Reagent dosage inference based on graph convolutional memory perception network for zinc roughing flotation

In froth flotation, the precise presetting of reagent dosage is crucial for enhancing mineral recovery and flotation efficiency. Considering that the manual operation method is susceptible to some subjective factors, many machine vision-based intelligent reagent dosage presetting approaches are developed. However, prevailing presetting methods primarily rely on empirical knowledge, and ignore the potential intelligent reasoning processes, resulting in a suboptimal flotation performance. In this paper, a reagent dosage inference method based on a graph convolutional memory perception network is proposed, which can leverage the power of both empirical knowledge and neural network to deduce the optimal reagent dosages. This method constructs a memory unit stored with representative operation data, which can be served as a reservoir of empirical knowledge, facilitating dosage inference by referencing relevant data according to the current flotation condition. Subsequently, a graph convolutional neural network is developed to acquire the logical connections between reagent dosage and key parameters of the flotation process. Finally, with the guidance of empirical knowledge, a multi-head attention perception module is employed to deduce the optimal reagent dosage according to the current flotation condition and the acquired logical connection. The effectiveness of the proposed method has been validated in the experiment. Compared to other methods, the proposed method obtains more accurate inference results, and its inferred reagent dosage achieves more satisfactory flotation effect.