Multi-timescale feature extraction method of wastewater treatment process based on adaptive entropy

doi:10.1016/j.cjche.2024.07.024

中国化学工程学报 ›› 2024, Vol. 76 ›› Issue (12): 264-271.DOI: 10.1016/j.cjche.2024.07.024

Multi-timescale feature extraction method of wastewater treatment process based on adaptive entropy

Honggui Han^1,2,3,4, Yaqian Zhao^2,3,4, Xiaolong Wu^1,3,4, Hongyan Yang^1,3,4

1. School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China;
2. College of Environmental Sciences and Engineering, Beijing University of Technology, Beijing 100124, China;
3. Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, China;
4. Engineering Research Center of Digital Community, Ministry of Education, Beijing 100124, China

收稿日期:2023-10-18 修回日期:2024-07-16 接受日期:2024-07-17 出版日期:2024-12-28 发布日期:2024-09-23
通讯作者: Honggui Han,E-mail:rechardhan@sina.com
基金资助:
The authors are thankful to the National Key Research and Development Program of China (2022YFB3305800-5), the National Natural Science Foundation of China (62125301, 62021003), the Beijing Outstanding Young Scientist Program (BJJWZYJH01201910005020), the Natural Science Foundation of Beijing Municipality (KZ202110005009) and Youth Beijing Scholar (037).

Multi-timescale feature extraction method of wastewater treatment process based on adaptive entropy

Honggui Han^1,2,3,4, Yaqian Zhao^2,3,4, Xiaolong Wu^1,3,4, Hongyan Yang^1,3,4

1. School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China;
2. College of Environmental Sciences and Engineering, Beijing University of Technology, Beijing 100124, China;
3. Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, China;
4. Engineering Research Center of Digital Community, Ministry of Education, Beijing 100124, China

Received:2023-10-18 Revised:2024-07-16 Accepted:2024-07-17 Online:2024-12-28 Published:2024-09-23
Contact: Honggui Han,E-mail:rechardhan@sina.com
Supported by:
The authors are thankful to the National Key Research and Development Program of China (2022YFB3305800-5), the National Natural Science Foundation of China (62125301, 62021003), the Beijing Outstanding Young Scientist Program (BJJWZYJH01201910005020), the Natural Science Foundation of Beijing Municipality (KZ202110005009) and Youth Beijing Scholar (037).

摘要/Abstract

摘要： In wastewater treatment systems, extracting meaningful features from process data is essential for effective monitoring and control. However, the multi-time scale data generated by different sampling frequencies pose a challenge to accurately extract features. To solve this issue, a multi-timescale feature extraction method based on adaptive entropy is proposed. Firstly, the expert knowledge graph is constructed by analyzing the characteristics of wastewater components and water quality data, which can illustrate various water quality parameters and the network of relationships among them. Secondly, multiscale entropy analysis is used to investigate the inherent multi-timescale patterns of water quality data in depth, which enables us to minimize information loss while uniformly optimizing the timescale. Thirdly, we harness partial least squares for feature extraction, resulting in an enhanced representation of sample data and the iterative enhancement of our expert knowledge graph. The experimental results show that the multi-timescale feature extraction algorithm can enhance the representation of water quality data and improve monitoring capabilities.

关键词: Feature extraction, Knowledge graph, Wastewater treatment process, Adaptive entropy

Abstract: In wastewater treatment systems, extracting meaningful features from process data is essential for effective monitoring and control. However, the multi-time scale data generated by different sampling frequencies pose a challenge to accurately extract features. To solve this issue, a multi-timescale feature extraction method based on adaptive entropy is proposed. Firstly, the expert knowledge graph is constructed by analyzing the characteristics of wastewater components and water quality data, which can illustrate various water quality parameters and the network of relationships among them. Secondly, multiscale entropy analysis is used to investigate the inherent multi-timescale patterns of water quality data in depth, which enables us to minimize information loss while uniformly optimizing the timescale. Thirdly, we harness partial least squares for feature extraction, resulting in an enhanced representation of sample data and the iterative enhancement of our expert knowledge graph. The experimental results show that the multi-timescale feature extraction algorithm can enhance the representation of water quality data and improve monitoring capabilities.

Key words: Feature extraction, Knowledge graph, Wastewater treatment process, Adaptive entropy

Honggui Han, Yaqian Zhao, Xiaolong Wu, Hongyan Yang. Multi-timescale feature extraction method of wastewater treatment process based on adaptive entropy[J]. 中国化学工程学报, 2024, 76(12): 264-271.

Honggui Han, Yaqian Zhao, Xiaolong Wu, Hongyan Yang. Multi-timescale feature extraction method of wastewater treatment process based on adaptive entropy[J]. Chinese Journal of Chemical Engineering, 2024, 76(12): 264-271.

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Multi-timescale feature extraction method of wastewater treatment process based on adaptive entropy

Multi-timescale feature extraction method of wastewater treatment process based on adaptive entropy

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