Graph-based deep learning for predictions on changes in microbiomes and biogas production in anaerobic digestion systems

IF 11.4 1区 环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL
Hyo Gyeom Kim , Sung Il Yu , Seung Gu Shin , Kyung Hwa Cho
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Abstract

Anaerobic digestion (AD), which relies on a complex microbial consortium for efficient biogas generation, is a promising avenue for renewable energy production and organic waste treatment. However, understanding and optimising AD processes are challenging because of the intricate interactions within microbial communities and the impact of volatile fatty acids (VFAs) on biogas production. To address these challenges, this study proposes the application of graph convolutional networks (GCNs) to comprehensively model AD processes. GCN models were developed to predict microbial dynamics and biogas production by integrating network analyses of high-throughput sequencing data and VFA inhibition effects. The models were trained based on the responses of anaerobic digesters to organic loading rate shock, starvation, and bioaugmentation for 281 d under various feeding conditions. Shifts in microbial community composition during AD stages and feeding conditions were successfully identified using next-generation sequencing tools. Graph topological features indicated a significant coupling between VFAs and microbial families, and the hydrogenotrophic archaeal families were most frequently connected to other families or residual acids. The GCN accurately predicted microbial abundances and gas production rates, achieving a mean squared error of 0.11 and 0.01 and a coefficient of determination of 0.72 and 0.87 for the testing dataset. These results provide valuable insights into the effects of starvation and bioaugmentation on the microbiome by utilising GCNs to model anaerobic treatment processes, predict microbial dynamics, and assess reactor productivity. Our study suggests a new modelling framework for understanding and improving AD systems by considering microbial interaction networks in relation to chemical parameter information at relevant operating scales.
基于图的深度学习,用于预测厌氧消化系统中微生物组和沼气产量的变化
厌氧消化(AD)依赖于一个复杂的微生物联合体来产生高效的沼气,是一种有前途的可再生能源生产和有机废物处理途径。然而,由于微生物群落内部复杂的相互作用以及挥发性脂肪酸(VFAs)对沼气生产的影响,理解和优化AD过程具有挑战性。为了解决这些挑战,本研究提出应用图卷积网络(GCNs)对AD过程进行全面建模。通过整合高通量测序数据和VFA抑制效应的网络分析,建立了GCN模型来预测微生物动力学和沼气产量。根据厌氧消化池在不同饲养条件下对281 d有机负荷率、冲击、饥饿和生物强化的反应对模型进行训练。利用新一代测序工具成功鉴定了AD阶段微生物群落组成和摄食条件的变化。图拓扑特征表明VFAs与微生物家族之间存在显著的耦合,而氢养古菌家族与其他家族或残留酸的联系最为频繁。GCN准确预测了微生物丰度和产气速率,测试数据集的均方误差为0.11和0.01,决定系数为0.72和0.87。这些结果通过利用GCNs模拟厌氧处理过程、预测微生物动力学和评估反应器生产率,为饥饿和生物增强对微生物组的影响提供了有价值的见解。我们的研究提出了一个新的建模框架,通过考虑微生物相互作用网络在相关操作尺度上的化学参数信息,来理解和改进AD系统。
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来源期刊
Water Research
Water Research 环境科学-工程:环境
CiteScore
20.80
自引率
9.40%
发文量
1307
审稿时长
38 days
期刊介绍: Water Research, along with its open access companion journal Water Research X, serves as a platform for publishing original research papers covering various aspects of the science and technology related to the anthropogenic water cycle, water quality, and its management worldwide. The audience targeted by the journal comprises biologists, chemical engineers, chemists, civil engineers, environmental engineers, limnologists, and microbiologists. The scope of the journal include: •Treatment processes for water and wastewaters (municipal, agricultural, industrial, and on-site treatment), including resource recovery and residuals management; •Urban hydrology including sewer systems, stormwater management, and green infrastructure; •Drinking water treatment and distribution; •Potable and non-potable water reuse; •Sanitation, public health, and risk assessment; •Anaerobic digestion, solid and hazardous waste management, including source characterization and the effects and control of leachates and gaseous emissions; •Contaminants (chemical, microbial, anthropogenic particles such as nanoparticles or microplastics) and related water quality sensing, monitoring, fate, and assessment; •Anthropogenic impacts on inland, tidal, coastal and urban waters, focusing on surface and ground waters, and point and non-point sources of pollution; •Environmental restoration, linked to surface water, groundwater and groundwater remediation; •Analysis of the interfaces between sediments and water, and between water and atmosphere, focusing specifically on anthropogenic impacts; •Mathematical modelling, systems analysis, machine learning, and beneficial use of big data related to the anthropogenic water cycle; •Socio-economic, policy, and regulations studies.
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