Integrating communication networks with reinforcement learning and big data analytics for optimizing carbon capture and utilization strategies
In recent years, the escalating impact of climate change has brought increasing attention to carbon-neutral strategies as a critical component of global environmental protection efforts. These strategies demand a comprehensive understanding of carbon emissions, which are influenced by a myriad of fa...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016824009992 |
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| author | Aichuan Li Rui Liu Shujuan Yi |
| author_facet | Aichuan Li Rui Liu Shujuan Yi |
| author_sort | Aichuan Li |
| collection | DOAJ |
| description | In recent years, the escalating impact of climate change has brought increasing attention to carbon-neutral strategies as a critical component of global environmental protection efforts. These strategies demand a comprehensive understanding of carbon emissions, which are influenced by a myriad of factors, including external conditions like seasonality and weather, as well as internal dynamics such as production and energy consumption. However, existing approaches often fail to account for these complex, dynamic interactions, resulting in suboptimal outcomes. To address these challenges, we propose an integrated model combining Autoformer, Deep Q-Network (DQN), and Deep Forest. This model is designed to dynamically respond to environmental changes using advanced time-series forecasting, adaptive decision-making, and robust feature extraction. Extensive experiments across multiple datasets reveal that our model significantly enhances carbon capture efficiency and accuracy, outperforming conventional methods. By providing a scalable and intelligent solution for carbon capture and utilization, this research not only supports the advancement of carbon-neutral strategies but also contributes to the broader goals of sustainable development and climate change mitigation. |
| format | Article |
| id | doaj-art-4e00c2815bf745fd8ca44b3f34662fd4 |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-4e00c2815bf745fd8ca44b3f34662fd42024-11-22T07:36:22ZengElsevierAlexandria Engineering Journal1110-01682024-12-01108937951Integrating communication networks with reinforcement learning and big data analytics for optimizing carbon capture and utilization strategiesAichuan Li0Rui Liu1Shujuan Yi2College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, 163319, Heilongjiang, China; Corresponding author.College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, 163319, Heilongjiang, ChinaCollege of Engineering, Heilongjiang Bayi Agricultural University, 163319, Heilongjiang, ChinaIn recent years, the escalating impact of climate change has brought increasing attention to carbon-neutral strategies as a critical component of global environmental protection efforts. These strategies demand a comprehensive understanding of carbon emissions, which are influenced by a myriad of factors, including external conditions like seasonality and weather, as well as internal dynamics such as production and energy consumption. However, existing approaches often fail to account for these complex, dynamic interactions, resulting in suboptimal outcomes. To address these challenges, we propose an integrated model combining Autoformer, Deep Q-Network (DQN), and Deep Forest. This model is designed to dynamically respond to environmental changes using advanced time-series forecasting, adaptive decision-making, and robust feature extraction. Extensive experiments across multiple datasets reveal that our model significantly enhances carbon capture efficiency and accuracy, outperforming conventional methods. By providing a scalable and intelligent solution for carbon capture and utilization, this research not only supports the advancement of carbon-neutral strategies but also contributes to the broader goals of sustainable development and climate change mitigation.http://www.sciencedirect.com/science/article/pii/S1110016824009992Carbon capture and utilizationReinforcement learningBig data analyticsDeep Q-networkCarbon neutrality |
| spellingShingle | Aichuan Li Rui Liu Shujuan Yi Integrating communication networks with reinforcement learning and big data analytics for optimizing carbon capture and utilization strategies Alexandria Engineering Journal Carbon capture and utilization Reinforcement learning Big data analytics Deep Q-network Carbon neutrality |
| title | Integrating communication networks with reinforcement learning and big data analytics for optimizing carbon capture and utilization strategies |
| title_full | Integrating communication networks with reinforcement learning and big data analytics for optimizing carbon capture and utilization strategies |
| title_fullStr | Integrating communication networks with reinforcement learning and big data analytics for optimizing carbon capture and utilization strategies |
| title_full_unstemmed | Integrating communication networks with reinforcement learning and big data analytics for optimizing carbon capture and utilization strategies |
| title_short | Integrating communication networks with reinforcement learning and big data analytics for optimizing carbon capture and utilization strategies |
| title_sort | integrating communication networks with reinforcement learning and big data analytics for optimizing carbon capture and utilization strategies |
| topic | Carbon capture and utilization Reinforcement learning Big data analytics Deep Q-network Carbon neutrality |
| url | http://www.sciencedirect.com/science/article/pii/S1110016824009992 |
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