ArticleScientific reports2023
Interference recommendation for the pump sizing process in progressive cavity pumps using graph neural networks.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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.
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Who cites it
4 citing papers in PubMed.
- Stacked graph attention network hypertuned by Heteroscedastic and Evolutionary Bayesian Optimization for fault identification on nuclear power plants robust to sensor drift.Scientific reports · 2026Article
- Optimized hybrid neural hierarchical interpolation time series with STL for flow forecasting in hydroelectric power plants.Scientific reports · 2026Article
- A deep learning-based approach for axle counter in free-flow tolling systems.Scientific reports · 2024Article
- A Review of Automation and Sensors: Parameter Control of Thermal Treatments for Electrical Power Generation.Sensors (Basel, Switzerland) · 2024Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Pump sizing is the process of dimensional matching of an impeller and stator to provide a satisfactory performance test result and good service life during the operation of progressive cavity pumps. In this process, historical data analysis and dimensional monitoring are done manually, consuming a large number of man-hours and requiring a deep knowledge of progressive cavity pump behavior. This paper proposes the use of graph neural networks in the construction of a prototype to recommend interference during the pump sizing process in a progressive cavity pump. For this, data from different applications is used in addition to individual control spreadsheets to build the database used in the prototype. From the pre-processed data, complex network techniques and the betweenness centrality metric are used to calculate the degree of importance of each order confirmation, as well as to calculate the dimensionality of the rotors. Using the proposed method a mean squared error of 0.28 is obtained for the cases where there are recommendations for order confirmations. Based on the results achieved, it is noticeable that there is a similarity of the dimensions defined by the project engineers during the pump sizing process, and this outcome can be used to validate the new design definitions.
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Registered trials
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