Prediction of Iron Ore Spheroidity Based on Image Texture Features and PCA-SVR

Abstract

Spheroidity is an important parameter to describe the granulation characteristics of iron ore. Traditionally, physical and chemical analysis methods are used to obtain the spheroidization of iron ore. However, these processes are time-consuming and labor-intensive, and it is difficult to control the accuracy of the results. In this study, image processing and neural networks are used to construct a support vector regression (SVR) iron ore sphericity prediction model from the perspective of information fusion. Three kinds of image texture feature extraction methods are used: Tamura texture feature, gray level co-occurrence matrix (GLCM), and gray level difference statistics (GLDS). Principal component analysis are used to dimensionality reduction of image texture feature parameters. Under the same operating conditions, the results using the SVR model with and without PCA are compared, and the prediction accuracy of these models for iron ore spheroidity are 96.7% and 79.8%, respectively. The results show that the model based on image texture features and PCA-SVR has excellent characteristics, such as fast operating time and high accuracy, for the prediction of iron ore spheroidity, has practical significance in guiding the sintering process of iron ore and can provide further efficient and accurate research on iron ore spheroidity in the future.