**4. Conclusion**

The implementation of artificial intelligence techniques such as artificial neural networks to the separation processes, provides promising results in matters of feasibility and process dynamics, in computational time the use of robust models for the calculation of properties compared to the use of networks. Similarly, with a significant decrease for neural network models, prediction-based fit and azeotrope separation based on variables such as temperature and pressure, neural networks provide better results compared to robust thermodynamic models with Aspen Plus®, which are models that in some cases implement statistical molecular mechanics. Fuzzy artificial neural networks adjust to the dynamics of the reactive column process, where separation of 99% is obtained, which implies that the azeotrope moves, in comparison with traditional models, adjusting the parameters according to the change in stoichiometry, one of the advantages in the ability to predict the change of azeotrope as a function of temperature and pressure; system, as well as the ability to establish the variables in permissible limits and limitations of the number of stages, without being a large design as the robust models mentioned, can give.
