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.
|Journal||MATEC Web of Conferences|
|Publication status||Published - 25 Oct 2017|
|Event||2017 UTP-UMP Symposium on Energy Systems, SES 2017 - Perak, Malaysia|
Duration: 26 Sep 2017 → 27 Sep 2017
All Science Journal Classification (ASJC) codes
- Materials Science(all)