Data processing: generic control systems or specific application – Generic control system – apparatus or process – Optimization or adaptive control
Reexamination Certificate
2002-12-26
2004-04-13
Voeltz, Emanuel Todd (Department: 2121)
Data processing: generic control systems or specific application
Generic control system, apparatus or process
Optimization or adaptive control
C700S028000, C700S044000, C700S150000
Reexamination Certificate
active
06721610
ABSTRACT:
TECHNICAL FIELD OF THE INVENTION
The invention relates to a universal method for pre-calculating or estimating parameters of industrial processes.
BACKGROUND OF THE INVENTION
Bigger, faster, better—these keywords characterize the development toward ever more efficient industrial plants. By contrast with the widespread mass production of consumer articles, large-scale plants to be newly set up are therefore often unprecedented, so that it is scarcely possible to resort to valid empirical values during the project planning, or during the first-time commissioning of such plants. One of the reasons for this is that, by contrast with small devices, large-scale plants of this type can only be modeled inadequately in laboratory tests and therefore can only be tested to a restricted extent. On the other hand, in the chemical industry and in the iron and steel industry new developments of improved materials are constantly taking place, materials which are not even known at the time when a plant is commissioned. Nevertheless, a plant is intended to be suitable also for processing materials of this type with still unknown properties, so that the high plant costs can be distributed over an adequate operating time period. For this application, the pre-estimation of properties, such as for example heat capacity, toughness, solidifying temperature, etc., of materials to be developed in the future is even required. Since, in steel production alone, a very wide variety of properties can be produced by admixing over 20 different alloying elements, in such an estimate of the chemical and physical properties of future steel alloys or other mixed materials “plain common sense”, which at the present time is the only cost-effective means of calculation available when extrapolating known material properties onto previously unknown products, is found to be completely inadequate because of the no longer comprehensible interrelationships. One of the reasons for this is that the information necessary for such a pre-estimate is usually scattered among many people and industrial enterprises and, on account of its volume, cannot be unified in a single person, even if this person had access to a large proportion of the information available, for example by way of patent specifications.
These disadvantages of the known prior art result in the problem initiating the invention, that of providing a method for pre-calculating or estimating parameters of industrial processes which, given access to a large volume of information, is rendered capable of determining the desired parameters in advance with greatest possible accuracy.
SUMMARY OF THE INVENTION
The solution to this problem is achieved by defining for a specialist technical field, for example the iron and steel industry, a vector of admissible input variables of an industrial process and/or product (hereafter: process input variables), with definition ranges assigned to each variable, and a vector of output variables to be determined of the industrial process and/or product (hereafter: process output variables), with the pre-calculable process and/or product parameters, known information on the process and/or the product being stored in a databank and this information being allocated ranges of validity for the process input variables, and exactly one process output vector being determined according to the information valid for it with respect to each input process input vector from a definition range which is admissible and provided with valid information.
A first step on the way toward solving the extremely extensive problem is a subdivision into specialist technical areas with expert knowledge that in each case is largely self-contained. For example, the processes in steel production can be largely detached from other chemical processes, since any combinations of such plants there may be are confined to auxiliary equipment for supporting the iron and steel industry. Then, in a further step, the input variables of the processes to be considered, which can be directly influenced externally, such as in particular the composition of a grade of steel, its current temperature, and, if appropriate, specific process steps of the production process, are distinguished from the output variables of the process, which although influenced by these factors are initially unknown, such as for example the chemical, physical and mechanical properties of the products produced by the working or processing, for example of new steel alloys. If further information on dependencies of the process output variables of less interesting state variables are known, for example of the density, additional state variables can be defined, the actual knowledge of which may be of no interest to the user but is indispensable for determining the desired output variables. Consequently, once the basic structure of the process to be considered has been defined in the form of its input variables, output variables and, if appropriate, state variables, in a further step the knowledge available on the internal interrelationships between these variables is brought together and stored in a databank. Owing to the wide diversity, for example of alloying elements which can be used in the iron and steel industry and of any additional process parameters, the available knowledge in virtually all applications will have to be classified as extremely sketchy, the knowledge of particularly frequent process parameters being more complete, the knowledge of exotic, and therefore rare, combinations of input variables being less complete. Accordingly, the accuracy of a prediction in the areas commonly used by technology will be significantly higher than in the areas where new technical territory is being entered. Nevertheless, even in the case of common processes, the interrelationship between output variables and input variables is likely to be known only for individual, specific process parameters, unless the chemical process concerned has been scientifically investigated completely and can be modeled with a closed system of equations. Such comprehensive information, as in the meantime achieved for example in the electrical drive sector, is nothing but a dream for the experts in other branches of industry. This is where the invention comes in, in that it uses the sketchy, but known information to provide an estimate of the output variables by interpolation for every conceivable application, as far as possible, i.e. combinations of input parameters, a constant motivation to optimize the method according to the invention being perceived in the endeavor to reduce the deviations of the parameters to be pre-calculated from the actual parameters to zero by an ongoing process of completing the data available. This can take place, for example, in the case of fields that are being newly worked and not yet fully understood scientifically, by the available information being stored in the form of measured values, it then being intended in the case of an inquiry concerning intermediate products for best possible interpolation to be performed between known values; as the information becomes increasingly complete, instead of individual measured points it is possible for example to store regression curves, which—even without full scientific understanding—permit a good approximation of the pre-estimate, and finally, after scientific study of the interrelationships, the functions found thereby can be programmed in, so that, over time, the precision of the method according to the invention asymptotically approaches the ideal of an error-free prediction of parameters of technically not yet realized processes. This capability of the method according to the invention of learning by adding to the databank, in order in this way constantly to increase its knowledge base, can be used to provide the user not only with the pre-calculated and/or estimated parameters but also with the degree of completeness of the knowledge required for this, or, derived from this, an estimate of the possible errors in calculation or estimation. If, in this cas
Gade Dirk
Peuker Thomas
LandOfFree
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