Issue |
E3S Web Conf.
Volume 520, 2024
4th International Conference on Environment Resources and Energy Engineering (ICEREE 2024)
|
|
---|---|---|
Article Number | 02004 | |
Number of page(s) | 5 | |
Section | Carbon Emission Control and Waste Resource Utilization | |
DOI | https://doi.org/10.1051/e3sconf/202452002004 | |
Published online | 03 May 2024 |
Research on the purification mechanism of heavy metal pollution by biochar composites driven by degree learning
Wuhan University of Technology School of Resources and Environmental Engineering, Wuhan, 430070, China
a dar@whut.edu.cn
b 1328000870@qq.com
c 354347179@qq.com
d 576312457@qq.com
This paper proposes an innovative approach by integrating deep learning technology, specifically employing the GRU recurrent neural network model based on the Seagull optimization algorithm, to enhance the accuracy of predicting biochar performance. The Seagull optimization algorithm, inspired by seagull predatory behavior, is adept at efficiently identifying optimal model parameters, thereby improving the model’s generalization ability and robustness. The GRU recurrent neural network, designed for sequence data processing, proves to be instrumental in capturing dynamic and nonlinear interactions between biochar and heavy metals. This, in turn, contributes to heightened prediction accuracy and model interpretability. The article unfolds in a structured manner, beginning with an introduction to the biochar preparation method and its characteristics. It then delves into an analysis of the sources and hazards of heavy metal pollution. Following this, the paper explains the principles and advantages of deep learning technology, providing a comprehensive foundation for the subsequent discussion. The construction and verification process of the proposed model is then detailed, concluding with the presentation of experimental results and in-depth analysis. In essence, this research introduces a pioneering idea and methodology for optimizing biochar design and effectively controlling heavy metal pollution, presenting a fresh perspective on addressing these environmental challenges.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.