A Machine Learning Approach for Fire-Fighting Detection in the Power Industry
Coal combustion; Coal fired power plant; Coal industry; Coal storage; Fires; Forecasting; Fossil fuel power plants; Multilayer neural networks; Sensitivity analysis; Spontaneous combustion; Clinker formation prediction model; Complex Processes; Fighting detections; Fire fighting; Machine learning ap...
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Hashemite University
2023
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my.uniten.dspace-263972023-05-29T17:09:58Z A Machine Learning Approach for Fire-Fighting Detection in the Power Industry Ismail F.B. Al-Bazi A. Al-Hadeethi R.H. Victor M. 58027086700 35098298500 57412220300 57526587800 Coal combustion; Coal fired power plant; Coal industry; Coal storage; Fires; Forecasting; Fossil fuel power plants; Multilayer neural networks; Sensitivity analysis; Spontaneous combustion; Clinker formation prediction model; Complex Processes; Fighting detections; Fire fighting; Machine learning approaches; Power industry; Prediction modelling; Reserved coals; Spontaneous combustion of coals; Storage yards; Coal Coal kept in the coal storage yard spontaneously catches on fire, which results in wastage and can even cause a massive fire to break out. This phenomenon is known as the spontaneous combustion of coal. It is a complex process that has non-linear relationships between its causing variables. Preventive measures to prevent the fire from spreading to other coal piles in the vicinity have already been implemented. However, the predictive aspect before the fire occurs is of great necessity for the power generation sector. This research investigates various prediction models for spontaneous coal combustion, explicitly selecting input and output parameters to identify a proper clinker formation prediction model. Feed-Forward Neural Network (FFNN) is proposed as a proper prediction model. Two Hidden Layers (2HL) network is found to be the best with 5 minutes prediction capability. A sensitivity analysis study is also conducted to determine the influence of random input variables on their respective response variables. � 2021 Jordan Journal of Mechanical and Industrial Engineering. All rights reserved Final 2023-05-29T09:09:58Z 2023-05-29T09:09:58Z 2021 Article 2-s2.0-85124127127 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124127127&partnerID=40&md5=372470b39cf48201179ce84967efc92c https://irepository.uniten.edu.my/handle/123456789/26397 15 5 475 482 Hashemite University Scopus |
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Malaysia |
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Universiti Tenaga Nasional |
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| description |
Coal combustion; Coal fired power plant; Coal industry; Coal storage; Fires; Forecasting; Fossil fuel power plants; Multilayer neural networks; Sensitivity analysis; Spontaneous combustion; Clinker formation prediction model; Complex Processes; Fighting detections; Fire fighting; Machine learning approaches; Power industry; Prediction modelling; Reserved coals; Spontaneous combustion of coals; Storage yards; Coal |
| author2 |
58027086700 |
| author_facet |
58027086700 Ismail F.B. Al-Bazi A. Al-Hadeethi R.H. Victor M. |
| format |
Article |
| author |
Ismail F.B. Al-Bazi A. Al-Hadeethi R.H. Victor M. |
| spellingShingle |
Ismail F.B. Al-Bazi A. Al-Hadeethi R.H. Victor M. A Machine Learning Approach for Fire-Fighting Detection in the Power Industry |
| author_sort |
Ismail F.B. |
| title |
A Machine Learning Approach for Fire-Fighting Detection in the Power Industry |
| title_short |
A Machine Learning Approach for Fire-Fighting Detection in the Power Industry |
| title_full |
A Machine Learning Approach for Fire-Fighting Detection in the Power Industry |
| title_fullStr |
A Machine Learning Approach for Fire-Fighting Detection in the Power Industry |
| title_full_unstemmed |
A Machine Learning Approach for Fire-Fighting Detection in the Power Industry |
| title_sort |
machine learning approach for fire-fighting detection in the power industry |
| publisher |
Hashemite University |
| publishDate |
2023 |
| _version_ |
1806425528669831168 |
| score |
13.219503 |