppage times and different amounts of parasite alumina fed. While a specific feeder is stopped under these circumstances, the feeding periods of the three active feeders are shortened in order to maintain the theoretical feeding rate necessary for the cell. Finally, based on the result from the previous section, the anode changed in the simulation is anode #2, which is located in the critical section of the cell (close to feeder 4) in order to illustrate the worst-case scenario for risks of anode effects.
The results presented in Figure 16 demonstrate that it is possible to determine precisely the correct duration of time that a specific feeder should be stopped if parasite alumina is anticipated, to avoid depletion of alumina, leading to anode effects or to avoid extra feeding that could lead to sludge generation. However, to use correctly this tool, a partnership with the electrolysis team (technician and engineers) is necessary to assure that the estimated parasite alumina feeding is representative of the real conditions.
Finally, the results also demonstrate that if the correct amount of alumina is provided to the cell from distant feeders, the area around the stopped feeder will eventually reach a new equilibrium concentration. In the case where no parasite alumina was provided, this new concentration is approximately 1 pct wt lower than the original cell concentration. For this reason, the risks of reaching anode effect conditions while stopping a single feeder should be minimal if the average alumina concentration was close to 3 pct prior to the stoppage. However, it is assumed that the other feeders perform optimally and that 100 pct of the dosed alumina eventually reaches the electrolysis bath, which might not always be the case, especially with an increased alumina feeding rate.