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صناعــــة الحديــــد والصلـــــب Iron and steel industry 

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The figure shows the stages of evolution in control theory. Control theory dates from 1868, when Maxwell made a theoretical analysis of the functions of a speed governor used to maintain a constant rotational speed in steam engines. Classical control theory was refined in the 1940s and 1950s, and is based on a mathematical model which gives the relationship between the input and output of the process to be controlled. Classical control theory deals with only one input and one output. However, control equipment called the generalpurpose PID controller, which permits proportional, integral, and differential control, has been developed and is used in many processes and plants. Subsequently, modern control theory has been developed to handle multivariable systems with many inputs and outputs. Control based on classical and modern control theories requires the preparation of an appropriate mathematical model of the process to be controlled. If the mathematical model is satisfactory, the expected control can be obtained in a quantitative way. However, with control systems involving substantial nonlinearity, there are many parameters that must be determined by experiment in order to construct the most appropriate mathematical model. It is thus difficult to prepare satisfactory mathematical models, and hence the effect of control is limited. Remarkable progress has been made in recent years in (i) fuzzy theory, which can handle ambiguity, (ii) expert systems, which utilize the knowledge of experts, and (iii) neural networks, which are very effective for pattern recognition and learning. These methods are called intellectual information processing techniques and are employed effectively in various problemsolving systems. Control theory has developed by adopting these methods, and it has become clear that the concepts and means of control can be applied not only to manufacturing products, but also to all problems related to human activities, products, and information, such as production planning, inventory, and material distribution. Control theory was first applied by the steel industry to control one machine/device, and then to control a manufacturing line and process involving multiple connected devices. Such theory is now applied in the optimization of the operation of a whole steel works incorporating many lines. Moreover, control theory is already applied to some extent in total optimization of company performance, including logistics, production, and sales activities. 


profile control in sendzimir mill
The Sendzimir mill is a rolling mill with cluster rolls for cold rolling such hard materials as stainless steel. Work rolls of small diameter are used to apply a strong rolling force to hard materials, but this makes it difficult to ensure the desired surface flatness. To solve this problem, a cluster roll configuration and various profile control units are adopted in the Sendzimir mill, which, as illustrated in the figure, is equipped with the upper first and lower first intermediate rolls which are tapered in one direction and are capable of being shifted. In addition, six separate rolls are installed at the top and bottom, together with two ASU rolls. The ASU rolls are each supported by seven saddles which can adjust the rolling load on each segment of the rolls. The ASU rolls can be displaced along their axes to a different extent by the hydraulically driven saddles, resulting in a change in the profile of the rolled material in these axial directions. However, such a complicated roll structure and the large number of operating factors result in a nonlinear relationship between the operating factors and the profile of the rolled material, causing difficulty in quantification. For this reason, applications of classical or modern control theories are not sufficient to produce the desired effects. In the past, skilled workers visually determined the profile of the strip at the delivery side and operated the mills utilizing their experience and intuition. The next section gives an example of how satisfactory results can be obtained by applying the new control techniques of a neural network and fuzzy logic as part of a new control system . 


fuzeey logic to profile control
The new control system involves recognizing the profile patterns of the rolled material with a neural network. The system extracts the major elements of these patterns and compares them with the standard profile patterns which had been determined by learning and stored in memory. Fuzzy logic is used to command the operation by comparing the fractions of the extracted elements with the standard profile pattern. In this method, the profile of a strip at the delivery side is detected by the profile sensor in such a way that the heights from the base plane to the strip surface are measured at each set position in the width direction. The Figure(b) shows an example of measurement. After such measurements have been input as signals, the neural network outputs the mode of patterns and the fraction of the patterns included in 8 standard patterns given in advance. The output data from the neural network in Figure(b) shows the 8 standard profile patterns. The area of the hatched portion within each pattern shows the fraction of the standard patterns incorporated in the signals. Figure(b) shows an example in which the output for the leftmost pattern is included as the major element in the signals, and the fifth pattern from the left is included in part. This output is then transmitted to the fuzzy control section, where it is converted to values which command which rolls are to be manipulated, in what way, and by how much. Figure(c) shows that the No. 2 saddle of the ASU roll needs to be lifted, the upper roll of the first intermediate rolls needs to be shifted to the left, and the lower roll should be moved to the right. Repetition of the cycle of "Measuring Recognizing Converting to the operating variables Operating Measuring" improves the profile of the strip. As a result of applying this control system to the rolling of stainless steel, profile has reportedly been improved better than that obtained with manual control, but an unadjustable profile deviation still remains. Further analyses on the still incomplete profile control will be made, followed by a test of the modified control system. 
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