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Nearest Correlation-Based Input Variable Weighting for Soft-Sensor Design.

Koichi Fujiwara1, Manabu Kano1

  • 1Department of Systems Science, Kyoto University, Kyoto, Japan.

Frontiers in Chemistry
|June 7, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a new soft-sensor method, NC-based variable weighting (NCVW), simplifying parameter tuning for accurate estimations. NCVW effectively weights input variables, improving soft-sensor development in processes like pharmaceutical manufacturing.

Keywords:
calibration modelnear infrared spectroscopypartial least squaressoft-sensorvariable weighting

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Area of Science:

  • Process Analytical Technology
  • Chemometrics
  • Machine Learning

Background:

  • Soft-sensors are crucial for estimating variables when online analyzers are unavailable.
  • Accurate soft-sensor development relies heavily on effective data preprocessing, particularly input variable selection.
  • Existing methods like nearest correlation spectral clustering-based variable selection (NCSC-VS) require burdensome parameter tuning.

Purpose of the Study:

  • To propose an effective input variable weighting method as an alternative to complex variable selection.
  • To simplify the development of soft-sensors by reducing parameter tuning requirements.
  • To demonstrate the utility of the proposed method in a pharmaceutical process.

Main Methods:

  • Developed NC-based variable weighting (NCVW) method.
  • NCVW identifies input variables correlated with the output variable using the NC method.
  • Input variables are weighted based on calculated correlation similarities for model construction.

Main Results:

  • The proposed NCVW method has only one tuning parameter, simplifying development compared to NCSC-VS.
  • NCVW demonstrated its usefulness in a pharmaceutical process calibration model design.
  • The method effectively weights input variables, enhancing soft-sensor performance.

Conclusions:

  • NCVW offers a simplified and effective approach to soft-sensor development.
  • The method reduces the labor associated with parameter tuning in soft-sensor design.
  • NCVW is a valuable tool for improving estimation accuracy in various industrial processes.