Prediction of Critical Parameters of Reprocessing Non-uniform Conditions Based on Improved BP Neural-network

As the key process equipment of reprocessing plant, the extraction column and storage tank often have the condition of fluctuating solution concentration (i.e., non-uniform condition). When conducting critical safety analysis, technicians adopt the conservative method of enlarging concentration several times. Although this meets the conservative requirements, it introduces too much critical margin, which limits the treatment efficiency and capacity of reprocessing. In order to solve the above problems, based on the improved BP neural network method and the large-scale MC code MCNP, this study completed the gradient modeling of random concentration distribution for typical equipment structure size, and realized the critical safety analysis method of predicting effective proliferation factor (k_eff) based on concentration distribution. Data test results show that the average error of k_eff calculated under non-uniform conditions by this method is 1.82×10⁻⁴, and the convergence value of loss function MSE is 3.34×10⁻⁶, which is far smaller than the unimproved model (2.4450×10⁻⁴). At the same time, in comparison with the conservative method, the critical margin introduced by the proposed method is –1.31×10⁻³, which is much smaller than that of the traditional method (0.32951). The above results prove that the method in this study is more accurate and effective under the precondition of conservativeness, and provide a method reference for the critical safety analysis of reprocessing.