MsFfTsGP: Multi-source features-fused two-stage grade prediction of zinc tailings in lead-zinc flotation process via multi-stream 3D convolution with attention mechanism

Abstract

The tailings grade is a crucial indicator for controlling and optimizing mineral flotation processes. However, detecting/predicting tailings grade online faces challenges due to intricate physicochemical reactions in three-phase slurries with the strongly-coupled characteristics of the technical process. This paper introduces a novel multi-source features-fused two-stage tailings grade prediction model called MsFfTsGP by integrating multiple-source froth image sequences (FISs) with available traditional technical variables (TTVs, e.g., the reagent dosage and measured grades) from multiple flotation cells, mimicking the on-site two-stage prediction mode of experienced operators. Namely, an initial evaluation is made based on the source-ore characteristics using the FISs of the first rougher, while the second stage provides a sophisticated prediction of the tailing grade by considering both the source-ore characteristics and the flotation behavior of the whole circuit process. Specifically, FISs captured from the first rougher are fed into a multi-stream 3D convolutional network (Ms3DCN) for making an initial prediction. Then, the initial prediction result is combined with the FIS information from the last scavenger (also processed by the Ms3DCN) and available TTVs based on the attention mechanism to make a sophisticated prediction. Extensive experiments on a real industrial lead-zinc flotation process demonstrated a significant improvement of the proposed model in prediction accuracies. Integrating multisource FISs with TTVs from multiple coupled flotation cells shows great potential for achieving accurate tailings grade prediction, making MsFfTsGP a promising tool for enhancing the control and optimization of flotation processes.