Operator Skill & Strategy Identification in Process Industry

Doc. student research seminar4.4.2011Janne Pietilä

Objective

• Despite a high level of automation, the human operator nevertheless has a significant role in controlling industrial processes

• The objective is to survey the performance and operating practices of different operators, using data-based analysis methods

• The industrial plant in case is a flotation process of the Pyhäsalmi mine in central Finland

• Results are useful in e.g. operator training or transfer of latent knowledge

The Process

• The copper flotation process of the Pyhäsalmi mine – a complicated process, whose state is difficult to measure– a relatively high level of automation– the operator’s expertise and insight significantly affect the efficiency of

the process• The control variables and setpoints

– the air feeds and froth thicknesses and the chemical addition rates are the most significant

• Measurements– levels of the slurry and the froth surface, concentrations, froth image

analysis

grinding

copper flotation

zinc flotation(pyrite flotation)

thickeningdewatering

The Operator

• The role of the operator– optimizing grade and recovery

– monitors the operation and reacts to emergencies, failures etc.

– coordinates maintenance and repair tasks during the shift

• There are 5 operators at the Pyhäsalmi mine– work group includes also

maintenance personnel

• The concentrator operates in three shifts

Performance

• The essential variables describing the process operating performance– recovery (index)

– concentrate grades (quality index)

– economic index

– tailings grades – fed to the zinc flotation circuit

• Other important variables– the ore feed properties

• grades • particle size distribution after

grinding

The data

• Gathered from the process automation system’s database

• The sampling time of the data is 1 minute, and from this data– the outliers and measurement errors are removed– hourly averages are calculated– the data is grouped according to the operating shifts

• The time span for the comparison analysis is e.g. 2-3 weeks

• The compared variables are the recovery, grades and production indices

Data preprocessing

CuV tal.ind. CuR Cu CuV Cu saanti

Kokonaissyotto

Malmi Cu

Malmi Zn

Malmi S

Syötteen raekoko -20um

Syötteen raekoko -74um

Syötteen raekoko +149um

Compensated Variables

Inpu

t var

iabl

es

CuV MLR Model Parameters: 01.10.2010 00:00 - 30.11.2010 23:59

-0.5

0

0.5

• Feed compensation– a fair comparison is sought– changes in the ore properties are

independent of the operator– an MLR model from the feed

properties to all comparison variables– estimated separately for each

comparison period

Testing and pairwisecomparisons• The pairwise comparisons

indicate those groups that differ statistically significantly from the others

• By combining the analysis results from different comparison variables, differences in process operating practices can be discovered

Analysis of the results

• Based on the comparison results, the following observations of the operating practices can be made:– Group A is ”evenly good”; the

recovery, concentrate grade and the economic index are all reasonably good

– Group B pursues a high recovery, even if the concentrate grade becomes lower

– Group C aims for a high quality concentrate, but at the expense of recovery

– Groups D and E seem to have some room to improve

Saanti-pitoisuus ja taloudellinen indeksi

27 28 2993

94

95

96

97

CuR Cu%

Saa

nti %

Ryhmä E

27 28 2993

94

95

96

97

CuR Cu%

Saa

nti %

Ryhmä D

27 28 2993

94

95

96

97

CuR Cu%

Saa

nti %

Ryhmä C

27 28 2993

94

95

96

97

CuR Cu%

Saa

nti %

Ryhmä A

27 28 2993

94

95

96

97

CuR Cu%

Saa

nti %

Ryhmä B

Tal

oude

lline

n in

deks

i

-1.5

-1

-0.5

0

0.5

1

Esimerkki 1: Pitkän aikavälin operaattorikohtainen vertailu• Kuparirikastuspiirin ohjaaminen on kesällä vaikeampaa

– Lietteen lämpötila vaikuttaa mineraalien käyttäytymiseen– Operaattorien väliset erot tulevat selvemmin esiin

01 02 03 04 05 06 07 08 09 10 11 12 01

94.5

95

95.5

96

96.5

97

Month

Rec

over

y (%

)

A

B

CD

E

Kuparin saanti 2010

Esimerkki 2: Syöttötason ylläpito

• Operaattori voi vaikuttaa syöttötasoon jauhatuksen aktiivisella valvonnalla

A B C D E

158

160

162

164

166

168

Med

ian

flota

tion

feed

(t/

h)

Operator

Syöttötaso

A B C D E0

20

40

60

Operator

% o

f ho

urs

incl

udin

g ch

ange

s

Operaattorin aktiivisuus

Vertailutyökalu

• Rikastamolle kehitetty automaattinen vuorojenanalysointi-työkalu– Operaattorien vertailu

halutulta ajanjaksolta– Datakompensointi– Suoritusindeksit– Jakaumat– Raportointi