In recent decades, separation of stable isotopes due to their substantial role in human health has been widely increased. The present research deals with square cascades optimization in order to separate the ¹²³Te by the Gray Wolf Optimization algorithm (GWO). The separation of ¹²³Te has significant application in medical science, and production of radioisotopes. In this study, attempts have been made to find the desired concentration of product (99.9%) for a given amount of natural Tellurium feed within four connected cascades. In this analysis, instead of solving nonlinear equations of concentration distribution in cascades, two different artificial neural networks (ANN) are trained to predict the objective functions. Two test cases for ¹²³Te separation with different objective functions have been considered. The aim is to gain the maximum product from a specified amount of feed in different configurations. In the first case, the neural network has 20 inputs and considers four connected cascades. To train the network, 5000 randomly generated data from the results is used. In the second case, the network has 22 inputs and 10,000 random data is used. In both cases, the Levenberg-Marquardt algorithm with 40 hidden layers is selected to train the networks. Prediction of the objective functions using a neural network leads to a 98% reduction in execution time and significantly improves the speed of the optimization process. Using this method, the optimal cascades for separation of ¹²³Te with 99.9% concentration from 15 kg of natural Tellurium during a year are introduced.

近几十年来，由于稳定​​同位素在人类健康中的重要作用，它们的分离已广泛增加。本研究涉及方形级联优化，以便通过灰狼优化算法 (GWO)分离¹²³ Te。¹²³ Te的分离在医学科学和放射性同位素的生产中具有重要应用。在这项研究中，已尝试在四个相连的级联中找到给定数量的天然碲进料所需的产品浓度 (99.9%)。在此分析中，不是求解级联中浓度分布的非线性方程，而是训练两个不同的人工神经网络 (ANN) 来预测目标函数。^{123 的}两个测试用例已经考虑了具有不同目标函数的分离。目的是在不同配置下从指定数量的饲料中获得最大的产品。在第一种情况下，神经网络有 20 个输入并考虑四个连接的级联。为了训练网络，使用了从结果中随机生成的 5000 个数据。在第二种情况下，网络有 22 个输入，使用了 10,000 个随机数据。在这两种情况下，都选择了具有 40 个隐藏层的 Levenberg-Marquardt 算法来训练网络。使用神经网络预测目标函数可将执行时间减少 98%，并显着提高优化过程的速度。使用这种方法，分离¹²³的最佳级联一年内从 15 公斤天然碲中提取浓度为 99.9% 的碲。