Multisource data ensemble modeling for clinker free lime content estimate in rotary kiln sintering processes

IEEE Transactions on Systems, Man, and Cybernetics: Systems

Volumn 45, Issue 2, 2015, Pages 303-314

  Li, Weitao ^a   Wang, Dianhui ^b,c   Chai, Tianyou ^c  

^a HEFEI UNIVERSITY OF TECHNOLOGY   (China)

^b LA TROBE UNIVERSITY   (Australia)

^c STATE KEY LABORATORY OF SYNTHETICAL AUTOMATION FOR PROCESS INDUSTRIES   (China)

Author keywords

Ensemble modeling; f CaO content; multisource data; neural networks with random weights; soft sensor

Indexed keywords

CALCIUM OXIDE; CEMENTS; CLOSED LOOP CONTROL SYSTEMS; FEEDFORWARD NEURAL NETWORKS; IMAGE SEGMENTATION; LIME; ROTARY KILNS; SINTERING;

COMPRESSED TECHNIQUE; ENSEMBLE MODELING; KERNEL PARTIAL LEAST SQUARES; LOCAL CONFIGURATIONS; MULTISOURCE DATA; PREDICTION PERFORMANCE; RANDOM WEIGHT; SOFT SENSORS;

LEARNING SYSTEMS;

EID: 84921350514     PISSN: 21682216     EISSN: 21682232     Source Type: Journal    
DOI: 10.1109/TSMC.2014.2332305     Document Type: Article

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