UTILIZING GAUSSIAN PROCESS REGRESSION FOR NONLINEAR MAGNETIC SEPARATION PROCESS IDENTIFICATION

Oleksandr Volovetskyi

ovolovetskyi@knu.edu.ua
Kryvyi Rih National University (Ukraine)
https://orcid.org/0009-0003-1703-387X

Abstract

This paper presents a novel approach utilizing Gaussian Process Regression (GPR) to identify dynamic models with nonlinear parameters in magnetic separation processes. It aims to address the complex and dynamic nature of these processes by employing advanced modeling methods. The effectiveness of GPR is demonstrated through its application to simulated signals representing real iron ore separation processes, highlighting its potential to enhance existing models and optimize processes. Conducted within the MATLAB, this research lays the groundwork for further advancement and practical implementation. The utilization of GPR in magnetic separation offers innovative modeling of nonlinear dynamic processes, promising improved efficiency and precision in industrial applications.


Keywords:

Gaussian process regression, magnetic separation, nonlinear modeling, dynamic systems

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Published
2024-09-30

Cited by

Volovetskyi, O. (2024). UTILIZING GAUSSIAN PROCESS REGRESSION FOR NONLINEAR MAGNETIC SEPARATION PROCESS IDENTIFICATION. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 14(3), 21–28. https://doi.org/10.35784/iapgos.5954

Authors

Oleksandr Volovetskyi 
ovolovetskyi@knu.edu.ua
Kryvyi Rih National University Ukraine
https://orcid.org/0009-0003-1703-387X

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