Assessing carbon–neutral supercapacitors in renewable energy systems with self-improving agent-based molecular fuzzy intelligent algorithms
Abstract Carbon–neutral supercapacitors play an important role in renewable energy investments as environmentally friendly devices that both function as energy storage and aim to reduce carbon footprint. This situation can cause waste of resources and wrong prioritization decisions. In this context,...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-12924-5 |
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| Summary: | Abstract Carbon–neutral supercapacitors play an important role in renewable energy investments as environmentally friendly devices that both function as energy storage and aim to reduce carbon footprint. This situation can cause waste of resources and wrong prioritization decisions. In this context, the main problem is that the most important factors affecting the technical investment performance of carbon–neutral supercapacitors have not been determined. To fill this gap, this study proposes an original decision-making model to determine the importance levels of variables affecting the performance of these devices and to present appropriate investment strategies. The proposed model includes the integrated use of Entropy-game-based expert weighting method, Q-learning algorithm, molecular fuzzy intelligence algorithms, Bayesian network-based weighting (BANEW) and ant colony optimization (ACO) techniques. This study contributes to making more accurate and effective technical decisions for sustainable energy investments by filling an important gap in the literature with its original decision model. It is determined that recyclability rate is the most significant factor because it has the highest weight (0.316). On the other side, the best investment choice for carbon–neutral supercapacitors in renewable energy systems is gravity-based energy storage with the greatest fitness value of 4.044. |
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| ISSN: | 2045-2322 |