utilization of artificial neural network in optimizing square cascades for separation of tellurium 130 isotope

utilization of artificial neural network in optimizing square cascades for separation of tellurium 130 isotope

separation of stable isotopes has been considered today due to their widespread use. the separation of the isotope 130te is important for medical applications, and the production of radioisotopes up to high concentrations. in this paper, square cascade optimization to achieve 99.9% concentration of this isotope by the gray wolf optimization algorithm is presented. in the optimization, instead of solving nonlinear equations of concentration distribution in the cascade, a trained neural network is used to predict the value of the objective function. to train the neural network, 400 randomly generated data from the simulation results were used. predicting the objective function using a neural network leads to a 98% reduction in optimization execution time. using this method, the optimal cascade separates 3409 g of 130te with 99.9% concentration from 10 kg of natural tellurium during one year.