Testing a new STARprobe^TM Measurement Based Ratio Control Algorithm Using a Dynamic

Cell Simulator

M. Dupuis, Jonquière, Jean-Pierre Gagné, Chicoutimi, Pascal Côté, Chicoutimi

In addition to the two main tasks an aluminium reduction cell controller has to perform, namely to

keep both the dissolved alumina concentration in the bath and the anode cathode distance (ACD)

under tight control [1], modern cell controllers are also in charge of keeping the bath ratio (or

excess AlF₃) concentration under control.

This task has proven to be quite challenging despite the fact that, at first glance at least, it looks

quite straightforward. Fluoride evolves out of the cell; a big fraction of that fluoride is captured by

the fresh alumina in the scrubber and returns to the cell as part of the secondary alumina feed to it.

The part that is not returning to the cell must be compensated by direct AlF₃ feeding in order to keep

constant the bath ratio in the cell. The cell controller performs that task using feedback control

algorithms based on regular measurements performed by cell operators.

Recently Alcoa has develop a revolutionary new technology to measured bath ratio in potroom

almost as quickly as you can measure bath temperature [2,3]. Furthermore, in addition of the excess

AlF₃ concentration, the new STARprobe^TM can also measures the bath temperature, the dissolved

alumina concentration and the cell superheat. That last information can be use as part of the cell

control logic as previously presented in [4] per example.

GeniSim’s Dyna/Marc dynamic aluminium reduction cell simulator cell simulator has been used to

compare the efficiency of the traditional combined bath sample/XRD analysis and bath temperature

measurement bath ratio control logic and a new control algorithm based on STARprobes^TM excess

AlF₃ concentration and superheat measurements.

Performing the AlF₃ mass balance

Using a 300 kA cell as example, the fluoride mass balance can be performed as follows. Fluoride

evolved out of the cell at a rate dictated by many factors like the bath ratio and temperature and the

state of the anode cover [5]. In the current example, the fluoride evolution rate is calculated to be

33.6 kg F / T Al with the cell conditions selected, namely 10% excess AlF₃, 970 °C and a good

anode cover. For a 300 kA cell producing 94.7 kg Al / hr, this represents the equivalent of 4.7 kg of

AlF₃ that evolves out of the cell and hence must be replaced each hour.

Assuming that after taking into consideration the neutralization of some of the fluoride absorbed by

the fresh alumina in the scrubber by the sodium already present into it, the equivalent of 3.6 kg /hr

of AlF₃ is fed back to the cell by the secondary alumina (on average or at the nominal 100% alumina

feeding rate). This leaves 1.1 kg /hr of AlF₃ that must be directly fed using a point breaker feeder

(PBF) under the supervision of the cell controller.

Considering that the cell contains close to 8 tons of bath and hence about 800 kg of excess AlF₃,

this means that if the direct AlF₃ is completely stopped for some reason, it would take about 72

hours for the mass of excess AlF₃ to be reduced by 80 kg and hence the bath excess AlF₃

concentration to drop by 1% to 9%. Considering that relatively slow response time of the cell, it

should be rather easy to keep the excess AlF₃ concentration under tight control, but since it is clearly

not the case in the great majority of smelters, some other factors must be complicating things.

Daily operations influence on bath ratio: In the above mass balance calculation, about 75% of the

AlF₃ is fed back to the cell as part of the alumina feeding, but in modern continuous tracking control

logic, the alumina is never fed at the nominal 100% rate to the cell. As a result, the excess AlF₃

concentration is evolving up and down according to the feeding cycle. The direct additions are also

performed in discrete events, per example 2 kg every 110 minutes, in order to average 1.1 kg / hr

per example. Those discrete additions also influence the short term variation of the excess AlF₃

concentration.

In addition of the irregular AlF₃ addition, the excess AlF₃ concentration variation is influenced by

thermal events affecting the AlF₃ evolution like the bath temperature but more importantly by the

ledge thickness variation: as ledge is mostly pure cryolite, ledge formation increases the

concentration of the excess AlF₃. Ledge formation occurs after anode change events per example.

Figure 1 shows the daily variation of the concentration of AlF₃in the bath in absence of control and

any AlF₃mass imbalance. The standard deviation on the average value is about 0.1%.

Sampling frequency and delayed XRD results: The next factor complicating things is the fact

that assessing the bath chemistry required manual interventions. The traditional way of proceeding

requires manual bath sampling, manual processing of the bath samples, at best semi-automatic

analysis of the bath samples by a XRD instrument and manual input of the results in a database

accessible to cell controllers. Considering the cost of a XRD analysis, it is typical to take bath

sample every second day and to get results at 8 to 24 hours after the actual bath sampling.

Figure 2 shows the variation of the AlF₃ for a period of 20 days again without control and any mass

imbalance. Figure 3 shows the results of the bath sampling performed once a day always at the same

time. The delay between the actual sampling and the time the results of the analysis becomes

available is clearly not an issue when the concentration is drifting very slowly. Yet any delay in the

feedback response can cause instability depending on the controller setup.

So far, despite the daily events “process noise”, the sparse sampling frequency and the delay in

getting the sampling analysis results, considering the very slow cell response time, there is no

serious problem that would explain why it is so difficult to control the bath ratio.

Bath sampling noise problem: But, a new problem affecting bath ratio control has recently been

identified: it is the bath sampling noise due to the fact that the bath is far from being homogeneous

[6]. The standard deviation of that bath sampling noise has been evaluated to be around 0.5 %

which is 5 times greater than the process noise generated by daily events. That bath sampling noise,

contrary to the daily events noise is completely unpredictable. Figure 4 shows the results of bath

sampling performed on the 20 days period presented in Figure 2 when a 0.5% white noise is added

to the noise free results presented in Figure 3.

Simulated process response using standard control without any process perturbation:

Now, we want to test a typical control logic where both the delayed XRD analysis from bath

samples and measured bath temperature are used to correct the direct AlF₃ feeding rate as it is

commonly done in the industry these days. The proportional band was set to 0.5 kg/hr% for the 24

hours delayed bath XRD analysis results and -0.1 kg/hr°C for the bath temperature measurement.

The bath sampling and the temperature measurement are done simultaneously every 24 hours.

A bath sampling noise having a standard deviation of 0.5% have been added to the XRD analysis

results following observation recently reported [6]. For the temperature measurement, a bath

sampling noise having a standard deviation of 2.5 °C have been added as reported in [6].

Figure 5 present the obtained results in the dynamic cell simulator for a period of 100 days. The top

graph presents the results obtained without any control in the absence of process perturbation. The

initial bump is an indication that the steady state conditions used as initial transient conditions are

not 100% representative of the long term pseudo steady state conditions.

The bottom graph presents the results obtained with feedback control active. Unfortunately, it is not

as good as the results without control indicating that the bath sampling noise combined with the 24

delay in the bath sampling analysis result is affecting the stability of this feedback control loop.

Simulated process response using standard control with a significant process perturbation:

In order to more seriously test the stability of the feedback control loop, a major perturbation is

added to the simulation. On day 14, about half of the anodes cover material is removed in doing so

increasing the anode panel heat loss by about 30 kW from 230 kW to 260 kW. As we can see in

figure 6, as a natural response, the cell must reduce its cathode heat loss of the same amount by

reducing its superheat by about 1 °C and increasing its ledge thickness by about 5 cm. As a result, of

this extra ledge formation, the excess AlF₃ increase by about 2% and remains close to 12% if the

direct AlF₃ additions remain unchanged.

This is clearly a case where some feedback control is required. Figure 7 presents the results obtained

using the standard control described above. After the change of superheat, the 970 °C temperature

target is no longer compatible with the 10% excess AlF₃ target, this combined with the 1 day offset

between the AlF₃ feedback and the temperature feedback generates a cyclic response characteristic

of somewhat unstable feedback control. That type of oscillations with a wave length of about 20

days and an amplitude of about 2.5% are very common place in smelters. Those undesired

oscillations occur despite the fact that the values of the proportional constants were careful selected

in order to avoid feedback loop instabilities.

The new STARprobe^TM

The STARprobe^TM is a portable device that takes real-time measurements of bath properties, such

as Superheat, Temperature, Alumina concentration and bath Ratio or acidity (STAR), in electrolysis

cells. This synchronicity of measurements is a most important step forward in improving the control

and efficiency of electrolysis cells. It unites the conventional processes of temperature measurement

and bath sampling-analysis into one online measurement, simplifies and greatly shortens the process

and time space from measurement/sampling to pot control decision. The pot control decision can

therefore be based on the real time cell conditions rather than those from few hours ago or from as

long as 24 hours ago.

This integrated real-time measurement system consists of four major components:

•

Reusable probe tip

•

Portable stand to fit various pot configurations

•

Electronics for data acquisition and analysis, and wireless communications for data transfer

•

Tablet PC with programs to perform all necessary tasks during measurements.

Considering the great advantages of the STARprobe^TM, Alcoa has decided to share the technology

with the rest of the aluminium industry starting from 2012. In this regard, Alcoa has just appointed

STAS, a well recognized leader in the aluminium industry (www.stas.com ), to commercialize the

new STARprobe^TM analyzing system.

Simulated process response using STARprobe^TM measurements based control with a

significant process perturbation: The exact same major perturbation is use to test the efficiency of

a STARprobe^TM measurements based feedback control loop. The same 1 day measurement

frequency is used and the same 0.5 kg/hr% proportional constant for the AlF₃ feedback loop.

Obviously in this case however, the measurement results are available without delay. In addition,

the measured superheat is also used in a separate feedback loop where the target cell resistance is

adjusted based on the offset between the target superheat and the measured superheat.

The measured superheat is also affected by a very significant bath sampling noise. That bath

sampling noise was estimated to have a standard deviation of about 2°C in [6] so a 2°C standard

deviation white sampling noise was added to the simulation.

The obtained results are presented in the figure 8. We can see that the response to the perturbation is

slower than in the previous case because there is no longer a correction based on the temperature

offset and a ±1 °C deadband on the superheat target was used in order not to wrongly respond to

the noise in the superheat measurements. Yet after a 25 days transient response to the perturbation,

the excess AlF₃ concentration goes back to its target value and remains on target without

oscillations after that.

Figure 9 shows the evolution of the target cell resistance, a result, a 0.01 micro-ohm correction on

the target cell resistance was apply each for 15 days for a total 0.15 micro-ohm correction after a

delay of 6 days. This 0.15 micro-ohm “permanent” correction ensure that the superheat remains

within the 3.5 °C to 5.5 °C range despite the fact that the anode panel now dissipates an extra 30

kW of heat loss.

Conclusions

This study demonstrates study the value of using a dynamic cell simulator to optimize existing cell

controller algorithms or to test new ones without putting real cells at risk. The Dyna/Marc cell

simulator used in this study is available to the whole aluminium industry through GeniSim Inc.

Version 14 supports adding the observed bath sampling noise to the AlF₃ measurements and using

STARprobe^TMmeasurements instead of bath samples/XRD analysis to perform bath ratio control.

A new control logic scheme based on independent control of the excess AlF₃ and the cell superheat

made possible with the revolutionary new STARprobe^TM measurement tool was demonstrated to be

superior to the standard single feedback control loop using two target variables namely the excess

AlF₃ and the operating temperature to control a single control action namely the direct AlF₃

additions.

The STARprobe^TM developed by Alcoa [2,3] is now available to the hole aluminium industry

through STAS (http://www.stas.com/en/starprobetm.html ).

References

[1] M. Dupuis, Testing cell controller algorithms using a dynamic cell simulator, ALUMINIUM • 1-

2/2012, (2012), 50-55.

[2] Wang, X., Hosler, B. and Tarcy, G. Alcoa STARprobe^TM, Light Metals, (2011), pp 483-489

[3] Wang, X., Tarcy, G., Batista, E. and Wood, G. Active pot control using Alcoa STARprobe^TM,

Light Metals, (2011), pp 491-496

[4] T. Rieck, M. Iffert, P.White, R. Rodrigo and R. Kelchtermans, Increased Current Efficiency and

Reduced Energy Consumption at the TRIMET Smelter Essen using 9 Box Matrix Control, Light

Metals, (2003), pp 449-456.

[5] W. Haupin, H. Kvande, Mathematical Model of Fluoride Evolution from Hall-Héroult Cells,

Proceedings from the International Jomar Thonstad Symposium, ed. by A. Solheim and G. M.

Haarberg, Trondheim, Norway, October 16 - 18, (2002), 53 - 65.

[6] M. Dupuis, P. Bouchard and J. P. Gagné, Measuring bath properties using the STARprobe^TM ,

19th International ICSOBA Symposium (2012), to be published.

Author

Dr. Marc Dupuis is a consultant specialized in the applications of mathematical modeling for the

aluminium industry since 1994, the year when he founded his own consulting company GeniSim Inc

(www.genisim.com). Before that, he graduated with a Ph.D. in chemical engineering from Laval

University in Quebec City in 1984, and then worked ten years as a research engineer for Alcan

International. His main research interests are the development of mathematical models of the Hall-

Héroult cell dealing with the thermo-electric, thermo-mechanic, electro-magnetic and hydrodynamic

aspects of the problem. He was also involved in the design of experimental high amperage cells and

the retrofit of many existing cell technologies.

Jean-Pierre Gagné is the Director of the ALCOA - STAS R&D Team. Master degree in Active

Control of Vibrations, I obtained in 1993. Since 1997, I am working for STAS mainly in R&D

projects. STAS is a company specialised in the design, fabrication and manufacturing of equipment

for the aluminum industry as well as in commercialisation of new technologically advanced

equipment. STAS is located in the heart of Quebec, Canada, where more than 10% of the world's

primary aluminum is produced in the most technologically advanced plants.

Pascal Côté is the Director of the Development & Innovation group for STAS. Professional

engineer specialized in the development of new technologies for the aluminium industry. Member

of the International scientific committee on processing of semi-solid alloys and composites.