Evidence map›Paper›PMID 33923611›Full record

ArticleEntropy (Basel, Switzerland)2021

Estimation of Feeding Composition of Industrial Process Based on Data Reconciliation.

Yusi Luan, Mengxuan Jiang, Zhenxiang Feng, Bei Sun

Abstract read
In one paragraph

Article in Entropy (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Yusi LuanSchool of Automation, Central South University, Changsha 410083, China.
Mengxuan JiangSchool of Automation, Central South University, Changsha 410083, China.
Zhenxiang FengSchool of Automation, Central South University, Changsha 410083, China.
Bei SunSchool of Automation, Central South University, Changsha 410083, China.

Funding

National Natural Science Foundation of China 61973321the National Key R&D Program of China 2019YFB1704703the Natural Science Foundation of Hunan Province 2019JJ50823
6 · The paper itself

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.

Indexed as

data reconciliationfeeding compositiongross error detectionrobust estimator

Identifiers

PMID33923611
PMCPMC8073053

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.