In recent years, the technology of deep learning has made great achievements in the field of machine learning. In this study, with the help of the transfer learning method, a kind of soft sensor is designed for the classification of iron ore tailings grade. Firstly, a sample database of froth images of flotation tailings was established. Secondly, the three most reliable models are determined after comparing the accuracy of 13 deep neural network models applied in the flotation froth image. A more accurate hybrid deep neural network model is established, with an accuracy of 97%. Finally, a software system is designed and developed, which can operate stably in the flotation plant. The experimental results show the effectiveness of the proposed hybrid deep neural network in the field of iron ore froth flotation.

No abstract

Concentrate grade and tailings grade are two vital parameter indexes in a flotation process. To detect the grade succinctly and continuously, a soft sensor based on case-based reasoning (CBR) is proposed. Historic production data is first switched into the form of a case. The case problem includes feed grade, raw ore grade, raw ore ferrous oxide content, raw ore magnetic iron content, target concentrate grade, target tailings grade, dosage of four kinds of reagents; the case solution includes concentrate grade and tailings grade. Simulation result shows that the CBR soft sensor has a higher accuracy and speed in forecasting both concentrate grade and tailings grade when compared with soft sensors supported by other algorithms. The application result in a Chinese iron core dressing mill indicates that the soft sensor presented by this paper causes no damage to people and it can forecast product quality in real-time.

No abstract

No abstract