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Interference recommendation for the pump sizing process in progressive cavity pumps using graph neural networks.

Leandro Starke1, Aurélio Faustino Hoppe1, Andreza Sartori1,2

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This study introduces a graph neural network prototype to optimize progressive cavity pump sizing. The AI model accurately recommends rotor dimensionality, reducing manual effort and validating engineering designs.

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Area of Science:

  • Engineering
  • Data Science

Background:

  • Pump sizing for progressive cavity pumps is manual, time-consuming, and requires extensive expertise.
  • Current methods involve historical data analysis and dimensional monitoring, leading to significant labor costs.

Purpose of the Study:

  • To develop a prototype using graph neural networks (GNNs) to automate and improve the pump sizing process.
  • To recommend optimal interference for impeller and stator matching in progressive cavity pumps.

Main Methods:

  • Utilized historical application data and control spreadsheets to build a comprehensive database.
  • Applied complex network techniques and betweenness centrality to determine rotor dimensionality and order confirmation importance.
  • Developed a GNN-based prototype for interference recommendations.

Main Results:

  • Achieved a mean squared error of 0.28 for order confirmation recommendations.
  • Demonstrated a high degree of similarity between AI-generated dimensions and those defined by experienced engineers.
  • The prototype successfully validates new design definitions.

Conclusions:

  • Graph neural networks offer a viable solution for automating progressive cavity pump sizing.
  • The developed prototype can significantly reduce manual effort and enhance the accuracy of pump design.
  • The findings support the use of AI in validating and refining engineering design processes.