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Benefits of Metallurgical Level 2 Model

Great value can be achieved by successful integration of the metallurgical models into the Level 2 systems. In addition to the high accuracy which leads to the high shape quality and high yield, metallurgical issues can be fully addressed in the pass schedule creation and as a result, better product properties can be achieved in a particular facility.

High Accuracy of the Force Prediction

A Level 2 improvement project was completed to solve the shape problems incurred in some hard and thin grades [7]. Analyses revealed high force errors up to 40% in the finish passes. Improvements were conducted in the two phases. In the first phase, following work was done:

  • System learning: applied the so-called guided two-parameter learning, which uses C1 and C2 as learning parameters, and about 6000 sets of carefully designed C3 and C4 as the fixed values

  • Metallurgical interaction: considered effects of retained strain, etc. The effects of the retained strain was integrated into the 6000 sets of the flow stress coefficients (C3 and C4).

  • After the first improvement, the force prediction accuracy was significantly improved and the shape problem was solved. The force error data from the tested samples of the troubled grades are showed in the Table 3. The 1% of passes still with the error over 15% was considered as the cause of phase transformation.

    Table 3 Force error and quality level after first improvement

    Error Range

    All grades, before

    Troubled grade, before

    Troubled grades, after

    < 5%

    73% passes

    57% passes

    over 80% passes

    < 10%

    90.9% passes

    87% passes

    over 90% passes

    < 15%

    96.4% passes

    94% passes

    over 99% passes

    The second improvement further increased model force prediction accuracy by modifying the force prediction for the resume pass and by expanding the flow stress valid range.

    It is anticipated that by applying metallurgical models and improving learning, it is possible to reduce force prediction error by 5-10% for many Level 2 systems.

    Accurate prediction of separating force during rolling is vital to the flatness of the rolled products. Draft distribution is tied to the force distribution, and a certain force distribution over the passes leads to good flatness of the plate/coil and high utilization of the equipment. In many cases, temperature from pass to pass is calculated based on the measured force (measured flow stress), so force prediction error also causes temperature error. In addition, a rolling schedule based on inaccurate force prediction may have more passes than needed; this leads to lower productivity and higher costs (energy, equipment, labor, etc.).

    Besides the shape and dimension problems, an inaccurate Level 2 force model may lead to poor mechanical properties of the rolled product. For a product order, the required properties (often, mechanical properties) of steel are specified. The steel plant usually designates a certain steel grade plus certain rolling procedure to produce the required steel. If the automation system is sufficiently accurate, the finish product should satisfy all the requirements. However, the automation system may have significant error, so the roll separating force, temperature and draft, etc. may be different from what are expected, and as the result, the product quality may be below what is planed. To assure the satisfaction of the property requirements, the steel producer has to plan higher quality of the product than that is required by the customer. This causes unnecessary costs for the steel plant. In addition, some plants conduct mill trials to select the proper rolling procedure for a new product; the cost of these trials is significant.A force error reduction by 10% for the Level 2 system has a great economic value, as estimated in the Table 4.

    Table 4 Benefit for 10% force error reduction



    Annual Total (US$)

    Annual Saving (US$)


    Investment Saving 1) 2)




    Equipment life span: 40 years

    Slab grade saving 3)




    50% of sales price

    Energy Saving 4)




    5% of sales price

    Yield increase




    1% yield increase

    Mill test saving for new products 5)




    0.5% of sales price




    1. Data in the table are based on a mill with US$800 million equipment and US$800 million annual sales.
    2. The saving is based on the increase of equipment utilization of 10%, equivalent to 15% of investment.
    3. When significant force error occurs, higher grade of steel has to be scheduled for an order to guarantee the rolled steel properties.
    4. The increase of energy consumption due to higher grade scheduled.
    5. Some plants conduct mill trial-and-errors for scheduling of new products.

    Improved pass schedule and slab selection

    As the metallurgical processes are modeled and integrated into the Level 2, two valuable improvements are possible. The first improvement would be to use the models in the design of the passes through the mill. The models would be included in the design of an optimum set of passes that maximize the toughness of the steel for the conditions of the mill and rolls, while achieving the precise targets of mechanical strength and dimensions. The second application would be the use of the models in the selection of slabs for particular orders. Each steel order has property requirements that depend upon the attributes of the slabs and the capability of the mill, so the optimum chemical composition and dimensions for the slab would be matched with the plan for rolling and quenching of the steel.

    Pass design - In this application, the design of the particular rolling passes for slab to produce an ordered set of plates would be achieved by the metallurgical level 2. All level 2 models use a heuristic or trial-and-error approach to searching for a set of rolling passes including roll gap, quench, speed and roll shape settings. In this search a metallurgical level 2 with a complete set of models would calculate the probable strength, toughness, flatness and dimensions for a pass design. The calculations would project the grain size, shape, phases present and the strain conditions for each rolling pass. The final projection would be compared to the target properties for the ordered set of plates. Then the gap, quench, speed and roll shape settings would be modified to improve the projections, until a final set of recommendations were complete.

    The data recorded at the mill would be stored in the database and analyzed to refine the constants in the models. A systematic and statistically valid approach should be used to refine the constants. The performance of the model in predicting forces, torques, temperature, yield strength, ultimate strength and dimensional uniformity must reported, and a process engineers should evaluate the reports

    The use of the metallurgical models in the design of the rolling passes would provide the maximum toughness with a more accurate achievement of the final target properties without unnecessary loss of steel or processing time.

    Slab selection - The metallurgical models could be used when the line up of slabs is prepared for the rolling mill. This preparation is processed on the level 2 or the level 3 systems, where an inventory of slabs is available to be selected for ordered steel products, or a list of heats may be available to produce in the melt shop. In either case the metallurgical models would be used with the chemical analyses of the slabs or heats, and the least-cost slabs or heats would be selected with the best yields.

    The process of selecting slabs for the ordered plates is a complex mathematical optimization procedure which includes the effect of the slab size and chemical analysis. With the use of the metallurgical models the properties, such as strength and toughness, would be predicted with a selected rolling and quenching plan, and the best slab would be selected. Since the metallurgical models allow more precise prediction of the force and torque limits of the mill, larger reductions would be planned and slabs with lowest content of molybdenum, vanadium and niobium would be selected.



    [1] B. Li and J. Nauman, Metallurgical, Modeling and Software Engineering Issues in the Further Development of the Steel Mill Level 2 Models, AIST Annual Conference 2008, May 5 8 2008.

    [2] I. Tamura, et al., Thermomechanical Processing of High-strength Low-alloy Steels. Butterworths & Co. 1988. ISBN 0-408-11034-1.

    [3] A. Hensel & T. Spittel, Kraft- und Arbeitsbedarf bildsameer Formgebungsverfahren. VEB Deutscher Verlag fur Grundstoffindustrie, Leipzig, Germany. 1977.

    [4] Y. Saito, et al., The mathemarical model of hot deformation resisitance with reference to microstructural changes during rolling in plate mill. Transaction ISIJ, 1985, 25(11).

    [5] Suzuki, et al, Studies on the Flow Stress of Metal and Alloys. Univ. of Tokyo. 1968.

    [6] B. Li and J. Nauman, Metal Pass 108 Mill-Related Projects. Online at www.metalpass.com/consulting.

    [7] B. Li, D. Cyr and P. Bothma, Level 2 Model Improvements at Evraz Oregon Steel Mills. To be published.


    Metal Pass Resources on Level 2 Model Improvement

    Metal Pass Work List on Level 2 and Mill Modeling

    Metal Pass Research Reports

    Technical papers published in the February and March of 2008. Those publications are primarily on Level 2, Level 2 model and process automation.

    List of available process models for steel mill developed by Metal Pass (Dr. Benjamin Li)

    Metal Pass software applications

    List of computer classes completed by Dr. Benjamin Li

    Metal Pass IT & Software Technology Resources

    See the profile for the primary consultant Dr. Benjamin Li. You may also view our company and personnel profiles. Please contact us via email admin@metalpass.com or by phone (001) 503 516 9625 for top quality consulting services.


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