Two-Stage Hybrid Model for Efficiency Prediction of Centrifugal Pump.

Yi Liu, Zhaoshun Xia, Hongying Deng, Shuihua Zheng

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2022. 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

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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.

Convolutional Neural Network-Based Machine Vision for Non-Destructive Detection of Flooding in Packed Columns.Sensors (Basel, Switzerland) · 2023
Article

4 · The record

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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.

Yi LiuInstitute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310023, China.ORCID 0000-0002-4066-689X

Zhaoshun XiaInstitute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310023, China.

Hongying DengInstitute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310023, China.

Shuihua ZhengInstitute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310023, China.ORCID 0000-0002-7112-2940

Funding

National Natural Science Foundation of China 51976193 and 62022073Zhejiang Provincial National Science Foundation of China LGG22E060011

6 · The paper itself

Abstract

Accurately predict the efficiency of centrifugal pumps at different rotational speeds is important but still intractable in practice. To enhance the prediction performance, this work proposes a hybrid modeling method by combining both the process data and knowledge of centrifugal pumps. First, according to the process knowledge of centrifugal pumps, the efficiency curve is divided into two stages. Then, the affinity law of pumps and a Gaussian process regression (GPR) model are explored and utilized to predict the efficiency at their suitable flow stages, respectively. Furthermore, a probability index is established through the prediction variance of a GPR model and Bayesian inference to select a suitable training set to improve the prediction accuracy. Experimental results show the superiority of the hybrid modeling method, compared with only using mechanism or data-driven models.

Indexed as

Bayes TheoremNormal Distributionaffinity lawcentrifugal pump efficiencyGaussian process regressionhybrid model

Identifiers

PMID35684920

DOI10.3390/s22114300

PMCPMC9185542

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LicenceCC BY

Read underepoch 390

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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.