Activated sludge modelling: past, present and future

2006 ◽  
Vol 53 (3) ◽  
pp. 111-119 ◽  
Author(s):  
W. Gujer
Keyword(s):  
Population Dynamics ◽  
Activated Sludge ◽  
Parameter Identification ◽  
Model Development ◽  
Model Complexity ◽  
Computational Power ◽  
Practical Limit ◽  
Simulation Parameter ◽  
Mc Simulation

Model complexity in activated sludge modelling has increased over 30 years in parallel with the computational power of computers. Today, the complexity of biokinetics has reached a practical limit. Future advances may be in the direction of enhanced spacial resolution (CFD, single organisms) respectively, in repetitive computations (MC simulation, parameter identification). Further model development may be in niches such as population dynamics, micropollutants, etc.

Relationships between Population Dynamics and Operating Parameters in an Activated Sludge-Plant by Statistical Analysis

1992 ◽  
Vol 25 (4-5) ◽  
pp. 399-400 ◽  
Author(s):  
L. Cingolani ◽  
M. Cossignani ◽  
R. Miliani
Keyword(s):  
Population Dynamics ◽  
Statistical Analysis ◽  
Activated Sludge ◽  
Treatment Plant ◽  
Operating Conditions ◽  
Statistical Analyses ◽  
Operating Parameters ◽  
Aerobic Treatment

Statistical analyses were applied to data from a series of 38 samples collected in an aerobic treatment plant from November 1989 to December 1990. Relationships between microfauna structure and plant operating conditions were found. Amount and quality of microfauna groups and species found in activated sludge proved useful to suggest the possible causes of disfunctions.

scholarly journals Physical Parameter Identification Algorithm Modeling for Complex Microsystem via Triangulation Functions

2018 ◽  
Vol 7 (4.36) ◽  
pp. 962
Author(s):  
Elena N. Meshcheryakova ◽  
. .
Keyword(s):  
Parameter Identification ◽  
Physical Parameter ◽  
Delaunay Triangulation ◽  
Model Development ◽  
Physical Object ◽  
Identification Algorithm ◽  
Classification Analysis ◽  
Advantages And Disadvantages ◽  
Object Parameters ◽  
D Model

This article describes the possibility of triangulation function use for the classification, analysis and identification of complex microsystem physical object parameters. They analyzed the existing methods and identification algorithms, their advantages and disadvantages are highlighted. The existing methods of triangulation are considered, the possibility of Delaunay triangulation is described for surfactant signal 3-D model development and analysis. They developed the algorithm to identify the state of an object using the triangulation function that takes into account the change of node coordinates and the length of the triangulation grid edges. They presented the visual UML model. The conclusions are drawn about the possibility of triangulation function use for the analysis of complex microsystem state.  

A systematic approach for model verification: application on seven published activated sludge models

2010 ◽  
Vol 61 (4) ◽  
pp. 825-839 ◽  
Author(s):  
H. Hauduc ◽  
L. Rieger ◽  
I. Takács ◽  
A. Héduit ◽  
P. A. Vanrolleghem ◽  
...  
Keyword(s):  
Activated Sludge ◽  
Systematic Approach ◽  
Model Development ◽  
Model Verification ◽  
Software Implementation ◽  
Model Description ◽  
Kinetic Rate ◽  
Simulation Results ◽  
Activated Sludge Models

The quality of simulation results can be significantly affected by errors in the published model (typing, inconsistencies, gaps or conceptual errors) and/or in the underlying numerical model description. Seven of the most commonly used activated sludge models have been investigated to point out the typing errors, inconsistencies and gaps in the model publications: ASM1; ASM2d; ASM3; ASM3 + Bio-P; ASM2d + TUD; New General; UCTPHO+. A systematic approach to verify models by tracking typing errors and inconsistencies in model development and software implementation is proposed. Then, stoichiometry and kinetic rate expressions are checked for each model and the errors found are reported in detail. An attached spreadsheet (see http://www.iwaponline.com/wst/06104/0898.pdf) provides corrected matrices with the calculations of all stoichiometric coefficients for the discussed biokinetic models and gives an example of proper continuity checks.

Activated sludge population dynamics

1994 ◽  
Vol 30 (11) ◽  
pp. 159-169 ◽  
Author(s):  
Jiri Wanner
Keyword(s):  
Population Dynamics ◽  
Activated Sludge ◽  
Organic Pollution ◽  
Paper Briefly ◽  
Microbial Composition ◽  
Selection Mechanisms ◽  
Sludge Settling

The paper briefly surveys topics covered by the IAWQ Specialist Group on Activated Sludge Population Dynamics. The activated sludge population dynamics has been formulated as a branch of water science and technology concerned with phenomena governing the relationships between activated sludge microorganisms and their functions. The characterization of organic pollution fractions in wastewaters according to their rate of biodegradation has been discussed and the role of wastewater as an inoculum stressed. The characterization of activated sludge biomass has been evaluated from two viewpoints: grouping according to metabolic abilities and identification and classification of activated sludge microorganisms. The basic selection mechanisms influencing the microbial composition of activated sludge have been described. The problems with activated sludge settling and thickening properties have been mentioned as a typical example of applied population dynamics research.

scholarly journals Inferring an Observer’s Prediction Strategy in Sequence Learning Experiments

Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 896 ◽  
Author(s):  
Abhinuv Uppal ◽  
Vanessa Ferdinand ◽  
Sarah Marzen
Keyword(s):  
Markov Model ◽  
Sequence Learning ◽  
Computational Time ◽  
Cognitive Systems ◽  
Computational Power ◽  
Data Points ◽  
Practical Limit ◽  
Parameter Values ◽  
Special Case ◽  
Prediction Strategy

Cognitive systems exhibit astounding prediction capabilities that allow them to reap rewards from regularities in their environment. How do organisms predict environmental input and how well do they do it? As a prerequisite to answering that question, we first address the limits on prediction strategy inference, given a series of inputs and predictions from an observer. We study the special case of Bayesian observers, allowing for a probability that the observer randomly ignores data when building her model. We demonstrate that an observer’s prediction model can be correctly inferred for binary stimuli generated from a finite-order Markov model. However, we can not necessarily infer the model’s parameter values unless we have access to several “clones” of the observer. As stimuli become increasingly complicated, correct inference requires exponentially more data points, computational power, and computational time. These factors place a practical limit on how well we are able to infer an observer’s prediction strategy in an experimental or observational setting.

Control of Activated Sludge Filamentous Bulking – IV. Effect of Sludge Regeneration

1982 ◽  
Vol 14 (1-2) ◽  
pp. 73-93 ◽  
Author(s):  
J Chudoba ◽  
M Dohányos ◽  
P Grau
Keyword(s):  
Population Dynamics ◽  
Activated Sludge ◽  
Concentration Gradient ◽  
Substrate Concentration ◽  
Laboratory Experiments ◽  
Growth Theory ◽  
Significant Relation ◽  
Filamentous Bulking ◽  
Accumulation Capacity ◽  
Feeding Cycle

In addition to the previously published growth theory based on the relation of μ^ to ks , the paper deals with an accumulation-regeneration theory of microorganisms selection. The accumulation-regeneration phenomenon can play a role in population dynamics only in the systems where the accumulated substrate can be subsequently oxidized and thus the accumulation capacity restored in one feeding cycle. This condition can be realized in properly designed systems with the substrate concentration gradient along the reactor. As a criterion of accumulation capacity restoration it is suggested to use the oxidized proportion of the substrate removed in one feeding cycle. Extensive laboratory experiments have shown a significant relation between the SVI and the percentage of the substrate oxidized. Practical consequences of these findings for the control of filamentous activated sludge bulking are also discussed in the present paper.

scholarly journals Parameter Identification of an Activated Sludge Wastewater Treatment Process Based on Particle Swarm Optimization Method

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Intissar Khoja ◽  
Taoufik Ladhari ◽  
Anis Sakly ◽  
Faouzi M’sahli
Keyword(s):  
Wastewater Treatment ◽  
Particle Swarm Optimization ◽  
Activated Sludge ◽  
Parameter Identification ◽  
Treatment Process ◽  
Classical Method ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Wastewater Treatment Process ◽  
Swarm Optimization

The current paper is entirely devoted to show the applicability of Particle Swarm Optimization (PSO) algorithm as a parameter identification method for a representative model of an Activated Sludge Wastewater Treatment Process (ASWWTP) with alternating phases. The model of identification is composed of two linear submodels: one for the aerobic phase and the other for the anoxic phase. In order to prove the efficiency of the proposed method, its performance is compared with another classical method called Simplex Search Algorithm (SSA) as well as with the experimental data.

scholarly journals Bacterial Population Dynamics in a Laboratory Activated Sludge Reactor Monitored by Pyrosequencing of 16S rRNA

2013 ◽  
Vol 28 (1) ◽  
pp. 65-70 ◽  
Author(s):  
Hiroyasu Satoh ◽  
Kenshiro Oshima ◽  
Wataru Suda ◽  
Purnika Ranasinghe ◽  
Ning Li ◽  
...  
Keyword(s):  
Population Dynamics ◽  
16S Rrna ◽  
Activated Sludge ◽  
Bacterial Population ◽  
Activated Sludge Reactor
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