A comparative study of black-box models for cement fineness prediction using SCADA measurements of a closed circuit grinding

• Published in: Revista IEEE América Latina Vol. 14; no. 2; pp. 673 - 680
• Main Authors: Minchala, Luis Ismael, Sanchez, Christian, Yungaicela, Noe Marcelo, Mora, Alfredo, Mata, Jean Paul
• Format: Journal Article
• Language: English
• Published: Los Alamitos IEEE 01.02.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; black-box model; Cements; Decision support systems; Discrete wavelet transforms; Fineness; fineness of cement; Fuzzy logic; Learning theory; Mathematical models; Neural networks; prediction; Supervisory control and data acquisition
• ISSN: 1548-0992; 1548-0992
• DOI: 10.1109/TLA.2016.7437209

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.