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.
Highlights
Malaysia’s largest thermal power plant uses coal as fuel source to fuel their furnace
There is few method was tried such as usage on infrared light sensor to measure carbon intensity in fly ash [3], Computational Fluid Dynamic (CFD) analysis [4] and combination of Artificial Neural Network (ANN) with other method to predict the presence of unburned carbon in fly ash [5,6,7,8]
This was proved by using CFD simulation method and a basic study was done on the production of unburned carbon for each parameter and was discussed briefly
Summary
Malaysia’s largest thermal power plant uses coal as fuel source to fuel their furnace. The ash usually contains some percentage of unburned carbon that was produced due to incomplete combustion in the boiler. An intelligent monitoring system was designed to detect and predict the unburned carbon in short period of time. This system is based on Artificial Neural Network (ANN) theory. This approach was selected because it proves to produce prediction using real time data at faster and accurate compare to other existing prediction system
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