Journal
INFORMATION SCIENCES
Volume 323, Issue -, Pages 94-105Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2015.06.035
Keywords
f-CaO content; Soft sensor; Multi-source segmental dataset; Ensemble modeling; DNNE
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Funding
- National Natural Science Foundation of China [61305029, 61273177]
- Natural Science Foundation of Anhui Province [1408085QF133]
- Key Support Plan of Overseas Experts, China Postdoctoral Science Foundation [2013M541820]
- Fundamental Research Funds for the Central Universities [2013HGBH0010, 2013HGQC0012]
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The excess free lime (f-CaO) content in clinker is the main cause of cement instability. Thus, it is crucial to effectively measure f-CaO content in real time for implementing a closed-loop control. To improve the estimation accuracy and sensor's reliability, improved multi-source modeling techniques are developed in this paper. In this work, fuzzy entropy is employed to compress the feature vectors of a segmental point dataset to enhance the sensor model's generalization power, and a decorrelated neural-net ensemble (DNNE) with random weights is employed to build the soft sensor. In this way, experiments with comprehensive comparisons can be carried out. Experimental results indicate that the improved soft sensor performs favorably in terms of both prediction accuracy and model reliability, compared with other soft sensor models. (C) 2015 Elsevier Inc. All rights reserved.
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