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Graduate School of Business

Aluminium Smelting and Communities of Practice

by

Miles G Nicholls



This presentation is based on a paper delivered to the 36th Meeting of the Decision Sciences Institute in San Francisco, USA, November 19th – 22nd, 2005:

“The Role of Communities of Practice in Determining Best Practice in Production Processes Involving ‘Alchemy’ – A Mixed-

mode Modelling Approach”

Miles G Nicholls Barbara J CargillGraduate School of Business Faculty of Business and EnterpriseRMIT University Swinburne University of Technology



The Portland Aluminium Smelter

Initially a consulting project

The aim was to model the entire smelter with a view to determining ‘best practice’ (commenced 1988 – finished 1998)

The smelter is an approximately $2b plant, a green-field 250 acre site near Portland, Victoria, Australia (largest smelter in Southern Hemisphere)

Unique management structures operating and providing an unusual occurrence of a ‘real’ bi-level model.



Problem Summary

The smelter takes in raw materials (primarily alumina, coke and

pitch) and places them in a “bath” of chemicals in a special “pot”.

Large quantities of electricity are then passed through the bath

via anodes (blocks of carbon) suspended in the bath (the cathodes

are underneath). The molten aluminium then collects at the bottom

of the pot and is syphoned off.

The task was to model the entire plant in order to “maximise

production of aluminium and keep ancillary plant activities to a

minimum”.



The Site



The Process



The Monthly Mathematical Model



The Monthly Mathematical Model

Only two variables, kilo Amperes (kA) and Setting Cycle (SC)

(1.1) relates to all kA based raw material consumption

(1.2) relates to all SC based raw material consumption

(1.3) ensures the anode will not be ‘overused’

(1.4) relates to kA and SC based consumption of Coke

(1.5) and (1.6) are maximum and minimum limits of ‘spent’ anodes that are used in the making of new anodes

kA and SC are typically 300 and 28 respectively

The multiple month model more complicated and involves more variables.



The ‘Behind the Scenes’ Link

Theoretically, the production of aluminium is calculated thus:

ATt = 0.008052 kAt PDt (2)

Estimated output is arrived at as follows:

AEt = 0.008052 kAt CEt-1 PDt (3)where:

CEt-1 = ASt-1 / ATt-1 (4)

and ASt is the actual aluminium siphoned from the pot

Note that AEt and CEt-1 are averages over all pots and that every constraint (other than (1.2)) contains CEt-1



Real World Problems

The Current Efficiency (CE), a key parameter, has three major problems :

it is a lagged estimation (CEt-1)

it is variable and often highly unpredictable on a pot to pot basis (limited success in modelling pots in a ‘real world’, ‘on- line’ and in a real time mode – thus no accurate CE)

it is inaccurate because the actual production of aluminium cannot be measured (other than in a laboratory pot)

Therefore the solution of the model provides a “best practice” solution that could be some distance from the “optimal”



The ‘Alchemy’

Pots are very individual in their behaviour. If they run too hot, or drop to too low a DC voltage they will become less productive and may take many days to recover

Pots often suffer an ‘anode effect’ which is a disaster for a pot. This effect sees molten aluminium behaving like a wave and this disrupts productivity, often for many days

Power is frequently turned off by the suppliers on agreed ‘power outages’ arrangements and this also destabilises the pot

All these problems are left to the ‘Operators’ to deal with in their own way based on their ‘experience’ and ‘shared knowledge’. This is the ‘alchemy’.



Communities of Practice

Aluminium smelting has been around since the late 1890’s and much “folk lore” has built up over that time as to how to deal with pots

To some extent the smelter’s Operators had already formed a ‘Community of Practice’, but it was very tenuous and fledgling

With encouragement, such a Community of Practice could potentially save millions (a 1% increase in CE could mean a $2m - $5m increase in the bottom line depending on the size of the smelter)

Communities of Practice are not only limited to ‘pot rooms’ in a smelter, but it is the point of biggest impact




Communities of Practice (C of P) are essentially informal groups of

people in a single organization (as in the case of ‘commercial in

confidence’ operations of smelters and other manufacturing entities)

or in more ‘common knowledge’ areas, across organizations as well.

As Louis (2005), Davies et al (2003) and Burk (2000) indicate, the C

of P are founded on common knowledge or common work tasks

where the coming together of people to share stories and discuss

work practices (i.e., sharing explicit and tacit knowledge) gives

support, knowledge and a sense of belonging to people. These

interactions can occur in a real or virtual sense depending on the

nature of the organization and whether the C of P exists beyond one

such work site.




Communities of Practice (C of P) in this paper are classified as a hybrid of ‘best practice’ and ‘knowledge stewarding’ (Vestal, 2003)

Membership of C of P is not necessarily fixed but may vary considerably (as will its leadership)

The aluminium smelting industry is ideally suited to the informal emergence of C of P which are essentially (in this instance) a sharing of experiences of the pot room Operators

Anecdotally, at least one C of P was in existence within the Portland smelter (Urpani, 1996)




It is clear, where there exists aspects of the production process (i.e., the sub-process) that are not well understood and for which a ‘model’ can’t be constructed (‘soft’ or ‘hard’) in order to determine the ‘best practice’ operation of the process, a combined approach of ‘hard’ modelling coupled with a C of P associated with the sub-process will provide the focus, knowledge improvement and long term better understanding of the operations

Further, it should be noted that a critical value of the C of P is the learning loop for the perpetuation of knowledge in the operators over time. Unless there is some process in place that nurtures the C of P and also attempts to bring the tacit knowledge into more explicit forms, there is a risk to the enterprise of knowledge loss over time



Mixed-mode Modelling



The Solution Methodology

The solution approach used for this problem is illustrated in Figure 3 and is termed ‘mixed-mode modelling’

Mixed-mode modelling in this application sees the combination of hard and soft models ‘solved’ by a heuristic involving C of P and non-linear bi-level programming (for the multiple month model)

Employing this approach will not only see an increase in the ‘bottom line’ of the business but also a retention of the bodies of explicit and tacit knowledge possessed by the Operators and thus the company



The Solution Methodology



Conclusion

Many problems in OR/MS appear ‘crisp’ and their parameters seem robust and meaningful

However, in many industries, the key process parameters and indeed parts of the processes themselves are not really understood or able to be reliably estimated or codified respectively. The aluminium smelting industry is such an example

This paper suggests a mixed mode modelling approach (combining Communities of Practice and mathematical programming) to arrive at best practice and also perpetuate the existence of essential explicit and tacit knowledge within the aluminium smelter



Conclusion – Gains

A decrease in the variability of a pot’s production

An increase in the quality (purity) the aluminium produced

Better behaved pots and a consequential more even temperature leading towards less build up of bath around the sides of the pot leading to a more accurate estimation of a pots production

A reduction in pot disturbances leading to the greater reliability is any estimates of production obtained from it

Consequential increases in current efficiency (which is also a more reliable ‘guestimated’ figure)

Providing a more realistic basis for determining best practice from the solution of the macro model of the smelter

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