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.