Enhanced TDLAS ammonia concentration detection with an OSP-SVR optimization algorithm for water vapor interference...

Area of Science:

• Analytical Chemistry
• Spectroscopy
• Environmental Monitoring

Background:

• Tunable diode laser absorption spectroscopy (TDLAS) is crucial for ammonia (NH3) detection in industrial settings.
• Water vapor interference in TDLAS leads to inaccurate ammonia concentration measurements.
• Existing methods struggle with multicomponent interference and baseline drift.

Purpose of the Study:

• To develop a hybrid algorithm for high-precision ammonia detection under water vapor interference.
• To eliminate spectral interference from background gases using orthogonal subspace projection (OSP).
• To improve concentration inversion accuracy by suppressing nonlinear noise and baseline drift with support vector regression (SVR).

Main Methods:

• Hybrid algorithm integrating Orthogonal Subspace Projection (OSP) and Support Vector Regression (SVR).
• OSP used to eliminate water vapor spectral contributions from the NH3-H2O mixed spectrum.
• SVR model constructed on purified NH3 signals to address residual noise and drift.

Main Results:

• The OSP-SVR algorithm effectively eliminates water vapor interference in TDLAS ammonia detection.
• Reduced water vapor-induced secondary harmonic interference peak by up to 98.54% compared to conventional methods.
• Achieved significant reductions in residual standard error (62.65% and 61.44%) for low and high ppm ammonia concentrations.

Conclusions:

• The OSP-SVR enhanced TDLAS method provides accurate ammonia measurements in complex industrial environments.
• This approach overcomes limitations of traditional methods in the presence of water vapor and other interferences.
• The developed algorithm is highly suitable for real-time monitoring of ammonia in dynamic industrial settings.