A physics-enhanced deep learning method for predicting anode furnace lining lifetime

To improve the accuracy of refractory lining life prediction in copper smelting rotary anode furnaces, this paper proposes a prediction method integrating physical mechanisms and deep learning. Aiming at the problem of sparse and unevenly distributed brick thickness measurement data in on-site high-temperature and high-dust environments, the model establishes a nonlinear exponential relationship between temperature and erosion rate based on the analysis of metallurgical reaction kinetics, constructs a data augmentation model under physical constraints, and introduces Monte Carlo noise simulation to expand discrete and sparse measurement records into a high-frequency time-series dataset. On this basis, a multilayer perceptron (MLP) model is constructed to realize the online evolution prediction of refractory brick thickness. Experimental results show that this method effectively solves the modeling difficulties under small-sample conditions.The model achieved a coefficient of determination (R²) greater than 0.99 and a root mean square error (RMSE) of approximately 0.1 mm on the test set, verifying the effectiveness of the mechanism and data dual-driven mode in the service life prediction of industrial thermal equipment.