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Specifically, first, the LSTM model was built with the Keras deep learning framework, and the accuracy of the model was further improved by selecting appropriate super-parameters through experiments. Secondly, the flue gas oxygen content, as the leading variable, was combined with the mechanism and boiler process primary auxiliary variables. Based on the actual production data collected from a coal-fired power plant in Yulin, China, the data sets were preprocessed. Moreover, a selection model of auxiliary variables based on grey relational analysis is proposed to construct a new data set and divide the training set and testing set. Finally, this model is compared with the traditional soft-sensing modelling methods (i.e. the methods based on support vector machine and BP neural network). The RMSE of LSTM model is 4.51% lower than that of GA-SVM model and 3.55% lower than that of PSO-BP model. The conclusion shows that the oxygen content model based on LSTM has better generalization and has certain industrial value.<\/jats:p>","DOI":"10.1177\/0142331220932390","type":"journal-article","created":{"date-parts":[[2020,6,26]],"date-time":"2020-06-26T04:35:41Z","timestamp":1593146141000},"page":"78-87","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":36,"title":["LSTM-based soft sensor design for oxygen content of flue gas in coal-fired power plant"],"prefix":"10.1177","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0390-6188","authenticated-orcid":false,"given":"Hongguang","family":"Pan","sequence":"first","affiliation":[{"name":"College of Electrical and Control Engineering, Xi\u2019an University of Science and Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Su","sequence":"additional","affiliation":[{"name":"College of Electrical and Control 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