Contenido principal del artículo

Juliana Sobral Barros de Queiroz
Universidad de Sevilla
España
Bismark Claure Torrico
Universidade Federal do Ceará (UFC)
Brasil
Fabrício González Nogueira
Universidade Federal do Ceará (UFC)
Brasil
Carlos Bordons
Universidad de Sevilla
España
Miguel Angel Ridao
Universidad de Sevilla
España
Núm. 45 (2024), Ingeniería de Control
DOI: https://doi.org/10.17979/ja-cea.2024.45.10894
Recibido: jun. 4, 2024 Aceptado: jul. 1, 2024 Publicado: jul. 17, 2024
Derechos de autor

Resumen

This article addresses developing and applying a model-based controller for a PEM (Proton Exchange Membrane) electrolyser. The primary objective is to optimise temperature control, aiming for greater efficiency in hydrogen production and extended system lifespan. These two benefits are compromised when the electrolyser is subject to high temperatures exceeding its nominal temperature. Such conditions can occur when the system is powered by renewable sources, which can operate at high current densities due to their variability and intermittency. The proposed controller employs an MPC (Model Predictive Control) combined with a disturbance model to promote decoupling in handling disturbances and introduce an additional degree of freedom to the control strategy. Simulation results demonstrate the robust performance of the controller in managing system nonlinearities, ensuring desired reference tracking for the process.

Detalles del artículo

Citas

Benghanem, M., Almohamadi, H., Haddad, S., Mellit, A., Chettibi, N., 2024. The effect of voltage and electrode types on hydrogen production powered by photovoltaic system using alkaline and pem electrolyzers. International Journal of Hydrogen Energy 57, 625–636. DOI: https://doi.org/10.1016/j.ijhydene.2023.12.232

Camacho, E., Bordons, C., 2007. Model Predictive control. Advanced Textbooks in Control and Signal Processing. Springer, London. DOI: https://doi.org/10.1007/978-0-85729-398-5

Carmo, M., Fritz, D. L., Mergel, J., Stolten, D., 2013. A comprehensive review on pem water electrolysis. International Journal of Hydrogen Energy 38, 4901–4934. DOI: https://doi.org/10.1016/j.ijhydene.2013.01.151

Espinosa-Lopez, M., Darras, C., Poggi, P., Glises, R., Baucour, P., Rakotondrainibe, A., Besse, S., Serre-Combe, P., 2018. Modelling and experimental validation of a 46 kw pem high pressure water electrolyzer. Renewable Energy 119, 160–173. DOI: https://doi.org/10.1016/j.renene.2017.11.081

Keller, R., Rauls, E., Hehemann, M., M¨uller, M., Carmo, M., 2022. An adaptive model-based feedforward temperature control of a 100 kw pem electrolyzer. Control Engineering Practice 120, 104992. DOI: https://doi.org/10.1016/j.conengprac.2021.104992

Maia, R. G. T., Garcia, K. C., 2023. What they say, what they do and how they do it: An evaluation of the energy transition and ghg emissions of electricity companies. Energy Policy 174, 113462. DOI: https://doi.org/10.1016/j.enpol.2023.113462

Molina, P., Rios, C., de Leon, C. M., Brey, J., 2024. Heat management system design and implementation in a pem water electrolyser. International Journal of Hydrogen Energy. DOI: https://doi.org/10.1016/j.ijhydene.2024.04.089

Mora, M., Bordons, C., 2022. Desarrollo y validación experimental del modelo dinámico de un electrolizador pem de 1kw para su integración con generación renovable. XLIII Jornadas de Automática: libro de actas, 560–567. DOI: https://doi.org/10.17979/spudc.9788497498418.0560

Ogumerem, G. S., Pistikopoulos, E. N., 2020. Parametric optimization and control for a smart proton exchange membrane water electrolysis (pemwe) system. Journal of Process Control 91, 37–49. DOI: https://doi.org/10.1016/j.jprocont.2020.05.002

United Nations, 2015. The 2030 agenda for sustainable development. https://sustainabledevelopment.un.org/post2015/transformingourworld, accessed: 2024-05-28.