Combining TDLAS and multi-fusion algorithms for methane gas concentration detection

High-precision methane gas detection is of great importance in industrial safety, energy production and environmental protection, etc. However, in the existing measurement techniques, the methane gas concentration information is susceptible to noise, which leads to its useful signal being drowned by noise. A fusion algorithm of variational modal decomposition(VMD) and improved wavelet threshold filtering is proposed, which is used in combination with tunable diode laser absorption spectroscopy(TDLAS) to implement a non-contact, high-resolution methane gas concentration detection. The fusion algorithm can perform noise reduction and further segmentation of the methane gas detection signal. And the simulation and experiment verify the effectiveness of the fusion algorithm, and the experimental results show that for the detection of air containing 10 ppm, 30 ppm, 60 ppm, 80 ppm, and 99 ppm methane, the errors are 12.75%, 8.18%, 3.37%, 2.46%, and 1.78%, respectively.

Combined Transmission Interference Spectrum of No Core Fiber and BP Neural Network for Concentration Sensing Research

To investigate wavelength response of the no core fiber（NCF）interference spectrum to concentration,a three-layer back propagation（BP）neural network model was established to optimize the concentration sensing data.In this method,the measured wavelength and the corresponding concentration were trained by a BP neural network,so that the accuracy of the measurement system was optimized.The wavelength was used as the training set and got into the input layer of the three layer BP network model which is used as the input value of the network,and the corresponding actual concentration value was used as the output value of the network,and the optimal network structure was trained.This paper discovers a preferable correlation between the predicted value and the actual value,where the former is approximately equal to the latter.The correlation coefficients of the measured and predicted values for a sucrose concentration were 1.000 89 and 1.003 94;similarly,correlations of0.999 51 and 1.018 8 for a glucose concentration were observed.The results demonstrate that the BP neural network can improve the prediction accuracy of the nonlinear relationship between the interference spectral data and the concentration in NCF sensing systems.

Investigating the minimum detectable activity concentration and contributing factors in airborne gamma-ray spectrometry

In this study,the theory of minimum detectable activity concentration(MDAC)for airborne gamma-ray spectrometry(AGS)was derived,and the relationship between the MDAC and the intrinsic effi-ciency of a scintillation counter,volume,and energy res-olution of scintillation crystals,and flight altitude of an aircraft was investigated.To verify this theory,experi-mental devices based on NaI and CeBr 3 scintillation counters were prepared,and the potassium,uranium,and thorium contents in calibration pads obtained via the stripping ratio method and theory were compared.The MDACs of AGS under different conditions were calculated and analyzed using the proposed theory and the Monte Carlo method.The relative errors found via a comparison of the experimental and theoretical results were less than 4%.The theory of MDAC can guide the work of AGS in probing areas with low radioactivity.

Capability of Raman lidar for monitoring the variation of atmospheric CO_2 profile

Lidar （Light detection and ranging） has special capabilities for remote sensing of many different behaviours of the atmosphere. One of the techniques which show a great deal of promise for several applications is Raman scattering. The detecting capability, including maximum operation range and minimum detectable gas concentration is one of the most significant parameters for lidar remote sensing of pollutants. In this paper, based on the new method for evaluating the capabilities of a Raman lidar system, we present an evaluation of detecting capability of Raman lidar for monitoring atmospheric CO2 in Hefei. Numerical simulations about the influence of atmospheric conditions on lidar detecting capability were carried out, and a conclusion can be drawn that the maximum difference of the operation ranges caused by the weather conditions alone can reach about 0.4 to 0.5km with a measuring precision within 30ppmv. The range of minimum detectable concentration caused by the weather conditions alone can reach about 20 to 35 ppmv in vertical direction for 20000 shots at a distance of 1 km on the assumption that other parameters are kept constant. The other corresponding parameters under different conditions are also given. The capability of Raman lidar operated in vertical direction was found to be superior to that operated in horizontal direction. During practical measurement with the Raman lidar whose hardware components were fixed, aerosol scattering extinction effect would be a significant factor that influenced the capability of Raman lidar. This work may be a valuable reference for lidar system designing, measurement accuracy improving and data processing.

Design and development of a plastic scintillator based whole body β/γ contamination monitoring system

Plastic scintillation detectors based whole body β/γ contamination monitors are developed for use in radiation facilities.This microcontroller-based multi-detector system uses 13 plastic scintillator detectors,with minimized dead detection zones,monitoring the whole body,and conforming to the contamination limit prescribed by the regulatory authority.This system has the features for monitoring hands,feet,head,and faceβ/γusing contamination monitors and portal exit monitors.It can detect gamma sources at a dose rate of 10 n Gyh^(-1).The system is calibrated using b sources^(90)Sr/^(90)Y,^(204)Tl,and^(36)Cl,and the efficiency is found to be 29%,22%,and 18%,respectively.Theminimumdetectableβ/γcontaminationis0.15 Bqcm^(-2),which is significantly less than the minimum detection objectives on head,face,hands,and feet.