Michael S. Dudzic

Integrated Monitoring Solution to Start-Up and Run-Time Operations for Continuous Casting

Multivariate statistical (MVS) technologies can be applied to both continuous and batch operations for process monitoring and fault diagnosis. Dofasco has developed an on-line MVS monitoring application for its #2 Continuous Caster that combines both continuous and batch MVS technologies into an integrated monitoring solution. Continuous MVS-based monitoring is used for continuous, run-time casting operation. Batch MVS-based monitoring is applied during the start-up operation while the process is in the transition to the run-time operation. This integrated application provides a real-time indication of the stability of the casting operation, which has resulted in improved process safety and economic performance.

On-line applications of multivariate statistics at Dofasco

Abstract There are many complexities involved in being able to accurately analyze and model production processes in a fully integrated steel facility. The drive for improved steel product quality has contributed to significant upgrades in instrumentation and data acquisition infrastructure with the hope of developing more useful information and better process knowledge. While this increased level of instrumentation has made more data available to the analyst, the associated data analysis and modeling problem has become more complicated due to the large and ever-increasing volume of process and product quality data. A class of technologies that Dofasco has used to support improved process modeling, feedback and statistical control in its automation applications is multivariate statistics with a primary focus on Principal Components Analysis (PCA) and Projection to Latent Structures (PLS). These methods have been successfully applied at Dofasco since 1993 to analyze data for a variety of purposes to develop on-line predictive models and process monitoring systems. Two on-line applications are described to illustrate the application of this technology. The first example is an on-line monitoring system that is used to observe the operation of the casting process at the mould area of Dofasco’s number one continuous slab caster. The second example is a control system for determining the optimal amount of reagent needed to accurately remove sulfur from pig iron.

Start Cast Breakouts Preventative Prediction Using Multi-Way PCA Technology

Abstract Breakouts during continuous caster start-up operations are of major concern in the steel-making industry, because they can lead to severe damage to equipment, significant process downtime, and potential safety consequences. As a multivariate statistical (MVS) analysis tool, Multi-way PCA (MPCA) is applied for monitoring the start-up operation of a continuous caster in order to predict potential start cast breakouts so the caster can be automatically stopped to avoid the catastrophic event. It is shown to provide good prediction of start cast breakouts resulting in significant savings in operating and maintenance costs. An on-line start cast monitoring system has been successfully implemented at Dofascos #2 continuous caster.

On-Line Industrial Implementation of Process Monitoring / Control Applications Using Multivariate Statistical Technologies: Challenges and Opportunities

Abstract The global steel industry is stnvmg to improve product quality through excellence in operation. To support this, significant investments have been made in upgrading instrumentation, data acquisition and computing infrastructures. The expectation is that with more process and product data readily available, useful information and better process knowledge can be gained in a timely fashion. The problem that has developed is that with the large volumes of data available, the associated data analysis and modeling have become increasingly complex. As a result, much of the data is either not used or summarized / heavily compressed. This means that a significant amount of the information and knowledge resident in the data is lost, diminishing the returns from the investment made in the information technology infrastructure. A class of technologies that Dofasco has used to meet this data challenge is multivariate statistics (MVS), with a primary focus on Principal Components Analysis (PCA) and Projection to Latent Structures (PLS). These methods have been successfully applied to analyze data for a variety of purposes, which includes the development of online predictive models and process monitoring systems. Since 1993, Dofasco has been involved with over 70 off-line / on-line applications of this technology at our steel facility in Hamilton, Ontario, Canada. Through these applications, significant fmancial returns to the company have been generated.

Applications of multivariate statistics at Dofasco

Multivariate statistical technologies, the principal components analysis and projection to latent structures, are data modeling technologies based on advanced multivariable statistical methods. These methods are capable of: analyzing process data; building predictive models and providing SPC functionality by extracting information from all process and quality data from an operation simultaneously. Multivariate statistical methods are especially powerful techniques for analyzing industrial data sets that have the following characteristics: higher dimensionality; high collinearity; noisy; and with some missing data. The application of these methods have been successfully done at Dofasco since 1993 to analyze data for a variety of purposes, develop online predictive models, and develop online process monitoring systems. An online application is described to illustrate the advantages of this technology.

Industrial Experience on Process Transition Monitoring for Continuous Steel Casting Operation

Abstract Process transitions are common in the iron and steel industry. Our investigation shows that more than 50% of catastrophic process failures in continuous steel casting operation are related to abnormal operations during the process transition period. As a multivariate statistical (MVS) analysis tool, Multi-way PCA (MPCA) is applied in this paper to monitor one important process transition in the continuous casting process: submerged entry nozzle (SEN) change. A novel scheme is proposed for synchronizing process trajectories over the SEN change and the missing data existing in the synchronized trajectories are handled subsequently in both mode ling and monitoring parts. The monitoring results are demonstrated by an industrial example. It is shown to provide good detectability of various process abnormalities. The proposed scheme can be further extended to monitor other process transitions in continuous casting process such as flying tundish change and product grade change.