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January 1, 2026International Journal of Simulation and Process ModellingOpen Access

A Bayesian multimodal deep learning model for process simulation and anomaly diagnosis in electrofusion magnesium furnaces

Qiuxia Qu Hong Yang Wan Jia Qi Zhai

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Key Points

The aim is to develop a Bayesian multilayer model for efficient simulation and diagnosis of anomalies in electrofusion magnesium furnaces.
Developed a deep learning model integrating multiple data modalities.
Conducted simulations to assess process performance and detect anomalies.
Utilized Bayesian statistics for model optimization and inference.
The model significantly reduced diagnosis time by 30%, improving operational efficiency.
Successfully identified 95% of anomalies during simulations with a 98% accuracy rate.
Demonstrated robust performance across various operational scenarios.

Implication

Demonstrates improved anomaly diagnosis in electrofusion magnesium furnaces, indicating enhanced operational efficiency.

Abstract

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Cite This Study

Qu et al. (2026) studied this question.

synapsesocial.com/papers/6a34df4a65a5b0777af2e7ca https://doi.org/10.1504/ijspm.2026.10079195

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