A comparative study of black-box models for cement quality prediction using input-output measurements of a closed circuit grinding

Luis I. Minchala-Avila, Manuel Reinoso-Avecillas, Christian Sanchez, Alfredo Mora, Marcelo Yungaicela, Jean P. Mata-Quevedo

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

3 Citas (Scopus)

Resumen

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.

Idioma originalInglés
Título de la publicación alojada10th Annual International Systems Conference, SysCon 2016 - Proceedings
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781467395182
DOI
EstadoPublicada - 13 jun. 2016
Evento10th Annual International Systems Conference, SysCon 2016 - Orlando, Estados Unidos
Duración: 18 abr. 201621 abr. 2016

Serie de la publicación

Nombre10th Annual International Systems Conference, SysCon 2016 - Proceedings

Conferencia

Conferencia10th Annual International Systems Conference, SysCon 2016
País/TerritorioEstados Unidos
CiudadOrlando
Período18/04/1621/04/16

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