Neural Network-Based Analysis of Flame States in Pulverised Coal and Biomass Co-Combustion
In the European Union, coal consumption in the power industry has been declining over time. Energy sources such as renewable energy, nuclear energy, and natural gas are being used on an increasing scale. Despite this, fossil fuels continue to be an important pillar of the energy industry in many cou...
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2025-01-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/2/258 |
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| author | Żaklin Grądz Waldemar Wójcik Baglan Imanbek Bakhyt Yeraliyeva |
| author_facet | Żaklin Grądz Waldemar Wójcik Baglan Imanbek Bakhyt Yeraliyeva |
| author_sort | Żaklin Grądz |
| collection | DOAJ |
| description | In the European Union, coal consumption in the power industry has been declining over time. Energy sources such as renewable energy, nuclear energy, and natural gas are being used on an increasing scale. Despite this, fossil fuels continue to be an important pillar of the energy industry in many countries around the world. There are various methods for diagnosing the combustion process, and one of them is based on a fibre-optic system for monitoring changes in flame intensity. Thanks to its innovative design, it allows information to be extracted from the flame under conditions of high temperatures and high dusting. The article presents an analysis of measurement signals for the recognition of states of flame intensity resulting from changes in the operating point of a power boiler. Trends in the flame that occur during the combustion process, which exceed the ranges specified by experts, can cause disturbances in combustion stability. The measurement data after preprocessing were classified using artificial neural networks to determine the conditions for flame stability. Based on the recurrent neural network models used, a classification accuracy of more than 99% was achieved. This allowed for the recognition of flame states in the combustion process. |
| format | Article |
| id | doaj-art-8fae843a856243f3861cfd94304e92be |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-8fae843a856243f3861cfd94304e92be2025-01-24T13:30:48ZengMDPI AGEnergies1996-10732025-01-0118225810.3390/en18020258Neural Network-Based Analysis of Flame States in Pulverised Coal and Biomass Co-CombustionŻaklin Grądz0Waldemar Wójcik1Baglan Imanbek2Bakhyt Yeraliyeva3Department of Electronics and Information Technology, Lublin University of Technology, Nadbystrzycka 38D, 20-618 Lublin, PolandDepartment of Electronics and Information Technology, Lublin University of Technology, Nadbystrzycka 38D, 20-618 Lublin, PolandFaculty Artificial Intelligence and Big Data, Al-Farabi Kazakh National University, Al-Farabi Avenue 71, Almaty 050040, KazakhstanFaculty of Information Technology, Taraz University Named After M.Kh. Dulaty, Suleymenova St. 7, Taraz 080000, KazakhstanIn the European Union, coal consumption in the power industry has been declining over time. Energy sources such as renewable energy, nuclear energy, and natural gas are being used on an increasing scale. Despite this, fossil fuels continue to be an important pillar of the energy industry in many countries around the world. There are various methods for diagnosing the combustion process, and one of them is based on a fibre-optic system for monitoring changes in flame intensity. Thanks to its innovative design, it allows information to be extracted from the flame under conditions of high temperatures and high dusting. The article presents an analysis of measurement signals for the recognition of states of flame intensity resulting from changes in the operating point of a power boiler. Trends in the flame that occur during the combustion process, which exceed the ranges specified by experts, can cause disturbances in combustion stability. The measurement data after preprocessing were classified using artificial neural networks to determine the conditions for flame stability. Based on the recurrent neural network models used, a classification accuracy of more than 99% was achieved. This allowed for the recognition of flame states in the combustion process.https://www.mdpi.com/1996-1073/18/2/258combustion processflamerecurrent neural networksclassification |
| spellingShingle | Żaklin Grądz Waldemar Wójcik Baglan Imanbek Bakhyt Yeraliyeva Neural Network-Based Analysis of Flame States in Pulverised Coal and Biomass Co-Combustion Energies combustion process flame recurrent neural networks classification |
| title | Neural Network-Based Analysis of Flame States in Pulverised Coal and Biomass Co-Combustion |
| title_full | Neural Network-Based Analysis of Flame States in Pulverised Coal and Biomass Co-Combustion |
| title_fullStr | Neural Network-Based Analysis of Flame States in Pulverised Coal and Biomass Co-Combustion |
| title_full_unstemmed | Neural Network-Based Analysis of Flame States in Pulverised Coal and Biomass Co-Combustion |
| title_short | Neural Network-Based Analysis of Flame States in Pulverised Coal and Biomass Co-Combustion |
| title_sort | neural network based analysis of flame states in pulverised coal and biomass co combustion |
| topic | combustion process flame recurrent neural networks classification |
| url | https://www.mdpi.com/1996-1073/18/2/258 |
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