EOLI : Gestione Efficiente degli Impianti di Trattamento delle

Acque Reflue Urbane

Denis Dochain

Project coordinator

CESAME & IMAP

Université catholique de Louvain, Belgium

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Content

• Project context

• Objectives of EOLI

• Project organization

• Experimental facilities

• Some results

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Project Context

• Efficient Operation of Urban Wastewater Treatment Plants (EOLI) : an European project dedicated to sequential batch reactors (SBR’s)

• INCO/DEV programme, i.e. INternational COoperation with DEVeloping countries (here Latin America (Mexico + Uruguay))

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What is a Sequential Batch Reactor?

Anoxic(30 min)

Idle (30 min)

Settle (60 min)

Anoxic fill (30 min)

Aerobic(9 hs)

Draw (30 min)

Anoxic phase : denitrificationAerobic phase : nitrification

Reactions ;

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Objectives of EOLI

• Design of a low-cost, modular and reliable monitoring and control system for wastewater treatment processes dedicated to the treatment of wastewater from urban settlements

• SBR’s : well adapted for developing countries (low investment and operation costs, process stability, operation reliability)

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• 3 types of wastewater : 1) one from a dairy industry contaminated with organic

carbon components and nitrogen 2) one for an area including chemical industry

containing toxic or recalcitrant compounds 3) one domestic wastewater which occasionally

contains toxic or organic overloads

• Monitoring of the biomass concentration due to possible settling problems of the suspended solids

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Project organization

WP1Process

ExperimentsPartners # 2,4,6,7,8WP 2

Model selectionand parameterIdentification

Partners # 1,6, 7

WP4SoftwareSensors

Partner # 1, 5

WP3HardwareSensors

Partners #1,3,4,5,6, 8

WP5ControlDesign

Partners # 5,6,7,8

WP6Fault Detectionand Isolation

Partners # 2,5,6, 8

WP7Supervision

SystemPartners #, 2, 5, 6, 7, 8

WP8Integration

Partners # 1,2, 3, 4, 5, 6,

7, 8

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Project partners

• European academic partners :– Univ. catholique de Louvain

(UCL)

– Laboratoire de Biotechnologie de l’Environnement (LBE), INRA, Narbonne

– Gradient, Université de Technologie de Compiègne (UTC)

– POLIMI (+ ENEA)

• Latin American academic partners :– Universidad Nacional

Autonoma Mexico (UNAM)

– Universidad de la Republica Oriental del Uruguay (UU)

• Industrial partners– SPES

– IBTech (Mexico)

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Uruguay (Montevideo)

Mexico(UNAM, IBTech)

Italy (POLIMI, ENEA, SPES)

France (INRA,Narbonne)

France (UTCompiègne)Belgium (UCL)

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Experimental Facilities

8 lab-scale and field-scale reactors :• Gradient : 4 L• UU : 2 x 20 L• POLIMI : 30 L• UNAM : 30 L• LBE : 200 L• ENEA : 500 L• IBTech : 1000 L

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UU’s reactor

pH, NO3-, NH4+, DO sensors

air input temperature sensor

Recycling pump

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UNAM’s reactor

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LBE’s reactor

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1 2 3 4 5

1. DO 2. pH 3. O2 gas 4. CO2 gas 5. Sludge level 6. Temperature 7. Redox 8. withdraw valve

Input

Output

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IBTech’s Reactor

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Some Results

• Experimental data

• Dynamical model

• Software sensor

• Control design

• (Hardware sensors)

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Experimental dataLBE-INRA

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Dynamical Modelling

2 models : • EM1 : carbon removal (Mexico) :

SC + SO --> X

• EM2 : carbon & nitrogen removal (denitrification/nitrification) : Anoxic phase : NO3 reduction : SC + SNO3 --> Xh + SNO2

NO2 reduction : SC + SNO2 --> Xh + N2 Ammonification : SN --> SNH

Aerobic phase : NH4 oxidation : SO + SNH --> Xa + SNO2

NO2 oxidation : SO + SNO2 --> Xa + SNO3

C-removal : SC + SO --> Xh

Ammonification : SN --> SNH

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Experimental protocol for parameter calibration :

• Datasets distributed in two (calibration set, validation set)

• Mathematical transformation : distribution of the parameters in 3 sets (transfer coefficients, yield coefficients, kinetic parameters) in order to provide independent calibration

• A priori identifiability analysis

• A posteriori statistical analysis of the calibration results

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Validation data (POLIMI)

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Software Sensors

• Objective : to provide on-line values of key process components that are not accessible for on-line measurement in presence of uncertainty in the model (kinetics)

• Example : EM1- On-line measurement : dissolved oxygen S0

- Software measurement : biomass concentration X- Uncertain parameter : maximum specific growth rate 0

(known mean value)- Calibration challenge : fast convergence

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Control Design

SBRSOV

eQin

Event Driven TOCEvent DetectorγEstimator

SequencerTimerQAirStir

γ

( 1)cSBRSOV

eQin

Event DrivenTOC EventDetectorγEstimator

SequencerTimerQAirStir

γ

( 1)c

S*SfSplSphVoVfV fast zone

γS( 2)cγ∗%Pγ∗bB

endnear

*SSfSplSphVoVfV fast zone

γS( 2)cγ∗%Pγ∗bB

endnearEvent Driven Time Optimal Control (UNAM)

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Optimal determination of time durations for anoxic and aerobic phases (LBE-INRA)

« target »(final valuesof C and N)

anoxic phasesaerobic phases

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Conclusions

• EOLI : last EC project of Alberto Rozzi

• EOLI objectives : to provide a low-cost, modular and reliable monitoring and control system for Sequential Batch Reactors (SBR’s)

• Hardware sensor development plays an essential role in EOLI