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Title: A comparative study of black-box models for cement quality prediction using input-output measurements of a closed circuit grinding
Authors: Minchala Avila, Luis Ismael
Sanchez, C
Yungaicela, M
Keywords: adaptive neuro-fuzzy inference system
artificial neural networks
black-box model
Fineness of the cement
Issue Date: 18-Apr-2016
metadata.dc.ucuenca.embargoend: 1-Jan-2022
Orlando Florida
metadata.dc.type: Article
This paper presents the methodology of design of three different modeling techniques for predicting cement quality using input-output measurements of the closed circuit grinding in a cement plant. The modeling approaches used are: statistical, artificial neural networks (ANN), and adaptive neuro-fuzzy inference systems (ANFIS). The data set for generating the predictive models are obtained from a database of the operation of the cement plant, UCEM-Guapan. An OPC (OLE for process control) network configuration in the SCADA system allows online validations of the proposed models in order to select the best approach for real-time prediction of cement quality.
ISBN: 9781467395182
Appears in Collections:Artículos

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