AI versus Classic Methods in Modelling Isotopic Separation Processes: Efficiency Comparison

Abstract

In the paper, the comparison between the efficiency of using artificial intelligence methods and the efficiency of using classical methods in modelling the industrial processes is made, considering as a case study the separation process of the 18O isotope. Firstly, the behavior of the considered isotopic separation process is learned using neural networks. The comparison between the efficiency of these methods is highlighted by the simulations of the process model, using the mentioned modelling techniques. In this context, the final part of the paper presents the proposed model being simulated in different scenarios that can occur in practice, thus resulting in some interesting interpretations and conclusions. The paper proves the feasibility of using artificial intelligence methods for industrial processes modeling; the obtained models being intended for use in designing automatic control systems.

Modeling and Simulation of High Voltage Power Lines under Transient and Persistent Faults

“…The mathematical model that describes the defect propagation can be expressed in the form of a second-order transfer function (representing the simplest form of a higher-order model) [31][32][33][34]. The input signal that produces the variations of the output signal presented in previous figures is considered to be a unitary step (u(t) = 1; U(s) = L{u(t)} = 1/s) having the physical meaning of the event that triggered the fault.…”