Hybrid Simulated Annealing Particle Swarm Optimization Support Vector Machine Based Temperature-Pressure Error Compensation Approach for TDLAS Gas Detection

Abstract

ABSTRACT The target gas concentration in TDLAS (Tunable Diode Laser Absorption Spectroscopy) gas detection is nonlinearly related to the photodetector output signal, which is effortlessly disturbed by the temperature and pressure inside the gas absorption cell, and it will decrease the detection accuracy of the system. This paper proposes an SA-PSO-SVM (Simulate Anneal-Particle Swarm Optimization-Support Vector Machine) based temperature-pressure error compensation approach to improve the accuracy of TDLAS gas detection. First, an experimental system concerning the effects of gas temperature-pressure on the absorption line distortion was built. Meanwhile, a method for processing and exploring normalized data about the temperature and pressure effects on the gas concentration was proposed. Second, an SA-PSO-SVM-based model of temperature-pressure compensation for TDLAS gas detection was constructed, and a multi-point correction was performed. Finally, the developed model was compared with traditional BP (Back Propagation), SVM, and PSO-SVM-based temperature-pressure compensation models for TDLAS gas detection, to verify the effectiveness in terms of mean absolute error, mean relative error, and mean square error, which could significantly improve the detection accuracy of TDLAS system. The results provide guidance and lay a technical foundation for the study of multi-component gas cross-aliasing absorption line separation.