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

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Listed:
  • 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
    Facultad de Ingeniería Mecánica y Eléctrica de la Universidad de Colima, Carretera Colima-Coquimatlan Km 9, Valle de las Huertas, Coquimatlán 28400, 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)

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.

Suggested Citation

  • 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. Rente, 2023. "Control for Bioethanol Production in a Pressure Swing Adsorption Process Using an Artificial Neural Network," Mathematics, MDPI, vol. 11(18), pages 1-26, September.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:18:p:3967-:d:1242667
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    References listed on IDEAS

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    1. Małgorzata Smuga-Kogut & Tomasz Kogut & Roksana Markiewicz & Adam Słowik, 2021. "Use of Machine Learning Methods for Predicting Amount of Bioethanol Obtained from Lignocellulosic Biomass with the Use of Ionic Liquids for Pretreatment," Energies, MDPI, vol. 14(1), pages 1-16, January.
    2. Aubaid Ullah & Nur Awanis Hashim & Mohamad Fairus Rabuni & Mohd Usman Mohd Junaidi, 2023. "A Review on Methanol as a Clean Energy Carrier: Roles of Zeolite in Improving Production Efficiency," Energies, MDPI, vol. 16(3), pages 1-35, February.
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