Prediction Model of SCR Outlet NOx Based on LSTM Algorithm

Abstract

Pollutants emissions is strictly controlled in modern power plants, and Nitrogen Oxides (NOx), which is the main contaminants is the exhaust gas. The Selective Catalytic Reduction process (SCR) is commonly used for denitration. For achieving an effective the SCR outlet NOx concentration control, an accurate outlet NOx concentration model is necessary. A model using historical data is proposed, and long-short term memory(LSTM) algorithm is applied, which could describe relevance in time series. The accuracy performances for proposed data-driven model are verified, and root mean square error (RMSE) and mean absolute error (MAPE) for training set are, 0.706 mg/m3 and 1.99%, respectively, which for test set are 1.44 mg/m3 and 2.90%, respectively, The verification reveals that the accuracy for data-driven model is acceptable for control system design.