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

Mehar Ullah, Daniel Gutierrez-Rojas, Eero Inkeri, Tero Tynjälä and Pedro H. J. Nardelli ()
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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

Energies, 2022, vol. 15, issue 21, 1-17

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.

Keywords: power-to-X; IoT; big data; machine learning; electrolysis; methanation; synthetic gas (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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