This paper presents a solution for the modelling, implementation and simulation of the fractional-order process of producing the enriched ¹³C isotope, through the chemical exchange between carbamate and carbon dioxide. To achieve the goal of implementation and simulation of the considered process, an original solution for the approximation of fractional-order systems at the variation of the system’s differentiation order is proposed, based on artificial intelligence methods. The separation process has the property of being strongly non-linear and also having fractional-order behaviour. Consequently, in the implementation of the mathematical model of the process, the theory associated with the fractional-order system’s domain has to be considered and applied. For learning the dynamics of the structure parameters of the fractional-order part of the model, neural networks, which are associated with the artificial intelligence domain, are used. Using these types of approximations, the simulation and the prediction of the produced ¹³C isotope concentration dynamics are made with high accuracy. In order to prove the efficiency of the proposed solutions, a comparation between the responses of the determined model and the experimental responses is made. The proposed model implementation is made based on using four trained neural networks. Moreover, in the final part of the paper, an original method for the online identification of the separation process model is proposed. This original method can identify the process of fractional differentiation order variation in relation to time, a phenomenon which is quite frequent in the operation of the real separation plant. In the last section of the paper, it is proven that artificial intelligence methods can successfully sustain the system model in all the scenarios, resulting in the feasible premise of designing an automatic control system for the ¹³C isotope concentration, a method which can be applied in the case of other industrial applications too.

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.

The COVID-19 infectious disease spread in the world represents, by far, one of the most significant moments in humankind’s recent history, affecting daily activities for a long period of time. The data available now allow important modelling developments for the simulation and prediction of the process of an infectious disease spread. The current work provides strong insight for estimation and prediction mathematical model development with emphasis on differentiation between three distinct methods, based on data gathering for Romanian territory. An essential aspect of the research is the quantification and filtering of the collected data. The current work identified five main categories considered as the model’s inputs: inside temperatures (°C), outside temperatures (°C), humidity (%), the number of tests and the quantified value of COVID-19 measures (%) and, as the model’s outputs: the number of new cases, the number of new deaths, the total number of cases or the total number of deaths. Three mathematical models were tested to find the optimal solution: transfer vector models using transfer functions as elements, autoregressive-exogenous (ARX) models, and autoregressive-moving-average (ARMAX) models. The optimal solution was selected by comparing the fit values obtained after the simulation of all proposed models. Moreover, the manuscript includes a study of the complexity of the proposed models. Based on the gathered information, the structure parameters of the proposed models are determined and the validity and the efficiency of the obtained models are proven through simulation.

This paper presents a comparative analysis of two adaptive control methods used in case of time varying plant parameters. The first adaptive control method is based on the stability theory of Lyapunov and the other one is based on fuzzy logic. Also, the performances of the proposed control algorithms are evaluated with simulation environment. These control structures are designed for local compensation of the non-linear interactions and unknown payload variations in flexible-link gear drive