Data Mining Applications in Steel Industry

Data Mining Applications in Steel Industry

Joaquín Ordieres-Meré (University of La Rioja, Spain), Manuel Castejón-Limas (University of León, Spain) and Ana González-Marcos (University of León, Spain)
Copyright: © 2009 |Pages: 6
DOI: 10.4018/978-1-60566-010-3.ch063
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Abstract

The industrial plants, beyond subsisting, pursue to be leaders in increasingly competitive and dynamic markets. In this environment, quality management and technological innovation is less a choice than a must. Quality principles, such as those comprised in ISO 9000 standards, recommend companies to make their decisions with a based on facts approach; a policy much easily followed thanks to the all-pervasive introduction of computers and databases. With a view to improving the quality of their products, factory owners are becoming more and more interested in exploiting the benefits gained from better understanding their productive processes. Modern industries routinely measure the key variables that describe their productive processes while these are in operation, storing this raw information in databases for later analysis. Unfortunately, the distillation of useful information might prove problematic as the amount of stored data increases. Eventually, the use of specific tools capable of handling massive data sets becomes mandatory. These tools come from what it is known as ‘data mining’, a discipline that plays a remarkable role at processing and analyzing massive databases such as those found in the industry. One of the most interesting applications of data mining in the industrial field is system modeling. The fact that most frequently the relationships amongst process variables are nonlinear and the consequent difficulty to obtain explicit models to describe their behavior leads to data-based modeling as an improvement over simplified linear models.
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Background

The iron and steel making sector, in spite of being a traditional and mature activity, strives to approach new manufacturing technologies and to improve the quality of its products. The analysis of process records, by means of efficient tools and methods, provides deeper knowledge about the manufacturing processes, therefore allowing the development of strategies to cut costs down, to improve the quality of the product, and to increase the production capability.

On account of their anticorrosive properties, the galvanized steel is a product experiencing an increasing demand in multiple sectors, ranging from the domestic appliances manufacturing to the construction or automotive industry. Steel making companies have established a continuous improvement strategy at each of the stages of the galvanizing process in order to lead the market as well as to satisfy the, every time greater, customer requirements (Kim, Cheol-Moon, Sam-Kang, Han, C. & Soo-Chang, 1998; Tian, Hou & Gao, 2000; Ordieres-Meré, González-Marcos, González & Lobato-Rubio, 2004; Martínez-de-Pisón, Alba, Castejón & González, 2006; Pernía-Espinoza, Castejón-Limas, González-Marcos & Lobato-Rubio, 2005).

The quality of the galvanized product can be mainly related to two fundamental aspects (Lu & Markward, 1997; Schiefer, Jörgl, Rubenzucker & Aberl, 1999; Tenner, Linkens, Morris & Bailey, 2001):

  • As to the anticorrosive characteristics, the quality is determined by the thickness and uniformity of the zinc coat. These factors basically depend on the base metal surface treatment, the temperature of its coating and homogenization, the bath composition, the air blades control and the speed of the band.

  • As to the steel properties, they mainly depend on the steel composition and on the melting, rolling and heat treatment processes prior to the immersion of the band into the liquid zinc bath.

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