PEDRO PABLO CORREA, ALDO CIPRIANO
Electrolytic refining is the last step of pyrometallurgical copper production. Here, smelted copper is converted into high-quality cathodes through electrolysis. Cathodes that do not meet the physical quality standards are rejected and further reprocessed or sold at a minimum profit. Prediction of cathodic rejection is therefore of utmost importance to accurately forecast the electrorefining cycle economic production. Several attempts have been made to estimate this process outcomes, mostly based on physical models of the underlying electrochemical reactions. However, they do not stand the complexity of real operations. Data-driven methods, such as deep learning, allow modeling complex non-linear processes by learning representations directly from the data. We study the use of several recurrent neural network models to estimate the cathodic rejection of a cathodic cycle, using a series of operational measurements throughout the process. We provide an ARMAX model as a benchmark. Basic recurrent neural network models are analyzed first: a vanilla RNN and an LSTM model provide an initial approach.
@article{901ed058-dbcc-4f92-b118-dd0831321d58,
title={Forecasting Copper Electrorefining Cathode Rejection by Means of Recurrent Neural Networks With Attention Mechanism},
author={PEDRO PABLO CORREA and ALDO CIPRIANO},
year={2021},
language={es}
}TY - JOUR TI - Forecasting Copper Electrorefining Cathode Rejection by Means of Recurrent Neural Networks With Attention Mechanism AU - PEDRO PABLO CORREA AU - ALDO CIPRIANO PY - 2021 LA - es ER -
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