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Experimental Study On The Effect Of Envirnmental Factor On222Rn And 220Rn Emanation Coefficient Of Uranium Tailing Sands

Posted on:2020-04-08Degree:MasterType:Thesis
Country:ChinaCandidate:S LiFull Text:PDF
GTID:2381330578466434Subject:Safety science and engineering
Abstract/Summary:PDF Full Text Request
A certain amount of tailings containing uranium series or thorium radionuclides will be produced during the development of underground uranium resources.These tailings will continue to release radioactive gases such as 222Rn and 220Rn into the ambient atmosphere,which are the main radioactive sources in uranium mine areas.In order to evaluate the radioactive pollution potential of these solid wastes to the surrounding environment,scientific and reasonable radiation protection measures are proposed,which is of great significance to the study of the radon release characteristics of uranium tailings under complex environment heat and humidity conditions.Emanation coefficient and diffusion coefficient are important physical parameters for determining the release of radon from uranium tailings.The tailings from a uranium tailing reservoir in southern China are selected as experimental objects.Firstly,the determination method of 222Rn emanation coefficient of uranium tailing sand is determined.The 222Rn emanation coefficient measuring device was designed to control temperature and humidity.The influence of temperature,humidity and tailings particle size on 222Rn emanation coefficient of uranium tailings was studied by selecting uranium tailings with different diameters.Secondly,based on the prediction model of BP neural network and taking temperature,humidity and particle size of tailings as input parameters,the prediction model of222Rn emanation coefficient of uranium tailings is established.In the end,the determination method and design experiment of the equivalent diffusion coefficient and emanation coefficient of the uranium tailing220Rn are developed,and the effect of the environmental humidity on the equivalent diffusion coefficient and emanation coefficient of the 220Rn is studied.The contents and conclusions of the study are as follows:?1?When the temperature is 10-30?,the 222Rn emanation coefficient of uranium tailings increases slowly.When the temperature exceeds 30?,the emanation coefficient begins to increase rapidly.When the environmental humidity is 0-40%,the 222Rn emanation coefficient of uranium tailings increases rapidly,and increases slowly when the humidity is more than 40%.When the temperature and humidity is constant,the tailing sand with small particle size has a large emanation coefficient,and the change of the temperature and the humidity have a small influence on the emanation coefficient of the 222Rn of the uranium tailing sand with large particle size,and the influence on the small-particle-size uranium tailing sand is large.?2?The prediction model of radon emanation coefficient of uranium tailings based on the prediction theory of BP neural network has met the requirement of precision after 7974 training times.The maximum relative error between the predicted model of emanation coefficient and the measured value is 2.68%.It is shown that the prediction model of radon emanation coefficient of uranium tailings is in good agreement with the measured value,and can be used to analyze the effects of ambient temperature,humidity and medium size on the emanation coefficient of particle-packing emanation media.?3?The free thoron generation rate and emanation coefficient of uranium tailings increased with the increase of environmental humidity.When the humidity increased to 60%,the increasing trend of the free thoron generation rate and emanation coefficient gradually became slower.The thoron diffusion coefficient of the uranium tailings decreases with the increase of the humidity.The measurement method has high requirements for the accuracy of the thoron concentration measuring device and the error control during the measurement process.
Keywords/Search Tags:Uranium tailing sand, Radon, Thoron, Emanation coefficient, BP neural network
PDF Full Text Request
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