NOx Prediction for Deep Peaking Regulation of Circulating Fluidized Bed Units Based on Data-Driven

ObjectivesUnder the deep peaking regulation operation of circulating fluidized bed (CFB) unit, NOx emission will fluctuate significantly, which will have a serious impact on the stability and economy of the denitrification control system. Therefore, a data-driven NOx prediction model based on the deep peaking regulation of CFB unit is proposed.MethodsThe generation mechanism and influencing factors of NOx are analyzed in depth. Combined with the field operation data of a 300 MW CFB unit, the whale optimization algorithm (WOA) is used to optimize the parameters of the long short-term memory (LSTM) neural network, and the WOA-LSTM neural network model is established.ResultsThe model realizes the online prediction of NOx emission mass concentration of CFB unit under deep peaking regulation. Compared with other neural network models, the proposed model has better prediction results. The mean absolute error of the model is 2.49 mg/m3, the mean absolute percentage error reaches 6.8%, and the model correlation coefficient reaches 0.99.ConclusionsThe model can accurately reflect the NOx emission trend under the wide range of variable load operation of the unit. The results of the study are a good guide for the operation of the automatic control system of denitrification in the field.