Proceedings of the 2025 7th International Conference on Civil Engineering, Environment Resources and Energy Materials (CCESEM 2025)

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Research on the Lifetime Prediction of Natural Gas Mercury Removal Agent Based on Improved GRU Neural Network

Authors
Chen Wang1, 2, Menglin Lin1, 2, Guiyuan Hu1, 2, Yueming Wang1, 2, *, Wei Yang1, 2, Lei Che1, 2, Huanghu Peng1, 2, Shengji Wu1, 2
1Zhejiang Key Laboratory of Industrial Solid Waste Thermal Hydrolysis Technology and Intelligent Equipment, Huzhou University, No. 759, East 2nd Road, Huzhou, 313000, China
2College of Engineering, Huzhou University, No. 759, East 2nd Road, Huzhou, 313000, China
*Corresponding author. Email: 468119083@qq.com
Corresponding Author
Yueming Wang
Available Online 16 December 2025.
DOI
10.2991/978-94-6463-902-5_39How to use a DOI?
Keywords
Natural gas mercury removal agent; lifetime prediction; GRU neural network; second-order difference; time-lag optimization
Abstract

Mercury contamination in natural gas systems presents dual hazards: compromising human health and corroding critical transmission infrastructure, ultimately jeopardizing the operational stability of gas purification facilities. Accurate prediction of mercury absorbent service life is therefore essential for optimizing replacement cycles and mitigating economic impacts. This study develops a novel lifetime prediction model utilizing Gated Recurrent Unit (GRU) neural networks, trains and validates using experimental adsorption data from simulated natural gas mercury removal processes. While the baseline GRU model demonstrates competent performance in tracking mercury removal efficiency trends, analysis reveals a persistent prediction latency. To resolve this limitation, a second-order difference algorithm for data preprocessing is implemented, successfully eliminating temporal dependencies and substantially enhancing model accuracy and responsiveness. The optimized model achieves exceptional performance metrics, including a test set coefficient of determination (R2) of 0.9918 and mean absolute percentage error (MAPE) of merely 0.49%, conclusively demonstrating the method’s technical superiority. This research establishes a robust, data-driven framework for mercury absorbent lifespan assessment, offering significant practical value for natural gas processing operations.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2025 7th International Conference on Civil Engineering, Environment Resources and Energy Materials (CCESEM 2025)
Series
Advances in Engineering Research
Publication Date
16 December 2025
ISBN
978-94-6463-902-5
ISSN
2352-5401
DOI
10.2991/978-94-6463-902-5_39How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Chen Wang
AU  - Menglin Lin
AU  - Guiyuan Hu
AU  - Yueming Wang
AU  - Wei Yang
AU  - Lei Che
AU  - Huanghu Peng
AU  - Shengji Wu
PY  - 2025
DA  - 2025/12/16
TI  - Research on the Lifetime Prediction of Natural Gas Mercury Removal Agent Based on Improved GRU Neural Network
BT  - Proceedings of the 2025 7th International Conference on Civil Engineering, Environment Resources and Energy Materials (CCESEM 2025)
PB  - Atlantis Press
SP  - 396
EP  - 408
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-902-5_39
DO  - 10.2991/978-94-6463-902-5_39
ID  - Wang2025
ER  -