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dc.contributor.authorYuan, Zhaolin
dc.contributor.authorHu, Jinlong
dc.contributor.authorWu, Di
dc.contributor.authorBan, Xiaojuan
dc.date.accessioned2022-05-11T06:14:24Z
dc.date.available2022-05-11T06:14:24Z
dc.date.created2020-05-14T12:28:15Z
dc.date.issued2020
dc.identifier.citationSensors. 2020, 20 (5), 1-18.en_US
dc.identifier.issn1424-8220
dc.identifier.urihttps://hdl.handle.net/11250/2995146
dc.description.abstractThis paper focuses on the time series prediction problem for underflow concentration of deep cone thickener. It is commonly used in the industrial sedimentation process. In this paper, we introduce a dual attention neural network method to model both spatial and temporal features of the data collected from multiple sensors in the thickener to predict underflow concentration. The concentration is the key factor for future mining process. This model includes encoder and decoder. Their function is to capture spatial and temporal importance separately from input data, and output more accurate prediction. We also consider the domain knowledge in modeling process. Several supplementary constructed features are examined to enhance the final prediction accuracy in addition to the raw data from sensors. To test the feasibility and efficiency of this method, we select an industrial case based on Industrial Internet of Things (IIoT). This Tailings Thickener is from FLSmidth with multiple sensors. The comparative results support this method has favorable prediction accuracy, which is more than 10% lower than other time series prediction models in some common error indices. We also try to interpret our method with additional ablation experiments for different features and attention mechanisms. By employing mean absolute error index to evaluate the models, experimental result reports that enhanced features and dual-attention modules reduce error of fitting ~5% and ~11%, respectively.en_US
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleA Dual-Attention Recurrent Neural Network Method for Deep Cone Thickener Underflow Concentration Predictionen_US
dc.title.alternativeA Dual-Attention Recurrent Neural Network Method for Deep Cone Thickener Underflow Concentration Predictionen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.pagenumber1-18en_US
dc.source.volume20en_US
dc.source.journalSensorsen_US
dc.source.issue5en_US
dc.identifier.doi10.3390/s20051260
dc.identifier.cristin1811000
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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