Soft Sensor for Online Cement Fineness Predicting in Ball Mills
- 10.2991/aer.k.201221.069How to use a DOI?
- Cement fineness, artificial neural network, ball mill, product quality
The cement fineness is a determining factor in product quality. Estimating this variable in real-time can be extremely useful to maintain the desired characteristics of the product during the cement grinding process, which will also allow a significant increase in the system energy efficiency. This paper describes the design and implementation of a soft sensor based on a backpropagation neural network model to predict the cement fineness online in a ball mill. The input variables of these models were selected by studying the cement grinding process, applying Spearman’s rank correlation, and the mutual information (MI) algorithm. The fineness results of laboratory tests were collected to obtain the output variable and for training the models. The procedure of extracting, analyzing, treating, and cleaning raw data received from the factory and the intensified hyperparameter adjustment of the predicting model provided excellent soft sensor performance. The developed system was tested in a cement grinding process and demonstrated the ability to provide information about the variables previously obtained only through offline laboratory tests.
- © 2020, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Karina Andreatta AU - Filipe Apóstolo AU - Reginaldo Nunes PY - 2020 DA - 2020/12/22 TI - Soft Sensor for Online Cement Fineness Predicting in Ball Mills BT - Proceedings of the International Seminar of Science and Applied Technology (ISSAT 2020) PB - Atlantis Press SP - 422 EP - 428 SN - 2352-5401 UR - https://doi.org/10.2991/aer.k.201221.069 DO - 10.2991/aer.k.201221.069 ID - Andreatta2020 ER -