Efficient Acoustic Thermometry Method for Power Plant Boilers Based on the LMS Quadratic Cross-Correlation

Area of Science:

• Engineering
• Signal Processing
• Thermometry

Background:

• Accurate temperature measurement is crucial for power plant boiler efficiency and safety.
• Acoustic thermometry offers a non-invasive method for boiler temperature monitoring.
• Existing time delay estimation methods struggle with noise in harsh industrial environments.

Purpose of the Study:

• To develop and validate a robust time delay estimation algorithm for acoustic thermometry in power plant boilers.
• To improve the accuracy of single-path acoustic temperature measurements.
• To address the challenges posed by noise and harsh conditions in boiler environments.

Main Methods:

• A novel algorithm combining least-mean-square (LMS) adaptive filtering and generalized quadratic cross-correlation was developed.
• Numerical simulations were performed to evaluate the algorithm's performance.
• Experimental validation was conducted in laboratory settings and on a 750 tons/day waste incineration boiler test platform.
• The proposed algorithm was compared against the generalized quadratic cross-correlation algorithm.

Main Results:

• The LMS quadratic cross-correlation algorithm effectively filtered noise in both numerical simulations and laboratory conditions.
• Accurate time delay estimation was achieved even in the harsh environment of power plant boilers.
• A significant improvement in the accuracy of single-path acoustic temperature measurement was demonstrated.

Conclusions:

• The proposed LMS quadratic cross-correlation algorithm provides a reliable solution for time delay estimation in acoustic thermometry for power plant boilers.
• The algorithm's robustness in noisy and harsh environments makes it suitable for industrial applications.
• This advancement enhances the precision of acoustic temperature monitoring in critical infrastructure.