Selection of variables using FDA for the state identification of an Anaerobic UASB-UAF hybrid Pilot ...
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M. Castellano 1 , G. Ruiz 2 , W. González 1 , E. Roca 3 and J.M. Lema 3 PowerPoint PPT Presentation


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Selection of variables using FDA for the state identification of an Anaerobic UASB-UAF hybrid Pilot Plant, fed with winery effluents. M. Castellano 1 , G. Ruiz 2 , W. González 1 , E. Roca 3 and J.M. Lema 3 1 Dep. of Statistics and O.R. University of Santiago de Compostela, Spain

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M. Castellano 1 , G. Ruiz 2 , W. González 1 , E. Roca 3 and J.M. Lema 3

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M castellano 1 g ruiz 2 w gonz lez 1 e roca 3 and j m lema 3

Selection of variables using FDA for the state identification of an Anaerobic UASB-UAF hybrid Pilot Plant, fed with winery effluents.

M. Castellano1, G. Ruiz 2, W. González1, E. Roca3 and J.M. Lema3

1Dep. of Statistics and O.R. University of Santiago de Compostela, Spain

2School of Biochemical Engineering. Catholic University of Valparaiso, Chile

3Dep. of Chemical Engineering. School of Engineering. University of Santiago de Compostela, Spain

IV International Specialized Conference on Sustainable Viniculture: Winery Wastes and Ecology Impact Management

Viña del Mar – Chile, November 2006

Winery2006


This is about

This is about...

  • The Anaerobic Wastewater Treatment

  • The Monitoring & Control Variables

  • Discrimination Statistical Techniques

  • Application of FDA

  • Experimentation

  • Results and Conclusions

Winery2006, Viña del Mar


This is about1

This is about...

  • The Anerobic Wastewater Treatment

    The Monitoring & Control Variables

    Discrimination Statistical Techniques

    Application of FDA

    Experimentation

    Results and Conclusions

Winery2006, Viña del Mar


The anerobic wastewater treatment

Changes in the Operation Conditions

The Anerobic Wastewater Treatment

The treatment characteristics

Requires low energy& Generates low sludges.

The problem

Variations over Influent properties and composition

Monitoring Diagnosis and Control System (MD&C) FORStable Operation Conditions

Winery2006, Viña del Mar


The anerobic wastewater treatment1

First requirement: Selectingprocess variables

The Anerobic Wastewater Treatment

The solution

Monitoring Diagnosis and Control System (MD&C) :

  • early and automatic detection of perturbations

    (overload, presence of toxic, inhibitory compounds, suddenly changes in pH)

Winery2006, Viña del Mar


This is about2

This is about...

The Problem

  • The Monitoring & Control Variables

    Discrimination Statistical Techniques

    Application of FDA

    Experimentation

    Results and Conclusions

Winery2006, Viña del Mar


The monitoring control variables

The Monitoring & Control Variables

Selection Criteria

  • Low response delay

  • High sensibility

  • Low cost of both, sensor itself and its operation-maintenance requirements.

  • Previously

    • Gas flow rate and H2/CH4 in the gas phase

    • H2/CO in the gas phase

    • H2 in the gas phase

    • Gas flow rate and CH4 in the gas phase

    • Alkalinities (total and partial) in the liquid phase

    • pH in the liquid phase and gas flow rate

Winery2006, Viña del Mar


The monitoring control variables1

FDA

Classify between different S.S.

Diagnose the process performance.

Group of variables

All combination of variables

Usefull for diagnosis?

The Monitoring & Control Variables

The statistical analysis

Functional Discriminant Analysis (FDA)

Classification

Select the minimum number of variables for process state identification purpose.

Winery2006, Viña del Mar


This is about3

This is about...

The Problem

The Monitoring & Control Variables

  • Discrimination Statistical Techniques

    Application of FDA

    Experimentation

    Results and Conclusions

Winery2006, Viña del Mar


Discrimination statistical techniques

Together

Discrimination Statistical Techniques

Functional Discriminant Analysis (FDA)

  • Simple Statistical Classification Tool

  • Linear Transformation of process variables

  • Requires: A priori knowledge about groups

  • Objectives:

    • Minimize the missclassification error

    • Minimize variance into each group

    • Maximizevariance between groups

Winery2006, Viña del Mar


Discrimination statistical techniques1

Men

Women

Women’s mean

Men’s mean

Discrimination Statistical Techniques

Men

Weight

Men’s mean

Women

Women’s mean

Height

Winery2006, Viña del Mar


Discrimination statistical techniques2

Only complex to explain, not to USE

Discrimination Statistical Techniques

Other techniques of classification

Consider more sophisticated functions lead to more sophisticated classification techniques. Some of the more popular and useful

  • Quadratic discrimination

  • Non parametric density estimation functions

  • Neural networks

Winery2006, Viña del Mar


This is about4

This is about...

The Problem

The Monitoring & Control Variables

Discrimination Statistical Techniques

  • Application of FDA

    Experimentation

    Results and Conclusions

Winery2006, Viña del Mar


Application of fda

FDA

All combination of variables

Missclassification

Error

Application of FDA

Selection of Variable using FDA

FDA assigns data to different groups.

The FDA classification is tested using all the possible combinations of the variables in order to select the best ones, so the most useful variables for MD&C.

Winery2006, Viña del Mar


This is about5

This is about...

The Problem

The Monitoring & Control Variables

Discrimination Statistical Techniques

Application of FDA

  • Experimentation

    Results and Conclusions

Winery2006, Viña del Mar


Experimentation

Experimentation

The pilot plant and its instrumentation

A UASB-UAF pilot plant fed with diluted wine. 26 variables were used to follow the process. Measurement devices

  • feed and recycling flow meters

  • pH meter

  • inflow and reactor Pt100

  • gas flow meter

  • infrared gas analyser (CH4 and CO)

  • gas hydrogen analyser

  • TOC/TIC combustion analyser

    Other parameters were calculated: methane and hydrogen flow rate (Q CH4) (QH2) and organic loading rate (OLR).

Winery2006, Viña del Mar


Experimentation1

Experimentation

The experimental conditions

Winery2006, Viña del Mar


This is about6

This is about...

The Problem

The Monitoring & Control Variables

Discrimination Statistical Techniques

Application of FDA

Experimentation

  • Results and Conclusions

Winery2006, Viña del Mar


Results and conclusions

Results and Conclusions

Selection of Variable using FDA

Classification analysis was made using 1 variable, all the combination of 2 variables and so on.

Winery2006, Viña del Mar


Results and conclusions1

Results and Conclusions

Selection of Variable using FDA

137 of the combination of 2 variables achieve a 100% of goodness classification.

The solution is not unique, so another criteria should be used to select the variables for monitoring

Winery2006, Viña del Mar


Results and conclusions2

Results and Conclusions

Other criteria

  • Constant temperature, influent pH and recirculation flow rate.

  • Specific substance determinations in the liquid phase are rare in industrial application

  • Qgas and P highly are correlated

  • High cost of the on line equipment for TIC/TOC on line measurement

  • Variables in the liquid phase are supposed to present higher response time than the gas phase variables

Winery2006, Viña del Mar


Results and conclusions3

Results and Conclusions

The selected variables were QH2, H2, Qg, QCH4 , CH4

Winery2006, Viña del Mar


Results and conclusions4

Results and Conclusions

  • Not subjective technique to select the variables that should be used for an MD&C system was developed.

  • Not only one group of variables that must be selected, but many combinations can achieve same performance.

  • Economical and technical criteria have been considered.

  • Gas phase variables obtain good results, even if only one variable is selected (H2)

Winery2006, Viña del Mar


For more information

For more information...

María Castellano Méndez

[email protected]

Dep. of Statistics and O.R. University of Santiago de Compostela, Spain

Gonzalo Ruiz Filippi

[email protected]

School of Biochemical Engineering. Catholic University of Valparaiso, Chile

Winery2006, Viña del Mar


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