Thickness measurement of material plates using low-activity sources with various energies in gamma-ray transmission technique.

This study proposes an approach based on the gamma-ray transmission technique to accurately determine the thickness of material plates using a low-activity source. For this purpose, we have set up an experimental configuration without collimators for both source and detector. Besides, a Monte Carlo simulation model using MCNP6 code has been created with the same geometric parameters as the empirical one. The calibration curves of thickness measurement were constructed for various energies of the incident gamma rays in the range of 123-661.7 keV and two materials of aluminum and copper using Monte Carlo simulation data. The thickness of the material plate was determined by applying experimental data to a known calibration curve. For a given material and gamma energy, the measurable thickness range (MTR) was estimated by investigating the dependence of the expected relative error on the thickness of the material plate. The obtained results show that the approach is feasible with the relative deviation between the measured and reference thicknesses of mostly less than 2% and the relative uncertainty of less than 3%. Such an approach could suggest a practical and cost-effective evaluation tool for optimizing the configuration to achieve a given accuracy corresponding to each type of material.

An approach based on gamma backscattering technique to measuring the density of liquid using the low-intensity radioactive source.

This work aims to develop a practical solution to measure the density of a liquid. Two purposes of this study: (1) using a low-activity source to measure the density of a liquid, and (2) simplifying the experimental arrangement to reduce the size and weight of the measuring system. The proposed solution is to develop a measurement technique without both detector and source collimators, while it considers an appropriate technique for analyzing the backscattering spectrum. To validate the proposed method, we used two groups of liquid: one group of liquids with a certified density and one group of liquids collected from the market. For the first group, the obtained results showed that the relative errors between the measured density and the reference one are below 6.8% and the uncertainties in density are below 4%, which confirms the feasibility of the proposed approach. For the second group, the liquids collected from the market include 70 percent alcohol, cooking oil, saltwater, fresh milk, diesel oil, dishwashing liquid, machine oil, and wine. The results obtained show that the relative errors between the densities determined by the proposed method and densities determined by the traditional method using density kit are less than 4.3%, the uncertainties in density when using the proposed method are below 3.2%. These results initially confirm that the proposed solution is completely applicable in measuring the density of a liquid.

A study on the sensitivity of the measurement of liquid density at different scattering angles using a gamma scattering technique.

This study aims to evaluate the sensitivity of the measurement of liquid density at different scattering angles using a gamma scattering technique. To perform this, the linear calibration curves of the ratio R (R is the ratio of the area under a single scattering peak for a liquid relative to that for water) versus the liquid density were constructed at different scattering angles. The sensitivity of the measurement is defined as the slope coefficient of these linear calibration curves. The results obtained from the Monte Carlo simulation data showed that the sensitivity of the measurement at different scattering angles including 70°, 80°, 90°, 100°, 110°, 120°, 130°, and 140° changes in the range from 0.44 to 0.48. Also, the results obtained from the experiment when performing the measurements at scattering angles of 90°, 100°, 110°, and 120° ranged from 0.46 to 0.48. This confirms that the dependence of the sensitivity of the measurement on scattering angle is insignificant. Besides, for every experimental dataset, we used each of 8 above-obtained calibration curves, in turn, to determine the densities of 8 liquids which yield the relative deviation between the measured density and the reference one is mostly less than 5%, the relative deviation of remaining cases (64 of 256 measurements) is in the range of 5.0%-9.9%.

An artificial neural network based approach for estimating the density of liquid applied in gamma transmission and gamma scattering techniques.

The study presents a new ANN-based approach to determine the density of a liquid applied in the gamma transmission and gamma scattering techniques. This approach used the Monte Carlo simulation combined with an artificial intelligence technique and experimental data to estimate the density of liquids. Two advantages of the proposed approach: (1) it is able to determine the density of a liquid by only measuring the gamma spectrum (transmission spectrum or scattering spectrum) without knowing the composition of the liquid, and (2) it is able to determine the density of a liquid when it is contained in a tube of various diameters. The artificial neural network model was trained by data obtained from simulation and then was used to predict the density of seven liquids with density in the range of 0.6 g cm-3 to 2.0 g cm-3 for the purpose of validating the proposed approach. For the gamma transmission technique, there are 25/28 samples with relative deviations between reference and predicted densities of less than 5%. The remaining three samples have deviations in the range from 5.2% to 6.3%. For the gamma scattering technique, there are 17/21 samples with a relative deviation of less than 5%. The remaining four samples have a deviation in the range from 5.2% to 6.9%. The results proved that the artificial intelligence technique combined with Monte Carlo based on gamma transmission and gamma scattering techniques is an effective approach for estimating the density of a liquid.

ANN coupled with Monte Carlo simulation for predicting the concentration of acids.

The present study proposes a new approach for determining the concentration of acids. The method is based on the combination of Monte Carlo simulation and artificial neural network (ANN) technique for predicting the concentration of acids. Firstly, a Monte Carlo simulation model is validated based on the comparison of simulation data with experimental data. Then, the whole data derived from the Monte Carlo simulation using the MCNP code was used to train the ANN model. The trained ANN model was used to predict the percentage concentrations of 14 acid samples, which yields the maximum relative deviation between the predicted and the reference concentrations is less than 3.5%.