The closed chamber method is widely used in measuring radon exhalation rate, which can avoid the error caused by the leakage and anti-diffusion phenomena. Firstly, considering the actual situation that uranium ore is difficult to obtain and have a high radioactivity, the uranium-like rock was made according to the similarity theory. Secondly, the diffusion length and intrinsic radon exhalation rate were obtained by using the closed chamber method. Thirdly, the theoretical values of radon exhalation rate made by uranium-like ore block were calculated, compared with the measured values. This study shows that the uranium-like rock block made by the best mass ratio is helpful for the subsequent experiment, and the error between the theoretical calculation and the measured value is no more than 9.14%. This indicates it is reliable to estimate radon exhalation rate by diffusion length and intrinsic radon exhalation rate and can also provide a foundation for rapidly gaining radon exhalation rate of the same type material by the closed chamber method. This study can further promote the study of the radon exhalation rate under the complex physical conditions and then better guide the protection work of radon radiation in underground mining.
To immobilize radionuclides in the soil of uranium mill tailing (UMT), the microwave vitrification method is considered as a promising alternative. In order to examine how changing microwave power and sample size affects microwave heating, a numerical model is constructed here that takes electromagnetic and heat transmission into account. The simulation findings demonstrate that a silicon carbide (SiC) susceptor and microwave aid can be used to treat the soil in UMT using microwave power. Due to the economic advantages, a 400W microwave input power adds significantly to thermal heterogeneity. The size of the soil samples is also important since it affects how microwaves heat the soil and how the heat is distributed. The 20mm in diameter, 10mm thick soil sample performed the best under the 400W input power.
This paper focuses on regression applications of the Support Vector Machine (SVM) in the process industry. The support vector regression machines are employed to build soft sensing models in the paper. Soft sensor modeling, in a sense, is a kind of regression problems in industrial processes. First we review the development history of the Vapnik Chervonenkis (VC) theory and SVM. And then, the basic idea behind the SVM is introduced and some famous SVM regression algorithms are talked about. After that, the standard QP and SMO implementations to Vapnik's soft margin epsiv-SVM regression algorithm are discussed in detail. Using these two implementing methods, we perform some experiments, to predict pulp Kappa numbers, over a real-life dataset retrieved from a kraft pulp cooking process. Some useful conclusions are drawn finally.
Based on the principle of similarity, uranium ore samples were prepared from raw materials such as uranium tailings, quartz sand and refined iron powder, and then the samples were treated with different packages for measuring the sample accumulated radon concentration. In the actual measurement process, due to the characteristics of radionuclide decay, instrument reasons and human factors, the data will be a certain deviation. Therefore, the method of wavelet analysis is used to denoise the accumulated radon concentration and obtain radon exhalation rate. The results of the study show: the correlation coefficient of cumulative radon concentration fitted by wavelet denoising is improved greatly, and all of them are above 0.99, the recalculated radon exhalation rate of the single side of the sample is decreased by 0.06Bq · m−2 · s−1, and double-edged is decreased by 0.02Bq · m−2 · s−1. The experiment proved that wavelet theory can be used to correct calculated value of radon exhalation rate of uranium-like rock. At the same time, it provides a new method for further study of uranium mine radiation protection parameters.
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