CA2497489A1 - Real-time system and method of monitoring transient operations in continuous casting process for breakout prevention - Google Patents
Real-time system and method of monitoring transient operations in continuous casting process for breakout prevention Download PDFInfo
- Publication number
- CA2497489A1 CA2497489A1 CA002497489A CA2497489A CA2497489A1 CA 2497489 A1 CA2497489 A1 CA 2497489A1 CA 002497489 A CA002497489 A CA 002497489A CA 2497489 A CA2497489 A CA 2497489A CA 2497489 A1 CA2497489 A1 CA 2497489A1
- Authority
- CA
- Canada
- Prior art keywords
- transient
- transient operation
- stage
- test statistics
- data
- 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.)
- Granted
Links
Classifications
-
- 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
-
- 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
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
- G05B23/0224—Process history based detection method, e.g. whereby history implies the availability of large amounts of data
- G05B23/024—Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks
Abstract
A real-time system and method for online monitoring a transient operation in a continuous casting process. The transient operation refers to, but is not limited to, submerged entry nozzle changes, flying tundish changes, product grade changes, etc. This invention treats the aforementioned transient operations as batch processes and utilizes multiway principal component analysis to develop a multivariate statistical model to characterize normal process transitions based on carefully selected historical process data. Such a model is used by an online monitoring system to determine if a continuous caster transient operation is normal. This monitoring system can further be used to predict an impending breakout, one type of catastrophic process failures which may occur in a continuous casting process, during the transient operation. Process variables that are most likely related to the predicted breakout are identified by the system such that appropriate control actions can be taken to prevent an actual breakout occurrence.
Claims (29)
1. A method for online monitoring of transient operations in a continuous caster and predicting an impending transient-cast breakout or other process abnormality, comprising the following steps:
retrieving historical process data of a plurality of selected process variables during a pre-defined transient operation duration, the resulting historical process data covering most of normal transient operation regions and being used to construct a modeling dataset;
dividing modeling data in each transient operation into two stages, and synchronizing the modeling data separately with respect to a set of synchronization scales pre-defined by casting speed and casting strand length, respectively, in said two stages to define a series of observations forming a synchronized modeling dataset;
performing a multi-way principal component analysis (MPCA) on said synchronized modeling dataset to develop a multivariate statistical model to benchmark normal transient operations; and calculating a loading matrix P, and values of principal components for each transient operation in the modeling dataset;
computing test statistics at each observation over a predefined transient operation duration, for each transient operation in the modeling dataset, based on the multivariate statistical model;
determining control limits for said test statistics and their contributions from each selected process variable;
acquiring on-line process data of each selected process variable from a pre-defined start point to a current time t, in a new transient operation to be monitored;
synchronizing the acquired online data based on the synchronization scales defined in the two stages, and predicting future process trajectories for the rest of said transient operation duration, namely, from said current time t to a pre-defined end point for said transient operation duration to create complete process trajectories;
computing test statistics based on the multivariate statistical model for the resulting complete process trajectories of said new transient operation;
comparing the test statistics computed from said new transient operation to their corresponding control limits; and generating at least one indication signal indicating whether said new transient operation is statistically different from its historical benchmark of normal operation in a continuous casting process.
retrieving historical process data of a plurality of selected process variables during a pre-defined transient operation duration, the resulting historical process data covering most of normal transient operation regions and being used to construct a modeling dataset;
dividing modeling data in each transient operation into two stages, and synchronizing the modeling data separately with respect to a set of synchronization scales pre-defined by casting speed and casting strand length, respectively, in said two stages to define a series of observations forming a synchronized modeling dataset;
performing a multi-way principal component analysis (MPCA) on said synchronized modeling dataset to develop a multivariate statistical model to benchmark normal transient operations; and calculating a loading matrix P, and values of principal components for each transient operation in the modeling dataset;
computing test statistics at each observation over a predefined transient operation duration, for each transient operation in the modeling dataset, based on the multivariate statistical model;
determining control limits for said test statistics and their contributions from each selected process variable;
acquiring on-line process data of each selected process variable from a pre-defined start point to a current time t, in a new transient operation to be monitored;
synchronizing the acquired online data based on the synchronization scales defined in the two stages, and predicting future process trajectories for the rest of said transient operation duration, namely, from said current time t to a pre-defined end point for said transient operation duration to create complete process trajectories;
computing test statistics based on the multivariate statistical model for the resulting complete process trajectories of said new transient operation;
comparing the test statistics computed from said new transient operation to their corresponding control limits; and generating at least one indication signal indicating whether said new transient operation is statistically different from its historical benchmark of normal operation in a continuous casting process.
2. A method according to Claim 1 in which an alarm signal is generated when test statistics exceed their control limits for more than a pre-defined number of consecutive sampling intervals, said alarm signal being indicative of an impending transient-cast breakout or other process abnormality.
3. A method according to Claim 2 in which a list of process variables that are most likely contributors to the alarm signal is generated.
4. A method according to Claim 1, in which a transient operation is characterized by slowing down the casting speed, the casting speed of the continuous caster remaining unchanged for a short period of time and finally the casting speed is ramping up gradually back to its normal operating conditions over several minutes.
5. A method according to Claim 4 in which the transient operations include SEN changes, flying tundish changes, and product grade changes of a continuous caster.
6. The method according to Claim 1, in which the process variables of a continuous caster are selected from the group comprising: mold thermocouple readings, temperature differences between pre-defined thermocouple pairs, stopper rod position, tundish car net weight, mold cooling water flows, temperature difference between inlet and outlet mold cooling water, and heat flux transferred through each mold face.
7. The method according to Claim 6, wherein the heat flux transferred through each mold face of a continuous caster is calculated from the temperature difference between the inlet and the outlet cooling water flows for each mold face.
8. A method according to Claim 1, in which the duration of a transient operation in a continuous casting process is defined by two stages, namely;
a D-stage beginning at the said start point when the casting speed is decreased for a transient operation and ending at an end point when the casting speed is increased from a pre-defined holding speed; and a U-stage beginning at the end of D-stage, and ending at an end point where the length of steel strand cast in U-stage reaches a predetermined length.
a D-stage beginning at the said start point when the casting speed is decreased for a transient operation and ending at an end point when the casting speed is increased from a pre-defined holding speed; and a U-stage beginning at the end of D-stage, and ending at an end point where the length of steel strand cast in U-stage reaches a predetermined length.
9. The method according to Claim 8, in which said casting speed decreases continuously in the D-stage to a desired value which is controlled by an automatic controller.
10. A method according to Claim 8, in which the length of steel strand cast in U-stage is equal to 2.4 meters.
11. A method according to Claim 8, in which said length of steel strand is calculated as the integral of the casting speed over time, and it increases monotonically in the U-stage.
12. A method according to Claim 8, in which process trajectories in D-stage are synchronized based on a set of uniform synchronization scales defined by using the casting speed, and missing data exist at the beginning and/or the end of D-stage due to the applied process trajectory synchronization method.
13. A method according to Claim 8, in which process trajectories in U-stage are synchronized based on a set of non-uniform synchronization scales defined by using the casting strand length such that an online monitoring calculation is conducted more frequently at the beginning of the U-stage than at the end of the U-stage.
14. A method according to Claim 1, in which said multivariate statistical model is developed using MPCA technology and the number of principal components is determined such that a predetermined percentage of operation-to-operation variance existing in the historical modeling dataset has been captured.
15. A method according to Claim 1 in which the test statistics are selected from the group consisting of Squared Prediction Error (SPE) and "Hotelling T" (HT).
16. A method according to Claim 1, in which said control limits for said test statistics and contributions of each selected process variable are determined based on the historical data in the modeling dataset, and an adjustable multiplier is used online to adjust said control limits.
17. A method according to Claim 16, in which said multiplier for the control limits of said test statistics has different values in the D- and U-stage.
18. A method according to Claim 3, in which the process variables that are most likely related to the predicted breakouts or process abnormalities are identified by high contribution values, in comparison with their corresponding control limits, at the most current observation.
19. A real-time system for online monitoring of transient operations in a continuous caster and predicting an impending transient-cast breakout or other process abnormality, comprising:
a plurality of measurement sensors for obtaining real-time process data of a continuous caster;
a data access module to acquire said real-time process data from said sensors, and supply them to other modules in the system, as required;
a process state determination module to determine a process state selected from the following: start-up state, shut-down state, run-time state, transient operation state and to select a model calculation module to monitor operations of a continuous caster;
a model calculation module, selected by the process state determination module according to said determined process state, to receive said real-time process data, to perform MPCA calculations and to compute test statistics; and a human machine interface for displaying current transient operating conditions according to the determined process state.
a plurality of measurement sensors for obtaining real-time process data of a continuous caster;
a data access module to acquire said real-time process data from said sensors, and supply them to other modules in the system, as required;
a process state determination module to determine a process state selected from the following: start-up state, shut-down state, run-time state, transient operation state and to select a model calculation module to monitor operations of a continuous caster;
a model calculation module, selected by the process state determination module according to said determined process state, to receive said real-time process data, to perform MPCA calculations and to compute test statistics; and a human machine interface for displaying current transient operating conditions according to the determined process state.
20. A system according to Claim 19, in which said transient operation state includes submerged entry nozzle (SEN) changes, flying tundish changes, and product grade changes.
21. A system according to Claim 19, in which said human machine interface provides operating information including any one of the following:
identification of said process state, slab tracking identifiers, real-time sensor readings and monitoring information including test statistics, control limits associated with the test statistics and identified process variables that are most likely contributors to generate alarms.
identification of said process state, slab tracking identifiers, real-time sensor readings and monitoring information including test statistics, control limits associated with the test statistics and identified process variables that are most likely contributors to generate alarms.
22. A system according to Claim 21, in which said test statistics and contribution values are scaled to [0,1] with respect to corresponding control limits over a defined transient operation duration.
23. A real-time system for online monitoring of transient operations in a continuous caster and predicting an impending transient-cast breakout or other process abnormality comprising:
a model development module to receive and divide modeling data from transient operations into two stages, and synchronize the modeling data separately with respect to a set of synchronization scales pre-defined by casting speed and casting strand length, respectively, in said two stages, said model development module defining a series of observations which form a synchronized modeling dataset; to perform a multi-way principal component analysis (MPCA) on said synchronized modeling dataset to develop a multivariate statistical model, which captures operation-operation variance existing in historical data to benchmark normal transient operations; to calculate a loading matrix P, and values of principal components for each transient operation in the modeling dataset; to compute test statistics at each observation over a pre-defined transient operation duration, for each transient operation in the modeling dataset, based on the multivariate statistical model;
and to determine control limits for said test statistics and their contributions from selected process variables;
a plurality of measurement sensors for acquiring on-line process data of selected process variables from a pre-defined start point to a current time t in a new transient operation being monitored;
a synchronization module to synchronize the acquired online process data based on the synchronization scales defined in the two stages, and predict future process trajectories for the rest of said transient operation duration, namely from said current time t to a pre-defined end point for said transient operation duration to create complete process trajectories;
a calculation module to compute test statistics based on the multivariate statistical model for the resulting complete process trajectories of said new transient operation and to compare the test statistics computed from said new transient operation to their corresponding control limits; and a human machine interface for displaying current transient operating conditions.
a model development module to receive and divide modeling data from transient operations into two stages, and synchronize the modeling data separately with respect to a set of synchronization scales pre-defined by casting speed and casting strand length, respectively, in said two stages, said model development module defining a series of observations which form a synchronized modeling dataset; to perform a multi-way principal component analysis (MPCA) on said synchronized modeling dataset to develop a multivariate statistical model, which captures operation-operation variance existing in historical data to benchmark normal transient operations; to calculate a loading matrix P, and values of principal components for each transient operation in the modeling dataset; to compute test statistics at each observation over a pre-defined transient operation duration, for each transient operation in the modeling dataset, based on the multivariate statistical model;
and to determine control limits for said test statistics and their contributions from selected process variables;
a plurality of measurement sensors for acquiring on-line process data of selected process variables from a pre-defined start point to a current time t in a new transient operation being monitored;
a synchronization module to synchronize the acquired online process data based on the synchronization scales defined in the two stages, and predict future process trajectories for the rest of said transient operation duration, namely from said current time t to a pre-defined end point for said transient operation duration to create complete process trajectories;
a calculation module to compute test statistics based on the multivariate statistical model for the resulting complete process trajectories of said new transient operation and to compare the test statistics computed from said new transient operation to their corresponding control limits; and a human machine interface for displaying current transient operating conditions.
24. A system according to Claim 23 having an alarm for generating an alarm signal when test statistics exceed their control limits for more than a predetermined number of consecutive sampling intervals, said alarm signal being indicative of an impending transient-cast breakout or other process abnormality.
25. A system according to Claim 24 in which the human machine interface presents the alarm signal.
26. A system according to Claim 24 in which the human machine interface displays a list of process variables that are most likely contributors to the alarm.
27. A system according to Claim 23 in which the model development module divides the modeling data in each transient operation into two stages, namely:
a D-stage beginning at the said start point when the casting speed is decreased for a transient operation and ending at an end point when the casting speed is increased from a pre-defined holding speed; and a U-stage beginning at the end of D-stage, and ending at an end point where the length of steel strand cast in U-stage reaches a predetermined length.
a D-stage beginning at the said start point when the casting speed is decreased for a transient operation and ending at an end point when the casting speed is increased from a pre-defined holding speed; and a U-stage beginning at the end of D-stage, and ending at an end point where the length of steel strand cast in U-stage reaches a predetermined length.
28. A system according to Claim 23 having an adjustable multiplier to adjust control limits online.
29. A system according to Claim 23 having means for handling missing data generated by sensor failures, non-functional thermocouples due to changes of continuous caster mold width, and by said synchronization module.
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US10/854,217 | 2004-05-27 | ||
| US10/854,217 US6885907B1 (en) | 2004-05-27 | 2004-05-27 | Real-time system and method of monitoring transient operations in continuous casting process for breakout prevention |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CA2497489A1 true CA2497489A1 (en) | 2005-11-27 |
| CA2497489C CA2497489C (en) | 2012-01-03 |
Family
ID=34435983
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CA2497489A Active CA2497489C (en) | 2004-05-27 | 2005-02-17 | Real-time system and method of monitoring transient operations in continuous casting process for breakout prevention |
Country Status (5)
| Country | Link |
|---|---|
| US (1) | US6885907B1 (en) |
| AT (1) | AT500365B1 (en) |
| CA (1) | CA2497489C (en) |
| DE (1) | DE102005022922A1 (en) |
| FR (1) | FR2870762B1 (en) |
Families Citing this family (36)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004047885A (en) * | 2002-07-15 | 2004-02-12 | Matsushita Electric Ind Co Ltd | Monitoring system and monitoring method of semiconductor manufacturing apparatus |
| US7799273B2 (en) | 2004-05-06 | 2010-09-21 | Smp Logic Systems Llc | Manufacturing execution system for validation, quality and risk assessment and monitoring of pharmaceutical manufacturing processes |
| US7444197B2 (en) | 2004-05-06 | 2008-10-28 | Smp Logic Systems Llc | Methods, systems, and software program for validation and monitoring of pharmaceutical manufacturing processes |
| US7444191B2 (en) | 2005-10-04 | 2008-10-28 | Fisher-Rosemount Systems, Inc. | Process model identification in a process control system |
| US8036760B2 (en) * | 2005-10-04 | 2011-10-11 | Fisher-Rosemount Systems, Inc. | Method and apparatus for intelligent control and monitoring in a process control system |
| KR100709000B1 (en) * | 2005-10-04 | 2007-04-18 | 주식회사 포스코 | A on-line quality prediction system for stainless steel slab and the predicting method using it |
| US7738975B2 (en) * | 2005-10-04 | 2010-06-15 | Fisher-Rosemount Systems, Inc. | Analytical server integrated in a process control network |
| US20070088448A1 (en) * | 2005-10-19 | 2007-04-19 | Honeywell International Inc. | Predictive correlation model system |
| CN100337773C (en) * | 2005-12-27 | 2007-09-19 | 上海宝信软件股份有限公司 | Method for grouping continuous casting batch in continuous casting tech. of steel melting |
| US20070265801A1 (en) * | 2006-05-05 | 2007-11-15 | Honeywell International Inc. | Multivariate monitoring of operating procedures |
| US7720553B2 (en) | 2006-08-31 | 2010-05-18 | Dell Products L.P. | Service metric portfolio management |
| US7853431B2 (en) * | 2006-09-29 | 2010-12-14 | Fisher-Rosemount Systems, Inc. | On-line monitoring and diagnostics of a process using multivariate statistical analysis |
| PL2097794T5 (en) | 2006-11-03 | 2018-01-31 | Air Prod & Chem | System and method for process monitoring |
| US7680553B2 (en) * | 2007-03-08 | 2010-03-16 | Smp Logic Systems Llc | Methods of interfacing nanomaterials for the monitoring and execution of pharmaceutical manufacturing processes |
| US20090084517A1 (en) | 2007-05-07 | 2009-04-02 | Thomas Brian G | Cooling control system for continuous casting of metal |
| DE102007058109A1 (en) * | 2007-12-03 | 2009-06-04 | Sms Demag Ag | Device for controlling or regulating a temperature |
| DE102008028481B4 (en) * | 2008-06-13 | 2022-12-08 | Sms Group Gmbh | Method for predicting the formation of longitudinal cracks in continuous casting |
| WO2010075166A1 (en) | 2008-12-23 | 2010-07-01 | Shell Oil Company | System and method for monitoring an industrial production process |
| CN106094568B (en) | 2010-05-21 | 2019-04-02 | 费希尔-罗斯蒙特系统公司 | Multistage process modeling approach |
| US8880203B2 (en) | 2010-05-21 | 2014-11-04 | Fisher-Rosemount Systems, Inc. | On-line alignment of a process analytical model with actual process operation |
| US8630728B2 (en) * | 2011-01-28 | 2014-01-14 | Abb Technology Ag | System and method for generating indices to quantify operating transition performance of a continuous process |
| CN102527971B (en) * | 2012-02-27 | 2014-01-22 | 宝山钢铁股份有限公司 | Online forecasting method for internal crack defect of casting blank |
| US9509706B2 (en) * | 2012-08-31 | 2016-11-29 | Hitachi, Ltd. | Service performance monitoring method |
| CN102866630B (en) * | 2012-10-11 | 2015-03-18 | 西北工业大学 | Aircraft modeling method based on maximum information amount on-line identification criterion |
| JP6119640B2 (en) * | 2014-02-28 | 2017-04-26 | Ｊｆｅスチール株式会社 | Method and apparatus for determining surface defects in continuously cast slabs |
| JP6119807B2 (en) * | 2014-08-18 | 2017-04-26 | Ｊｆｅスチール株式会社 | Method and apparatus for determining surface defects of continuous cast slab, and method for producing steel slab using the surface defect determination method |
| US10455349B2 (en) * | 2016-03-14 | 2019-10-22 | Aveva Software, Llc | Contextual filtering in a historian system |
| CN106055836B (en) * | 2016-06-27 | 2019-01-29 | 重庆大学 | Continuous casting unit pours the Multipurpose Optimal Method of heat selection, sequence and casting time decision |
| US11899417B2 (en) * | 2019-05-02 | 2024-02-13 | Fisher-Rosemount Systems, Inc. | Methods and apparatus to implement predictive analytics for continuous control system processes |
| EP3819727B1 (en) * | 2019-11-11 | 2023-05-24 | Siemens Aktiengesellschaft | Control system for a technical installation with a trend curve diagram |
| CN113369451B (en) * | 2020-03-10 | 2022-11-29 | 中冶京诚工程技术有限公司 | Automatic monitoring method and device for bleed-out in continuous casting production |
| US11861494B2 (en) * | 2020-06-26 | 2024-01-02 | Intel Corporation | Neural network verification based on cognitive trajectories |
| CN112387947B (en) * | 2020-11-06 | 2021-10-08 | 福建三宝钢铁有限公司 | Preparation method of cold forging steel |
| CN116274916A (en) * | 2021-12-20 | 2023-06-23 | 斯凯孚公司 | Real-time monitoring method and stability analysis method for billet continuous casting process |
| CN114653914B (en) * | 2021-12-30 | 2023-12-29 | 白居冰 | Crystallizer steel leakage early warning method based on morphological reconstruction and electronic device |
| CN116944446B (en) * | 2023-07-27 | 2024-02-09 | 常州润来科技有限公司 | Abnormal monitoring method for technological parameters of horizontal continuous casting copper tube blank |
Family Cites Families (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US3478808A (en) | 1964-10-08 | 1969-11-18 | Bunker Ramo | Method of continuously casting steel |
| US4235276A (en) | 1979-04-16 | 1980-11-25 | Bethlehem Steel Corporation | Method and apparatus for controlling caster heat removal by varying casting speed |
| US4949777A (en) | 1987-10-02 | 1990-08-21 | Kawasaki Steel Corp. | Process of and apparatus for continuous casting with detection of possibility of break out |
| US5020585A (en) | 1989-03-20 | 1991-06-04 | Inland Steel Company | Break-out detection in continuous casting |
| JPH04238662A (en) | 1991-01-09 | 1992-08-26 | Toshiba Mach Co Ltd | Method for displaying casting data in die casting machine |
| US5257206A (en) | 1991-04-08 | 1993-10-26 | Praxair Technology, Inc. | Statistical process control for air separation process |
| DE4338607B4 (en) | 1993-11-11 | 2005-10-06 | Siemens Ag | Method and device for managing a process in a controlled system |
| SE9304246L (en) * | 1993-12-22 | 1995-06-23 | Asea Brown Boveri | Procedure for monitoring multivariate processes |
| JP3035688B2 (en) | 1993-12-24 | 2000-04-24 | トピー工業株式会社 | Breakout prediction system in continuous casting. |
| US5659667A (en) | 1995-01-17 | 1997-08-19 | The Regents Of The University Of California Office Of Technology Transfer | Adaptive model predictive process control using neural networks |
| US5904202A (en) | 1995-04-03 | 1999-05-18 | Siemens Aktiengesellschaft | Device for early detection of run-out in continuous casting |
| US5819258A (en) | 1997-03-07 | 1998-10-06 | Digital Equipment Corporation | Method and apparatus for automatically generating hierarchical categories from large document collections |
| DE19718262A1 (en) | 1997-04-30 | 1998-11-05 | Schenck Panel Production Syste | Method and system for generating a process model of a technical problem |
| DE19725433C1 (en) | 1997-06-16 | 1999-01-21 | Schloemann Siemag Ag | Method and device for early breakthrough detection in the continuous casting of steel with an oscillating mold |
| ATE281260T1 (en) * | 1998-07-21 | 2004-11-15 | Dofasco Inc | SYSTEM BASED ON A MULTIVARIABLE STATISTICAL MODEL FOR REPRESENTING THE OPERATION OF A CONTINUOUS CASTING PLANT AND DETECTING IMPENDING BREAKTHROUGH |
| US7130781B2 (en) * | 2002-04-10 | 2006-10-31 | 3M Innovative Properties Company | One-dimensional modeling of the manufacture of multi-layered material |
| CA2414167A1 (en) * | 2002-12-12 | 2004-06-12 | Dofasco Inc. | Method and online system for monitoring continuous caster start-up operation and predicting start cast breakouts |
-
2004
- 2004-05-27 US US10/854,217 patent/US6885907B1/en active Active
-
2005
- 2005-02-17 CA CA2497489A patent/CA2497489C/en active Active
- 2005-05-13 DE DE102005022922A patent/DE102005022922A1/en not_active Ceased
- 2005-05-13 FR FR0504876A patent/FR2870762B1/en not_active Expired - Fee Related
- 2005-05-24 AT AT0088605A patent/AT500365B1/en active
Also Published As
| Publication number | Publication date |
|---|---|
| FR2870762A1 (en) | 2005-12-02 |
| DE102005022922A1 (en) | 2005-12-29 |
| AT500365A2 (en) | 2005-12-15 |
| CA2497489C (en) | 2012-01-03 |
| US6885907B1 (en) | 2005-04-26 |
| FR2870762B1 (en) | 2007-06-15 |
| AT500365B1 (en) | 2009-04-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CA2497489A1 (en) | Real-time system and method of monitoring transient operations in continuous casting process for breakout prevention | |
| US7039552B2 (en) | Method and online system for monitoring continuous caster start-up operation and predicting start cast breakouts | |
| US6564119B1 (en) | Multivariate statistical model-based system for monitoring the operation of a continuous caster and detecting the onset of impending breakouts | |
| DK3250425T3 (en) | BRAKE COATING FORECAST | |
| Adamy | Breakout prediction for continuous casting by fuzzy mealy automata | |
| CN104936725B (en) | The monitoring method for continuous cast mold including establishing database | |
| CN103192047A (en) | Automatic control system and control method for cooling water of crystallizer for novel continuous casting machine | |
| US10095199B2 (en) | Energy consumption predicting device for rolling line | |
| JP2007224918A (en) | Method of judging excess of limit value | |
| Tirian et al. | Adaptive control system for continuous steel casting based on neural networks and fuzzy logic | |
| CN106413942A (en) | Method, device and program for determining casting state in continuous casting | |
| Zhang et al. | Integrated monitoring solution to start-up and run-time operations for continuous casting | |
| Zhang et al. | Industrial application of multivariate SPC to continuous caster start-up operations for breakout prevention | |
| US9063541B2 (en) | Method and means for tracking corrosion-related plant operation costs | |
| CN111683766A (en) | Method and device for monitoring a continuous casting process | |
| CN116274916A (en) | Real-time monitoring method and stability analysis method for billet continuous casting process | |
| US20140374051A1 (en) | Method for Controlling a Casting Plant | |
| JP5741060B2 (en) | Cooling zone diagnostic method, rolled material cooling method, cooling zone diagnostic device, rolled material cooling device | |
| JP7232028B2 (en) | Operation monitoring device and method | |
| JPH06154982A (en) | Method and device for monitoring mold temperature in continuous casting | |
| Zhang et al. | Industrial Experience on Process Transition Monitoring for Continuous Steel Casting Operation | |
| JPH10277716A (en) | Method for measuring thickness of solidified shell in continuous casing and instrument therefor | |
| JP6908930B2 (en) | Information generator and information generation method | |
| JP2018067051A (en) | Quality monitoring apparatus, quality monitoring system, quality monitoring method and program | |
| JPS6054256A (en) | Method for diagnosing abnormality of continuous casting machine |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| EEER | Examination request |