A Dive into the Specific Electric Energy Consumption in Steelworks
The paper describes an application of optimization techniques for the minimization of the specific electrical energy consumption related to the production of steel for a steelworks situated in Italy. The major electrical consumption derives from two internal plants: the Electric Arc Furnace and the Ladle Furnace. This work addresses the problem of understanding the best settings (based on predefined models) to produce a specific steel, which is mainly characterized by its steelgrade and quality, with the minimum energy consumption.
A fuzzy logic-based method for outliers detection
The paper presents a method based on a fuzzy inference system that is capable of pointing out outliers in a series of data. The proposed algorithm has been adopted in order to process data coming from a real industrial context. The overall purpose of the work is to point out anomalous data due to several causes such as erroneous measurements, errors made by the operator that filled the database or anomalous process conditions. A standard statistical technique for outlier detection as been exploied for the same purpose: the performance obtained by the two methods are compared and discussed.
Statistical feature extraction and selection for IC test pattern analysis
Complexity reduction and automatic test point selection are discussed in the context of statistical pattern classification. Different types of feedforward neural networks capable of IC fault diagnosis are examined. To reduce diagnostic complexity, principal component analysis (PCA) and full stepwise feature selection are employed to reduce network input dimension without sacrificing accuracy. For fault analysis purposes, it seems that feature selection by stepwise variable selection appears much more useful than feature extraction by PCA, since the latter requires that all original test measurements be made while the former helps eliminate redundant measurements.<<ETX>>