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A statistically based filter.

R Russell Rhinehart1

  • 1School of Chemical Engineering, Oklahoma State University, Stillwater 74078-5021, USA. rrr@okstate.edu

ISA Transactions
|June 20, 2002
PubMed
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A novel statistical process control method effectively filters noise from process variables. This algorithm is demonstrated on a pilot-scale process with significant noise and frequent level changes.

Area of Science:

  • Process Engineering
  • Data Analysis
  • Control Systems

Background:

  • Process variables often contain significant noise, complicating analysis and control.
  • Traditional filtering methods may struggle with dynamic processes exhibiting frequent changes.

Purpose of the Study:

  • To develop a simple and effective noise filtering procedure for process variables.
  • To utilize statistical process control (SPC) concepts for enhanced data quality.

Main Methods:

  • A new procedure based on SPC principles was developed.
  • Algorithm code was implemented for practical application.
  • Experimental validation was performed on a pilot-scale process.

Main Results:

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  • The developed procedure successfully filtered noise from the process variable.
  • The method demonstrated effectiveness even with significant and frequent process changes.
  • The algorithm's performance was validated in a real-world pilot-scale setting.

    • Conclusions:

    • The SPC-based noise filtering procedure offers a robust solution for improving process data quality.
    • This method is particularly suitable for dynamic industrial processes.
    • The presented algorithm and experimental results provide a practical tool for process engineers.