Key Takeaways & Executive Findings
- •• Proposes a novel multi-timescale feature extraction method using adaptive entropy to handle varying sampling frequencies in wastewater treatment. • Integrates expert knowledge graph with multiscale entropy analysis and partial least squares to enhance data representation and monitoring. • Demonstrates improved water quality data representation and monitoring capabilities through experimental validation. • Addresses the challenge of information loss and errors from data interpolation in multi-timescale wastewater data.
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.
1. Introduction
With the increasing awareness of environmental protection, wastewater treatment has become a crucial issue [1e4]. In order to meet the effluent quality requirements, the wastewater treatment process (WWTP) and effluent must be monitored and reported. In recent years, water quality monitoring systems that use sensors to collect real-time data of water environment have been extensively applied [5e7]. At the same time, massive amounts of historical data are collected and stored. Data analysis plays a critical role in supporting the development and optimization of wastewater treatment systems [8e11]. The raw data often contains a lot of redundant information, noise, and irrelevant features that can interfere with subsequent analysis [12,13]. Feature extraction which can improve data quality, reduce dimensionality, increase model performance, enhance interpretability, and facilitate data visualization, is essential for analyzing data [14e16]. Wastewater treatment involves multi-timescale, as the treatment process includes transient events and long-term trends [17,18]. However, in practice, the multi-timescale data caused by different sampling frequencies will make the feature extraction insufficient. Specifically, there may be loss of information and additional errors introduced due to data interpolation. Therefore, multi-timescale feature extraction of WWTP is an important and necessary step before applying data analysis.
WWTP involves a large number of water quality parameters. The relationship between the parameters is complex and there is a coupling relationship. This makes it necessary to have expertise in the field of wastewater treatment prior to data analysis. Knowledge graph (KG) is a graph-based data structure used to describe concepts and their relationships in the physical world [19,20]. KG technology can obtain structured knowledge from massive data, achieve unified expression and efficient storage, and is an effective means to solve the problem of massive data management in the process of wastewater treatment. In order to support domain-specific applications, domain KG have also received attention. Based on multi-source heterogeneous data, a knowledge graph was built as information integration system for contaminated site [21].
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Honggui Han, Yaqian Zhao, Xiaolong Wu, Hongyan Yang (2024). Multi-timescale feature extraction method of wastewater treatment process based on adaptive entropy. Chinese Journal of Chemical Engineering. https://doi.org/10.1016/j_cjche_1448
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Frequently Asked Questions
What is the main challenge addressed by this paper?
The paper addresses the challenge of extracting meaningful features from multi-timescale data in wastewater treatment processes, where different sampling frequencies can lead to information loss and errors due to data interpolation.
How does the proposed method work?
The method constructs an expert knowledge graph to represent water quality parameters and their relationships, uses multiscale entropy analysis to capture inherent multi-timescale patterns, and applies partial least squares for feature extraction, iteratively enhancing the knowledge graph.
What are the key benefits of the proposed approach?
The approach minimizes information loss, optimizes timescale uniformly, enhances data representation, and improves monitoring capabilities in wastewater treatment systems.
What is the role of the knowledge graph in this method?
The knowledge graph illustrates various water quality parameters and their relationships, providing structured domain knowledge that supports feature extraction and is iteratively enhanced during the process.
What are the experimental results?
The experimental results show that the multi-timescale feature extraction algorithm enhances the representation of water quality data and improves monitoring capabilities, validating the effectiveness of the proposed method.
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