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Machine Learning Approach to Predict the Surface Charge Density of Monodispersed Particles in Gas-Solid Fluidized

Junyu Lu^1,2, Chenlong Duan¹, Yuemin Zhao¹

¹School of Chemical Engineering and Technology, China University of Mining and Technology, Xuzhou 221116, China.

|March 31, 2022

Summary

Machine learning models accurately predict particle surface charge density in gas-solid fluidized beds. The multilayer perceptron (MLP) model offers a reliable tool for estimating electrostatic behavior in these complex systems.

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Area of Science:

Chemical Engineering
Particle Technology
Computational Modeling

Background:

Gas-solid fluidized beds exhibit complex particle electrostatic behavior influenced by multiple factors.
Existing research often examines single factors, lacking a comprehensive understanding of interactions.
Developing mathematical models for multi-factor particle charging is challenging due to system complexity.

Purpose of the Study:

To develop a predictive model for particle surface charge density in monodispersed gas-solid fluidized beds.
To investigate the application of machine learning for analyzing particle charging behavior.
To identify the most reliable machine learning model for this complex phenomenon.

Main Methods:

Utilized machine learning techniques including kernel ridge regression (KRR), support vector machine regression (SVR), and multilayer perceptron (MLP).
Trained models using literature and experimental data for predicting surface charge density.
Performed sensitivity analysis to evaluate model reliability.

Main Results:

SVR and MLP models achieved high prediction accuracy, with R-squared values of 0.980 and 0.979, respectively.
Sensitivity analysis indicated that the MLP model demonstrated greater reliability compared to the SVR model.
The developed models accurately predict particle surface charge density within a defined range.

Conclusions:

Machine learning is a feasible approach for analyzing particle charging in fluidized beds.
The proposed MLP model serves as an effective correlative tool for rapid estimation of particle surface charge density.
This study provides a novel method for understanding complex electrostatic interactions in gas-solid systems.