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