Physics-Informed Machine Learning for Grade Prediction in Froth Flotation
This addresses the challenge of accurate grade prediction for automatic control and optimization in mineral processing, representing an incremental improvement by combining existing physics and data-driven methods.
The paper tackled the problem of predicting concentrate gold grade in froth flotation cells by integrating classical mathematical models with deep learning to create physics-informed neural networks, resulting in superior generalization and predictive performance compared to purely data-driven models on simulated data.
In this paper, physics-informed neural network models are developed to predict the concentrate gold grade in froth flotation cells. Accurate prediction of concentrate grades is important for the automatic control and optimization of mineral processing. Both first-principles and data-driven machine learning methods have been used to model the flotation process. The complexity of models based on first-principles restricts their direct use, while purely data-driven models often fail in dynamic industrial environments, leading to poor generalization. To address these limitations, this study integrates classical mathematical models of froth flotation processes with conventional deep learning methods to construct physics-informed neural networks. These models demonstrated superior generalization and predictive performance compared to purely data-driven models, on simulated data from two flotation cells, in terms of mean squared error and mean relative error.