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Tytuł:
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Two-Stage Hybrid Model for Efficiency Prediction of Centrifugal Pump.
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Autorzy:
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Liu Y; Institute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310023, China.
Xia Z; Institute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310023, China.
Deng H; Institute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310023, China.
Zheng S; Institute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310023, China.
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Źródło:
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Sensors (Basel, Switzerland) [Sensors (Basel)] 2022 Jun 06; Vol. 22 (11). Date of Electronic Publication: 2022 Jun 06.
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Typ publikacji:
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Journal Article
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Język:
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English
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Imprint Name(s):
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Original Publication: Basel, Switzerland : MDPI, c2000-
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MeSH Terms:
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Bayes Theorem*
Normal Distribution
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References:
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ISA Trans. 2021 Jun;112:386-401. (PMID: 33341238)
Sensors (Basel). 2020 Jun 09;20(11):. (PMID: 32526895)
Sensors (Basel). 2022 Mar 09;22(6):. (PMID: 35336277)
Sensors (Basel). 2021 Dec 28;22(1):. (PMID: 35009719)
Environ Res. 2022 Aug;211:112942. (PMID: 35189104)
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Grant Information:
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51976193 and 62022073 National Natural Science Foundation of China; LGG22E060011 Zhejiang Provincial National Science Foundation of China
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Contributed Indexing:
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Keywords: Gaussian process regression; affinity law; centrifugal pump efficiency; hybrid model
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Entry Date(s):
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Date Created: 20220610 Date Completed: 20220613 Latest Revision: 20220716
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Update Code:
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20240105
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PubMed Central ID:
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PMC9185542
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DOI:
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10.3390/s22114300
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PMID:
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35684920
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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.
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