Torque-Limited Attitude Control for Rigid Spacecraft with Motion Constraints

Bai, Zhining; Liu, Yueyang; Hu, Qinglei

doi:10.23919/ccc52363.2021.9550402

Cited by 1 publication

(1 citation statement)

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“…For the prediction of sinter ore composition, there has been a lot of representative research work. For example, Bai et al [ 29 ] proposed an integrated model combining multi‐source information fusion and a long short‐term memory (LSTM) network to predict the FeO content in sinter ore. In addition, a combination model of an adaptive particle swarm optimization (APSO) algorithm and a least square support vector machine (LS‐SVM) algorithm was proposed to predict the FeO content of sinter ore. [ 52 ] Li et al [ 30 ] developed a novel dynamic time feature expanding and extracting framework for the FeO content prediction in the sintering process.…”

Section: Review Of Data‐driven Methods In the Sintering Processmentioning

confidence: 99%

Data‐driven modelling methods in sintering process: Current research status and perspectives

et al. 2022

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The sintering process, as a primary modus of the blast furnace ironmaking industry, has enormous economic value and environmental protection significance for the iron and steel enterprises. Recently, with the emergence of artificial intelligence and big data, data‐driven modelling methods in the sintering process have increasingly received the researchers' attention. But now, there is still no systematic review of the data‐driven modelling approaches in the sintering process. Therefore, in this article, we conduct a comprehensive overview and prospects on the data‐driven models for the purpose of intelligent sintering. First, the mechanism and characteristics of the sintering process are introduced and analyzed elaborately. Second, the detailed research status of the sintering process is illustrated from four aspects: key parameters prediction, control, optimization, and others. Finally, several challenges and promising modelling methods such as deep learning in the sintering process are outlined and discussed for future research.

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Section: Review Of Data‐driven Methods In the Sintering Processmentioning

confidence: 99%