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

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.

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

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

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

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

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

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

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

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

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

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

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