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Operation of Power-to-X-Related Processes Based on Advanced Data-Driven Methods: A Comprehensive Review

Author

Listed:
  • Mehar Ullah

    (School of Energy Systemes, Lappeenranta–Lahti University of Technology, Yliopistonkatu 34, 53850 Lappeenranta, Finland)

  • Daniel Gutierrez-Rojas

    (School of Energy Systemes, Lappeenranta–Lahti University of Technology, Yliopistonkatu 34, 53850 Lappeenranta, Finland)

  • Eero Inkeri

    (School of Energy Systemes, Lappeenranta–Lahti University of Technology, Yliopistonkatu 34, 53850 Lappeenranta, Finland)

  • Tero Tynjälä

    (School of Energy Systemes, Lappeenranta–Lahti University of Technology, Yliopistonkatu 34, 53850 Lappeenranta, Finland)

  • Pedro H. J. Nardelli

    (School of Energy Systemes, Lappeenranta–Lahti University of Technology, Yliopistonkatu 34, 53850 Lappeenranta, Finland)

Abstract

This study is a systematic analysis of selected research articles about power-to-X (P2X)-related processes. The relevance of this resides in the fact that most of the world’s energy is produced using fossil fuels, which has led to a huge amount of greenhouse gas emissions that are the source of global warming. One of the most supported actions against such a phenomenon is to employ renewable energy resources, some of which are intermittent, such as solar and wind. This brings the need for large-scale, longer-period energy storage solutions. In this sense, the P2X process chain could play this role: renewable energy can be converted into storable hydrogen, chemicals, and fuels via electrolysis and subsequent synthesis with CO 2 . The main contribution of this study is to provide a systematic articulation of advanced data-driven methods and latest technologies such as the Internet of Things (IoT), big data analytics, and machine learning for the efficient operation of P2X-related processes. We summarize our findings into different working architectures and illustrate them with a numerical result that employs a machine learning model using historic data to define operational parameters for a given P2X process.

Suggested Citation

  • Mehar Ullah & Daniel Gutierrez-Rojas & Eero Inkeri & Tero Tynjälä & Pedro H. J. Nardelli, 2022. "Operation of Power-to-X-Related Processes Based on Advanced Data-Driven Methods: A Comprehensive Review," Energies, MDPI, vol. 15(21), pages 1-17, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:21:p:8118-:d:959324
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    References listed on IDEAS

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