Research Progress on Flotation Prediction Based on Froth Characteristics

Flotation is a technical method for separating valuable minerals from ores based on the differences in physical and chemical properties of mineral particles. The real−time detection of its process indicators is an effective prerequisite for improving resource utilization. However, due to the long sampling period, long sample delivery and analysis time, the traditional off−line test method shows a serious lag, and it is difficult to achieve real−time control of mineral processing. In recent years, with the continuous development of machine vision and artificial intelligence technology, flotation froth image has become an important data source in the soft measurement research of flotation process indicators because of its rich process state information. With the help of modeling, the feature extraction and quantification of flotation froth images can provide reliable support for dynamic real−time monitoring and indicators prediction of flotation process, so as to promote the effective utilization of resources, efficiency improvement and intelligent development in the field of mineral processing. This paper reviews the research status of the whole process technology from data acquisition and processing to feature extraction, and then to prediction model establishment and optimization. In the aspect of preliminary data preparation, the flotation froth image acquisition device, sampling method and image preprocessing method are summarized. In the feature extraction process, the typical techniques of froth static feature and dynamic feature extraction are reviewed, such as bubble size, shape, texture, froth motion behavior and other dimensions. Based on this, the application of deep learning algorithm in the prediction of ash content, grade and other indicators is further reviewed. The key roles of different intelligent optimization algorithms such as genetic algorithm and particle swarm optimization algorithm in model optimization are summarized. It is expected to provide some reference for the intelligent transformation of flotation technology.