通过机器学习增强微生物群落管理以实现生物废水处理
Enhancement of microbiome management by machine learning for biological wastewater treatment.
作者信息
Cai Wenfang, Long Fei, Wang Yunhai, Liu Hong, Guo Kun
机构信息
School of Chemical Engineering and Technology, Xi'an Jiaotong University, Xi'an, 710049, China.
Department of Environmental Science and Engineering, Xi'an Jiaotong University, Xi'an, 710049, China.
出版信息
Microb Biotechnol. 2021 Jan;14(1):59-62. doi: 10.1111/1751-7915.13707. Epub 2020 Nov 22.
Abstract
Here, we propose to develop microbiome-based machine learning models to predict the response of biological wastewater treatment systems to environmental or operational disturbances or to design specific microbiomes to achieve a desired system function. These machine learning models can be used to enhance the stability of microbiome-based biological systems and warn against the failure of these systems.
摘要
在此，我们提议开发基于微生物组的机器学习模型，以预测生物废水处理系统对环境或运行干扰的响应，或设计特定的微生物组以实现所需的系统功能。这些机器学习模型可用于增强基于微生物组的生物系统的稳定性，并对这些系统的故障发出警告。
相似文献
1
Enhancement of microbiome management by machine learning for biological wastewater treatment.通过机器学习增强微生物群落管理以实现生物废水处理
Microb Biotechnol. 2021 Jan;14(1):59-62. doi: 10.1111/1751-7915.13707. Epub 2020 Nov 22.
2
Universal Dynamics of Microbial Communities in Full-Scale Textile Wastewater Treatment Plants and System Prediction by Machine Learning.全尺寸纺织废水处理厂中微生物群落的普遍动态及机器学习系统预测
Environ Sci Technol. 2023 Feb 28;57(8):3345-3356. doi: 10.1021/acs.est.2c08116. Epub 2023 Feb 16.
3
Machine-learning insights into nitrate-reducing communities in a full-scale municipal wastewater treatment plant.机器学习在大型城市污水处理厂硝酸盐还原菌群落中的应用研究。
J Environ Manage. 2021 Dec 15;300:113795. doi: 10.1016/j.jenvman.2021.113795. Epub 2021 Sep 21.
4
Machine learning reveals the complex ecological interplay of microbiome in a full-scale membrane bioreactor wastewater treatment plant.机器学习揭示了全尺寸膜生物反应器污水处理厂中微生物群落复杂的生态相互作用。
Environ Res. 2023 Apr 1;222:115366. doi: 10.1016/j.envres.2023.115366. Epub 2023 Jan 25.
5
Artificial intelligence and machine learning-based monitoring and design of biological wastewater treatment systems.基于人工智能和机器学习的生物废水处理系统监测与设计
Bioresour Technol. 2023 Feb;369:128486. doi: 10.1016/j.biortech.2022.128486. Epub 2022 Dec 14.
6
Different Engineering Designs Have Profoundly Different Impacts on the Microbiome and Nitrifying Bacterial Populations in Municipal Wastewater Treatment Bioreactors.不同的工程设计方案对城市污水处理生物反应器中的微生物组和硝化细菌种群有着深远的影响。
Appl Environ Microbiol. 2021 Aug 26;87(18):e0104421. doi: 10.1128/AEM.01044-21.
7
Revealing General Patterns of Microbiomes That Transcend Systems: Potential and Challenges of Deep Transfer Learning.揭示超越系统的微生物组的一般模式：深度迁移学习的潜力和挑战。
mSystems. 2022 Feb 22;7(1):e0105821. doi: 10.1128/msystems.01058-21. Epub 2022 Jan 18.
8
Machine learning approaches for data-driven process monitoring of biological wastewater treatment plant: A review of research works on benchmark simulation model No. 1(BSM1).基于机器学习的数据驱动式生物污水处理厂过程监测方法：基准模拟模型 No.1(BSM1)相关研究工作述评。
Environ Monit Assess. 2023 Jul 4;195(8):916. doi: 10.1007/s10661-023-11463-8.
9
Application of machine learning algorithms and feature selection methods for better prediction of sludge production in a real advanced biological wastewater treatment plant.应用机器学习算法和特征选择方法，以更好地预测实际先进生物污水处理厂中的污泥产量。
J Environ Manage. 2023 Dec 15;348:119448. doi: 10.1016/j.jenvman.2023.119448. Epub 2023 Nov 6.
10
Machine learning methods for microbiome studies.微生物组研究中的机器学习方法。
J Microbiol. 2020 Mar;58(3):206-216. doi: 10.1007/s12275-020-0066-8. Epub 2020 Feb 27.
引用本文的文献
1
Beta-glucans in oncology: revolutionizing treatment with immune power & tumor targeting.肿瘤学中的β-葡聚糖：凭借免疫能力和肿瘤靶向作用革新治疗方法。
Naunyn Schmiedebergs Arch Pharmacol. 2025 Aug 27. doi: 10.1007/s00210-025-04519-8.
2
Harnessing Engineered Microbial Consortia for Xenobiotic Bioremediation: Integrating Multi-Omics and AI for Next-Generation Wastewater Treatment.利用工程化微生物群落进行异生素生物修复：整合多组学和人工智能用于下一代废水处理。
J Xenobiot. 2025 Aug 19;15(4):133. doi: 10.3390/jox15040133.
3
Photocatalytic degradation of drugs and dyes using a maching learning approach.采用机器学习方法对药物和染料进行光催化降解
RSC Adv. 2024 Mar 18;14(13):9003-9019. doi: 10.1039/d4ra00711e. eCollection 2024 Mar 14.
4
Niche differentiation in microbial communities with stable genomic traits over time in engineered systems.在工程系统中，具有稳定基因组特征的微生物群落随时间发生生态位分化。
ISME J. 2024 Jan 8;18(1). doi: 10.1093/ismejo/wrae042.
5
A microbial evolutionary approach for a sustainable future.微生物进化方法助力可持续未来。
Microb Biotechnol. 2023 Oct;16(10):1895-1899. doi: 10.1111/1751-7915.14331. Epub 2023 Aug 21.
6
Big data and computational advancements for next generation of Microbial Biotechnology.面向下一代微生物生物技术的大数据与计算进展
Microb Biotechnol. 2022 Jan;15(1):107-109. doi: 10.1111/1751-7915.13936. Epub 2021 Oct 29.
7
The future of Microbial Biotechnology.微生物生物技术的未来。
Microb Biotechnol. 2022 Jan;15(1):79-80. doi: 10.1111/1751-7915.13920. Epub 2021 Oct 6.
本文引用的文献
1
Prediction of anaerobic digestion performance and identification of critical operational parameters using machine learning algorithms.使用机器学习算法预测厌氧消化性能并确定关键操作参数。
Bioresour Technol. 2020 Feb;298:122495. doi: 10.1016/j.biortech.2019.122495. Epub 2019 Dec 2.
2
Microbial Community Predicts Functional Stability of Microbial Fuel Cells.微生物群落预测微生物燃料电池的功能稳定性。
Environ Sci Technol. 2020 Jan 7;54(1):427-436. doi: 10.1021/acs.est.9b03667. Epub 2019 Dec 16.
3
Common principles and best practices for engineering microbiomes.工程微生物组的通用原则和最佳实践。
Nat Rev Microbiol. 2019 Dec;17(12):725-741. doi: 10.1038/s41579-019-0255-9. Epub 2019 Sep 23.
4
Incorporating microbial community data with machine learning techniques to predict feed substrates in microbial fuel cells.将微生物群落数据与机器学习技术相结合，以预测微生物燃料电池中的饲料底物。
Biosens Bioelectron. 2019 May 15;133:64-71. doi: 10.1016/j.bios.2019.03.021. Epub 2019 Mar 13.
5
Electroactive microorganisms in bioelectrochemical systems.生物电化学系统中的电活性微生物。
Nat Rev Microbiol. 2019 May;17(5):307-319. doi: 10.1038/s41579-019-0173-x.
6
Predicting Microbial Fuel Cell Biofilm Communities and Bioreactor Performance using Artificial Neural Networks.使用人工神经网络预测微生物燃料电池生物膜群落和生物反应器性能。
Environ Sci Technol. 2017 Sep 19;51(18):10881-10892. doi: 10.1021/acs.est.7b01413. Epub 2017 Aug 25.
7
Microbiology and potential applications of aerobic methane oxidation coupled to denitrification (AME-D) process: A review.好的，我将为你翻译该文本：
有氧甲烷氧化偶联反硝化（AMO-D）过程的微生物学及潜在应用：综述。
Water Res. 2016 Mar 1;90:203-215. doi: 10.1016/j.watres.2015.12.020. Epub 2015 Dec 17.
8
Deciphering microbial community robustness through synthetic ecology and molecular systems synecology.通过合成生态学和分子系统共生学来破译微生物群落的稳健性。
Curr Opin Biotechnol. 2015 Jun;33:305-17. doi: 10.1016/j.copbio.2015.03.012. Epub 2015 Apr 14.
9
Microbial management of anaerobic digestion: exploiting the microbiome-functionality nexus.厌氧消化中的微生物管理：利用微生物组-功能关系。
Curr Opin Biotechnol. 2015 Jun;33:103-11. doi: 10.1016/j.copbio.2015.01.008. Epub 2015 Feb 13.
10
Outlining microbial community dynamics during temperature drop and subsequent recovery period in anaerobic co-digestion systems.概述厌氧共消化系统中温度下降及随后恢复期的微生物群落动态。
J Biotechnol. 2014 Dec 20;192 Pt A:179-86. doi: 10.1016/j.jbiotec.2014.10.007.
Zotero 插件