Measurement Method for Mold Slag Thickness in Continuous Casting Mold Using Millimeter-Wave Radar and Eddy Current...

Area of Science:

• Materials Science and Engineering
• Sensor Technology
• Process Monitoring

Background:

• Traditional mold slag thickness measurement methods face challenges like sensor degradation at high temperatures and limited detection capabilities.
• Existing non-contact methods often only detect the upper slag surface, failing to provide comprehensive thickness data.
• Accurate, real-time monitoring of mold slag is crucial for optimizing continuous casting processes and ensuring steel quality.

Purpose of the Study:

• To develop an integrated, non-contact sensing approach for accurate mold slag thickness measurement in continuous casting.
• To overcome the limitations of existing contact and non-contact measurement techniques.
• To enable synchronous measurement of solid slag thickness, liquid slag layer inversion, and molten steel level.

Main Methods:

• Fusion of millimeter-wave radar with an improved FFT-CZT^2 algorithm for high-resolution upper slag surface topography scanning (±1 mm error).
• Application of Mel-frequency cepstral coefficients (MFCC) and an enhanced PSO-BP neural network for solid slag thickness prediction (±5 mm error).
• Integration with an eddy current sensor for monitoring the liquid slag-molten steel interface position (±1 mm error) and data fusion for 3D spatial registration.

Main Results:

• Successful reconstruction of upper slag surface topography with high precision.
• Accurate prediction of solid slag layer thickness and molten steel level through dual-sensor data fusion.
• Demonstrated stable performance in high-temperature, complex environments, resolving issues like uneven material distribution.

Conclusions:

• The proposed integrated millimeter-wave radar and eddy current sensor fusion method provides a real-time, accurate, and full-cross-section monitoring solution for mold slag.
• This non-contact approach effectively addresses practical challenges in continuous casting mold monitoring.
• The technology offers significant value for the development of intelligent and automated steel plants.