Energy Optimisation of Industrial Limestone Grinding Using ANN
This paper presents methods for modelling and optimising the industrial limestone grinding process carried out using a real limestone plant. Two key process evaluation indicators were developed: specific electric energy consumption and an extended indicator that also includes gas usage. Using proces...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/14/7702 |
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| author | Dagmara Kołodziej Patryk Bałazy Paweł Knap Krzysztof Lalik Damian Krawczykowski |
| author_facet | Dagmara Kołodziej Patryk Bałazy Paweł Knap Krzysztof Lalik Damian Krawczykowski |
| author_sort | Dagmara Kołodziej |
| collection | DOAJ |
| description | This paper presents methods for modelling and optimising the industrial limestone grinding process carried out using a real limestone plant. Two key process evaluation indicators were developed: specific electric energy consumption and an extended indicator that also includes gas usage. Using process data collected from the SCADA system and results from industrial factorial experiments, regression artificial neural network models were developed, with controllable process parameters used as inputs. In the next phase, black-box optimisation was performed using Bayesian and genetic algorithms to identify optimal mill operating settings. The results demonstrate significant improvements in energy efficiency, with energy savings reaching up to 48% in selected cases. The proposed methodology can be effectively applied to enhance energy performance in similar industrial grinding processes. |
| format | Article |
| id | doaj-art-b0958c9e5da44bf8a4101e6c3a60b1b2 |
| institution | Matheson Library |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-b0958c9e5da44bf8a4101e6c3a60b1b22025-07-25T13:12:06ZengMDPI AGApplied Sciences2076-34172025-07-011514770210.3390/app15147702Energy Optimisation of Industrial Limestone Grinding Using ANNDagmara Kołodziej0Patryk Bałazy1Paweł Knap2Krzysztof Lalik3Damian Krawczykowski4Department of Environmental Engineering, Faculty of Civil Engineering and Resource Management, AGH University of Krakow, 30059 Krakow, PolandDepartment of Process Control, Faculty of Mechanical Engineering and Robotics, AGH University of Krakow, 30059 Krakow, PolandDepartment of Process Control, Faculty of Mechanical Engineering and Robotics, AGH University of Krakow, 30059 Krakow, PolandDepartment of Process Control, Faculty of Mechanical Engineering and Robotics, AGH University of Krakow, 30059 Krakow, PolandDepartment of Environmental Engineering, Faculty of Civil Engineering and Resource Management, AGH University of Krakow, 30059 Krakow, PolandThis paper presents methods for modelling and optimising the industrial limestone grinding process carried out using a real limestone plant. Two key process evaluation indicators were developed: specific electric energy consumption and an extended indicator that also includes gas usage. Using process data collected from the SCADA system and results from industrial factorial experiments, regression artificial neural network models were developed, with controllable process parameters used as inputs. In the next phase, black-box optimisation was performed using Bayesian and genetic algorithms to identify optimal mill operating settings. The results demonstrate significant improvements in energy efficiency, with energy savings reaching up to 48% in selected cases. The proposed methodology can be effectively applied to enhance energy performance in similar industrial grinding processes.https://www.mdpi.com/2076-3417/15/14/7702energy efficiencyartificial neural networksindustrial process optimisationBayesian optimisationlimestone grinding |
| spellingShingle | Dagmara Kołodziej Patryk Bałazy Paweł Knap Krzysztof Lalik Damian Krawczykowski Energy Optimisation of Industrial Limestone Grinding Using ANN Applied Sciences energy efficiency artificial neural networks industrial process optimisation Bayesian optimisation limestone grinding |
| title | Energy Optimisation of Industrial Limestone Grinding Using ANN |
| title_full | Energy Optimisation of Industrial Limestone Grinding Using ANN |
| title_fullStr | Energy Optimisation of Industrial Limestone Grinding Using ANN |
| title_full_unstemmed | Energy Optimisation of Industrial Limestone Grinding Using ANN |
| title_short | Energy Optimisation of Industrial Limestone Grinding Using ANN |
| title_sort | energy optimisation of industrial limestone grinding using ann |
| topic | energy efficiency artificial neural networks industrial process optimisation Bayesian optimisation limestone grinding |
| url | https://www.mdpi.com/2076-3417/15/14/7702 |
| work_keys_str_mv | AT dagmarakołodziej energyoptimisationofindustriallimestonegrindingusingann AT patrykbałazy energyoptimisationofindustriallimestonegrindingusingann AT pawełknap energyoptimisationofindustriallimestonegrindingusingann AT krzysztoflalik energyoptimisationofindustriallimestonegrindingusingann AT damiankrawczykowski energyoptimisationofindustriallimestonegrindingusingann |