Modeling and Control of an Isotope Separation Process Using Artificial Intelligence Techniques

In this paper, an original solution for modeling and control of the isotope separation process of the 18O isotope, using artificial intelligence techniques, is proposed. The main difficulties of the approached problem are implied by the following facts: the two separation columns from the structure of the considered separation cascade for the 18O isotope have strong nonlinear operation; the separation process is an extremely slow one, having time constants of the order of days; the separation process associated to the second (final) column from the separation plant structure is a fractional-order one; the refluxing system associated to the two separation columns are equipment with a high grade of complexity. In order to model the behavior of the separation cascade, experimental data obtained from the real plant are presented and processed. The control of the 18O isotope concentration at the output of the two separation columns is made using two interconnected control structures based on internal model control strategy. In order to implement the nonlinearities of the mathematical models of the two separation columns, neural networks are used. In the end of the paper, some interesting simulations results are presented which prove the efficiency of the proposed solutions.