Flotation froth content estimation on the basis of a machine learning process

The flotation froth content assessment can be carried out in various ways. One of them is the Machine Learning (ML) process applied to the preprocessed and parametrized flotation froth images. The ML procedure can be conducted on the basis of Artificial Neural Network (ANN) or Linear Discriminant Analysis (LDA). This paper presents the LDA application in a ML process. The ML algorithms have their origins in the family of classification algorithms. They are constructed on the basis of training groups of data and enable us to classify the unknown data to one of the training groups. Theoretically, if we have more training groups, we should be able to obtain more accurate estimation of data. However, such approach is not efficient. Each training group should have a high number of data sets, which in most cases is difficult to obtain. This paper presents the estimation of the flotation froth content in the mineral processing plant on the basis of images of the froth surface. The experiment was performed in the Pb Mineral Processing Plant. The images of the flotation froth surface were registered for seven different configurations of the flotation process parameters. The flotation parameters were stabilized 30 minutes before the image registration process. The images registered in the stabilized technological conditions constituted the training group of images for the ML process. For each of the technological configurations of the flotation process, ten collective froth samples were collected for direct chemical analysis. This means that each of seven training groups of the froth images contained ten subgroups of images with well-defined froth content. The ML process enabled us to construct the froth content estimation algorithms for the flotation process in the experiment.