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An Algorithm for Soft Sensor Development for a Class of Processes with Distinct Operating Conditions.

Darko Stanišić1, Luka Mejić1, Bojan Jorgovanović1

  • 1Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia.

Sensors (Basel, Switzerland)
|March 28, 2024
PubMed
Summary

This study introduces an automated algorithm for developing soft sensors, crucial for real-time process monitoring. The method, using radial basis function neural networks, simplifies soft sensor creation for industrial applications with distinct operating conditions.

Keywords:
cement finenessdistinct operating conditionsindustrial applicationneural networkssoft sensor

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Area of Science:

  • Chemical Engineering
  • Process Control
  • Artificial Intelligence

Background:

  • Soft sensors offer real-time process insights, reducing reliance on offline lab analysis.
  • Current soft sensor development is application-specific, demanding extensive process analysis and manual model selection.
  • Industrial adoption necessitates automated, broadly applicable soft sensor development methods.

Purpose of the Study:

  • To present an automated algorithm for developing soft sensors for processes with distinct operating conditions.
  • To enable widespread industrial application of soft sensors through a highly automated development process.
  • To minimize user intervention in model structure selection and parameter determination.

Main Methods:

  • Development of an algorithm based on radial basis function artificial neural networks (RBF-ANN).
  • Algorithm facilitates automatic selection of model structure and determination of model parameters.
  • Utilizes only the training data set for model development, requiring minimal user input.

Main Results:

  • The algorithm demonstrates a high level of automatism in soft sensor development.
  • Testing on a cement production process, a typical case of distinct operating conditions, yielded successful results.
  • The developed soft sensors exhibit high estimation performance.

Conclusions:

  • The presented algorithm offers a highly automated and efficient approach to soft sensor development.
  • It is suitable for industrial implementation, particularly for processes with distinct operating conditions.
  • The method ensures high estimation performance while simplifying the development lifecycle.