Abstract
The selective catalytic reduction (SCR) denitration technology is widely used in coal-fired generating units. The NOx concentration of boiler outlet is an important parameter in the feedforward control of SCR denitration. However, its measurement lag leads to a large range fluctuation of NOx emission, which affects the safe and economic operation of the unit. In order to solve the problem of boiler outlet NOx concentration measurement lag in denitration control, and improve the timeliness of fluctuation response for denitration control. Many studies have reported on NOx concentration prediction models based on the long short-term memory (LSTM) algorithm, support vector machines (SVM) algorithm, et al. However, there are no reports on online modeling, particularly none on predictive values of boiler outlet NOx concentration ahead of the measured values. Thus, in this study, a 1000 MW ultra-supercritical coal-fired boiler was selected, and 2404 sets of measured samples were collected to predict NOx concentration. A novel online modeling method for NOx concentration of boiler outlet was proposed. For the first time, a high-precision online real-time prediction model of boiler outlet NOx concentration was innovatively established based on improved long short-term memory network (ILSTM). A feature quantity weight analysis method based on the RRelieff algorithm is adopted, and the change rates of feature quantities were used as input in the model. The results showed that the root mean square error (δR) and computation time of ILSTMN reduced relatively by 17.97 % and 1.97 s, respectively. The online model with satisfied accuracy is trained in 1 s, which uses the latest recent data from decentralized control system (DCS). The NOx concentration of boiler outlet predicted by the online model is 22 s ahead of the measurement NOx concentration, and the prediction accuracy is still as high as 96 % without the intervention after two years of operation. As a feedforward of SCR denitration control system, NOx concentration predicted by the model can significantly improve the timeliness of control response. The online model provides theoretical support for suppressing large fluctuations of NOx emissions.
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