A time-frequency dual-branch feature dynamic fusion prediction network for tail gas sulfur content prediction in the wet flue gas desulfurization process

Key Points

• Sulfur content prediction accuracy improves significantly with the dual-branch approach, enhancing the monitoring process.
• Key metrics show that the prediction network outperforms traditional methods by at least 20% in accuracy.
• The analysis uses a time-frequency dual-branch feature dynamic fusion network for real-time predictions during the desulfurization process.
• This method highlights the need for efficient monitoring solutions, especially as it uses advanced prediction technology.