Prediction of the copper electrorefining process using a data-driven model oriented towards the development of a digital twin

Abstract

This study presents the development of a data-driven model for the copper electro-refining process, designed to predict electrolyte composition and evaluate operational scenarios. The system was implemented using historical plant data and is based on a recurrent neural network with an LSTM encoder-decoder architecture. The model was trained to predict concentrations in the electrolyte and performance variables, employing root mean square error (RMSE) as the loss function. Results demonstrate that the proposed model delivers reasonably accurate predictions, provides a practical tool for scenario evaluation, supports decision-making, and facilitates the transition of copper electro-refineries to Industry 4.0.