A novel robust dynamic method for <scp>NOx</scp> emissions prediction in a thermal power plant
Ying Liu, Jianxin Zhou, Wei Fan
Abstract
Abstract NOx is a harmful by‐product of coal‐fired boilers, and accurate prediction of NOx emissions in the outlet of a boiler is essential for environmental protection. In recent years, data‐driven models have been widely studied and applied in this area. However, dynamic characteristics are ignored by many existing models, leading to sub‐optimal performance. Besides, outliers that occur in the operation data have adverse effects on the efficacy of these prediction models. To address these issues, this paper presents a novel method for predicting NOx concentration via integrating a robust dynamic probabilistic approach and the long short‐term memory (LSTM). First, mutual information (MI) is applied to determine the input variables. Subsequently, a robust probabilistic method is proposed to extract dynamic latent features considering outliers. On this basis, the generated latent variables are further utilized to train the LSTM‐based model, with which the intrinsic relation between inputs and NOx values are obtained. Based on the application to a 660 MW thermal power plant, the superiority of the proposed method is demonstrated in terms of high prediction accuracy.