Predictive Controller Design for a Cement Ball Mill Grinding Process under Larger Heterogeneities in Clinker Using State-Space Models
Chemical process industries are running under severe constraints, and it is essential to maintain the end-product quality under disturbances. Maintaining the product quality in the cement grinding process in the presence of clinker heterogeneity is a challenging task. The model predictive controller...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-09-01
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| Series: | Designs |
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| Online Access: | https://www.mdpi.com/2411-9660/4/3/36 |
| _version_ | 1797553570675949568 |
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| author | Sivanandam Venkatesh Kannan Ramkumar Rengarajan Amirtharajan |
| author_facet | Sivanandam Venkatesh Kannan Ramkumar Rengarajan Amirtharajan |
| author_sort | Sivanandam Venkatesh |
| collection | DOAJ |
| description | Chemical process industries are running under severe constraints, and it is essential to maintain the end-product quality under disturbances. Maintaining the product quality in the cement grinding process in the presence of clinker heterogeneity is a challenging task. The model predictive controller (MPC) poses a viable solution to handle the variability. This paper addresses the design of predictive controller for the cement grinding process using the state-space model and the implementation of this industrially prevalent predictive controller in a real-time cement plant simulator. The real-time simulator provides a realistic environment for testing the controllers. Both the designed state-space predictive controller (SSMPC) in this work and the generalised predictive controller (GPC) are tested in an industrially recognized real-time simulator ECS/CEMulator available at FLSmidthPvt. Ltd., Chennai, by introducing a grindability factor from 33 to 27 (the lower the grindability factor, the harder the clinker) to the clinkers. Both the predictive controllers can maintain product quality for the hardest clinkers, whereas the existing controller maintains the product quality only up to the grindability factor of 30. |
| first_indexed | 2024-03-10T16:18:24Z |
| format | Article |
| id | doaj.art-38efaf63e4d64373bfe1e767c88d9b58 |
| institution | Directory Open Access Journal |
| issn | 2411-9660 |
| language | English |
| last_indexed | 2024-03-10T16:18:24Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Designs |
| spelling | doaj.art-38efaf63e4d64373bfe1e767c88d9b582023-11-20T13:50:09ZengMDPI AGDesigns2411-96602020-09-01433610.3390/designs4030036Predictive Controller Design for a Cement Ball Mill Grinding Process under Larger Heterogeneities in Clinker Using State-Space ModelsSivanandam Venkatesh0Kannan Ramkumar1Rengarajan Amirtharajan2School of Electrical & Electronics Engineering, SASTRA Deemed University, Thanjavur 613 401, IndiaSchool of Electrical & Electronics Engineering, SASTRA Deemed University, Thanjavur 613 401, IndiaSchool of Electrical & Electronics Engineering, SASTRA Deemed University, Thanjavur 613 401, IndiaChemical process industries are running under severe constraints, and it is essential to maintain the end-product quality under disturbances. Maintaining the product quality in the cement grinding process in the presence of clinker heterogeneity is a challenging task. The model predictive controller (MPC) poses a viable solution to handle the variability. This paper addresses the design of predictive controller for the cement grinding process using the state-space model and the implementation of this industrially prevalent predictive controller in a real-time cement plant simulator. The real-time simulator provides a realistic environment for testing the controllers. Both the designed state-space predictive controller (SSMPC) in this work and the generalised predictive controller (GPC) are tested in an industrially recognized real-time simulator ECS/CEMulator available at FLSmidthPvt. Ltd., Chennai, by introducing a grindability factor from 33 to 27 (the lower the grindability factor, the harder the clinker) to the clinkers. Both the predictive controllers can maintain product quality for the hardest clinkers, whereas the existing controller maintains the product quality only up to the grindability factor of 30.https://www.mdpi.com/2411-9660/4/3/36ball mill grindingstate-space modelpredictive controllerreal-time simulator |
| spellingShingle | Sivanandam Venkatesh Kannan Ramkumar Rengarajan Amirtharajan Predictive Controller Design for a Cement Ball Mill Grinding Process under Larger Heterogeneities in Clinker Using State-Space Models Designs ball mill grinding state-space model predictive controller real-time simulator |
| title | Predictive Controller Design for a Cement Ball Mill Grinding Process under Larger Heterogeneities in Clinker Using State-Space Models |
| title_full | Predictive Controller Design for a Cement Ball Mill Grinding Process under Larger Heterogeneities in Clinker Using State-Space Models |
| title_fullStr | Predictive Controller Design for a Cement Ball Mill Grinding Process under Larger Heterogeneities in Clinker Using State-Space Models |
| title_full_unstemmed | Predictive Controller Design for a Cement Ball Mill Grinding Process under Larger Heterogeneities in Clinker Using State-Space Models |
| title_short | Predictive Controller Design for a Cement Ball Mill Grinding Process under Larger Heterogeneities in Clinker Using State-Space Models |
| title_sort | predictive controller design for a cement ball mill grinding process under larger heterogeneities in clinker using state space models |
| topic | ball mill grinding state-space model predictive controller real-time simulator |
| url | https://www.mdpi.com/2411-9660/4/3/36 |
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