| 학회 |
한국화학공학회 |
| 학술대회 |
2022년 봄 (04/20 ~ 04/23, 제주국제컨벤션센터) |
| 권호 |
28권 1호, p.129 |
| 발표분야 |
[주제 2] 기계학습 |
| 제목 |
Framework to Predict NOx-SOx Emissions from a Circulating Fluidized Bed Boiler Power Plant using Deep Neural Network |
| 초록 |
Circulating fluidized bed boiler is a modern green energy technology that has garnered much attention in recent times because of its fuel flexibility and low emission power generation. As a result of the stringent limitations imposed by environmental regulatory agencies on coal fired plants to reduce the quantity of NOx-SOx emissions, their prediction and control have become necessary. it has therefore become imperative to develop solutions to help power plant operators to minimize harmful emissions from the stack while running the operation cleanly and efficiently. This study focuses on the application of Deep Neural Network modeling to predict the emission of NOx and SOx in a 500MW CFB plant. Commercial plant data was used to train the DNN model, and dropout strategy, and modified early stopping was adopted to improve the performance of the model. The model has higher prediction accuracy, faster training time, stronger generalization time and is more competitive in the modeling of NOx and SOx emission. Thus, DNN Machine Learning method is capable of predicting the SOx-NOx emissions from coal-fired boilers and is superior to other traditional times series prediction techniques. |
| 저자 |
한대원1, 박준규1, APPIAH PIUS1, 김동원2, 박상빈2, 오민1
|
| 소속 |
1한밭대, 2한국전력공사 |
| 키워드 |
공정시스템(Process Systems Engineering) |
| E-Mail |
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| 원문파일 |
초록 보기 |
