Operating Performance Assessment Based on Semi-Supervised Cluster Generative Adversarial Networks for Gold Flotation Process

Operating performance assessment of gold flotation process plays an important role in improving the metallurgical performances and pursues the best comprehensive economic benefits. The appearance of froth is a good indicator of flotation performance; however, labeling the data is costly. To assess the operational performance of flotation process with a small amount of labeled data, this study proposes a semi-supervised cluster generative adversarial network (SSClusterGAN). First, latent variables are sampled from one-hot encoded variables and continuous normal distribution variables. While projecting all data into the latent variable space, the specific types of clustering in the latent space is realized by controlling the type of distribution of the learned latent codes, so that the training objectives of labeled data and unlabeled data are consistent. Then, by adapting the generative adversarial networks (GANs) framework, a pair of stacked discriminators are used to learn the conditional distribution of attributes for labeled and unlabeled data, respectively. In addition, using semi-supervised learning (SSL) as the basic learning technology, the high-confidence pseudo-labels obtained by training with unlabeled data are combined with the labeled training data to increase the training samples and effectively improve the accuracy of the model. Finally, the method was applied to the gold flotation process. The experimental results show that the method effectively utilizes unlabeled samples to expand the labeled dataset, combines the advantages of SSL and GAN, obtains accurate assessment results, and effectively improves the comprehensive economic benefits of the gold flotation process.