Doc. student research seminar
4.4.2011
Janne Pietilä

• 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 databased 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

grinding copper flotation zinc flotation
(pyrite flotation) thickening dewatering
• 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

• 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

• 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

• 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

• Feed compensation
– a fair comparison is sought
– changes in the ore properties are independent of the operator
– an MLR model from the feed
CuV MLR Model Parameters: 01.10.2010 00:00 - 30.11.2010 23:59 properties to all comparison variables
Compensated Variables
– estimated separately for each comparison period
Kokonaissyotto
Malmi Cu
CuV tal.ind.
CuR Cu CuV Cu saanti
0.5
Malmi Zn
Malmi S 0
Syötteen raekoko -20um
-0.5
Syötteen raekoko -74um
Syötteen raekoko +149um

• 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

• 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

Ryhmä C Ryhmä A Ryhmä B
97 97 97
96 96 96
95
97
96
94
93
27 28
CuR Cu%
Ryhmä D
95
94
93
27 28
CuR Cu%
95
29
29
94
93
27 28
CuR Cu%
Ryhmä E
95
94
97
96
93
27 28
CuR Cu%
29
29
95
94
93
27 28
CuR Cu%
1
0.5
0
-0.5
-1
-1.5
29

• Kuparirikastuspiirin ohjaaminen on kesällä vaikeampaa
– Lietteen lämpötila vaikuttaa mineraalien käyttäytymiseen
– Operaattorien väliset erot tulevat selvemmin esiin
Kuparin saanti 2010
97
96.5
A
B
C
D
E
96
95.5
95
94.5
01 02 03 04 05 06 07 08 09 10 11 12 01
Month

• Operaattori voi vaikuttaa syöttötasoon jauhatuksen aktiivisella valvonnalla
Operaattorin aktiivisuus
Syöttötaso
60
168
166
40
164
20
162
160
158
0
A B C
Operator
D E A B C
Operator
D E

• Rikastamolle kehitetty automaattinen vuorojenanalysointityökalu
– Operaattorien vertailu halutulta ajanjaksolta
– Datakompensointi
– Suoritusindeksit
– Jakaumat
– Raportointi