Intelligence based modeling and optimisation of tandem cold rolling process

The requirements for the manufacturers of steel have been increased in many respects in recent years. The harsh competition between the manufacturers requires a continuous reduction of the production costs and a successive improvement of product quality. The work presented herein is a part of efforts towards maximizing rolling mill throughput and minimizing processing costs and crop losses by computational intelligence based process modeling and optimisation in tandem cold rolling. It is the first time that the computational intelligence based optimisation has been applied to the rolling schedules of tandem cold rolling mills as is shown in this thesis. Rolling schedule is essential for a tandem cold strip mill operation. This involves the determination of inter-stand gauges, tensions and rolling speeds at individual stands. As the rolling process is examined and shown to be a nonlinear programming problem incorporated the complex interactions of multiple rolling parameters such as reductions, roll force and torque, rolling speeds and tensions. Each of the parameters can be set to a value in its admissible data range of float type, which is determined by threading conditions. The rolling schedule is a set of such rolling parameters. Therefore, different combinations of these rolling parameters generate numerous rolling schedules. Although some empirical rolling schedules may be reasonably satisfactory, however, it is hard to declare which schedule is the best solution. In this study, an intelligent searching mechanism is introduced to optimize the rolling schedule by assessing rolling constraints and the combined cost function of tension, shape and power distribution. The optimisation results have been compared with current rolling practices based on empirical models. It is shown that the proposed model can significantly reduce the power distribution cost, maximize the safe level of strip tension, and obtain good strip shape. The proposed model is generic for complex engineering problem optimisation, and is capable for Multiple-Objective Problem Solving (MOPS). A hybrid model of Influence Coefficient Method and Spring-Beam Method is proposed and verified for strip shape analysis and prediction. From the point of view of product quality, shape is an important parameter in the rolling process. Bad shape is a frequent cause for threading delay, rejection or rectification by costly means. Particularly for cold strip, the thinner the strip, the greater is the difficulty in maintaining good shape, which requires the optimum relationship of roll force, strip tension, work roll crowns and/or bending force. A physically based shape predictive model has been established, on the basis of which, a heuristic optimisation approach is employed to search for the optimum setting of the bending forces for those mill stands with bending systems, and to optimize reduction schedule for those without roll bending system. The proposed model has also been extended to optimize shape problem during threading by using optimal roll ground crown or optimum roll bending force. A comparison is made between results predicted by the proposed model and test results obtained on a production mill. The control of head and tail off-gauge is becoming increasingly important for many companies in the steel industry so as to maximize yield from their rolling operations. there are various kinds of fluctuations in unsteady states such as threading, acceleration, deceleration, and tailing out processes, and these fluctuations always accompany non-linearity and hysteresis, it is very difficult to accurately model, and then optimize and/or control these processes. Therefore, dynamic simulation and analysis of mill performance during these transient rolling phases, is vital in order to ensure that thickness is controlled within the desired tolerance. The significant advantage to be gained from the simulation is that the logic errors potentially existing in a rolling mill operation could be corrected and the mill performance could be improved. Innovative models for the unsteady rolling process simulation have been presented. The threading and acceleration processes have been dynamically simulated. The results generated from the simulation have been compared with the measurements of the strip produced from a production mill. The interactive relationships between roll force, exit thickness and inter-stand tensions are presented for both threading and acceleration processes. The speed dependent rolling parameters for acceleration process are analyzed and taken into account in the simulation. Deceleration and tailing-out processes are also addressed, as they are similar to acceleration and threading processes, respectively. The proposed model has also been examined with respect to changes in initial conditions such as friction between work rolls and strip, strip properties and work roll profile. The proposed models and simulations have been implemented and verified with plant data. The numerical simulations together with their result analysis will be beneficial to fine-tune the rolling process parameters and are effective in achieving significant cost savings and product quality improvements