Predictive Modeling of Ball Mill Load Parameters Using Hybrid Physics-Informed Neural Networks
Yiwen Liu (Taiyuan University of Technology)
Gaowei Yan (Taiyuan University of Technology)
Yusong Pang (TU Delft - Transport Engineering and Logistics)
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Abstract
Wet ball mill plays a key role in the grinding process, and its load state directly affects production efficiency, energy consumption and product quality. Aiming at the problems of poor interpretability of pure data-driven models and complex modeling of mechanism models under variable working conditions, a hybrid prediction model DAPINN combining deep learning and physical information is proposed. By introducing the deep hidden physics model principle and using the characteristics of neural networks to approximate arbitrary functions to simulate complex physical partial differential equations, the physical interpretability of the model is enhanced. At the same time, the model introduces domain adaptation technology to improve the prediction accuracy and generalization of the model under variable working conditions. Experiments were conducted on data collected from a small ball mill in the laboratory. The experimental results show that under variable working conditions, the prediction accuracy of the DAPINN model is better than that of the pure data-driven model.