Flotation equipment automation and intelligent froth feature extraction in flotation process: a review

Abstract Flotation is the most widely used technology for mineral separation and purification. The flotation production process has complex mechanism characteristics and is influenced by multiple variables that are coupled with each other, which has always been a difficulty in controlling the beneficiation process. The flotation system of mineral processing plants mostly relies on manual control, which is influenced by subjective factors such as worker experience, technical level, and sense of responsibility, making it difficult to optimize control parameters and maximize production efficiency. This paper systematically summarized the automation systems of flotation equipment such as automatic dosing device, automatic liquid level detection device, automatic feed concentration adjustment device, and automatic feed flow adjustment device. The accurate extraction methods of physical and dynamic characteristics such as color, texture, size, and moving speed of flotation froth were reviewed. The traditional data-driven model and image feature-based prediction methods for prediction of the grade, recovery rate, ash content in the concentrate, and tailings were combed. On this basis, a technical route for achieving intelligent flotation process was proposed with the aim of providing theoretical and practical references through the collaborative operation of flotation devices, detection sensors, and machine learning algorithms.