A Temporal Network Based on Characterizing and Extracting Time Series in Copper Smelting for Predicting Matte Grade

Abstract

Addressing the issues of low prediction accuracy and poor interpretability in traditional matte grade prediction models, which rely on pre-smelting input and assay data for regression, we incorporate process sensors’ data and propose a temporal network based on Time to Vector (Time2Vec) and temporal convolutional network combined with temporal multi-head attention (TCN-TMHA) to tackle the weak temporal characteristics and uncertain periodic information in the copper smelting process. Firstly, we employed the maximum information coefficient (MIC) criterion to select temporal process sensors’ data strongly correlated with matte grade. Secondly, we used a Time2Vec module to extract periodic information from the copper smelting process variables, incorporates time series processing directly into the prediction model. Finally, we implemented the TCN-TMHA module and used specific weighting mechanisms to assign weights to the input features and prioritize relevant key time step features. Experimental results indicate that the proposed model yields more accurate predictions of copper content, and the coefficient of determination (R2) is improved by 2.13% to 11.95% and reduced compared to the existing matte grade prediction models.

Keywords

• copper smelting process sensors’ data
• temporal representation and feature extraction
• temporal convolutional network
• matte grade prediction