Intelligent monitoring system of unburned carbon of fly ash for coal fired power plant boiler
Coal; Coal ash; Coal combustion; Coal fueled furnaces; Fly ash; Forecasting; Monitoring; Neural networks; Artificial neural network modeling; Coal fired plants; Coal-fired power plant; Conventional methods; Electricity demands; Incomplete combustion; Intelligent monitoring systems; Unburned carbon i...
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EDP Sciences
2023
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my.uniten.dspace-230782023-05-29T14:37:42Z Intelligent monitoring system of unburned carbon of fly ash for coal fired power plant boiler Gurusingam P. Basim Ismail F. Gunnasegaran P. Sundaram T. 57196439549 58027086700 35778031300 57196438069 Coal; Coal ash; Coal combustion; Coal fueled furnaces; Fly ash; Forecasting; Monitoring; Neural networks; Artificial neural network modeling; Coal fired plants; Coal-fired power plant; Conventional methods; Electricity demands; Incomplete combustion; Intelligent monitoring systems; Unburned carbon in fly ashes; Fossil fuel power plants Coal fired power plant becoming preferable power plant type to support electricity demand mainly in Asia due to stable coal price and low maintenance. However, most coal fired plant operator struggle with condition where coal undergo incomplete combustion and produced unburned carbon where can be found in ashes especially in fly ash. Higher percentage of unburned carbon in fly ash reflects the lower efficiency of furnace and contributes to financial loses for plant operators. This problem also leads to technical issues such as slagging and clinkering and further reduces the efficiency of furnace. The plant operator determines the amount of unburned carbon by using conventional method and this proves be a challenge to identify and rectify the problem on day basis due time constraint to obtain results of unburned carbon. Thus in this paper, best Artificial Neural Network model was derived to develop intelligent monitoring system to predict unburned carbon level on more daily basis. By this model, the power producer can predict the unburned carbon level by using data in power plant to predict the unburned carbon level in short period of time. � The authors, published by EDP Sciences, 2017. Final 2023-05-29T06:37:42Z 2023-05-29T06:37:42Z 2017 Conference Paper 10.1051/matecconf/201713102003 2-s2.0-85033236050 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85033236050&doi=10.1051%2fmatecconf%2f201713102003&partnerID=40&md5=000df79491f0c15ea7b5c7755f2f4228 https://irepository.uniten.edu.my/handle/123456789/23078 131 2003 All Open Access, Gold, Green EDP Sciences Scopus |
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| description |
Coal; Coal ash; Coal combustion; Coal fueled furnaces; Fly ash; Forecasting; Monitoring; Neural networks; Artificial neural network modeling; Coal fired plants; Coal-fired power plant; Conventional methods; Electricity demands; Incomplete combustion; Intelligent monitoring systems; Unburned carbon in fly ashes; Fossil fuel power plants |
| author2 |
57196439549 |
| author_facet |
57196439549 Gurusingam P. Basim Ismail F. Gunnasegaran P. Sundaram T. |
| format |
Conference Paper |
| author |
Gurusingam P. Basim Ismail F. Gunnasegaran P. Sundaram T. |
| spellingShingle |
Gurusingam P. Basim Ismail F. Gunnasegaran P. Sundaram T. Intelligent monitoring system of unburned carbon of fly ash for coal fired power plant boiler |
| author_sort |
Gurusingam P. |
| title |
Intelligent monitoring system of unburned carbon of fly ash for coal fired power plant boiler |
| title_short |
Intelligent monitoring system of unburned carbon of fly ash for coal fired power plant boiler |
| title_full |
Intelligent monitoring system of unburned carbon of fly ash for coal fired power plant boiler |
| title_fullStr |
Intelligent monitoring system of unburned carbon of fly ash for coal fired power plant boiler |
| title_full_unstemmed |
Intelligent monitoring system of unburned carbon of fly ash for coal fired power plant boiler |
| title_sort |
intelligent monitoring system of unburned carbon of fly ash for coal fired power plant boiler |
| publisher |
EDP Sciences |
| publishDate |
2023 |
| _version_ |
1806424034249801728 |
| score |
13.222552 |