Prediction of the NOx emissions from thermal power plant using long-short term memory neural network

Guotian Yang, Yingnan Wang and Xinli Li

Energy, 2020, vol. 192, issue C

Abstract: Coal combustion in thermal power plant is the main source of the NOx emission. An effective prediction model should be established for reducing NOx emission. This paper focuses on the application of long-short term memory (LSTM) neural network in modeling the relationship between operational parameters and NOx emission of a 660 MW boiler. Principal component analysis (PCA) method is used to eliminate the coupling between original variables, then the NOx emission model based on LSTM is established. The dropout strategy and Adam optimizer are adopted to improve the network performance. Compared with the least squares support vector machine (LSSVM), the proposed model has higher prediction accuracy, faster response speed, stronger generalization ability, and is more competitive in the modeling of NOx emission. The difference between the LSTM model and the traditional recurrent neural network (RNN) model is also compared. The results show that the performance of LSTM model is better than that of RNN model under the same model structure and parameters. In addition, a NOx prediction model for high-dimensional data is established and achieves good prediction performance. Thus, LSTM is capable to model the NOx emission for coal-fired boilers and is superior to other traditional modeling methods.

Keywords: NOx emission; LSTM; Coal fired boiler; Machine learning (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (22)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:192:y:2020:i:c:s0360544219322923

DOI: 10.1016/j.energy.2019.116597

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