Abstract
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.
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