State evaluation of copper flotation process based on transfer learning and a layered and blocked framework

Abstract

State evaluation is vital to ensure the process operating optimality for copper flotation processes. Specifically, the froth image is the comprehensive embodiment of raw ore properties and process operations, which is one of the key factors to realize condition recognition and state evaluation. Firstly, a feature mosaic technique‐based neural network framework is proposed. The input image features are extracted from the different network structures, which can achieve higher precision in condition recognition and state evaluation than a single neural network framework. Then, an improved deep convolutional generative adversarial networks (DCGAN) model based on feature matching and maximize mean discrepancy (MMD) distance is investigated so that the froth images with high similarity, integrity, and balance to the original images can be generated. Therefore, the problem of small image sets and the lack of labelled images for some sub‐processes can be solved. Finally, a layered and blocked state evaluation model is constructed based on the improved DCGAN model and transfer learning (TL) so that the state evaluation of the copper flotation process with multiple sub‐processes, long process, and small image sets of some sub‐processes is solved. The effectiveness of the proposed method is verified through a series of data experiments on a copper flotation industrial process.