Analysis and design of suitable model structures for activated sludge tanks with circulating flow

1999 ◽  
Vol 39 (4) ◽  
Keyword(s):  
Activated Sludge ◽  
Suitable Model ◽  
Analysis And Design ◽  
Model Structures

Analysis and design of suitable model structures for activated sludge tanks with circulating flow

1999 ◽  
Vol 39 (4) ◽  
pp. 55-60 ◽  
Author(s):  
J. Alex ◽  
R. Tschepetzki ◽  
U. Jumar ◽  
F. Obenaus ◽  
K.-H. Rosenwinkel
Keyword(s):  
Activated Sludge ◽  
Large Scale ◽  
Wastewater Treatment Plants ◽  
Treatment Plant ◽  
High Sensitivity ◽  
Suitable Model ◽  
Model Structure ◽  
Analysis And Design ◽  
Hydraulic Behaviour ◽  
Model Structures

Activated sludge models are widely used for planning and optimisation of wastewater treatment plants and on line applications are under development to support the operation of complex treatment plants. A proper model is crucial for all of these applications. The task of parameter calibration is focused in several papers and applications. An essential precondition for this task is an appropriately defined model structure, which is often given much less attention. Different model structures for a large scale treatment plant with circulation flow are discussed in this paper. A more systematic method to derive a suitable model structure is applied to this case. Results of a numerical hydraulic model are used for this purpose. The importance of these efforts are proven by a high sensitivity of the simulation results with respect to the selection of the model structure and the hydraulic conditions. Finally it is shown, that model calibration was possible only by adjusting to the hydraulic behaviour and without any changes of biological parameters.

scholarly journals Aquifer parameter and zonation structure estimation using meshless local Petrov–Galerkin method and particle swarm optimization

2017 ◽  
Vol 20 (2) ◽  
pp. 457-467 ◽  
Author(s):  
Boddula Swathi ◽  
T. I. Eldho
Keyword(s):  
Parameter Estimation ◽  
Particle Swarm Optimization ◽  
Measurement Errors ◽  
Particle Swarm ◽  
Least Square ◽  
Suitable Model ◽  
Swarm Optimization ◽  
Aquifer Parameter ◽  
Marquardt Algorithm ◽  
Model Structures

Abstract The simulation-optimization (SO) modeling approach can be effectively used for aquifer parameter estimation. In this study, a numerical approach based on meshless local Petrov–Galerkin (MLPG) method is used for groundwater flow simulation and coupled with particle swarm optimization model for optimization. The study deals with the identification of the most suitable model structure for a hypothetical heterogeneous confined aquifer from a number of alternate models using zonation method of parameter estimation. A range of alternate models starting from homogeneous to more complex model structures are considered for the zonation. Inverse modeling of different model structures is carried out based on weighted least square performance criterion. The suitable models are selected and reliability analysis ascertained by computing three parameters of composite scaled sensitivity, coefficient of variation, and confidence interval, and the best model is selected. Sensitivity of estimated parameters is investigated by considering different sets of head data involving possible measurement errors. The solutions are compared with another inverse model using the MLPG and Levenberg–Marquardt algorithm. Based on the results, it is found that the proposed methodology can be utilized in the estimation of different unknown parameters in a regional groundwater system.

scholarly journals Reverse engineering model structures for soil and ecosystem respiration: the potential of gene expression programming

2017 ◽  
Vol 10 (9) ◽  
pp. 3519-3545 ◽  
Author(s):  
Iulia Ilie ◽  
Peter Dittrich ◽  
Nuno Carvalhais ◽  
Martin Jung ◽  
Andreas Heinemeyer ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Ecosystem Respiration ◽  
Gene Expression Programming ◽  
Carbon Assimilation ◽  
Water Dynamics ◽  
Support Vector ◽  
Climate Projections ◽  
Suitable Model ◽  
Model Structures

Abstract. Accurate model representation of land–atmosphere carbon fluxes is essential for climate projections. However, the exact responses of carbon cycle processes to climatic drivers often remain uncertain. Presently, knowledge derived from experiments, complemented by a steadily evolving body of mechanistic theory, provides the main basis for developing such models. The strongly increasing availability of measurements may facilitate new ways of identifying suitable model structures using machine learning. Here, we explore the potential of gene expression programming (GEP) to derive relevant model formulations based solely on the signals present in data by automatically applying various mathematical transformations to potential predictors and repeatedly evolving the resulting model structures. In contrast to most other machine learning regression techniques, the GEP approach generates readable models that allow for prediction and possibly for interpretation. Our study is based on two cases: artificially generated data and real observations. Simulations based on artificial data show that GEP is successful in identifying prescribed functions, with the prediction capacity of the models comparable to four state-of-the-art machine learning methods (random forests, support vector machines, artificial neural networks, and kernel ridge regressions). Based on real observations we explore the responses of the different components of terrestrial respiration at an oak forest in south-eastern England. We find that the GEP-retrieved models are often better in prediction than some established respiration models. Based on their structures, we find previously unconsidered exponential dependencies of respiration on seasonal ecosystem carbon assimilation and water dynamics. We noticed that the GEP models are only partly portable across respiration components, the identification of a general terrestrial respiration model possibly prevented by equifinality issues. Overall, GEP is a promising tool for uncovering new model structures for terrestrial ecology in the data-rich era, complementing more traditional modelling approaches.

scholarly journals Reverse engineering model structures for soil and ecosystem respiration: the potential of gene expression programming

2016 ◽  
Author(s):  
Iulia Ilie ◽  
Peter Dittrich ◽  
Nuno Carvalhais ◽  
Martin Jung ◽  
Andreas Heinemeyer ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Ecosystem Respiration ◽  
Traditional Approach ◽  
Gene Expression Programming ◽  
Water Dynamics ◽  
Support Vector ◽  
Climate Projections ◽  
Suitable Model ◽  
Model Structures

Abstract. Accurate modelling of land-atmosphere carbon fluxes is essential for future climate projections. However, the exact responses of carbon cycle processes to climatic drivers often remain uncertain. Presently, knowledge derived from experiments complemented with a steadily evolving body of mechanistic theory provides the main basis for developing the respective models. The strongly increasing availability of measurements may complicate the traditional hypothesis driven path to developing mechanistic models, but it may facilitate new ways of identifying suitable model structures using machine learning as well. Here we explore the potential to derive model formulations automatically from data based on gene expression programming (GEP). GEP automatically (re)combines various mathematical operators to model formulations that are further evolved, eventually identifying the most suitable structures. In contrast to most other machine learning regression techniques, the GEP approach generates models that allow for prediction and possibly for interpretation. Our study is based on two cases: artificially generated data and real observations. Simulations based on artificial data show that GEP is successful in identifying prescribed functions with the prediction capacity of the models comparable to four state-of-the-art machine learning methods (Random Forests, Support Vector Machines, Artificial Neural Networks, and Kernel Ridge Regressions). The case of real observations explores different components of terrestrial respiration at an oak forest in south-east England. We find that GEP retrieved models are often better in prediction than established respiration models. Furthermore, the structure of the GEP models offers new insights to driver selection and interactions. We find previously unconsidered exponential dependencies of respiration on seasonal ecosystem carbon assimilation and water dynamics. However, we also noticed that the GEP models are only partly portable across respiration components; equifinality issues possibly preventing the identification of a "general" terrestrial respiration model. Overall, GEP is a promising tool to uncover new model structures for terrestrial ecology in the data rich era, complementing the traditional approach of model building.

scholarly journals The behaviour of f2 coliphage in activated sludge treatment

1978 ◽  
Vol 80 (2) ◽  
pp. 237-242 ◽  
Author(s):  
S. A. Balluz ◽  
M. Butler ◽  
H. H. Jones
Keyword(s):  
Activated Sludge ◽  
Treatment Plant ◽  
Full Scale ◽  
Suitable Model ◽  
Sludge Treatment ◽  
Low Background ◽  
Mixed Liquor

SUMMARYA model activated sludge treatment plant was used which was functionally very similar to a full-scale plant. It was inoculated with f2 coliphage and the titres of virus in the influent, the mixed liquor and the effluent were monitored regularly. The distribution of the virus in the solids and liquid fractions of the mixed liquor was in the ratio of 18:82 and 20–4 % of the influent virus was recovered in the effluent. After inoculation was stopped the titre of virus in the solids fraction of the mixed liquor remained high and unaltered for up to 70 h, whereas the value for effluent reverted to the low background titre originally present. These results are discussed in relation to those reported for poliovirus and it is concluded that f2 coliphage is not a suitable model for studies of the behaviour of human enteroviruses.

scholarly journals Parameter Sensitivity and Uncertainty of Radiation Interception Models for Intercropping System

2020 ◽  
Vol 27 (3) ◽  
pp. 437-456
Author(s):  
Wenzhi Zeng ◽  
Yuchao Lu ◽  
Amit Kumar Srivastava ◽  
Thomas Gaiser ◽  
Jiesheng Huang
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Suitable Model ◽  
Area Index ◽  
Radiation Interception ◽  
Sensitivity Indices ◽  
Intercepted Radiation ◽  
Main Effects ◽  
Sensitive Parameters ◽  
Model Structures

AbstractEstimating the interception of radiation is the first and crucial step for the prediction of production for intercropping systems. Determining the relative importance of radiation interception models to the specific outputs could assist in developing suitable model structures, which fit to the theory of light interception and promote model improvements. Assuming an intercropping system with a taller and a shorter crop, a variance-based global sensitivity analysis (EFAST) was applied to three radiation interception models (M1, M2 and M3). The sensitivity indices including main (Si) and total effects (STi) of the fraction of intercepted radiation by the taller (ftaller), the shorter (fshorter) and both intercrops together (fall) were quantified with different perturbations of the geometric arrangement of the crops (10-60 %). We found both ftaller and fshorter in M1 are most sensitive to the leaf area index of the taller crop (LAItaller). In M2, based on the main effects, the leaf area index of the shorter crop (LAIshorter) replaces LAItaller and becomes the most sensitive parameter for fshorter when the perturbations of widths of taller and shorter crops (Wtaller and Wshorter) become 40 % and larger. Furthermore, in M3, ftaller is most sensitive to LAItaller while fshorter is most sensitive to LAIshorter before the perturbations of geometry parameters becoming larger than 50 %. Meanwhile, LAItaller, LAIshorter, and Ktaller are the three most sensitive parameters for fall in all three models. From the results we conclude that M3 is the most plausible radiation interception model among the three models.

scholarly journals A knowledge-based method for the automatic determination of hydrological model structures

2019 ◽  
Vol 21 (6) ◽  
pp. 1163-1178 ◽  
Author(s):  
Jingchao Jiang ◽  
A-Xing Zhu ◽  
Cheng-Zhi Qin ◽  
Junzhi Liu
Keyword(s):  
Hydrological Model ◽  
Integrated Modelling ◽  
Suitable Model ◽  
Selection Algorithm ◽  
Process Selection ◽  
Knowledge Based ◽  
Description Framework ◽  
Model Structures ◽  
Resource Description

Abstract To determine a suitable hydrological model structure for a specific application context using integrated modelling frameworks, modellers usually need to manually select the required hydrological processes, identify the appropriate algorithm for each process, and couple the algorithms' software components. However, these modelling steps are difficult and require corresponding knowledge. It is not easy for modellers to master all of the required knowledge. To alleviate this problem, a knowledge-based method is proposed to automatically determine hydrological model structures. First, modelling knowledge for process selection, algorithm identification, and component coupling is formalized in the formats of the Rule Markup Language (RuleML) and Resource Description Framework (RDF). Second, the formalized knowledge is applied to an inference engine to determine model structures. The method is applied to three hypothetical experiments and a real experiment. These experiments show how the knowledge-based method could support modellers in determining suitable model structures. The proposed method has the potential to reduce the knowledge burden on modellers and would be conducive to the promotion of integrated modelling frameworks.

Intelligent Modeling of Complex Manufacturing Processes Using Hierarchical Fuzzy Basis Function Networks

2001 ◽  
Author(s):  
Cheol W. Lee ◽  
Taejoon Choi ◽  
Yung C. Shin
Keyword(s):  
Experimental Data ◽  
Basis Function ◽  
Process Models ◽  
Analytical Models ◽  
Manufacturing Processes ◽  
Suitable Model ◽  
Training Algorithm ◽  
Intelligent Modeling ◽  
The Hierarchical Structure ◽  
Model Structures

Abstract This paper presents a generalized modeling approach to modeling of complex manufacturing processes. Fuzzy basis function networks with a novel training algorithm are used to capture the cause-effect relationships of complex manufacturing processes. The modeling scheme allows for utilization of the existing knowledge in the form of analytical models, experimental data and heuristic rules in developing a suitable model. The method is implemented for the surface grinding processes based on the hierarchical structure of fuzzy basis function networks proposed by Lee and Shin [21]. Process models for surface roughness and residual stress are developed based on the available grinding model structures with a small number of experimental data to demonstrate the concept. The accuracy of developed models is validated through independent sets of grinding experiments.

Chemical modification of the bacterial wall to determine sites of metal deposition

1978 ◽  
Vol 36 (2) ◽  
pp. 350-351
Author(s):  
T. J. Beveridge
Keyword(s):  
Electron Scattering ◽  
Metal Salt ◽  
Metal Deposition ◽  
Suitable Model ◽  
X Ray Diffraction ◽  
Subtilis Cell ◽  
Thin Sections ◽  
X Ray ◽  
Section Data ◽  
Metal Impregnation

The Bacillus subtilis cell wall provides a protective sacculus about the vital constituents of the bacterium and consists of a collection of anionic hetero- and homopolymers which are mainly polysaccharidic. We recently demonstrated that unfixed walls were able to trap and retain substantial amounts of metal when suspended in aqueous metal salt solutions. These walls were briefly mixed with low concentration metal solutions (5mM for 10 min at 22°C), were well washed with deionized distilled water, and the quantity of metal uptake (atomic absorption and X-ray fluorescence), the type of staining response (electron scattering profile of thin-sections), and the crystallinity of the deposition product (X-ray diffraction of embedded specimens) determined.Since most biological material possesses little electron scattering ability electron microscopists have been forced to depend on heavy metal impregnation of the specimen before obtaining thin-section data. Our experience with these walls suggested that they may provide a suitable model system with which to study the sites of reaction for this metal deposition.

Localization of DNA in chromatin using ESI and osmium ammine staining

1990 ◽  
Vol 48 (3) ◽  
pp. 116-117
Author(s):  
C.L. Woodcock ◽  
R.A. Horowitz ◽  
D. P. Bazett-Jones ◽  
A.L. Olins
Keyword(s):  
Spectroscopic Imaging ◽  
Energy Losses ◽  
Electron Spectroscopic Imaging ◽  
Feulgen Reaction ◽  
Specific Location ◽  
Eukaryotic Nucleus ◽  
Chromatin Fibers ◽  
Patiria Miniata ◽  
Model Structures

In the eukaryotic nucleus, DNA is packaged into nucleosomes, and the nucleosome chain folded into ‘30nm’ chromatin fibers. A number of different model structures, each with a specific location of nucleosomal and linker DNA have been proposed for the arrangment of nucleosomes within the fiber. We are exploring two strategies for testing the models by localizing DNA within chromatin: electron spectroscopic imaging (ESI) of phosphorus atoms, and osmium ammine (OSAM) staining, a method based on the DNA-specific Feulgen reaction.Sperm were obtained from Patiria miniata (starfish), fixed in 2% GA in 150mM NaCl, 15mM HEPES pH 8.0, and embedded In Lowiciyl K11M at -55C. For OSAM staining, sections 100nm to 150nm thick were treated as described, and stereo pairs recorded at 40,000x and 100KV using a Philips CM10 TEM. (The new osmium ammine-B stain is available from Polysciences Inc). Uranyl-lead (U-Pb) staining was as described. ESI was carried out on unstained, very thin (<30 nm) beveled sections at 80KV using a Zeiss EM902. Images were recorded at 20,000x and 30,000x with median energy losses of 110eV, 120eV and 160eV, and a window of 20eV.

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