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Estimation of Feeding Composition of Industrial Process Based on Data Reconciliation

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-ra...

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Detalles Bibliográficos
Autores principales: Luan, Yusi, Jiang, Mengxuan, Feng, Zhenxiang, Sun, Bei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8073053/
https://www.ncbi.nlm.nih.gov/pubmed/33923611
http://dx.doi.org/10.3390/e23040473
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author Luan, Yusi
Jiang, Mengxuan
Feng, Zhenxiang
Sun, Bei
author_facet Luan, Yusi
Jiang, Mengxuan
Feng, Zhenxiang
Sun, Bei
author_sort Luan, Yusi
collection PubMed
description 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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spelling pubmed-80730532021-04-27 Estimation of Feeding Composition of Industrial Process Based on Data Reconciliation Luan, Yusi Jiang, Mengxuan Feng, Zhenxiang Sun, Bei Entropy (Basel) Article 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. MDPI 2021-04-16 /pmc/articles/PMC8073053/ /pubmed/33923611 http://dx.doi.org/10.3390/e23040473 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Luan, Yusi
Jiang, Mengxuan
Feng, Zhenxiang
Sun, Bei
Estimation of Feeding Composition of Industrial Process Based on Data Reconciliation
title Estimation of Feeding Composition of Industrial Process Based on Data Reconciliation
title_full Estimation of Feeding Composition of Industrial Process Based on Data Reconciliation
title_fullStr Estimation of Feeding Composition of Industrial Process Based on Data Reconciliation
title_full_unstemmed Estimation of Feeding Composition of Industrial Process Based on Data Reconciliation
title_short Estimation of Feeding Composition of Industrial Process Based on Data Reconciliation
title_sort estimation of feeding composition of industrial process based on data reconciliation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8073053/
https://www.ncbi.nlm.nih.gov/pubmed/33923611
http://dx.doi.org/10.3390/e23040473
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