Intelligent monitoring system of unburned carbon of fly ash for coal fired power plant boiler

Pogganeswaran Gurusingam, Firas Basim Ismail, Prem Gunnasegaran, Taneshwaren Sundaram

Research output: Contribution to journalConference article

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Article number02003
JournalMATEC Web of Conferences
Volume131
DOIs
Publication statusPublished - 25 Oct 2017
Event2017 UTP-UMP Symposium on Energy Systems, SES 2017 - Perak, Malaysia
Duration: 26 Sep 201727 Sep 2017

Fingerprint

Coal Ash
Coal
Fly ash
Boilers
Power plants
Carbon
Monitoring
Ashes
Furnaces
Coal ash
Electricity
Neural networks

All Science Journal Classification (ASJC) codes

  • Chemistry(all)
  • Materials Science(all)
  • Engineering(all)

Cite this

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title = "Intelligent monitoring system of unburned carbon of fly ash for coal fired power plant boiler",
abstract = "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.",
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Intelligent monitoring system of unburned carbon of fly ash for coal fired power plant boiler. / Gurusingam, Pogganeswaran; Ismail, Firas Basim; Gunnasegaran, Prem; Sundaram, Taneshwaren.

In: MATEC Web of Conferences, Vol. 131, 02003, 25.10.2017.

Research output: Contribution to journalConference article

TY - JOUR

T1 - Intelligent monitoring system of unburned carbon of fly ash for coal fired power plant boiler

AU - Gurusingam, Pogganeswaran

AU - Ismail, Firas Basim

AU - Gunnasegaran, Prem

AU - Sundaram, Taneshwaren

PY - 2017/10/25

Y1 - 2017/10/25

N2 - 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.

AB - 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.

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