Introducing optimum parameters of separation cascades for 123Te using GWO based on ANN

Abstract

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.