Method for controlling a primary industry plant of the processing industry

• US 5,727,127 A
• Filed: 06/05/1995
• Issued: 03/10/1998
• Est. Priority Date: 03/09/1995
• Status: Expired due to Term

• Current Assignee: Siemans Atkiengesellschaft
• Original Assignee: Siemans Atkiengesellschaft
• Inventors: Adamy, Juergen, Schulze Horn, Hannes
• Primary Examiner(s): Hafiz, Tariq R.
• Application Number:
  US08/463,446
• Time in Patent Office: 1,009 Days
• Field of Search: 395/23, 395/24, 395/10, 395/22, 395/167, 395/50, 395/53, 395/60-61, 364/472
• US Class Current: 706/52
• CPC Class Codes: B22D 11/16 Controlling or regulating processes or operations

Abstract

A method for controlling a primary industry plant of the processing industry, for example, in a steel plant or a rolling mill in order to, for instance, produce strips of steel or non-ferrous metals. The control method is designed in terms of computer engineering building on inputted advance knowledge, such that the method can automatically recognize the state of the installation and details of a manufacturing process taking place in the installation, for example in a continuous casting process for strips, and is able to give desired values and setpoints appropriate for the situation to achieve a reliable and successful production.

Claims

1. A method for controlling a primary industry plant, like one of a steel plant or a steel mill producing strips of one of steel and non-ferrous metals, the control method being implemented on one of a computer and a system of distributed computers, the control method comprising the steps of:

adapting a model of operation of the primary industry plant;

carrying out an optimization process using advance knowledge of the primary industry plant and knowledge about the status of the primary industry plant obtained from the model; and

calculating, in terms of the optimization process, at least one of a setpoint value and a desired value respecting one of safety, reliability, and throughput of the primary industry plant and quality of a processed product, for use in one of driving at least one actuator of the primary industry plant and feeding to at least one controller controlling the at least one actuator.

2. The control method according to claim 1, further comprising the steps of:

improving the advance knowledge by computer-generated knowledge gained from the model during production in the primary industry plant; and

accepting the computer-generated knowledge as a new advance knowledge in a data storage unit.

3. The control method according to claim 1, further comprising the steps of:

giving at least one instruction applicable to a situation directly to at least one primary industry plant component in the form of a selection value, for at least a position; and

giving at least one instruction applicable to a situation indirectly via the controller setpoint values, for at least a rotational speed.

4. The control method according to claim 1, wherein a basic function system for at least one primary industry plant component reliably converts the instructions from the knowledge gained computationally from the model into the primary industry plant control.

5. The control method according to claim 4, wherein the basic function system is designed as a basic automation system, which reliably renders each of the primary industry plant components operational.

6. The control method according to claim 4, wherein the basic function system obtains the setpoint values directly from an intelligent part of a control computer which determines the setpoint values from the results of one of the steps of the adaptation and the optimization processes on the model.

7. The control method according to claim 4, wherein the basic function system is developed as an autonomous subsystem that guarantees a reliable condition of the primary industry plant and of an emergency-condition release system, which instead of falling back on the computer-generated instructions, can fall back on positively identified operational values stored in the data storage unit.

8. The control method according to claim 4, wherein the basic function system has a starting and run-up routines, which can be entered in one of a manual and automatic manner, as well as a suboptimal normal operating routine, in which individual, otherwise computer-generated instructions can be replaced by constant setpoint values.

9. The control method according to claim 1, wherein the state of the primary industry plant and of the individual primary industry plant components is continually simulated for purposes of the optimization step on the basis of a process model, which in particular has a modular design, and which describes the performance characteristics among a plurality of process input variables, as well as a plurality of manipulated variables, and a plurality of process output variables.

10. The control method according to claim 9, wherein the process model has mathematical descriptive forms, at least in part, to the extent that it can be modelled on the basis of one of physico-mathematical, chemical, metallurgical, and biological laws.

11. The control method according to claim 9 wherein for the primary industry plant components for which there is existing process knowledge that can only be expressed linguistically, the process model has linguistically formulated model sections, which can be realized by one of fuzzy systems, neuro-fuzzy systems, expert systems, and tabular compilations.

12. The control method according to claim 9, wherein for the primary industry plant components for which it is not possible to produce a model based on the fundamentals of one of mathematical physics, chemistry, metallurgy and biology, and on a linguistically describable process knowledge, the process model has at least one self-learning system, such as a neural network.

13. The control method according to claim 12, wherein the starting values for an optimization are determined on the basis of the suboptimal operational data filed in a process data storage unit.

14. The control method according to claim 9, wherein the process model is continually adapted to the process and corrected on the basis of process data, which has been collected at the primary industry plant and filed in a process data base, and in that this is accomplished by means of one of adaptive methods and learning methods, by one of means of a back-propagation learning method, and a selection method for various submodels, such as a neural network.

15. The control method according to claim 9, wherein the process variables are optimized off-line by an optimizer at the process model so as to allow the model output variables, which, in particular, are quality parameters for the product, to conform as best as possible to preselected target values.

16. The control method according to claim 9, wherein the step of optimization takes place using a known optimization method such as one of a genetic algorithm, the Hooke-Jeeves method, a simulated annealing method and the like, and that the optimization method applied in each case is specified in dependence upon the situation and the problem and is selected from a data file in dependence upon one of the number of variables to be optimized and the formation of the minima to be expected.

17. The control method according to claim 16, wherein the criteria for breaking off the optimization methods, such as with the neural networks, are determined according to one of a method of pattern recognition and classical convergency criteria on the basis of the course of the optimization.

18. The control method according to claim 9, wherein the step of optimization takes place off-line on the basis of the process model, adjustable process variables, which were so determined that the characteristic values of the manufactured product simulated by the model conform as best as possible to the predetermined desired values, being given as setpoint values to the basic function system of the process, and the process being adjusted by the basic function system in accordance with the setpoint values.

19. The control method according to claim 9, wherein in the case of a malfunction of one of the model and the optimizer, the setpoint values can be generated directly from the data of the process data base, an interpolation being performed to improve the setpoint values, in particular between the stored operational data.

20. The control method according to claim 9 wherein the model takes into consideration one of the restrictions of the manipulated variables, the actuator time response and, in some instances, the process dynamic, preferably in and before the area of the casting rolls, such as in relation to the position of the merging zone of the solidification shells for the solidification shells deposited on the casting rolls.