Data Analysis and Prediction Model for Copper Matte Smelting Process

Wang, Guangbiao; Yang, Yingbao; Zhou, Shiwei; Li, Bo; Wei, Yonggang; Wang, Hua

doi:10.1007/s11663-024-03115-0

Metall Mater Trans B

2024

DOI: 10.1007/s11663-024-03115-0

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Data Analysis and Prediction Model for Copper Matte Smelting Process

Guangbiao Wang,

Yingbao Yang,

Shiwei Zhou

et al.

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2024

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Cited by 2 publications

References 29 publications

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Enhanced Generative Adversarial Networks for Isa Furnace Matte Grade Prediction under Limited Data

Ma,

Li,

Shu

et al. 2024

Metals

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Due to the scarcity of modeling samples and the low prediction accuracy of the matte grade prediction model in the copper melting process, a new prediction method is proposed. This method is based on enhanced generative adversarial networks (EGANs) and random forests (RFs). Firstly, the maximum relevance minimum redundancy (MRMR) algorithm is utilized to screen the key influencing factors of matte grade and remove redundant information. Secondly, the GAN data augmentation model containing different activation functions is constructed. And, the generated data fusion criterion based on the root mean squared error (RMSE) and the coefficient of determination (R2) is designed, which can tap into the global character distributions of the copper melting data to improve the quality of the generated data. Finally, a matte grade prediction model based on RF is constructed, and the industrial data collected from the copper smelting process are used to verify the effectiveness of the model. The experimental results show that the proposed method can obtain high-quality generated data, and the prediction accuracy is better than other models. The R2 is improved by at least 2.68%, and other indicators such as RMSE, mean absolute error (MAE), and mean absolute percentage error (MAPE) are significantly improved.

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Enhanced Generative Adversarial Networks for Isa Furnace Matte Grade Prediction under Limited Data

Ma,

Li,

Shu

et al. 2024

Metals

View full text Add to dashboard Cite

show abstract

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

Zhang,

Li,

Wang

et al. 2024

Sensors

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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.

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Data Analysis and Prediction Model for Copper Matte Smelting Process

Cited by 2 publications

References 29 publications

Enhanced Generative Adversarial Networks for Isa Furnace Matte Grade Prediction under Limited Data

Enhanced Generative Adversarial Networks for Isa Furnace Matte Grade Prediction under Limited Data

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

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