关键词: 模型, 粒度分布, 主元分析, 优化, 预测

Abstract:

The support vector machine (SVM) model in the machine learning algorithm was used to predict the size distribution of hydrotalcite, with ultrasonic attenuation signal as the main input variable and the size distribution measured by Malvern laser particle size analyzer as the output. The study firstly extracted and analyzed the features of the ultrasonic attenuation signal, combined with the strong learning ability of SVM, and introduced principal component analysis (PCA) to establish a size distribution prediction model. The model’‍s hyperparameters were optimized using grid search optimization. The experimental results showed that the SVM model could effectively capture the complex relationship between the ultrasonic attenuation signal and the size distribution of hydrotalcite, with the predicted peak shape similar to the measured value. The mean square error (MSE) was 0.1373, the coefficient of determination (R²) was 0.9758, the maximum absolute error of the cumulative size distribution was 0.0405, and the prediction accuracy was significantly higher than traditional statistical methods. The study showed that this method had great advantages in dealing with nonlinear complex data and provided a new idea for the nondestructive detection and real-time monitoring of the size distribution of hydrotalcite. It had high application value and potential for promotion.

Key words: model, particle size distribution, principal component analysis, optimization, forecast