EP1342138A1 - Verfahren und vorrichtung zur berechnung von prozessgrössen eines industriellen prozesses - Google Patents
Verfahren und vorrichtung zur berechnung von prozessgrössen eines industriellen prozessesInfo
- Publication number
- EP1342138A1 EP1342138A1 EP01995542A EP01995542A EP1342138A1 EP 1342138 A1 EP1342138 A1 EP 1342138A1 EP 01995542 A EP01995542 A EP 01995542A EP 01995542 A EP01995542 A EP 01995542A EP 1342138 A1 EP1342138 A1 EP 1342138A1
- Authority
- EP
- European Patent Office
- Prior art keywords
- variables
- empirical
- model
- core model
- process parameters
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Withdrawn
Links
- 238000000034 method Methods 0.000 title claims abstract description 62
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 11
- 230000006978 adaptation Effects 0.000 claims description 7
- 238000005259 measurement Methods 0.000 claims description 7
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000005096 rolling process Methods 0.000 description 4
- 230000003044 adaptive effect Effects 0.000 description 2
- 238000011478 gradient descent method Methods 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 1
- 238000005097 cold rolling Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000004801 process automation Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B17/00—Systems involving the use of models or simulators of said systems
- G05B17/02—Systems involving the use of models or simulators of said systems electric
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/41885—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by modeling, simulation of the manufacturing system
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/32—Operator till task planning
- G05B2219/32017—Adapt real process as function of changing simulation model, changing for better results
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/42—Servomotor, servo controller kind till VSS
- G05B2219/42136—Fuzzy feedback adapts parameters model
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Definitions
- the invention relates to a method and a device for calculating process variables.
- Predicting process variables with which the system is preset are optimized using measured process variables.
- Adaptive models which are used in the process automation of industrial processes, often consist of a physical core model.
- This core model describes the relationships that can be described mathematically and physically with the current level of knowledge with sufficient accuracy (DE 43 38 608 AI).
- Process variables for which a sufficiently precise mathematical-physical theory does not yet exist are nowadays determined using empirical models. These empirical models are either manually, e.g. B. during the commissioning of an industrial process plant, adjusted or adjusted from the direct comparison between measured and calculated process variables.
- the object of the invention is to provide a method or device which enables the empirical models to be adapted quickly and efficiently.
- the process according to the invention according to claim 1 u comprises a core model and one or more empirical models, the empirical models being adapted using what is known as a “partially inverse core model X ⁇ .
- Process variables for which no sufficiently precise mathematical-physical theory is known are calculated in the empirical model.
- only process variables are calculated in the physical core model for which, based on current knowledge, the mathematical-physical dependencies are known with sufficient accuracy.
- the input variables of the empirical models, the output variables of which are to be referred to as empirical variables are known process parameters.
- the empirical variables as well as known process parameters are used as input variables in the core model. In the output variables of the core model, a distinction is made between measurable process variables and other process variables. That for
- Core model of partially inversely constructed model (briefly referred to as "partially inverse core model” 1 ) has a suitable selection of measurable process variables as well as all known parameters that come into play in the core model.
- the output variables of the partially inverse core model are the empirical ones already mentioned above sizes.
- the core model and the inverse core model are compatible with one another except for numerical rounding errors and both models are online-compatible in terms of computing time.
- the Partially inverse core model exactly (except for the measurement accuracy of the selected measurable process variables) determine which values the empirical variables should have had at the time of measurement so that the model predictions of the core model match the selected measurement values as closely as possible. With this knowledge of the empirical quantities at the time of measurement, the empirical models can be adapted.
- Another advantageous embodiment of the invention is that by means of adaptation or training algorithms, such as. B. with a gradient descent method, an adaptation of the process variables in the sense of a reduction in the determined deviation.
- the inventive device comprises a computing system of an industrial process for calculating unknown process parameters, also referred to as empirical variables, depending on known process parameters in at least one empirical model, and for calculating process variables depending on the known process parameters and the empirical variables in a core model, the empirical model being adapted by means of a core model that is partially inverse to the core model.
- unknown process parameters also referred to as empirical variables
- empirical model being adapted by means of a core model that is partially inverse to the core model.
- the single figure shows an example of the execution of an empirical model, a core model and a partially inverse core model according to the invention.
- the exemplary embodiment shows the method according to the invention for calculating process variables 12 of an industrial process.
- the process model shown is e.g. B. used for the calculation of the rolling forces, the rolling moments, the rolling power and the advance for all rolling stands of a five-stand cold rolling mill (tandem mill).
- a five-stand cold rolling mill tilt mill
- the core model 9 and the partially inverse core model 14 are compatible with one another except for numerical rounding errors and that both models are online-compatible in terms of computing time.
- the partially inverse core model 14 can be used to determine exactly which values the empirical variables 15 should have at the time of measurement so that the measurable process variables 10 calculated (selected) by the core model match the actually measured process variables 13 as closely as possible .
- the empirical models 3, 5 can be adapted or optimized using the calculated empirical variables 15.
- the adaptation or optimization of the empirical models 3, 5 takes place via adaptation or training algorithms 2, 4.
- the adaptation or training algorithms 2, 4 have the calculated empirical variables 16, 17 and the known process parameters 1 as input variables.
- the adaptation or training algorithms 2, 4 associated with the empirical models 3, 5 implemented in the form of neural networks are based on a gradient descent method, i. H. that, depending on the deviation, there is an adaptive change in the model parameters contained in the neuronets in the sense of a reduction in the determined deviation.
- the model parameters adapted in this way are available for the calculation of the empirical variables 6, 7 at the beginning of the next process sequence.
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- General Physics & Mathematics (AREA)
- Manufacturing & Machinery (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Software Systems (AREA)
- Medical Informatics (AREA)
- Evolutionary Computation (AREA)
- Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- General Engineering & Computer Science (AREA)
- Quality & Reliability (AREA)
- Feedback Control In General (AREA)
- General Factory Administration (AREA)
Applications Claiming Priority (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
DE10059567 | 2000-11-30 | ||
DE10059567A DE10059567A1 (de) | 2000-11-30 | 2000-11-30 | Verfahren und Vorrichtung zur Berechnung von Prozessgrößen eines industriellen Prozesses |
PCT/DE2001/004467 WO2002044822A1 (de) | 2000-11-30 | 2001-11-28 | Verfahren und vorrichtung zur berechnung von prozessgrössen eines industriellen prozesses |
Publications (1)
Publication Number | Publication Date |
---|---|
EP1342138A1 true EP1342138A1 (de) | 2003-09-10 |
Family
ID=7665306
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
EP01995542A Withdrawn EP1342138A1 (de) | 2000-11-30 | 2001-11-28 | Verfahren und vorrichtung zur berechnung von prozessgrössen eines industriellen prozesses |
Country Status (5)
Country | Link |
---|---|
US (1) | US20030208287A1 (ja) |
EP (1) | EP1342138A1 (ja) |
JP (1) | JP2004514998A (ja) |
DE (1) | DE10059567A1 (ja) |
WO (1) | WO2002044822A1 (ja) |
Families Citing this family (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
DE102004011236A1 (de) * | 2004-03-04 | 2005-09-29 | Bayerische Motoren Werke Ag | Prozesssteuersystem |
WO2016012971A1 (fr) * | 2014-07-25 | 2016-01-28 | Suez Environnement | Procede de detection d'anomalies dans un reseau de distribution, en particulier distribution d'eau |
FR3024254B1 (fr) | 2014-07-25 | 2018-08-03 | Suez Environnement | Procede de detection d'anomalies dans un reseau de distribution, en particulier distribution d'eau |
Family Cites Families (12)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JPH04149663A (ja) * | 1990-10-09 | 1992-05-22 | Fujitsu Ltd | 逆モデル生成方法および該方法による制御システム |
DE4130164A1 (de) * | 1991-09-11 | 1993-03-18 | Bodenseewerk Geraetetech | Regler, insbesondere flugregler |
WO1994020887A2 (en) * | 1993-03-02 | 1994-09-15 | Pavilion Technologies, Inc. | Method and apparatus for analyzing a neural network within desired operating parameter constraints |
DE4316533C2 (de) * | 1993-05-18 | 1997-09-18 | Bodenseewerk Geraetetech | Neuronales Netz für dynamische Prozesse |
DE4338608B4 (de) * | 1993-11-11 | 2005-10-06 | Siemens Ag | Verfahren und Vorrichtung zur Führung eines Prozesses in einem geregelten System |
JPH08115103A (ja) * | 1994-10-18 | 1996-05-07 | Meidensha Corp | 制御系の制御方式 |
DE19545262B4 (de) * | 1995-11-25 | 2004-08-05 | Alstom Power Conversion Gmbh | Einrichtung zum Betrieb einer mehrgerüstigen Walzstraße |
US6047221A (en) * | 1997-10-03 | 2000-04-04 | Pavilion Technologies, Inc. | Method for steady-state identification based upon identified dynamics |
US5933345A (en) * | 1996-05-06 | 1999-08-03 | Pavilion Technologies, Inc. | Method and apparatus for dynamic and steady state modeling over a desired path between two end points |
DE19641432C2 (de) * | 1996-10-08 | 2000-01-05 | Siemens Ag | Verfahren und Einrichtung zur Vorausberechnung von vorab unbekannten Parametern eines industriellen Prozesses |
DE19642918C2 (de) * | 1996-10-17 | 2003-04-24 | Siemens Ag | System zur Berechnung des Enddickenprofils eines Walzbandes |
DE19756877A1 (de) * | 1997-12-19 | 1999-07-01 | Siemens Ag | Verfahren und Einrichtung zum Beschichten eines Metallbandes |
-
2000
- 2000-11-30 DE DE10059567A patent/DE10059567A1/de not_active Ceased
-
2001
- 2001-11-28 EP EP01995542A patent/EP1342138A1/de not_active Withdrawn
- 2001-11-28 JP JP2002546924A patent/JP2004514998A/ja active Pending
- 2001-11-28 WO PCT/DE2001/004467 patent/WO2002044822A1/de active Application Filing
-
2003
- 2003-05-30 US US10/449,625 patent/US20030208287A1/en not_active Abandoned
Non-Patent Citations (1)
Title |
---|
See references of WO0244822A1 * |
Also Published As
Publication number | Publication date |
---|---|
DE10059567A1 (de) | 2002-06-13 |
US20030208287A1 (en) | 2003-11-06 |
WO2002044822A1 (de) | 2002-06-06 |
JP2004514998A (ja) | 2004-05-20 |
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Legal Events
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Effective date: 20030401 |
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Free format text: STATUS: THE APPLICATION IS DEEMED TO BE WITHDRAWN |
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18D | Application deemed to be withdrawn |
Effective date: 20100601 |