딥러닝 기반 화력발전 보일러 출구 NOx 농도 선행 예측 모델

다국어 초록 (Multilingual Abstract)

In this study, we developed a deep learning model to forecast the NOx and oxygen concentration, and gas temperature at the boiler exit of a coal-fired power plant. The target boiler is a 500 MWe tangential firing boiler, which is one of 20 units often referred to as standard coal power plant. From the database of the power plant, 73 raw items of operation data with one-minute frequency were collected for a period of approximately 5 months. Through the feature selection procedure, the raw data items were condensed into 19 features which include coal feeder throughput to burners, air flow rate, and burner tilt. The features were then used to establish two types of data segments: segment #1 for current operation status and segment #2 for recent histories measured at the boiler exit. Considering the large fluctuations, the histories of the recent values at the boiler exit values were averaged over 5 min. After evaluating different prediction models with respect to the nature of the data segments, suitable models were applied in the form of ensemble model to forecast the boiler exit values 1 min in advance. When compared to measured data, the prediction quality was sufficiently high with a mean square error of 0.0123 for NOx emission.

목차 (Table of Contents)

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