Neural Network-Based Identification of a PSA Process for Production and Purification of Bioethanol

Abstract

The pressure swing adsorption (PSA) process, is a novel method for the purification and production of bioethanol. A highly non-linear rigorous model is implemented to simulate the cyclic dynamics of PSA, achieving purity of 99% wt of ethanol, which meets international standards to be used as fuel. The contribution of this work focuses on obtaining an identified model capable of capturing the important dynamics of the PSA process (with a fit above of 90%) and to be used for controller design purposes, since it is very complicated to design control in highly nonlinear models that are represented with partial differential equations (PDE). For proof of concept, a comparison between Hammersetein-Wienner and Artificial Neural-Networks showed the relevance of the proposed method, using the same input and output signals. Both identified models capture the important dynamics of the rigorous PSA model.