NDLI: Predicting ethanol concentration behavior of future harvests using Knowledge Discovery in Database

Content Provider	IEEE Xplore Digital Library
Author	da Cunha, M.J. Belini, V.L. Caurin, G.A.P.
Copyright Year	2012
Description	Author affiliation: Faculty of Electrical Engineering, Federal University of Uberlândia, Brazil (da Cunha, M.J.) \|\| Mechanical Engineering Department, São Carlos School of Engineering - University of São Paulo, Brazil (Belini, V.L.; Caurin, G.A.P.)
Abstract	The growing demand for higher productivity in the sugar-alcohol sector has required higher levels of automation in new and existing production plants. However, with increasing the number of sensors and equipment emerges a correspondent growth in the amount of data generated. Although the industry stores most of these data in dedicated data warehouse they are rarely used in future analysis due to the inherent technological challenge to properly cope with the large amount of data. This paper proposes the usage of a Knowledge Discovery in Database (KDD) process as a powerful tool to assist one in obtaining relevant industrial behavior from the stored data with the purpose of allowing quality and efficiency analysis. The experiments conducted with data collected in an industrial sugarcane plant successfully demonstrate that it is possible to apply the KDD to predict the ethanol concentration of future harvests.
Starting Page	1
Ending Page	6
File Size	571550
Page Count	6
File Format	PDF
ISBN	9781467324120
e-ISBN	9781467324113
DOI	10.1109/INDUSCON.2012.6451382
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-11-05
Publisher Place	Brazil
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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