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A preliminary stochastic model for managing microorganisms in a recirculating aquaculture system
Fu, Songzhe1,3; Liu, Ying2; Li, Xian2; Tu, Junling1; Lan, Ruiting3; Tian, Huiqin4
2015-06-01
Source PublicationANNALS OF MICROBIOLOGY
Volume65Issue:2Pages:1119-1129
SubtypeArticle
AbstractPredicting the growth of key microorganisms is essential to improve the efficiency of wastewater treatment of recirculating aquaculture systems (RAS). We have developed a stochastic model to assess quantitatively the microbial populations in RAS. This stochastic model encompassed the growth model into the Monte Carlo simulation and was constructed with risk analysis software. A modified logistic model combined with the saturation growth-rate model was successfully developed to regress the growth curves of six microorganisms. Monte Carlo simulation was employed to model the effects of chemical oxygen demand (COD) on the maximum specific growth rate. Probabilistic distributions and predictions under the different COD ranges were generated for each simulated scenario. The coefficient of determination (R (2)) and bias factor (Bf) were used to assess the performance of an established model. Logistic model produced a good fit to the growth curve of Flavobacterium sp. (R (2) = 0.9511), Acinetobacter baumannii (R (2) = 0.9970), Sphingomonas paucimobilis (R (2) = 0.9086), Vibrio natriegens (R (2) = 0.9993), Lutimonas sp. (R (2) = 0.9872) and Bacillus pumilus (R (2) = 0.9816). Bacterial population structure was determined by the construction of 16S rRNA gene libraries. A regular variation trend was observed for the dominant groups during the entire process, with a decrease of Cytophaga-Flavobacterium-Bacteroidetes from 37.6 to 18.7 % and an increase in Gammaproteobacteria from 8.5 to 30.6 %. The predicted model agreed well with observed values except for Flavobacterium sp., and the results can be applied to predict key microorganisms in actual environments. The results of this study provide a method to monitor the dynamics of key microorganisms, which can also help to evaluate the impacts of microorganisms on the operations of RAS.
KeywordRecirculating Aquaculture Systems Biofilter Monte Carlo Simulation Stochastic Model
DOI10.1007/s13213-014-0958-0
Indexed BySCI
Language英语
WOS IDWOS:000354724300052
Citation statistics
Document Type期刊论文
Identifierhttp://ir.qdio.ac.cn/handle/337002/33038
Collection海洋生物技术研发中心
Affiliation1.Nanchang Ctr Dis Control & Prevent, Nanchang 330038, Peoples R China
2.Chinese Acad Sci, Inst Oceanol, Qingdao 266701, Peoples R China
3.Univ New S Wales, Sch Biotechnol & Biomol Sci, Sydney, NSW, Australia
4.Ocean Univ China, Coll Fisheries, Qingdao 266701, Peoples R China
Recommended Citation
GB/T 7714
Fu, Songzhe,Liu, Ying,Li, Xian,et al. A preliminary stochastic model for managing microorganisms in a recirculating aquaculture system[J]. ANNALS OF MICROBIOLOGY,2015,65(2):1119-1129.
APA Fu, Songzhe,Liu, Ying,Li, Xian,Tu, Junling,Lan, Ruiting,&Tian, Huiqin.(2015).A preliminary stochastic model for managing microorganisms in a recirculating aquaculture system.ANNALS OF MICROBIOLOGY,65(2),1119-1129.
MLA Fu, Songzhe,et al."A preliminary stochastic model for managing microorganisms in a recirculating aquaculture system".ANNALS OF MICROBIOLOGY 65.2(2015):1119-1129.
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