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

doi:10.1109/SYSCON.2016.7490538

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

ASK Solutions
Universidad de Cuenca
Dep. Research and Development
Universidad Católica de Cuenca

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

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.

Original language	English
Title of host publication	10th Annual International Systems Conference, SysCon 2016 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781467395182
DOIs	https://doi.org/10.1109/SYSCON.2016.7490538
State	Published - 13 Jun 2016
Event	10th Annual International Systems Conference, SysCon 2016 - Orlando, United States Duration: 18 Apr 2016 → 21 Apr 2016

Publication series

Name	10th Annual International Systems Conference, SysCon 2016 - Proceedings

Conference

Conference	10th Annual International Systems Conference, SysCon 2016
Country/Territory	United States
City	Orlando
Period	18/04/16 → 21/04/16

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

adaptive neuro-fuzzy inference system
artificial neural networks
black-box model
Fineness of the cement

Access to Document

10.1109/SYSCON.2016.7490538

Cite this

Minchala-Avila, L. I., Reinoso-Avecillas, M., Sanchez, C., Mora, A., Yungaicela, M., & Mata-Quevedo, J. P. (2016). A comparative study of black-box models for cement quality prediction using input-output measurements of a closed circuit grinding. In 10th Annual International Systems Conference, SysCon 2016 - Proceedings Article 7490538 (10th Annual International Systems Conference, SysCon 2016 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SYSCON.2016.7490538

Minchala-Avila, Luis I. ; Reinoso-Avecillas, Manuel ; Sanchez, Christian et al. / A comparative study of black-box models for cement quality prediction using input-output measurements of a closed circuit grinding. 10th Annual International Systems Conference, SysCon 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. (10th Annual International Systems Conference, SysCon 2016 - Proceedings).

@inproceedings{7a0db81f476f487796a8617c97fa47ad,

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

abstract = "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.",

keywords = "adaptive neuro-fuzzy inference system, artificial neural networks, black-box model, Fineness of the cement",

author = "Minchala-Avila, \{Luis I.\} and Manuel Reinoso-Avecillas and Christian Sanchez and Alfredo Mora and Marcelo Yungaicela and Mata-Quevedo, \{Jean P.\}",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 10th Annual International Systems Conference, SysCon 2016 ; Conference date: 18-04-2016 Through 21-04-2016",

year = "2016",

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doi = "10.1109/SYSCON.2016.7490538",

language = "Ingl{\'e}s",

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publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Minchala-Avila, LI, Reinoso-Avecillas, M, Sanchez, C, Mora, A, Yungaicela, M & Mata-Quevedo, JP 2016, A comparative study of black-box models for cement quality prediction using input-output measurements of a closed circuit grinding. in 10th Annual International Systems Conference, SysCon 2016 - Proceedings., 7490538, 10th Annual International Systems Conference, SysCon 2016 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 10th Annual International Systems Conference, SysCon 2016, Orlando, United States, 18/04/16. https://doi.org/10.1109/SYSCON.2016.7490538

A comparative study of black-box models for cement quality prediction using input-output measurements of a closed circuit grinding. / Minchala-Avila, Luis I.; Reinoso-Avecillas, Manuel; Sanchez, Christian et al.
10th Annual International Systems Conference, SysCon 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. 7490538 (10th Annual International Systems Conference, SysCon 2016 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A comparative study of black-box models for cement quality prediction using input-output measurements of a closed circuit grinding

AU - Minchala-Avila, Luis I.

AU - Reinoso-Avecillas, Manuel

AU - Sanchez, Christian

AU - Mora, Alfredo

AU - Yungaicela, Marcelo

AU - Mata-Quevedo, Jean P.

PY - 2016/6/13

Y1 - 2016/6/13

N2 - 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.

AB - 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.

KW - adaptive neuro-fuzzy inference system

KW - artificial neural networks

KW - black-box model

KW - Fineness of the cement

UR - https://www.scopus.com/pages/publications/84979279823

U2 - 10.1109/SYSCON.2016.7490538

DO - 10.1109/SYSCON.2016.7490538

M3 - Contribución a la conferencia

AN - SCOPUS:84979279823

T3 - 10th Annual International Systems Conference, SysCon 2016 - Proceedings

BT - 10th Annual International Systems Conference, SysCon 2016 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 10th Annual International Systems Conference, SysCon 2016

Y2 - 18 April 2016 through 21 April 2016

ER -

Minchala-Avila LI, Reinoso-Avecillas M, Sanchez C, Mora A, Yungaicela M, Mata-Quevedo JP. A comparative study of black-box models for cement quality prediction using input-output measurements of a closed circuit grinding. In 10th Annual International Systems Conference, SysCon 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2016. 7490538. (10th Annual International Systems Conference, SysCon 2016 - Proceedings). doi: 10.1109/SYSCON.2016.7490538

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

Abstract

Publication series

Conference

UN SDGs

Keywords

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