Intelligent Error Compensation in Copper Concentrate Belt Conveyors Using LSTM Recurrent Neural Networks for Sustainable Mining Operations

This study presents the development and validation of an intelligent error compensator based on Long Short-Term Memory (LSTM) recurrent neural networks for dynamic weighing systems in copper concentrate belt conveyors. Conventional weighing systems fail to capture nonlinear temporal dynamics, leading to measurement inaccuracies during container filling operations. The methodology comprised data acquisition from load cells, speed sensors, and inclinometers; systematic hyperparameter optimization; and evaluation using Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and coefficient of determination (R2). Hyperparameter optimization identified an optimal configuration with one LSTM layer (20 units, learning rate 0.001, window size 20 steps). Evaluation on an independent test set showed that the compensator reduced MAPE from 8.5% (uncompensated system) to 3.01%, representing a 64.6% improvement, and reduced RMSE from 12.3 to 4.2 tons (65.9% improvement), with an R2 of 0.95. Feature importance analysis confirmed physical consistency, with load cell voltage as the dominant predictor (42%). These results demonstrate that LSTM-based compensation significantly enhances weighing accuracy. The study provides a replicable framework for industrial metrology modernization, contributing to sustainable mining operations through material loss reduction and logistics optimization. While the proposed model has been validated offline using historical data, its deployment in the live production environment remains pending.