Abstract
NOx is one of the main pollutants emitted by thermal power plants. Excessive NOx emissions not only cause many negative impacts on the environment but also cause great harm to human health. Power plant NOx prediction technology has drawn more and more attention from the industry. In this paper, a novel bidirectional long and short term memory network (Bi-LSTM) NOx soft-sensing model is proposed for the first time to dynamically predict NOx emissions from power plants in the form of time series. To get better prediction performance, a univariate model and a multivariate model are constructed for comparative study. Besides, Bi-LSTM and different algorithms are compared in the univariate model. In order to confirm the generalization ability of the model, two sets of NOx values of A and B emission outlets from different power plant historical data is used. The results show that the predictive power of univariate models is better than multivariate models. In univariate models, Bi-LSTM is better than other models. On the two sets of data with sampling intervals of 1 min and 2 min, the mean absolute percentage error (MAPE) could reach 2.105% and 4.45%.
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Wen, X., Li, K. (2024). Predicting NOx Emission in Thermal Power Plants Based on Bidirectional Long and Short Term Memory Network. In: Wang, B., Hu, Z., Jiang, X., Zhang, YD. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 535. Springer, Cham. https://doi.org/10.1007/978-3-031-50580-5_30
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