Author:
Luan Yusi,Jiang Mengxuan,Feng Zhenxiang,Sun Bei
Abstract
For an industrial process, the estimation of feeding composition is important for analyzing production status and making control decisions. However, random errors or even gross ones inevitably contaminate the actual measurements. Feeding composition is conventionally obtained via discrete and low-rate artificial testing. To address these problems, a feeding composition estimation approach based on data reconciliation procedure is developed. To improve the variable accuracy, a novel robust M-estimator is first proposed. Then, an iterative robust hierarchical data reconciliation and estimation strategy is applied to estimate the feeding composition. The feasibility and effectiveness of the estimation approach are verified on a fluidized bed roaster. The proposed M-estimator showed better overall performance.
Funder
National Natural Science Foundation of China
the Natural Science Foundation of Hunan Province
Subject
General Physics and Astronomy
Cited by
4 articles.
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