Multivariate assessment of activated sludge stability in lab-scale experiments

Kerkhof, Pieter Van den; Gins, Geert; Broeck, Rob Van den; Impe, Jan Van

doi:10.1016/j.procbio.2013.07.016

Cited by 5 publications

(6 citation statements)

References 13 publications

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“…Little literature about quantification methods for the acclimation period is available. A few studies suggested univariate and multivariate assessment approaches using the biochemical, physical, and morphological characteristics of AS [ 22 , 23 ]. These approaches suggest 20–40 days of minimal periods for microcosm startups (one to two times the SRT in the settings), comparable to those (1.7–2.4 the SRT) measured in our study.…”

Section: Resultsmentioning

confidence: 99%

“…Although previous studies have documented phenotype changes during AS microcosm startups [ 22 , 23 ], the time-dependent shifts in the community structure, diversity, and composition during startups remain to be further investigated. 16S rRNA gene-based community profiling was thus carried out for the I (at day 0) and AS samples of A, G, and S taken at day 28 (right after the acclimation period) and day 77, respectively.…”

Section: Resultsmentioning

confidence: 99%

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Machine Learning Approach Reveals the Assembly of Activated Sludge Microbiome with Different Carbon Sources during Microcosm Startup

Kim

Park

2021

Microorganisms

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Activated sludge (AS) microcosm experiments usually begin with inoculating a bioreactor with an AS mixed culture. During the bioreactor startup, AS communities undergo, to some extent, a distortion in their characteristics (e.g., loss of diversity). This work aimed to provide a predictive understanding of the dynamic changes in the community structure and diversity occurring during aerobic AS microcosm startups. AS microcosms were developed using three frequently used carbon sources: acetate (A), glucose (G), and starch (S), respectively. A mathematical modeling approach quantitatively determined that 1.7–2.4 times the solid retention time (SRT) was minimally required for the microcosm startups, during which substantial divergences in the community biomass and diversity (33–45% reduction in species richness and diversity) were observed. A machine learning modeling application using AS microbiome data could successfully (>95% accuracy) predict the assembly pattern of aerobic AS microcosm communities responsive to each carbon source. A feature importance analysis pinpointed specific taxa that were highly indicative of a microcosm feed source (A, G, or S) and significantly contributed for the ML-based predictive classification. The results of this study have important implications on the interpretation and validity of microcosm experiments using AS.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Machine Learning Approach Reveals the Assembly of Activated Sludge Microbiome with Different Carbon Sources during Microcosm Startup

Kim

Park

2021

Microorganisms

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“…Undoubtedly, in order to solve the number of problems occurring during activated sludge studies, it is reasonable to conduct investigations in a laboratory scale. The transfer from the industrial to laboratory scale requires a period of adaptation of the activated sludge community to the conditions of the SBR [19,20]. The adaptation period should be neither too short, or else sludge will not be stabilized, nor too long, or else experiment will be time consuming.…”

Section: Resultsmentioning

confidence: 99%

“…Setting of adaptation regimes and periods makes it possible to conduct the research without considering the reaction of activated sludge to stress caused by repeated volume reductions and stabilization of the main factors. To avoid mistakes in the experimental results, it is important to assess quantitatively in advance whether conditions in the activated sludge have stabilized [19,20]. The common practice assumes that the optimal period for adaptation lasts 2-2.5 sludge ages [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Bioindication assessment of activated sludge Adaptation in a Lab-Scale Experiment

Babko

Kuzmina

Jaromin-Gleń

et al. 2015

Ecological Chemistry and Engineering S

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The adaptation of activated sludge from the Hajdow sewage treatment plant in a laboratory SBR was studied. The structure of the ciliate assembly was considered as a criterion. 32 ciliate species were found during the experiment. The composition and changes in the ciliate community structure during the process of activated sludge adaptation was examined. In the process of adaptation, reduction was observed in the number of ciliate species together with an increase in assembly total abundance. The decrease in the Shannon diversity index and equitability index in the adaptation process was observed. In the process of adaptation, two states of ciliate assembly were marked out - unstable transient period and stable period. During the transient period, reduction of ammonium utilization efficiency down to 50% and its subsequent increase up to 80% in the stable period were observed. In the transient period, the Simpson dominance index remained low but increased in the stable period. At a temperature of 10°C, the transient period lasted from six to nine days. After the stabilization process, the diversity of the ciliate assemblage remained at a lower level. Rarefaction methods showed that the number of potential ecological niches of ciliate amounted to 30 in the adaptation period, whereas there were only 15-20 ecological niches in adapted sludge.

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“…can be measured on-line, and it is difficult (or even impossible) to build a mechanistic process model. Given the complexity of biological systems and the lack of quantitative analysis about the underlying mechanisms, multivariate statistical techniques have offered effective tools for bioprocess supervision [1,2].…”

Section: Introductionmentioning

confidence: 99%

Bioprocess statistical control: Identification stage based on hierarchical clustering

Cedeño

Rodríguez

Sánchez

2016

Process Biochemistry

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Multivariate assessment of activated sludge stability in lab-scale experiments

Abstract: Postprint)version)of)paper)published)in)Process)Biochemistry)2013,)vol.)48(11),)p.)1789-1793.

Cited by 5 publications

References 13 publications

Machine Learning Approach Reveals the Assembly of Activated Sludge Microbiome with Different Carbon Sources during Microcosm Startup

Machine Learning Approach Reveals the Assembly of Activated Sludge Microbiome with Different Carbon Sources during Microcosm Startup

Bioindication assessment of activated sludge Adaptation in a Lab-Scale Experiment

Bioprocess statistical control: Identification stage based on hierarchical clustering

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