arXiv 2509.015262025-09-01cs.LGcs.NE

基于DE-BP模型、SiTime-GAN模型与DPNG-EPMC集成聚类算法的污水处理厂微生物群落结构预测、生成及多特征属性聚类——微生物生态系统健康调控视角

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

Shanghai Jiao Tong University（上海交通大学）；East China University of Science and Technology（华东理工大学）；Beijing Jiaotong University（北京交通大学）

Mingzhi Dai, Weiwei Cai, Xiang Feng, Huiqun Yu, Weibin Guo, Miao Guo

备注：: 48 pages,25 figures, three major research sections: Prediction, Generation and Clustering

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AI总结：

本研究针对微生物组工程调控难题，提出DE-BP模型预测污水处理厂活性污泥微生物组成、DPNG-EPMC聚类算法分析厂站多特征属性、SiTime-GAN生成增量合成数据，深化了对活性污泥群落影响因素的理解。

摘要：

微生物组不仅支撑着地球的生物地球化学循环，还在土壤、污水处理、人类肠道等人工与自然生态系统中发挥着关键作用。然而，微生物组工程要实现预期的微生物调控优化目标，仍面临诸多亟待突破的重大障碍。本研究采用经差分进化优化的反向传播神经网络（DE-BP），对全球各地污水处理厂（WWTPs）采集的活性污泥（AS）系统微生物组成进行预测。此外，本文提出了一种名为定向位置非线性情感偏好迁移行为聚类（DPNG-EPMC）的新型聚类算法，用于对污水处理厂的各类特征属性开展聚类分析。最后，研究采用相似时间生成对抗网络（SiTime-GAN）合成全新的微生物组成与特征属性数据。实验结果表明，DE-BP模型对微生物组成的预测性能更优；DPNG-EPMC可有效应用于多特征属性下的污水处理厂分析；SiTime-GAN模型能够生成有价值的增量合成数据。本研究通过微生物群落预测与多特征属性下的污水处理厂分析，深化了对活性污泥群落影响因素的认知。

英文摘要：

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.

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