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Please use this identifier to cite or link to this item: http://dspace.ucuenca.edu.ec/handle/123456789/29057
Title: A comparative study of black-box models for cement fineness prediction using SCADA measurements of a closed circuit grinding
Authors: Minchala Avila, Luis Ismael
Mata, J. P.
Sanchez, C.
Yungaicela, N. M.
Keywords: Black-Box Model
Fineness Of Cement
Issue Date: 1-Feb-2016
metadata.dc.ucuenca.embargoend: 1-Jan-2022
metadata.dc.ucuenca.volumen: 14
metadata.dc.source: IEEE Latin America Transactions
metadata.dc.identifier.doi: 10.1109/TLA.2016.7437209
metadata.dc.type: Article
This paper presents a comparative study of three different modeling techniques for predicting cement fineness using input-output SCADA measurements of the closed circuit grinding in a cement plant. The modeling approaches used are the following: statistical, artificial neural networks (ANN), and adaptive neuro-fuzzy inference system (ANFIS). The data set for generating the predictive models are obtained from a database of the operation of the cement plant, UCEM-Guapan located in Azogues, Ecuador. Online validations of the proposed models allow the selection of the best approach and the most accurate models for cement fineness prediction, Blaine and percentage passing the sieve No. 325.
URI: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84964385401&doi=10.1109%2fTLA.2016.7437209&partnerID=40&md5=759a4ae6c97f49f27bdb945400c593ed
ISSN: 15480992
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