A novel method for gamma spectrum analysis of low-level and intermediate-level radioactive waste

The uncertainty of nuclide libraries in the analysis of the gamma spectra of low-and intermediate-level radioactive waste(LILW)using existing methods produces unstable results.To address this problem,a novel spectral analysis method is proposed in this study.In this method,overlapping peaks are located using a continuous wavelet transform.An improved quadratic convolution method is proposed to calculate the widths of the peaks and establish a fourth-order filter model to estimate the Compton edge baseline with the overlapping peaks.Combined with the adaptive sensitive nonlinear iterative peak,this method can effectively subtracts the background.Finally,a function describing the peak shape as a filter is used to deconvolve the energy spectrum to achieve accurate qualitative and quantitative analyses of the nuclide without the aid of a nuclide library.Gamma spectrum acquisition experiments for standard point sources of Cs-137 and Eu-152,a segmented gamma scanning experiment for a 200 L standard drum,and a Monte Carlo simulation experiment for triple overlapping peaks using the closest energy of three typical LILW nuclides(Sb-125,Sb-124,and Cs-134)are conducted.The results of the experiments indicate that(1)the novel method and gamma vision(GV)with an accurate nuclide library have the same spectral analysis capability,and the peak area calculation error is less than 4%;(2)compared with the GV,the analysis results of the novel method are more stable;(3)the novel method can be applied to the activity measurement of LILW,and the error of the activity reconstruction at the equivalent radius is 2.4%;and(4)The proposed novel method can quantitatively analyze all nuclides in LILW without a nuclide library.This novel method can improve the accuracy and precision of LILW measurements,provide key technical support for the reasonable disposal of LILW,and ensure the safety of humans and the environment.

Overview of Industrial Materials Detection Based on Prompt Gamma Neutron Activation Analysis Technology

Prompt gamma neutron activation analysis (PGNAA) is a non-destructive online measurement nuclear analysis method. With its unique advantages, it has been widely used in online analysis of industrial materials such as coal, cement, and minerals in recent years. However, there are many kinds of literature on PGNAA in the field of industrial materials detection, and there are still a few concluding articles. To this end, based on the principle of PGNAA online analysis, the status quo and development of the real-time online detection of industrial material components in the field are reviewed and discussed by consulting a large number of domestic and foreign PGNAA related literature and data, to facilitate the reference of relevant scientific researchers.

A novel approach for feature extraction from a gamma‑ray energy spectrum based on image descriptor transferring for radionuclide identification

This study proposes a novel feature extraction approach for radionuclide identification to increase the precision of identification of the gamma-ray energy spectrum set.For easier utilization of the information contained in the spectra,the vectors of the gamma-ray energy spectra from Euclidean space,which are fingerprints of the different types of radionuclides,were mapped to matrices in the Banach space.Subsequently,to make the spectra in matrix form easier to apply to image-based deep learning frameworks,the matrices of the gamma-ray energy spectra were mapped to images in the RGB color space.A deep convolutional neural network(DCNN)model was constructed and trained on the ImageNet dataset.The mapped gamma-ray energy spectrum images were applied as inputs to the DCNN model,and the corresponding outputs of the convolution layers and fully connected layers were transferred as descriptors of the images to construct a new classification model for radionuclide identification.The transferred image descriptors consist of global and local features,where the activation vectors of fully connected layers are global features,and activations from convolution layers are local features.A series of comparative experiments between the transferred image descriptors,peak information,features extracted by the histogram of the oriented gradients(HOG),and scale-invariant feature transform(SIFT)using both synthetic and measured data were applied to 11 classical classifiers.The results demonstrate that although the gamma-ray energy spectrum images are completely unfamiliar to the DCNN model and have not been used in the pre-training process,the transferred image descriptors achieved good classification results.The global features have strong semantic information,which achieves an average accuracy of 92.76%and 94.86%on the synthetic dataset and measured dataset,respectively.The results of the statistical comparison of features demonstrate that the proposed approach outperforms the peak-searching-based method,HOG,and SIFT on the synthetic and measured datasets.

A Sensitivity Dosimetry Study of the Setup Uncertainties during Machine Commissioning and Annual QA

This study is to investigate three common potential setup uncertainties during Linac commissioning and annual QA and to evaluate how these uncertainties propagate into the quality of beam profiles and patient dosimetry using gamma analysis. Three uncertainty scenarios were purposely introduced for gantry position tilted from 0˚- 3˚(scenario 1), isocenter position misaligned from 0 - 6 mm (scenario 2) and SAD changed from 99.5 - 103 cm (scenario 3). A 60 × 60 × 60 cm3 water phantom cube was created to replicate a 3D water tank in VarianEclipse (V.11) treatment planning system (Varian Medical Systems, Palo Alto, CA). For each scenario, beam data profiles (crossline and diagonal) and PDD curves were calculated at different field sizes and depths for three energies: 6 MV, 6 MV-FFF and 10 MV-FFF. Gamma analysis method was used to compare a total of 263 profiles to baseline using a 1%/1mm parameter with 90% gamma passing rate criteria. For scenario 1, a ≥90% gamma passing rate and ≤1% dose difference were seen on both crossline and diagonal profiles, and PDD curves for gantry tilted up to 2˚. For 3˚degree tilt, the gamma passing rate decreased to ≤90% at depth of ≥20 cm for 6MV/6MV-FFF and depth of ≥12 cm for 10MV-FFF. For scenario 2, a ≤90% gamma passing rate and ≥1% dose difference were seen at depths from dmax to 20 cm for all energies. For depths ≥20 cm, mostly ≥90% gamma passing rate and ≤1% dose difference were seen. For scenario 3, a ≥90% gamma passing rate and ≤1% dose difference were seen on ≤4 mm isocenter misalignments for all energies. In summary, gamma analysis of the beam profiles is a very sensitive test for SAD deviation scenarios and can reveal issues of sub millimeter setup uncertainty. However, it is not as sensitive for isocenter misalignment scenarios. The test is also more sensitive for FFF beams than flattening filter beams.

Optimization of PGNAA device and algorithm for testing basicity index of sinter mixture

Sinter is the main raw material in the blast furnace iron-making process,and basicity(CaO/SiO2)is an important quality index of sinter.Prompt gamma neutron activation analysis is a multi-elemental online detection technology that has been successfully applied in cement,coal,etc.Compared with cement as a raw material,sinter exhibits poor moderation ability and a large neutron absorption cross section.Therefore,cement detection devices are not suitable for sinter mixture detection.In this study,a prompt gamma neutron activation analysis equipment used for testing cement was re-optimized to render it suitable for measuring a sinter mixture.Using Monte Carlo simulation,the comprehensive detection efficiency of the detection device improved by 71.52%.Because of the gamma-ray self-shielding effect of the sinter mixture,the detection errors of CaO and SiO2 are significant.By applying the gamma-ray self-shielding correction algorithm,the detection accuracies improved,and their linear correlation coefficients R2 exceeded 0.99.Furthermore,by applying an improved analyzer to a sintering plant,the first-grade product rate of the factory increased by 4.64%.