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A Nonlinear Integrated Modeling Method of Extended Kalman Filter Based on Adaboost Algorithm.

Feng-Bo Zhou1,2, Chang-Geng Li2, Hong-Qiu Zhu3

  • 1School of Information Engineering, Shaoyang University, Shaoyang, China.

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|August 16, 2021
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Summary

This study presents a new method for accurately detecting trace nickel in zinc sulfate solutions. The Adaboost-based nonlinear integrated modeling with an extended Kalman filter improves nickel detection sensitivity and accuracy.

Keywords:
Adaboost algorithmextended Kalman filter spectrophotometryintegrated modelingultraviolet visible spectroscopyzinc hydrometallurgy

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Area of Science:

  • Analytical Chemistry
  • Hydrometallurgy

Background:

  • Detecting trace nickel in zinc sulfate solutions is challenging due to high zinc concentrations masking nickel signals.
  • Existing methods suffer from low sensitivity and nonlinear characteristics for nickel spectral signals.

Purpose of the Study:

  • To develop a robust method for accurate trace nickel detection in zinc sulfate solutions.
  • To address the limitations of low sensitivity and nonlinear spectral signals in nickel analysis.

Main Methods:

  • Established a nonlinear nickel model using nickel standard solutions.
  • Employed an extended Kalman filter (EKF) wavelength optimization based on correlation coefficients to select sensitive wavelengths.
  • Developed an Adaboost-based nonlinear integrated modeling method using EKFs as submodels.

Main Results:

  • Achieved accurate detection of trace nickel in zinc sulfate solutions.
  • The proposed method demonstrated an average relative error of 4.56% for nickel detection.
  • Successfully overcame the spectral signal interference from high zinc concentrations.

Conclusions:

  • The Adaboost-based nonlinear integrated modeling with EKF is effective for stable trace nickel detection.
  • This approach significantly enhances the sensitivity and accuracy of nickel analysis in complex matrices.
  • The method offers a viable solution for quality control in zinc hydrometallurgical processes.