ABSTRACT

Cathode ray tubes (CRTs) are glass materials that contain harmful heavy metal elements such as lead . CRTs have been widely used in electronic products sets for decades. At present, these discarded CRTs endanger the environment. Because the high-density heavy metal elements in CRTs have a good shielding effect for X/γ rays, these raw materials have become a good choice to produce radiation-proof concrete. In the selection and use of shielding materials, the buildup factor must be considered. Monte Carlo code MCNP is used to calculate the exposure buildup factor (EBF) of concrete with different proportions of CRT fragments in the photon energy range of 0.015–15 MeV for shielding thicknesses of up to 20 mean free paths (mfp). Back-propagation (BP) neural network is proposed to predict the EBF, and the prediction effect is evaluated, reliability of this method is verified; the average error is 3.4%, and the maximum error is 9.1%. Using this method, the EBF of concrete with an arbitrary proportion of CRTs, any energy level and any shielding thickness can be quickly obtained in the ranges of the parameters of the neural network training set (0.015–15 MeV and 0–20 mfp in this paper).

摘要

阴极射线管（CRT）是包含有害重金属元素（例如铅）的玻璃材料。数十年来，CRT已广泛用于电子产品中。目前，这些废弃的CRT危害环境。由于CRT中的高密度重金属元素对X /γ射线具有良好的屏蔽效果，因此这些原材料已成为生产抗辐射混凝土的理想选择。在选择和使用屏蔽材料时，必须考虑堆积因子。蒙特卡洛代码MCNP用于计算在0.015-15 MeV的光子能量范围内具有不同比例的CRT碎片的混凝土的暴露累积因子（EBF），屏蔽厚度最大为20条平均自由程（mfp）。提出了BP神经网络对EBF进行预测，并对预测效果进行了评估，验证了该方法的可靠性；平均误差为3.4％，最大误差为9.1％。使用这种方法，可以在神经网络训练集的参数范围内（本例中为0.015–15 MeV和0–20 mfp）快速获得具有任意比例的CRT，任何能级和任何屏蔽厚度的混凝土的EBF。纸）。