Morteza Imani - Engineering Department, Shahid Beheshti University, G.C., P.O. Box: ۱۹۸۳۹۶۳۱۱۳, Tehran, Iran
Mohammademad Adelikhah - Engineering Department, Shahid Beheshti University, G.C., P.O. Box: ۱۹۸۳۹۶۳۱۱۳, Tehran, Iran
Mehdi Aghaie - Engineering Department, Shahid Beheshti University, G.C., P.O. Box: ۱۹۸۳۹۶۳۱۱۳, Tehran, Iran

Separation of stable isotopes has been considered today due to their widespread use. The separation of the isotope ۱۳۰Te is important for medical applications, and the production of radioisotopes up to high concentrations. In this paper, square cascade optimization to achieve ۹۹.۹% 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, ۴۰۰ randomly generated data from the simulation results were used. Predicting the objective function using a neural network leads to a ۹۸% reduction in optimization execution time. Using this method, the optimal cascade separates ۳۴۰۹ g of ۱۳۰Te with ۹۹.۹% concentration from ۱۰ kg of natural tellurium during one year.

Square cascade, Separation, ۱۳۰Te, Gray wolf algorithm, Neural network