EP0813701B1 - Systeme de gestion pour installations de l'industrie primaire ou de l'industrie manufacturiere - Google Patents

Systeme de gestion pour installations de l'industrie primaire ou de l'industrie manufacturiere Download PDF

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Publication number
EP0813701B1
EP0813701B1 EP96905686A EP96905686A EP0813701B1 EP 0813701 B1 EP0813701 B1 EP 0813701B1 EP 96905686 A EP96905686 A EP 96905686A EP 96905686 A EP96905686 A EP 96905686A EP 0813701 B1 EP0813701 B1 EP 0813701B1
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Prior art keywords
control system
model
values
optimization
plant
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German (de)
English (en)
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EP0813701A1 (fr
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Hannes Schulze Horn
Jürgen ADAMY
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Siemens AG
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Siemens AG
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B22—CASTING; POWDER METALLURGY
    • B22D—CASTING OF METALS; CASTING OF OTHER SUBSTANCES BY THE SAME PROCESSES OR DEVICES
    • B22D11/00—Continuous casting of metals, i.e. casting in indefinite lengths
    • B22D11/16—Controlling or regulating processes or operations

Definitions

  • the invention relates to a control system for a plant Basic material or the processing industry or the like, e.g. For a metallurgical plant, for example for the production of strips made of steel or non-ferrous metals, the control system using computer technology, building on previous knowledge, the state of the facility and details of one that is running in the facility Manufacturing process, e.g. a continuous casting process for tapes, automatically recognizing and for achieving safe production success according to the situation giving, is trained.
  • a control system for a plant Basic material or the processing industry or the like e.g.
  • a metallurgical plant for example for the production of strips made of steel or non-ferrous metals
  • the control system using computer technology, building on previous knowledge, the state of the facility and details of one that is running in the facility Manufacturing process, e.g. a continuous casting process for tapes, automatically recognizing and for achieving safe production success according to the situation giving, is trained.
  • Expert systems as so-called intelligent systems, with whom the quality of the manufactured products also in relation on quality features that are difficult to control are also to be improved for plants in the raw materials industry known, e.g. from the article “Process optimization for maximum availability in continuous casting”, published in the journal “Metallurgical Plant and Technologie International 5/1994 ".
  • Such expert systems that can certainly improve the production success however not the principal weaknesses of the conventional scheme. These will be especially then visible when it comes to processes that (such as in the raw materials industry) due to the lack of more suitable ones Sensors, for example inside high-temperature processes, do not can be regulated directly.
  • the task is actually intelligently trained Control system solved, based on the previous knowledge entered, automatically appropriate instructions for a situation safe and as good (optimal) process management as possible. It is therefore a fully trained technical Intelligence that, surprisingly, is already with today available computing resources also for Process control systems of large plants can be realized.
  • Control system the appropriate instructions is computationally optimizing step by step. This will further increase intelligent behavior achieved that lead to a quality of litigation leads that by human operators or not at least not in the short, computationally achievable Time is achievable.
  • the prior knowledge entered that is, the one predefined by humans Process knowledge, preferably automatic, ongoing through on Process during production internally, e.g. in different operating points, knowledge gained improved and this self-generated process knowledge into one, in particular constantly updated, data storage as new Previous knowledge is adopted. So one becomes very advantageous constantly improving basis for further adaptation or Process optimization created.
  • the knowledge gained is not only limited to more precise parameters etc. but also particularly closes the principles of algorithms etc. used.
  • the control system has a basic functional system for the system components, which the instructions from the computational, e.g. from a process model, preferably an overall process model, gained knowledge, safely implemented in the plant management.
  • a process model preferably an overall process model
  • the control system has a basic functional system for the system components, which the instructions from the computational, e.g. from a process model, preferably an overall process model, gained knowledge, safely implemented in the plant management.
  • the situation-appropriate Instructions e.g. in the form of setting values
  • the system components directly in the form of control values, for example of positions or in particular indirectly, e.g. via controller setpoints, for speeds, for example.
  • the Instructions are particularly advantageous directly from the Process model sizes determined. This happens for Time-critical setpoints are advantageous on-line, otherwise off-line. This results in a particularly favorable response from the system to changed process conditions under advantageously possible Saving of setpoint calculators.
  • the basic function system advantageously also has start and Start-up routines that are entered manually or automatically can be, as well as suboptimal normal operating routines, in to those instructions, otherwise determined by calculation can be replaced by constant, safe guidelines.
  • A is such a configuration of the basic function system especially for the commissioning phases, for operation with erratic change of requirements etc. advantageous.
  • function of the intelligent computer part do not always need all model parts in specific adapted form are available. It is also beneficial a company with a partially elaborated and / or adapted overall process model possible.
  • the process model itself is in the form of an overall process model modular structure and describes the behavior between the process input variables and the manipulated variables and the process output variables, e.g. Quality parameters of the product created.
  • the modularity allows one particularly advantageous design and processing of Overall process model, as it is made up of individual, clearly visible, Part models can be assumed.
  • the process model is based advantageous as far as possible on mathematical forms of description. Where such mathematical forms of description are not possible, for example, is formulated on linguistically Model parts used, e.g. through fuzzy systems, Neuro-fuzzy systems, expert systems or similar be realized can. For, e.g.
  • the process model becomes advantageous due to the on the plant collected process data, which is archived in a process database are continuously adapted to the process and further improved, this advantageously using adaptive methods, Learning methods, e.g. through a backpropagation learning process or a selection process for different sub-models, such as neural networks or parts thereof. So results a largely self-taught model, the can be adapted or improved on-line or off-line.
  • the adjustable Process variables by the optimizer on the process model are optimized in such a way that the model output variables, which are in particular quality parameters of the product, as well as possible with given, e.g. the target, Values match.
  • the offline optimization can both on a separate processing unit parallel to the Model adaptation, as well as in breaks, e.g. at the weekend or in the event of repair downtimes on the computer, e.g. in the Operation of the reference variables of the basic functional system issues, take place.
  • the optimization is advantageously carried out using known optimization methods, especially about genetic algorithms.
  • the optimization process is selected according to the situation and problem-dependent. It can be done by a specification, e.g. based on an analysis of the course of the process, or by computational Selection from a collection of optimization methods respectively.
  • a simple "trial and error” procedure can be used for this applied, but it is recommended to reduce it the computing effort, the "trial and error” procedure through convergence criteria, methods of pattern recognition in Support the error acceptance process etc.
  • the respective start values for an optimization are advantageous on the basis of the archived in a process data memory suboptimal operating data determined. So reduced the optimization effort because the optimization calculation starts with pre-optimized values if they are certain recognized intermediate values are used as start values.
  • the overall system is improved at least in three stages.
  • the lowest level is the continuous improvement of the existing process knowledge, stored in the data memory, e.g. in Form of sub-optimal, safe operating points that automatically continuously brought to a better adapted level of knowledge , and from that again it is assumed becomes.
  • the second stage essentially consists of the model adaptation, that adapts the model behavior to the process behavior as well as possible.
  • the third stage is a continuous improvement of the situation-specific instructions from the process optimizer, e.g. about evolutionary strategies, genetic algorithms etc. These strategies require a lot of computing time and preferably run off-line.
  • the system improvement is also advantageous through external simulation calculations, model tests, possibly also through tests on the production plant with new aids etc., continuously supported.
  • control system according to the invention is exemplified below using a steel strip caster.
  • inventive details and Advantages from the drawing and the description of the drawing as well as from the subclaims are further, also inventive details and Advantages from the drawing and the description of the drawing as well as from the subclaims.
  • FIG. 1, 1 denotes the casting rolls of a two-roll casting device
  • the material between the casting rolls 1, liquid steel, from the ladle 4 over the tundish 5 and a dip tube 6 is entered and solidified into a band 3, that in one, through the circles 2 with movement arrows symbolized, rolling mill can be further deformed.
  • the downstream rolling mill can also be easily moved by conveyor rollers, a reel or similar be replaced when rolling out should not be done immediately after pouring.
  • the design the entire system is made according to requirements. Also training the, the pouring device downstream, system as a hot-cold rolling mill is possible and recommended at very high casting speeds because then the cold rolling section of the plant is sufficiently utilized can be.
  • the casting and rolling device preferably also has one only symbolically represented electrodynamic system 8.9 and an induction heating system 10.
  • the electrodynamic System part 8 advantageously serves to relieve weight, the cast, here still very soft and therefore at risk of constriction, Volume 3 and the electrodynamic system part 9 of guiding the tape 3 during the induction heating system 10 compliance with a predetermined temperature profile over the bandwidth if, e.g. a direct one Post-forming in a rolling mill. This is particularly advantageous for crack-sensitive steels.
  • the cast strip 3 is checked for cracks by a camera 73, which can be used to advantage that the crack pattern in the scale is influenced by cracks in the base material becomes.
  • the formation of a measured variable is advantageous through a neuro-fuzzy system.
  • FIG. 2 shows the structure of the intelligent part of the control system. This essentially consists of the parts process optimizer 15, model 20, model adaptation 16 and data storage 17. These parts of the control system interact in such a way that as good as possible via the setpoint output 13, Instructions appropriate to the situation via the data line V for Litigation can be made available. This Instructions are then converted into setpoints for basic automation implemented. The following is the task and the function of the individual parts is described.
  • the model output variables are typical quality parameters of the product.
  • the model description generally does not exactly grasp the process behavior, which is why y i and deviate more or less from each other.
  • the manipulated variables u i and the non-influenceable manipulated variables v i are transmitted via data lines I and II.
  • the model adaptation 16 has the task of improving the model, thus the model behavior as well as the process behavior corresponds. This can - at least for model parts - done online by making these model parts based on continuously recorded process data can be adapted or updated.
  • the adaptation can also be carried out off-line at certain times. This is done on the basis of a number m of process states representing the process ( u k i , v k i , y k i ), which are stored in the data memory 17.
  • the index k quantifies the respective process status.
  • the process optimizer has the task of using an optimization method and the process model to find manipulated variables u i which lead to the best possible process behavior.
  • the process optimizer works off-line at specific times, for example, which can be predetermined manually, as follows:
  • the manipulated variables v i that cannot be influenced, for which the optimization is to take place — for example the current ones — are kept constant and are fed to the model via the data line II.
  • the process optimizer is then connected to the model by means of switch 18. It gives control values u i to the model.
  • the initial values are based on the model certainly. These are compared with target output values y target, i , and it becomes the error certainly.
  • the error E should be minimized.
  • the process optimizer varies the manipulated variables u i in an iterative loop, which contains the calculation of y i and E as well as the new selection of u i , until the error cannot be reduced further or this optimization is terminated. Genetic algorithms, hill climbing methods, etc. can be used as optimization methods.
  • the main task of the data memory is to archive representative process states ( u i , v i , y i ). In doing so, he replaces old process data again and again with newly determined one, in order to enable a current, albeit selective, process description based on this data.
  • the data memory then supplies the model adaptation, as described above.
  • it also supplies start values u i for the process optimizer.
  • the start values are selected, for example, in such a way that the output values y i belonging to these start values correspond as well as possible to the target values y target, i .
  • model 20 and Process optimizer 15 e.g. genetic algorithms for e.g. evolutionary, model improvement
  • Process optimizer 15 works preferably off-line because of the complexity of a plant control model with its many possible Develop the computing time of an evolutionary optimization process becomes comparatively long.
  • Optimization strategies that e.g. based on an analysis of the probable model behavior are selected many optimization processes until a clear one is reached Calculate model improvement.
  • the results from the model calculation are continuously compared with the setpoint values.
  • the difference can be minimized through optimization. Since the difference in technical processes cannot generally become zero, the optimization process must be meaningfully limited, that is, it must be terminated.
  • FIG. 3 shows more precise details of the program structure with which the optimization is terminated and the new setpoint output is started.
  • 58 denotes an error function to be selected in each case, into which the detected errors (setpoint deviations) flow.
  • 61 now examines whether the error function meets the termination criteria of the optimization. If this is the case, further optimized control and regulating variables are output.
  • start values from the data memory continuously enter the start value specification 59, from which, in search steps in 60, not from the optimizer, but from the data memory, for example with the aid of fuzzy interpolation, control and regulating parameters for sub-optional process control are obtained.
  • Switching takes place after reaching the predetermined quality factor, which is adapted to the respective control system knowledge.
  • the minimization which can never be absolute, is stopped when the predetermined quality factor is reached.
  • the model will also be advantageous if it goes to the Process connected, i.e. Switch 1 is closed, too generates an alarm signal that is critical Operating states signaled. Such procedures are already known and can be found in the same way in conventional control systems.
  • FIG 4 which the structure of a model adaptation by means of a Optimization algorithm shows data from the start value specification 61 in a search step unit 62 and are from passed there as model parameters to model 63.
  • the Model 63 forms, together with data storage 64, a parameter improvement loop, the 65 in a known manner the compares formed and stored values.
  • the comparison values are fed to the error function 67, their Passes values to the termination criteria unit 66. Are the If the termination criteria are met, the model will not continue improved and worked with the existing values. Otherwise the optimization with further search steps and the Intermediate values continued in the data storage.
  • the essential sub-models of the overall process model of the embodiment, 46 denotes that Input model in which the external influences, such as the influences summarized from the quality of the material used are.
  • the quality of the steel used results e.g. of the Liquidus value, the solidus value, as well as others, the casting behavior characteristic sizes.
  • 47 denotes the tundish model, into e.g. the steel volume of the tundish, the dip tube position or the like, the stopper position and the steel discharge temperature come in.
  • the input models 46 and 47 are summarized in sub-model 56, which the status of the fed Material.
  • Such partial models can advantageously parallel to other sub-models, such as the casting area model, the rolling range model or similar be optimized.
  • the input model 48 contains the influences that the solidification influence, e.g. the casting roll cooling, the infrared heating etc.
  • the input model 49 contains the values that necessary for the heat balance, such as the steel casting roll temperature difference, the influence of lubricant as a function the amount of lubricant, the rate of crystal formation the respective steel grade and e.g. the roll surface condition.
  • the input model 50 contains e.g. the influences of Casting level characteristics, so the level of the casting level Slag layer thickness and the radiation coefficient.
  • the Input models 48, 49 and 50 are part of a model 54, the reflects the status of the casting area, summarized. This Model area summary is general for production areas advantageous as it is the overall model optimization simplified and improved.
  • the sub-models are among themselves partly still dependent on each other, for example to a considerable extent the input models 49 (input model heat balance) and 50 (Initial model of the mold level characteristic). Secondary dependencies are not shown for simplification.
  • the sub-model 51 contains all influences on the solidification front, i.e. to the area where the on the two chill rolls solidified metal shells meet. Essentially these influences are the forming work done by the casting rolls is achieved, the vibration width of the casting rolls or the emerging tape, the side gap sealing influences and the level of effort of the overall system, this is e.g. a fuzzy model.
  • the sub-model 52 gives the exit values again, e.g. the quality of the tape that Outlet temperature and distribution, but also the tendency to stick and the condition of the scale formed.
  • the sub-model 52 also goes the input model 53 and the input model 74 a, which relates to the temperature profile across Tape and refer to the surface condition of the tape. For the particularly advantageous case that it is a
  • the strip casting and rolling mill also works Rolling mill part models 54 in this special process model a, since the product training after leaving the Roll stands is the decisive criterion.
  • the sub-models are combined to form the product training model 57, which is the thickness profile of the band formed, the strip thickness, a possible error pattern, the grain structure of the tape, the surface structure etc. summarized.
  • the surface structure and especially the grain structure of the tape can only be determined with a considerable time delay. It is therefore advantageous to work with partial models here on the basis of neural networks for qualitative and quantitative determination of influencing variables.
  • 68 denotes the process data archive , 69 the model parameter storage part, 70 the part with the start values for the optimizer and 71 the memory part for the safe operating points. In 68 the respective Model training saved.
  • the basic automation with its regulations, controls, Interlocks etc., an indispensable part of the Control system, because it the safe functioning of the Attachment even if the model part of the Guaranteed control system according to the invention, must Perform a variety of functions.
  • the individual functions are not, finally, due to the symbolized individual "black box” in FIG 7.
  • Here means 21 in the embodiment, the mass flow control over the Single speed controller, 22 the regulation of the tundish heating, 23 the mold level control, 24 the tundish outflow control and 25 the heating power of the infrared or similar.
  • 26 means the regulation of the addition of lubricant, e.g. in shape of loose mold powder or of one applied to the casting rollers Casting powder paste, 27 the cooling water quantity control, 28 if necessary the roller oscillation control, 29 the electrical Drive control and 30 the roll gap setting.
  • 31 means the roller speed control and 32 if necessary the control of the Roller torque, 33 the setting of the cleaning system, consisting for example of a brush and a scraper for the casting rolls and 34 the regulation of the electrodynamic System for balancing the belt weight and the regulation the vibration width of the cast strip.
  • 36 means that Regulation of the individual parts of an electrodynamic system for side gap sealing and 37 the regulation of the heating for the side walls of the space between the casting rolls.
  • 38 means the temperature profile control of the induction heating system 10.
  • 39 as well as indicated other control units refer to regulations of the downstream deformation units, e.g. Rolling stands, the train between them Roll stands etc. On the above actuators, controllers etc. acts the time control 45, which outputs the manipulated variables etc. coordinated in time.
  • interlocks e.g. prevent Liquid steel can flow before the pair of casting rolls and the Deformation rollers are workable, etc.
  • they are further systems, not shown in the schematic, for the band edge separation required, e.g. by Lasers, for influencing scale formation, e.g. by Silicating, roller lubrication etc. available.
  • the manipulated variables VI are generated, over which the system is managed.
  • the casting and rolling process consists of a number of sub-processes, their training and influences are decisive for the End product.
  • Can be influenced and optimized according to the invention are the properties of the end product, e.g. its Thickness, its thickness profile and its surface formation, through a number of adjustable process variables, e.g. the Casting roll gap, the casting roll profile, the casting level etc., which in turn shows the location of the union zone on the Casting rollers affect separated, solidified metal shells.
  • an overall process model according to the invention which the Process behavior describes. Based on this process model can be the influencing factors with which the process influenced, step by step according to the process conditions be adapted and optimized.
  • the intelligent, self-improving, Part of the control system consisting of three essential elements: The process model, the model adaptation and the process optimizer.
  • the process model consists of subsystems (Modules) together, which vary depending on the process knowledge Will be type. With knowledge of the physical Connections can be classic, physical-mathematical Models are created. On the other hand, you only have experience or estimates, so are fuzzy or neuro-fuzzy systems related. In case you have little or nothing about knows the process behavior, such as cracking and At least in the beginning, surface training is set to neural Networks for process formation. Describes overall the model the relationship between the process variables, as in selected example of the mold level, the condition values and the quality of the potted material, the setting values the casting rolls etc. and the quality parameters of the Tape, e.g. the thickness, the profile and the surface formation.
  • the model Since the model may substantial, percentage based on uncertain knowledge, it is not exact. The The model must therefore be adapted and changed based on the process data obtained etc. This happens advantageously on the one hand using the well-known model adaptation based on data from past Process states set up. Based on this data the model parameters or similar so that the model behavior corresponds as closely as possible to that of the process. Also be modifying the models themselves, e.g. through genetic Algorithms, a combinatorial evolution etc. Corresponding Optimization strategies are known, e.g. from Ulrich Hoffmann, Hanns Hofmann "Introduction to Optimization", Verlag Chemie GmbH, 1971 Weinheim / Bergstrasse; H.P.

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  • Engineering & Computer Science (AREA)
  • Mechanical Engineering (AREA)
  • Feedback Control In General (AREA)
  • General Factory Administration (AREA)
  • Control Of Metal Rolling (AREA)
  • Testing And Monitoring For Control Systems (AREA)
  • Organic Low-Molecular-Weight Compounds And Preparation Thereof (AREA)

Claims (20)

  1. Système de gestion pour une installation de l'industrie primaire ou de l'industrie manufacturière, par exemple pour une installation métallurgique, comme pour la production de feuillards d'acier ou de métaux non ferreux, le système de gestion étant conçu pour détecter automatiquement, par une technique informatique et sur la base d'une connaissance préalable donnée, l'état de l'installation et des particularités d'un processus de fabrication se déroulant dans l'installation, par exemple d'un processus de coulée continue pour des feuillards, et pour prescrire des données de valeurs de consigne (V) adaptées à la situation en vue d'obtenir un résultat de production sûr, le cas échéant le meilleur possible,
       caractérisé par le fait que
    le système de gestion simule l'état de l'installation et des divers éléments de l'installation en vue d'une optimisation en continu à l'aide d'un modèle de processus à structure modulaire qui décrit les relations entre les grandeurs d'entrée de processus ainsi que des grandeurs réglantes et les grandeurs de sortie de processus, par exemple des valeurs caractéristiques de la qualité du produit fabriqué.
  2. Système de gestion selon la revendication 1,
       caractérisé par le fait que
    il est conçu pour optimiser les données de valeurs de consigne (V) adaptées à la situation, de préférence pour les optimiser automatiquement dans des routines d'optimisation prédéterminées.
  3. Système de gestion selon la revendication 1 ou 2,
       caractérisé par le fait que
    la connaissance préalable donnée est améliorée, de préférence automatiquement, en continu au moyen d'une connaissance obtenue en interne par une technique informatique sur le modèle de processus (20) pendant la production, par exemple en différents points de fonctionnement, et cette connaissance de processus produite automatiquement est prise en charge comme nouvelle connaissance préalable dans une mémoire de données (17) qui est notamment constamment mise à jour.
  4. Système de gestion selon la revendication 1, 2 ou 3,
       caractérisé par le fait que
    les données de valeurs de consigne (V) adaptées à la situation sont envoyées par exemple sous forme de valeurs de référence aux éléments de l'installation, directement sous forme de valeurs de commande, par exemple de positions, ou en particulier indirectement, par exemple par l'intermédiaire de valeurs de consigne de régulateur, par exemple pour des vitesses de rotation.
  5. Système de gestion selon la revendication 1, 2, 3 ou 4,
       caractérisé par le fait que
    il comporte un système de fonction de base pour les éléments de l'installation qui transfère de manière fiable dans la commande de l'installation les données de valeurs de consigne (V) provenant de la connaissance obtenue par des moyens informatiques, par exemple à partir d'un modèle de processus (20), de préférence à partir d'un modèle global de processus.
  6. Système de gestion selon la revendication 5,
       caractérisé par le fait que
    le système de fonction de base est conçu comme un système d'automatisation de base permettant le fonctionnement fiable des éléments de l'installation pris individuellement ou regroupés.
  7. Système de gestion selon la revendication 5 ou 6,
       caractérisé par le fait que
    le système de fonction de base reçoit ses valeurs prescrites directement de la partie intelligente du système de gestion qui détermine ces valeurs à partir des résultats de processus d'adaptation et/ou d'optimisation sur le modèle de processus (20).
  8. Système de gestion selon la revendication 5, 6 ou 7,
       caractérisé par le fait que
    le système d'automatisation de base est conçu comme un sous-système autonome garantissant un état sûr de l'installation et du processus (système de retour d'état à risque) qui peut accéder, à la place des données de valeurs de consigne (V) produites par des moyens informatiques, notamment à des valeurs de fonctionnement connues comme étant sûres et mémorisées dans la mémoire de données.
  9. Système de gestion selon la revendication 5, 6, 7 ou 8,
       caractérisé par le fait que
    le système de fonction de base comporte des routines de démarrage et de lancement qui peuvent être entrées manuellement ou automatiquement ainsi que des routines de fonctionnement normal sous-optimales dans lesquelles des données de valeurs de consigne (V), déterminées sinon par des moyens informatiques, peuvent être remplacées par des données constantes.
  10. Système de gestion selon la revendication 1,
       caractérisé par le fait que
    le modèle de processus comporte au moins en partie, dans la mesure où il peut être modélisé sur la base de lois générales mathématico-physiques, chimiques, métallurgiques ou biologiques, des formes de description mathématiques.
  11. Système de gestion selon la revendication 1 ou 10,
       caractérisé par le fait que
    le modèle de processus comporte, pour les éléments de l'installation pour lesquels il existe une connaissance de processus qui ne peut être exprimée que sous forme linguistique, des parties de modèle qui sont formulées par des moyens linguistiques et qui peuvent être mises en oeuvre par exemple par des systèmes flous, par des systèmes neuronaux flous, par des systèmes experts ou par des ensembles de tableaux.
  12. Système de gestion selon la revendication 1, 10 ou 11,
       caractérisé par le fait que le modèle de processus comporte des systèmes auto-adaptatifs, par exemple des réseaux neuronaux, pour les éléments de l'installation pour lesquels il n'est pas possible de former un modèle sur la base de principes mathématico-physiques, chimiques, métallurgiques ou biologiques ou sur la base d'une connaissance de processus pouvant être décrite par des moyens linguistiques.
  13. Système de gestion selon la revendication 1, 10, 11 ou 12,
       caractérisé par le fait que
    le modèle de processus est adapté en continu au processus, ou poursuit en continu le processus, en fonction de données de processus qui ont été collectées dans l'installation et qui sont archivées dans une base de données de processus et que cette adaptation ou poursuite s'effectue au moyen de procédés adaptatifs ou de procédés d'apprentissage, par exemple par un procédé d'apprentissage à rétropropagation ou par un procédé de sélection pour différents modèles partiels, par exemple des réseaux neuronaux.
  14. Système de gestion selon la revendication 1, 10, 11, 12 ou 13,
       caractérisé par le fait que
    les variables de processus réglables sont optimisées en différé par un dispositif d'optimisation sur le modèle de processus de telle sorte que les grandeurs de sortie de modèle, qui sont notamment des valeurs caractéristiques de la qualité du produit, coïncident le plus possible avec des valeurs prescrites, par exemple avec les valeurs recherchées.
  15. Système de gestion selon la revendication 1, 10, 11, 12, 13 ou 14,
       caractérisé par le fait que
    l'optimisation s'effectue avec un procédé d'optimisation connu, par exemple avec un algorithme génétique, avec le procédé de Hooke-Jeeves, avec un procédé du type Simulated Annealings ou avec d'autres procédés analogues, et que les procédés d'optimisation respectivement appliqués sont prédéterminés ou sélectionnés dans un fichier en fonction de la situation et du problème, par exemple en fonction du nombre des grandeurs à optimiser et/ou de la configuration des minima à escompter.
  16. Système de gestion selon la revendication 15,
       caractérisé par le fait que
    les critères d'interruption des procédés d'optimisation sont déterminés, par exemple avec des réseaux neuronaux, selon une méthode de reconnaissance de formes ou selon des critères de convergence classiques sur la base de l'allure de l'optimisation.
  17. Système de gestion selon la revendication 12, 13, 14, 15 ou 16,
       caractérisé par le fait que
    les valeurs initiales pour une optimisation sont déterminées sur la base des données de fonctionnement sous-optimales archivées dans une mémoire de données de processus.
  18. Système de gestion selon la revendication 1, 10, 11, 12, 13, 14, 15, 16 ou 17,
       caractérisé par le fait que
    l'optimisation s'effectue en différé à l'aide du modèle de processus, des variables de processus réglables qui ont été déterminées de telle sorte que les valeurs caractéristiques, simulées par le modèle, du produit fabriqué coïncident le plus possible avec les valeurs souhaitées prédéterminées données comme valeurs prescrites au système de fonction de base du processus et le processus étant réglé par ce dernier en fonction des valeurs prescrites.
  19. Système de gestion selon des revendications 1, 10, 11, 12, 13, 14, 15, 16, 17 ou 18,
       caractérisé par le fait que
    les valeurs prescrites pour le système de fonction de base peuvent être produites directement à partir des données de la base de données de processus lors d'un dysfonctionnement ou d'un événement analogue concernant le modèle ou le dispositif d'optimisation, une interpolation étant effectuée notamment entre les données de fonctionnement mémorisées afin d'améliorer les valeurs prescrites.
  20. Système de gestion selon l'une des revendications 1 ou 10 à 18,
       caractérisé par le fait que
    le modèle, par exemple dans le cas d'un processus de laminage direct en coulée continue pour des feuillards métalliques, prend en compte notamment les limitations des grandeurs réglantes, le comportement dans le temps des actionneurs et éventuellement la dynamique du processus, de préférence avant ou dans la zone de laminage direct, par exemple par rapport à la position de la zone de réunion des coquilles de solidification pour les coquilles de solidification séparées sur le laminage direct.
EP96905686A 1995-03-09 1996-03-06 Systeme de gestion pour installations de l'industrie primaire ou de l'industrie manufacturiere Revoked EP0813701B1 (fr)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
DE19508476 1995-03-09
DE19508476A DE19508476A1 (de) 1995-03-09 1995-03-09 Leitsystem für eine Anlage der Grundstoff- oder der verarbeitenden Industrie o. ä.
PCT/DE1996/000397 WO1996028772A1 (fr) 1995-03-09 1996-03-06 Systeme de gestion pour installations de l'industrie primaire ou de l'industrie manufacturiere

Publications (2)

Publication Number Publication Date
EP0813701A1 EP0813701A1 (fr) 1997-12-29
EP0813701B1 true EP0813701B1 (fr) 1999-10-13

Family

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Application Number Title Priority Date Filing Date
EP96905686A Revoked EP0813701B1 (fr) 1995-03-09 1996-03-06 Systeme de gestion pour installations de l'industrie primaire ou de l'industrie manufacturiere

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US (1) US5727127A (fr)
EP (1) EP0813701B1 (fr)
CN (1) CN1244032C (fr)
AT (1) ATE185626T1 (fr)
DE (2) DE19508476A1 (fr)
WO (1) WO1996028772A1 (fr)

Families Citing this family (61)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE19508474A1 (de) * 1995-03-09 1996-09-19 Siemens Ag Intelligentes Rechner-Leitsystem
DE19513801A1 (de) * 1995-04-11 1996-10-17 Siemens Ag Verfahren zur automatischen Erzeugung einer Steuerung
CN1119729C (zh) 1996-06-21 2003-08-27 西门子公司 用于开通工业设备、特别是原料工业设备的方法和系统
DE19640806C2 (de) * 1996-10-02 2002-03-14 Siemens Ag Verfahren und Einrichtung zum Gießen eines Stranges aus flüssigem Material
DE19704983B4 (de) * 1997-01-29 2006-07-06 Diehl Bgt Defence Gmbh & Co. Kg Autonomes System, insbesondere autonome Plattform
DE19706767A1 (de) * 1997-02-20 1998-09-03 Siemens Ag Verfahren und Einrichtung zur Simulation einer Anlage der Grundstoffindustrie
JP3802965B2 (ja) * 1997-03-21 2006-08-02 ヴイ．ウリヤノフ セルゲイ 非線形の物理的な制御対象の最適制御のための自己組織化方法及び装置
DE19715503A1 (de) 1997-04-14 1998-10-15 Siemens Ag Integriertes Rechner- und Kommunikationssystem für den Anlagenbereich
DE19731980A1 (de) * 1997-07-24 1999-01-28 Siemens Ag Verfahren zur Steuerung und Voreinstellung eines Walzgerüstes oder einer Walzstraße zum Walzen eines Walzbandes
DE19744815C1 (de) * 1997-10-02 1999-03-11 Mannesmann Ag Verfahren zur Materialflußbestimmung und -steuerung von stranggegossenen Brammen
DE19752548A1 (de) * 1997-11-27 1999-06-10 Schloemann Siemag Ag Verfahren zur Vorrichtung zum Einstellen und Halten der Temperatur einer Stahlschmelze beim Stranggießen
DE19807114B4 (de) * 1998-02-20 2006-11-23 Sms Demag Ag Verfahren zur Qualitätsüberwachung des Gießvorganges einer Stranggießanlage
AT409229B (de) * 1998-04-29 2002-06-25 Voest Alpine Ind Anlagen Verfahren zur verbesserung der kontur gewalzten materials und zur erhöhung der gewalzten materiallänge
DE19832762C2 (de) * 1998-07-21 2003-05-08 Fraunhofer Ges Forschung Gießwalzanlage, insbesondere Dünnbrammengießwalzanlage
DE19916190C2 (de) * 1998-12-22 2001-03-29 Sms Demag Ag Verfahren und Vorrichtung zum Stranggießen von Brammen
DE10001400C2 (de) * 1999-01-14 2003-08-14 Sumitomo Heavy Industries Vorrichtung zum Regeln des Gießspiegels einer Stranggußvorrichtung
DE10027324C2 (de) * 1999-06-07 2003-04-10 Sms Demag Ag Verfahren zum Gießen eines metallischen Strangs sowie System hierzu
DE19931331A1 (de) * 1999-07-07 2001-01-18 Siemens Ag Verfahren und Einrichtung zum Herstellen eines Stranges aus Metall
US6505475B1 (en) 1999-08-20 2003-01-14 Hudson Technologies Inc. Method and apparatus for measuring and improving efficiency in refrigeration systems
US6564194B1 (en) * 1999-09-10 2003-05-13 John R. Koza Method and apparatus for automatic synthesis controllers
DE19959204A1 (de) * 1999-12-08 2001-07-12 Siemens Ag Verfahren zur Ermittlung einer Beizzeit eines eine Zunderschicht aufweisenden Metallbandes
US7216113B1 (en) * 2000-03-24 2007-05-08 Symyx Technologies, Inc. Remote Execution of Materials Library Designs
AT409352B (de) * 2000-06-02 2002-07-25 Voest Alpine Ind Anlagen Verfahren zum stranggiessen eines metallstranges
US6581672B2 (en) 2000-09-29 2003-06-24 Nucor Corporation Method for controlling a continuous strip steel casting process based on customer-specified requirements
ATE349288T1 (de) * 2000-09-29 2007-01-15 Nucor Corp Herstellungsverfahren von angeforderten stahlbändern
US7591917B2 (en) * 2000-10-02 2009-09-22 Nucor Corporation Method of producing steel strip
US6801817B1 (en) * 2001-02-20 2004-10-05 Advanced Micro Devices, Inc. Method and apparatus for integrating multiple process controllers
US6615098B1 (en) * 2001-02-21 2003-09-02 Advanced Micro Devices, Inc. Method and apparatus for controlling a tool using a baseline control script
US6915172B2 (en) 2001-11-21 2005-07-05 General Electric Method, system and storage medium for enhancing process control
US7367018B2 (en) * 2002-10-25 2008-04-29 Aspen Technology, Inc. System and method for organizing and sharing of process plant design and operations data
US8463441B2 (en) * 2002-12-09 2013-06-11 Hudson Technologies, Inc. Method and apparatus for optimizing refrigeration systems
KR101258973B1 (ko) * 2002-12-09 2013-04-29 허드슨 테크놀로지스, 인코포레이티드 냉각 시스템 최적화 방법 및 장치
DE10306273A1 (de) * 2003-02-14 2004-09-02 Siemens Ag Mathematisches Modell für eine hüttentechnische Anlage und Optimierungsverfahren für den Betrieb einer hüttentechnischen Anlage unter Verwendung eines derartigen Modells
DE10310357A1 (de) * 2003-03-10 2004-09-30 Siemens Ag Gießwalzanlage zur Erzeugen eines Stahlbandes
JP2004315949A (ja) * 2003-04-21 2004-11-11 Internatl Business Mach Corp <Ibm> 物理状態制御用情報計算装置、物理状態制御用情報計算方法、物理状態制御用情報計算用プログラム及び物理状態制御装置
SE527168C2 (sv) * 2003-12-31 2006-01-10 Abb Ab Förfarande och anordning för mätning, bestämning och styrning av planhet hos ett metallband
JP4834988B2 (ja) * 2004-12-14 2011-12-14 横河電機株式会社 連続系プロセス制御方法および連続系プロセス制御システム
DE102007025447A1 (de) * 2006-10-09 2008-04-17 Siemens Ag Verfahren zur Steuerung und/oder Regelung eines industriellen Prozesses
DE102008020381A1 (de) * 2008-04-23 2009-10-29 Siemens Aktiengesellschaft System und Verfahren zum Sammeln von Daten aus industriellen Anlagen
RU2492023C2 (ru) * 2008-11-04 2013-09-10 Смс Зимаг Аг Способ и устройство для управления затвердеванием непрерывной заготовки в установке для непрерывного литья при запуске процесса литья
US8042602B2 (en) * 2009-06-16 2011-10-25 Nucor Corporation High efficiency plant for making steel
KR101130222B1 (ko) * 2009-12-24 2012-03-29 주식회사 오토스윙 용접 헬멧의 카트리지 제어방법
KR101299094B1 (ko) * 2010-08-30 2013-08-27 현대제철 주식회사 래들 교환시 용강 오염범위 예측 방법
EP2578333A1 (fr) * 2011-10-07 2013-04-10 Nemak Linz GmbH Procédé de commande d'un système de coulée
CN102520705B (zh) * 2011-12-31 2014-11-26 中国石油天然气股份有限公司 一种炼化生产过程优化分析方法及系统
CN103293951B (zh) * 2013-06-14 2015-07-29 湘潭大学 一种智能出钢材组炉组浇装置及方法
DE102013220657A1 (de) * 2013-07-26 2015-01-29 Sms Siemag Ag Verfahren und Vorrichtung zur Herstellung eines metallischen Bandes im kontinuierlichen Gießwalzverfahren
ITUD20130128A1 (it) * 2013-10-04 2015-04-05 Danieli Off Mecc Impianto siderurgico a linea di co-laminazione multipla e relativo metodo di produzione
AT516440B1 (de) * 2014-10-28 2017-03-15 Primetals Technologies Austria GmbH Strangführungssegment, Strangführungssystem und Verfahren zum Konfigurieren eines solchen Strangführungssystems
CN107003645B (zh) * 2014-12-17 2020-12-08 首要金属科技奥地利有限责任公司 具有运行方式优化的用于冶炼技术设备的运行方法
US20160275219A1 (en) * 2015-03-20 2016-09-22 Siemens Product Lifecycle Management Software Inc. Simulating an industrial system
EP3293594A1 (fr) * 2016-09-13 2018-03-14 Primetals Technologies Germany GmbH Utilisation d'intelligence artificielle globale dans des installations de l'industrie de base
EP3318342A1 (fr) 2016-11-07 2018-05-09 Primetals Technologies Austria GmbH Procédé de fonctionnement d'un ensemble de coulée-laminage
EP3511783A1 (fr) * 2018-01-15 2019-07-17 Covestro Deutschland AG Procédé permettant d'améliorer un processus de production chimique
EP3511784A1 (fr) * 2018-01-15 2019-07-17 Covestro Deutschland AG Procédé permettant d'améliorer un processus de production chimique
EP3511782A1 (fr) * 2018-01-15 2019-07-17 Covestro Deutschland AG Procédé permettant d'améliorer un processus de production chimique
EP3534278A1 (fr) * 2018-02-28 2019-09-04 Siemens Aktiengesellschaft Procédé et système de dessin assisté par ordinateur d'une usine
US11262734B2 (en) * 2018-08-29 2022-03-01 Siemens Aktiengesellschaft Systems and methods to ensure robustness for engineering autonomy
DE102019132029A1 (de) * 2019-11-26 2021-05-27 Thyssenkrupp Steel Europe Ag Herstellung eines gewünschten Metallwerkstücks aus einem Metallflachprodukt
CN113485267A (zh) * 2021-07-12 2021-10-08 湖南先登智能科技有限公司 一种镍基靶材生产自动控制系统
EP4354232A1 (fr) 2022-10-11 2024-04-17 Primetals Technologies Germany GmbH Procédé et système d'adaptation d'un processus de fabrication

Family Cites Families (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4368510A (en) * 1980-10-20 1983-01-11 Leeds & Northrup Company Automatic identification system for self tuning process controller
DE3133222A1 (de) * 1981-08-21 1983-03-03 Kraftwerk Union AG, 4330 Mülheim Verfahren zur ermittlung des augenblicklichen und des zukuenftigen zustandes eines technischen prozesses mit hilfe von nichtlinearen prozessmodellen
EP0138059A1 (fr) * 1983-09-19 1985-04-24 Hitachi, Ltd. Procédé et dispositif pour couler une bande métallique entre deux cylindres
US4678023A (en) * 1985-12-24 1987-07-07 Aluminum Company Of America Closed loop delivery gauge control in roll casting
JP2697908B2 (ja) * 1989-08-03 1998-01-19 新日本製鐵株式会社 双ロール式連続鋳造機の制御装置
US5212765A (en) * 1990-08-03 1993-05-18 E. I. Du Pont De Nemours & Co., Inc. On-line training neural network system for process control
DE4125176A1 (de) * 1991-07-30 1993-02-04 Lucas Nuelle Lehr Und Messgera Steuerfeld einer industriellen anlage mit einer speicherprogrammierbaren steuerung
JP3136183B2 (ja) * 1992-01-20 2001-02-19 株式会社日立製作所 制御方法
DE4209746A1 (de) * 1992-03-25 1993-09-30 Siemens Ag Verfahren zur Optimierung eines technischen Neuro-Fuzzy-Systems
US5412756A (en) * 1992-12-22 1995-05-02 Mitsubishi Denki Kabushiki Kaisha Artificial intelligence software shell for plant operation simulation
DE4310332A1 (de) * 1993-03-31 1994-10-06 Mueller Weingarten Maschf Verfahren zur Ermittlung von optimalen Parametern eines Gießprozesses insbesondere an Druckgießmaschinen
US5486998A (en) * 1993-06-14 1996-01-23 Amax Coal West, Inc. Process stabilizing process controller

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Publication number Publication date
CN1244032C (zh) 2006-03-01
DE19508476A1 (de) 1996-09-12
US5727127A (en) 1998-03-10
ATE185626T1 (de) 1999-10-15
EP0813701A1 (fr) 1997-12-29
WO1996028772A1 (fr) 1996-09-19
CN1179840A (zh) 1998-04-22
DE59603352D1 (de) 1999-11-18

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