Coarse Return Prediction in a Cement Industry’s Closed Grinding Circuit System through a Fully Connected Deep Neural Network (FCDNN) Model
Milling operations in various production processes are among the most important factors in determining the quality, stability, and consumption of energy. Optimizing and stabilizing the milling process is a non-linear multivariable control problem. In specific processes that deal with natural materia...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/4/1361 |
| _version_ | 1797416046698692608 |
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| author | Morad Danishvar Sebelan Danishvar Francisco Souza Pedro Sousa Alireza Mousavi |
| author_facet | Morad Danishvar Sebelan Danishvar Francisco Souza Pedro Sousa Alireza Mousavi |
| author_sort | Morad Danishvar |
| collection | DOAJ |
| description | Milling operations in various production processes are among the most important factors in determining the quality, stability, and consumption of energy. Optimizing and stabilizing the milling process is a non-linear multivariable control problem. In specific processes that deal with natural materials (e.g., cement, pulp and paper, beverage brewery and water/wastewater treatment industries). A novel data-driven approach utilizing real-time monitoring control technology is proposed for the purpose of optimizing the grinding of cement processing. A combined event modeling for feature extraction and the fully connected deep neural network model to predict the coarseness of cement particles is proposed. The resulting prediction allows a look ahead control strategy and corrective actions. The proposed solution has been deployed in a number of cement plants around the world. The resultant control strategy has enabled the operators to take corrective actions before the coarse return increases, both in autonomous and manual mode. The impact of the solution has improved efficiency resource use by 10% of resources, the plant stability, and the overall energy efficiency of the plant. |
| first_indexed | 2024-03-09T05:58:07Z |
| format | Article |
| id | doaj.art-4050f21cb58f4ae2bbbd9f9538a3a2a0 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T05:58:07Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-4050f21cb58f4ae2bbbd9f9538a3a2a02023-12-03T12:11:27ZengMDPI AGApplied Sciences2076-34172021-02-01114136110.3390/app11041361Coarse Return Prediction in a Cement Industry’s Closed Grinding Circuit System through a Fully Connected Deep Neural Network (FCDNN) ModelMorad Danishvar0Sebelan Danishvar1Francisco Souza2Pedro Sousa3Alireza Mousavi4Department of Electronics and Computer Engineering, College of Engineering, Design and Physical Sciences, Brunel University, Kingston Lane UB8 3PH, UKDepartment of Electronics and Computer Engineering, College of Engineering, Design and Physical Sciences, Brunel University, Kingston Lane UB8 3PH, UKOncontrol Technologies, 3000-174 Coimbra, PortugalOncontrol Technologies, 3000-174 Coimbra, PortugalDepartment of Electronics and Computer Engineering, College of Engineering, Design and Physical Sciences, Brunel University, Kingston Lane UB8 3PH, UKMilling operations in various production processes are among the most important factors in determining the quality, stability, and consumption of energy. Optimizing and stabilizing the milling process is a non-linear multivariable control problem. In specific processes that deal with natural materials (e.g., cement, pulp and paper, beverage brewery and water/wastewater treatment industries). A novel data-driven approach utilizing real-time monitoring control technology is proposed for the purpose of optimizing the grinding of cement processing. A combined event modeling for feature extraction and the fully connected deep neural network model to predict the coarseness of cement particles is proposed. The resulting prediction allows a look ahead control strategy and corrective actions. The proposed solution has been deployed in a number of cement plants around the world. The resultant control strategy has enabled the operators to take corrective actions before the coarse return increases, both in autonomous and manual mode. The impact of the solution has improved efficiency resource use by 10% of resources, the plant stability, and the overall energy efficiency of the plant.https://www.mdpi.com/2076-3417/11/4/1361coarse returnpredictiondeep learningcementmilling and grinding processevent modeling |
| spellingShingle | Morad Danishvar Sebelan Danishvar Francisco Souza Pedro Sousa Alireza Mousavi Coarse Return Prediction in a Cement Industry’s Closed Grinding Circuit System through a Fully Connected Deep Neural Network (FCDNN) Model Applied Sciences coarse return prediction deep learning cement milling and grinding process event modeling |
| title | Coarse Return Prediction in a Cement Industry’s Closed Grinding Circuit System through a Fully Connected Deep Neural Network (FCDNN) Model |
| title_full | Coarse Return Prediction in a Cement Industry’s Closed Grinding Circuit System through a Fully Connected Deep Neural Network (FCDNN) Model |
| title_fullStr | Coarse Return Prediction in a Cement Industry’s Closed Grinding Circuit System through a Fully Connected Deep Neural Network (FCDNN) Model |
| title_full_unstemmed | Coarse Return Prediction in a Cement Industry’s Closed Grinding Circuit System through a Fully Connected Deep Neural Network (FCDNN) Model |
| title_short | Coarse Return Prediction in a Cement Industry’s Closed Grinding Circuit System through a Fully Connected Deep Neural Network (FCDNN) Model |
| title_sort | coarse return prediction in a cement industry s closed grinding circuit system through a fully connected deep neural network fcdnn model |
| topic | coarse return prediction deep learning cement milling and grinding process event modeling |
| url | https://www.mdpi.com/2076-3417/11/4/1361 |
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