Expert Systems in Energy Transition as a Tool for Intelligent Support of Decarbonization and Sustainable Development

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Abstract
The article explores the evolution of energy transition research through a bibliometric co-occurrence analysis of author keywords extracted from 146 scientific publications. The analysis reveals a shift from the technical aspects of energy systems and traditional decision-support approaches (2014–2018) towards climate change, decarbonization, renewable energy, investments, and energy policy (2018–2021). Recent studies (2022–2024) increasingly emphasize renewable energy, sustainable development, and intelligent decision-support systems, reflecting the growing digitalization of energy systems and the transition towards intelligent energy management. Based on these findings, the study develops a Digital-Twin-Oriented Techno-Economic Decision-Support Framework (DTOTEDSF) for optimizing and managing carbon-reduction strategies under dynamic energy transition conditions. Rather than representing a fully implemented digital twin (DT), the proposed framework constitutes the analytical foundation for its future development. It integrates techno-economic modeling, optimization, scenario analysis, and sensitivity assessment into a unified decision-support methodology. To demonstrate its practical applicability, the framework was applied to four industrial CCS case studies in the cement sector using publicly available technical and economic data. Its analytical core combines technical, economic, and optimization models to evaluate CCS performance under alternative operating conditions. Consequently, the proposed framework provides a methodological basis for the future implementation of fully operational DTs and contributes to the development of intelligent decision-support tools for industrial decarbonization and the sustainable energy transition.
Funding Information
  • AGH University of Krakow

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