1. OBTAINING QUALITY MILL PERFORMANCE Dan Miller
IOM3 Meeting on Maximising Mill Performance London 4 December 2008

2. Abstract
It is a relatively easy exercise to calculate a mill capacity based on the product entry and exit gauges and mill speeds, but achieving the productivity off the mill this capacity might imply is a much more difficult task. It will be shown that good mill design can help maximise productivity by reducing the non-productive times of coil handling and maintenance, and that by removing constraints such as vibration or fear of strip breaks can be used to achieve the designed mill speeds and hence maximise productivity.
Maximising performance however implies that the quality dimensions will be maintained under all conditions. To do this effectively requires more than good mechanical design: it requires knowledge of the rolling process and how the process stream contributes to the final customer attributes. This will be demonstrated through the impact of scheduling on consistency of hot mill profile control as well as the consequences of poor coil sequencing through the cold mills. Through these examples it will be shown that optimising mill capability can occur only when the mill capabilities, process understanding and coil sequencing are dynamically linked.

3. Mill capacity calculations
All major mill suppliers (and many major metals producers) have some form of capacity model for a plant
These can be used to design a new mill or look at the consequences of a mill upgrade
At the simplest level they consist of
Pass schedules plus pass speed
A value for coil handling or set-up time
A value for yield from the mill (recovery value through a series of passes, including width changes due to edge trim)
An indication of coil sizes (weight & width) and of the product mix
From a design perspective, such models allow
Calculations on motor sizes to achieve the desired speeds and torque
Calculations on loads to ensure engineering constraints are not exceeded
From a user perspective, the challenge is to ensure
The mill is constrained by maximum motor power or maximum speed for all passes
The quality of the product is not compromised
The operation has as low as possible energy consumption & environmental impact

4. Capacity calculations
Product
One
Two
Three
Four
Five
Final gauge [mm]
0.1
0.22
0.5
0.8 2
Percent of production
33%
23%
22% 9%
13%
Rolling time [min] 54 18 10 6 3
Handling time [min] 30 20 15
Overall capacity [ktonne/yr]
110
Product
One
Two
Three
Four
Five
Final gauge [mm]
0.1
0.22
0.5
0.8
Percent of production
39%
28%
26% 7%
Rolling time [min] 54 18 10 6
Handling time [min] 30 20 15
Overall capacity [ktonne/yr] 83
Removal of heavy gauge product
Product
One
Two
Three
Four
Five
Final gauge [mm]
0.1
0.22
0.5
0.8
Percent of production
39%
28%
26% 7%
Rolling time [min] 49 17 9 6
Handling time [min] 30 20 15
Overall capacity [ktonne/yr] 87
10% increase in pass speeds

5. Typical mill configuration
Correct design of mechanical & thermal actuators on the mill
Good set-up models
Excellent control of thickness & flatness
Minimal head & tail losses
Efficient coil preparation & handling
Rapid roll changing facilities
Correct process- based scheduling

6. Keys to achieving ‘designed’ capacities
Being able to run at or above the designed speeds
Achieving a short coil change time
Avoiding the product mix changing towards a lower average exit gauge
Avoiding other constraints such as vibration, fear of strip breaks, edge quality
Maximising output is not maximising performance – performance is about producing quality products at acceptable or enhanced volumes

7. Example of productivity improvement through practices
In this example there are no changes in equipment, product mix, pass speed or reduction
Key steps
Identification of bottle-necks
Examination of variability
Education of operators
Visibility of results
Changes proposed to procedures for coil handling
Improvement in consistency
Improvement in performance

8. Understanding of the process
There are several stages during the rolling of strip where some of the critical input parameters are determined early in the process stream, such as strip profile
Profile is effectively set by the hot rolling process
It is a key limiting factor in narrow cut multi-slit finishing processes (and off-line flatness)
Profile is measured off some hot rolling mills and the results stored for quality control
There are many instances where profile measurement is used for coil-to-coil and intra-coil profile control
However, scheduling of hot mills can significantly alter the expected profile and place large demands on the control schemes

9. Predicted changes in crown due to a product change
Time →
Centre to edge thickness difference →
Range of profile change to be corrected by mechanical or thermal actuators

10. Graph showing profile changes with scheduling
Profile error generated through product change through a sequence of coils on the hot mill
Predicted profile error generated by altering the sequence of coils on the hot mill, taking into account changes caused by different products

11. Consequences of ignoring process knowledge in cold rolling
There are some sequences of coils of different products that can become impossible to roll well
Light reduction or temper passes generate so little heat flow to the rolls that AFC is ineffective and mechanical actuators can saturate
Although AFC is generally good, the mismatch in thermal crown caused by width changes or by significant differences in heat flow into the work-rolls places a large demand on the system
Edge control becomes more problematic
The length of material out of flatness tolerance at the start of the coil increases

12. Without good AGC & AFC quality of the products cannot be guaranteed
Conclusions
Mill capacity (and hence maximum productivity) depends on product mix, pass speeds & coil handling times
A well-designed mill takes all this into account
Without good AGC & AFC quality of the products cannot be guaranteed
These control schemes require good set-up models
Most control schemes work well when the deviations from the target are relatively small
The best set-up models track thermal transients (including the impact of roll change)
Process knowledge enables the prediction of the impact of product changes. This can be embedded in other systems, especially in scheduling.
It is only with all of the above, that the quality of products can be assured at all times
The measure of mill performance is the ability to achieve the maximum capacities but without any degradation in quality

13. Innoval Technology An independent company providing expertise to the metals industry