An integrated multi-mode model of froth flotation cell based on fusion of flotation kinetics and froth image features

Abstract Froth flotation is the most commonly used technology in minerals separation and beneficiation. A comprehensive and precise flotation model is the foundation for optimizing the recovery rate and grade of valuable minerals. In this paper, a phenomenological model is built to describe the underlying physicochemical principles of the flotation process. To account for the influence factors not explicitly included in the phenomenological model, a data-driven model based on Long Short-Term Memory (LSTM) is proposed for error compensation by learning from the sequentially collected froth images. Considering the time-varying nature of flotation dynamics, a multi-mode modeling framework is established in the context of comprehensive state space (CSS) description system. In the framework, the dynamics of the flotation cell is described using a twofold integration. For each working condition, the phenomenological model and the compensation model are integrated to describe the process dynamics. The process dynamics is formulated as an integration of the sub-models of each working condition. In order to follow the evolution of working condition, the weights of the sub-models vary with the location of the working point in the CSS, which are calculated using a SAE(Stacked Auto Encoder)-KD(K-dimensional tree)-TAN(Tree Augmented Naive Bayes) approach. The proposed approach utilizes both kinetics knowledge and froth image features, and can be adapted to the variation of working conditions. A case study illustrates the stability and accuracy of the proposed approach.