Microbial Ecology of Liquid Waste Treatment

Rivière, J. W. M. la

doi:10.1007/978-1-4615-8219-9_5

Cited by 35 publications

(26 citation statements)

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“…With some modifications it is also used as the model for waste-water treatment process [33,52]. The chemostat model can be considered as the starting point for many variations that yield more realistic biological models, e.g., the recombinant problem in genetically altered organisms [56,77] and the model of mammalian large intestine [36,37].…”

Section: Application To Ecology: a Chemostat Modelmentioning

confidence: 99%

Applied Nonautonomous and Random Dynamical Systems

Caraballo¹,

Han²

2016

SpringerBriefs in Mathematics

114

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PrefaceDuring the past two decades, the theory of nonautonomous dynamical systems and random dynamical systems has made substantial progress in studying the long term dynamics of open systems subject to time-dependent or random forcing. However, most of the existing pertinent literatures are fairly technical and almost impenetrable for a general audience, except the most dedicated specialists. Moreover, the concepts and methods of nonautonomous and random dynamical systems, though well-established, are extremely nontrivial to apply to real-world problems. The aim of this work is to provide an accessible and broad introduction to the theory of nonautonomous and random dynamical systems, with an emphasis on applications of the theory to problems arising in the applied sciences and engineering.The book starts with basic concepts in the theory of autonomous dynamical systems which are easier to understand, and used as the motivation for the study of non-autonomous and random dynamical systems. Then the framework of nonautonomous dynamical systems is set up, including various approaches to analyze the long time behavior of non-autonomous problems. The major emphasis is given to the novel theory of pullback attractors, as it can be regarded as a natural extension of the autonomous theory and allows a larger variety of time-dependence forcing than other alternatives such as skew-product flows or cocycles. In the end the theory of random dynamical systems and random attractors is introduced and shown to fairly informative to the study of long term behavior of stochastic systems with random forcing.Each set of theory is illustrated by being applied to three different models, the chemostat model, the SIR epidemic model, and the Lorenz-84 model, in their autonomous, nonautonomous, and stochastic formulations, respectively. The techniques and methods adopted can be applied to the study of the long term behavior of a wide range of applications arising in applied sciences and engineering.

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Section: Application To Ecology: a Chemostat Modelmentioning

confidence: 99%

Applied Nonautonomous and Random Dynamical Systems

Caraballo¹,

Han²

2016

SpringerBriefs in Mathematics

114

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“…The last aspect, related to the contribution to the formation of flocs, seems to be a mechanism of lower importance (La Riviére, 1977). The first two aspects (consumption of organic matter and of free bacteria) depend on the feeding mode of the protozoa, which varies with its type, as seen below (Horan, 1990):…”

Section: Protozoamentioning

confidence: 99%

“…The size of the floc is regulated by the balance between the forces of cohesion and shear stress caused by the artificial aeration and agitation (La Riviére, 1977). Among the microorganisms that constitute the floc, besides the bacteria and protozoa, fungi, rotifers, nematodes and occasionally even insect larvae can be found (Branco, 1978).…”

Section: The Biological Floc In Dispersed-growth Systemsmentioning

confidence: 99%

Basic principles of wastewater treatment

2008

Choice Reviews Online

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“…It is also often used as a mean to reproduce situations where (limiting) nutrients are fed to micro-organisms, typically in a liquid medium, such as in natural ecosystems [16,5] or anthropized environments [23]. More generally, the chemostat is largely used as a scientific investigation tool in microbial ecology [21,45].…”

Section: Introductionmentioning

confidence: 99%