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Advanced Control Strategies for Cleaner Energy Conversion in Biomass Gasification

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Listed:
  • Wilmer Velilla-Díaz

    (Department of Mechanical Engineering, Universidad de La Serena, La Serena 1720170, Chile)

  • Johann Monroy Barrios

    (Faculty of Engineering, Universidad Autonóma del Caribe, Barranquilla 080020, Colombia)

  • Jonathan Fábregas Villegas

    (Faculty of Engineering, Universidad Autonóma del Caribe, Barranquilla 080020, Colombia
    Department of Mechanical Engineering, Universidad Tecnológica de Bolivar, Cartagena 130001, Colombia)

  • Argemiro Palencia-Díaz

    (Department of Mechanical Engineering, Universidad Tecnológica de Bolivar, Cartagena 130001, Colombia)

Abstract

The escalating climate crisis necessitates urgent and decisive action to mitigate greenhouse gas emissions. Gasification stands out as a highly adaptable process for energy conversion, capable of handling a wide range of feedstocks, from coal to biomass. The process plays a significant role in improving sustainability by converting these feedstocks into synthesic gas (syngas), which can be used as a cleaner energy source or as a building block for producing various chemicals. The utilization of syngas obtained through gasification not only reduces the reliance on fossil fuels but also helps in reducing greenhouse gases (GHGs), thereby contributing to a more sustainable energy landscape. To maintain optimal operational conditions and ensure the quality and safety of the product, an effective control system is crucial in the gasification process. This paper presents a comparative analysis of three control strategies applied to a numerical model of rice husk gasification: classical control, fuzzy logic control, and dynamic matrix control. The analysis is based on a comprehensive model that includes the equations necessary to capture the dynamic behavior of the gasification process across its various stages. The goal is to identify the most effective control strategy, and the performance of each control strategy is evaluated based on the integral of the absolute value of the error (IAE). The results indicatethat fuzzy logic control consistently outperforms classical control techniques, demonstrating superior disturbance rejection, enhanced stability, and overall improved control accuracy. These findings highlight the importance of selecting an appropriate advanced control strategy to optimize sustainable gasification processes.

Suggested Citation

  • Wilmer Velilla-Díaz & Johann Monroy Barrios & Jonathan Fábregas Villegas & Argemiro Palencia-Díaz, 2024. "Advanced Control Strategies for Cleaner Energy Conversion in Biomass Gasification," Sustainability, MDPI, vol. 16(23), pages 1-16, December.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:23:p:10691-:d:1537799
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    References listed on IDEAS

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    1. Pereira, Emanuele Graciosa & da Silva, Jadir Nogueira & de Oliveira, Jofran L. & Machado, Cássio S., 2012. "Sustainable energy: A review of gasification technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(7), pages 4753-4762.
    2. Sara Maen Asaad & Abrar Inayat & Lisandra Rocha-Meneses & Farrukh Jamil & Chaouki Ghenai & Abdallah Shanableh, 2022. "Prospective of Response Surface Methodology as an Optimization Tool for Biomass Gasification Process," Energies, MDPI, vol. 16(1), pages 1-18, December.
    3. Matthias Leisin & Peter Radgen, 2023. "Holistic Assessment of Decarbonization Pathways of Energy-Intensive Industries Based on Exergy Analysis," Sustainability, MDPI, vol. 16(1), pages 1-16, December.
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