Reactive Distillation Modeling Using Artificial Neural Networks

Abstract

The use of artificial intelligence techniques in the design of processes has generated a line of research of interest, in areas of chemical engineering and especially in the so-called separation processes, in this chapter the combination of artificial neural networks (ANN) is presented and fuzzy dynamic artificial neural networks (DFANN). Applied to the calculation of thermodynamic properties and the design of reactive distillation columns, the ANN and DFANN are mathematical models that resemble the behavior of the human brain, the proposed models do not require linearization of thermodynamic equations, models of mass and energy transfer, this provides an approximate and tight solution compared to robust reactive distillation column design models. Generally, the models must be trained according to a dimensionless model, for the design of a reactive column a dimensionless model is not required, it is observed that the use of robust models for the design and calculation of thermodynamic properties give results that provide better results than those calculated with a commercial simulator such as Aspen Plus (R), it is worth mentioning that in this chapter only the application of neural network models is shown, not all the simulation and implementation are presented, mainly because it is a specialized area where not only requires a chapter for its explanation, it is shown that with a neural network of 16 inputs, 2 hidden layers and 16 outputs, it generates a robust calculation system compared to robust thermodynamic models that contain the same commercial simulator, a characteristic of the network presented is the minimization of overlearning in which the network by its very nature is low. In addition, it is shown that it is a dynamic model that presents adjustment as a function of time with an approximation of 96–98% of adjustment for commercial simulator models such as Aspen Plus (R), the DFANN is a viable alternative for implementation in processes of separation, but one of the disadvantages of the implementation of these techniques is the experience of the programmer both in the area of artificial intelligence and in separation processes.