Control for Bioethanol Production in a Pressure Swing Adsorption Process Using an Artificial Neural Network

Moises Ramos-Martinez, Carlos Alberto Torres-Cantero, Gerardo Ortiz-Torres, Felipe D. J. Sorcia-Vázquez, Himer Avila-George, Ricardo Eliú Lozoya-Ponce (), Rodolfo A. Vargas-Méndez, Erasmo M. Renteria-Vargas and Jesse Y. Rumbo-Morales
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Moises Ramos-Martinez: Departamento de Ciencias Computacionale e Ingenierías, Universidad de Guadalajara, Carretera Guadalajara-Ameca Km. 45.5 C.P., Ameca 46600, Jalisco, Mexico
Carlos Alberto Torres-Cantero: Tecnológico Nacional de Mexico Campus Colima, Av. Tecnológico # 1, Col. Liberación, Villa de Álvarez 28976, Colima, Mexico
Gerardo Ortiz-Torres: Departamento de Ciencias Computacionale e Ingenierías, Universidad de Guadalajara, Carretera Guadalajara-Ameca Km. 45.5 C.P., Ameca 46600, Jalisco, Mexico
Felipe D. J. Sorcia-Vázquez: Departamento de Ciencias Computacionale e Ingenierías, Universidad de Guadalajara, Carretera Guadalajara-Ameca Km. 45.5 C.P., Ameca 46600, Jalisco, Mexico
Himer Avila-George: Departamento de Ciencias Computacionale e Ingenierías, Universidad de Guadalajara, Carretera Guadalajara-Ameca Km. 45.5 C.P., Ameca 46600, Jalisco, Mexico
Ricardo Eliú Lozoya-Ponce: División de Estudios de Posgrado e Investigación, Tecnológico Nacional de México campus Chihuahua, Chihuahua 31310, Chih, Mexico
Rodolfo A. Vargas-Méndez: Department of Electronic Engineering, CENIDET, Cuernavaca 62490, Morelos, Mexico
Erasmo M. Renteria-Vargas: Departamento de Ciencias Computacionale e Ingenierías, Universidad de Guadalajara, Carretera Guadalajara-Ameca Km. 45.5 C.P., Ameca 46600, Jalisco, Mexico
Jesse Y. Rumbo-Morales: Departamento de Ciencias Computacionale e Ingenierías, Universidad de Guadalajara, Carretera Guadalajara-Ameca Km. 45.5 C.P., Ameca 46600, Jalisco, Mexico

Mathematics, 2023, vol. 11, issue 18, 1-26

Abstract: This paper introduces a new approach to controlling Pressure Swing Adsorption (PSA) using a neural network controller based on a Model Predictive Control (MPC) process. We use a Hammerstein–Wiener (HW) model representing the real PSA process data. Then, we design an MPC-controlled model based on the HW model to maintain the bioethanol purity near 99 % molar fraction. This work proposes an Artificial Neural Network (ANN) that captures the dynamics of the PSA model controlled by the MPC strategy. Both controllers are validated using the HW model of the PSA process, showing great performance and robustness against disturbances. The results show that we can follow the desired trajectory and attenuate disturbances, achieving the purity of bioethanol at a molar fraction value of 0.99 using the ANN based on the MPC strategy with 94 % of fit in the control signal and a 97 % fit in the purity signal, so we can conclude that our ANN can be used to attenuate disturbances and maintain purity in the PSA process.

Keywords: artificial neural networks; pressure swing adsorption; model predictive control; bioethanol (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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