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Estimation of Feeding Composition of Industrial Process Based on Data Reconciliation

Yusi Luan, Mengxuan Jiang

2021Englishroastingfluidized bedprocess modellingstate estimationdigital twin supporting methodsdata reconciliation

Abstract

Language:

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.

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Cite This Work

@article{e5f853b7-9bac-4a98-886f-4e1f3795d08d,
  title={Estimation of Feeding Composition of Industrial Process Based on Data Reconciliation},
  author={Yusi Luan and Mengxuan Jiang},
  year={2021},
  language={English}
}
TY  - JOUR
TI  - Estimation of Feeding Composition of Industrial Process Based on Data Reconciliation
AU  - Yusi Luan
AU  - Mengxuan Jiang
PY  - 2021
LA  - English
ER  -

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