Prediction, Generation of WWTPs microbiome community structures and Clustering of WWTPs various feature attributes using DE-BP model, SiTime-GAN model and DPNG-EPMC ensemble clustering algorithm with modulation of microbial ecosystem health

2025-09-01

AI Summary

This study addresses three key challenges in wastewater treatment plant (WWTP) microbiome analysis: (1) difficulty in accurately predicting microbial community structure, (2) scarcity of high-quality empirical measurements, and (3) poor understanding of multidimensional feature-driven ecological mechanisms. To tackle these, we propose an integrated framework combining deep learning and intelligent optimization: (i) a differential evolution-optimized backpropagation neural network (DE-BP) for high-accuracy microbial composition prediction; (ii) a similarity-aware time-series generative adversarial network (SiTime-GAN) to synthesize ecologically plausible synthetic microbiome data; and (iii) DPNG-EPMC—a novel ensemble clustering algorithm incorporating emotion-preference-inspired transfer learning—for interpretable, multi-source feature clustering across global WWTPs. Our approach significantly strengthens theoretical foundations and practical capabilities for microbial ecosystem health monitoring and regulation, establishing a new paradigm for intelligent operation and maintenance of activated sludge systems.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Learning & Optimization for NLP
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Web data generation and simulation
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Microbiomes not only underpin Earth's biogeochemical cycles but also play crucial roles in both engineered and natural ecosystems, such as the soil, wastewater treatment, and the human gut. However, microbiome engineering faces significant obstacles to surmount to deliver the desired improvements in microbiome control. Here, we use the backpropagation neural network (BPNN), optimized through differential evolution (DE-BP), to predict the microbial composition of activated sludge (AS) systems collected from wastewater treatment plants (WWTPs) located worldwide. Furthermore, we introduce a novel clustering algorithm termed Directional Position Nonlinear Emotional Preference Migration Behavior Clustering (DPNG-EPMC). This method is applied to conduct a clustering analysis of WWTPs across various feature attributes. Finally, we employ the Similar Time Generative Adversarial Networks (SiTime-GAN), to synthesize novel microbial compositions and feature attributes data. As a result, we demonstrate that the DE-BP model can provide superior predictions of the microbial composition. Additionally, we show that the DPNG-EPMC can be applied to the analysis of WWTPs under various feature attributes. Finally, we demonstrate that the SiTime-GAN model can generate valuable incremental synthetic data. Our results, obtained through predicting the microbial community and conducting analysis of WWTPs under various feature attributes, develop an understanding of the factors influencing AS communities.

Problem

• Predicting microbial composition in wastewater treatment plants
• Clustering WWTPs based on diverse feature attributes
• Generating synthetic microbiome data using advanced models

Innovation

• DE-BP model predicts microbial composition
• DPNG-EPMC algorithm clusters WWTP features
• SiTime-GAN generates synthetic microbiome data