Increasing Potlife of Hall–Héroult Reduction Cells Through Multivariate On-Line Monitoring of Preheating, Start-Up, and Early Operation

Aluminum is produced inside metallurgical reactors known as pots that are replaced at the end of their service life. New pots are preheated, started, and then enter a period known as early operation in which different control strategies are used before entering regular operation. It is known that how preheating, start-up, and early operation are performed can damage a well-designed pot and lead to a shorter service life. However, the impact of these phases with respect to potlife is not well documented quantitatively. In this article, multivariate statistical analysis techniques are used to investigate the impact of pot-to-pot variations during the three phases. A partial least squares regression model is first proposed for predicting potlife, within an error of 90 days, using process data gathered until the end of early operation. This model is also used to identify those variables having the greatest influence on potlife. Finally, multivariate statistical process control charts are proposed to monitor the three steps efficiently. These charts have a low false-alarm rate and can help find the root cause of abnormal operation occurring during the early phases. A few examples are used to illustrate how operators and engineers could use the charts to maintain consistent early operation and help improve mean potlife. Nomenclature: In this article, bold characters are used to identify vectors (bold lowercase), matrices (bold capital), and three-dimensional arrays (bold, underlined capital). Lowercase italics letters are used to define indices. Al—Aluminium; Al2O3—Alumina; C—Carbon; CO2—Carbon dioxide; kA—kilo-Amperes; Na—Sodium; Na3AlF6—Cryolite; V—Volt.

[1]  Theodora Kourti,et al.  Process analysis, monitoring and diagnosis, using multivariate projection methods , 1995 .

[2]  C. Jun,et al.  Performance of some variable selection methods when multicollinearity is present , 2005 .

[3]  Theodora Kourti,et al.  Multivariate SPC for startups and grade transitions , 2002 .

[4]  S. Wold Cross-Validatory Estimation of the Number of Components in Factor and Principal Components Models , 1978 .

[5]  Paul Geladi,et al.  Principal Component Analysis , 1987, Comprehensive Chemometrics.

[6]  Theodora Kourti,et al.  Application of latent variable methods to process control and multivariate statistical process control in industry , 2005 .

[7]  J. E. Jackson A User's Guide to Principal Components , 1991 .

[8]  Age K. Smilde,et al.  Generalized contribution plots in multivariate statistical process monitoring , 2000 .

[9]  John F. MacGregor,et al.  Multivariate SPC charts for monitoring batch processes , 1995 .

[10]  B. Kowalski,et al.  Partial least-squares regression: a tutorial , 1986 .

[11]  Theodora Kourti,et al.  Comparing alternative approaches for multivariate statistical analysis of batch process data , 1999 .

[12]  Salvador García Muñoz,et al.  Data-based latent variable methods for process analysis, monitoring and control , 2005, Comput. Chem. Eng..

[13]  Dimitrios I. Gerogiorgis,et al.  Light Metals 2003 , 2003 .

[14]  A. Höskuldsson PLS regression methods , 1988 .

[15]  Theodora Kourti,et al.  Process analysis and abnormal situation detection: from theory to practice , 2002 .

[16]  S. Wold,et al.  PLS-regression: a basic tool of chemometrics , 2001 .

[17]  J. Thonstad,et al.  Aluminium electrolysis : Fundamentals of the Hall-Héroult process , 2001 .

[18]  Michael S. Dudzic,et al.  An industrial perspective on implementing on-line applications of multivariate statistics , 2004 .

[19]  J. Edward Jackson,et al.  A User's Guide to Principal Components: Jackson/User's Guide to Principal Components , 2004 .