NOx emissions estimation of boiler based on mutual information feature reconstruction and optimization of extreme learning machine

Abstract

Abstract The measurement of NOx emissions in the selective catalytic reduction (SCR) system of boilers has problems with poor real-time performance and abnormal measurements during purging. It is necessary to accurately estimate NOx emissions. For this reason, the NOx emissions prediction method of boiler based on mutual information feature reconstruction and optimization of extreme learning machine (ELM) is proposed: firstly, delay estimation and data space reconstruction of input features are performed based on mutual information; Then the conditional mutual information based on greedy selection strategy is adopted to rank and choose the input features; Finally, the hybrid quantum sparrow search algorithm (QSSA) was proposed by combining Lévy flight strategy and quantum strategy in the sparrow search algorithm, and QSSA is used to optimize the weights and biases of the ELM. Taking the operation data of the SCR system of a 1000 MW thermal power unit as an example for verification. The results show that the proposed method can effectively improve the accuracy and generalization ability of the ELM, and provide a new method for NOx emissions estimation of boilers.