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Please use this identifier to cite or link to this item: http://dspace.ucuenca.edu.ec/handle/123456789/29168
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
metadata.dc.ucuenca.nombrerevista: 10th Annual International Systems Conference SysCon 2016
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
metadata.dc.source: 10th Annual International Systems Conference, SysCon 2016 - Proceedings
metadata.dc.identifier.doi: 10.1109/SYSCON.2016.7490538
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.
URI: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84979279823&doi=10.1109%2fSYSCON.2016.7490538&partnerID=40&md5=f53a51d7025bdeb048c214c70c991b0a
ISBN: 9781467395182
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