WeChat Mini Program
Old Version Features

Integrating Communication Networks with Reinforcement Learning and Big Data Analytics for Optimizing Carbon Capture and Utilization Strategies

ALEXANDRIA ENGINEERING JOURNAL(2024)

Heilongjiang Bayi Agr Univ

Cited 0|Views1
Abstract
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.
More
Translated text
Key words
Carbon capture and utilization,Reinforcement learning,Big data analytics,Deep Q-network,Carbon neutrality
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined