a batch respirometric analysis (1)

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5/8/12

A Batch Respirometric

Analysis

Canberk Sevim Nazlican Tezgel Nergis Gunindi

Ece Toklu



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IntroductionWhat is activated sluge process ?

•

is a process in sewage treatment in which air or oxygen isforced into sewage liquor to develop a biological flocwhich reduces the organic content of the sewage.



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In all activated sludge plants, once the sewage has received sufficient treatment, excessmixed liquor is discharged into settling tanks and the supernatant is run off to undergo

further treatment before discharge. Part of the settled material, the sludge, is returned to thehead of the aeration system to re-seed the new sewage entering the tank. The remaining

sludge is further treated prior to disposal.



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Components of a sludge system;

•

Substrate• Biomass

• Oxygen

Oxygen requirement and excess biologicalsludge relate;

•

Growth (biosynthesis)• Decay (maintenance)

• Hydrolysis



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Understanding and Controlling

the performance and efficiency requires;

“a proper kinetic description of

the reactions taking place in thebiological reactor”



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• What is Oxygen Uptake Rate (OUR) ?

is a parameter that can be used to evaluate the rate at whichmetabolic processes take place in activated sludge treatment

processes with sludge in suspension.



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The main uses of OUR method;

•

estimating the values of kinetic and stoichiometric parameters

• obtaining data required to set up a mass balance of

organic or nitrogenous material

• evaluating the sludge activity in terms of the maximum

and endogenous substrate utilization rate

• determining the degree of sludge stabilization after

aerobic digestion



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Respirometer

measure and interpret the biological oxygen consumptionrate under well-definedexperimental conditions

is a useful technique for;• monitoring• modeling•

controlling



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The BOD test measures the

oxygen demand

of biodegradable pollutants

The COD test measures the

oxygen demand of biogradable pollutants plus the oxygen

demand of non-biodegradableoxidizable pollutants



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• What is AQUASIM?

is a program that;• perform simulations using different models

• assess the identifiability

• estimate the values of model parameters and to estimate

prediction uncertainty



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Model simulations and parameter

estimation works were performedusing AQUASIM program. For this

experiment Aquasim was used to

perform analyses for sludge systemdetails and modeling.



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Experimental Setup• Surface-saturation-type hydrolysis & growth mechanisms

were investigated for a domestic wastewater.

• COD was used to characterize organic matter in thesewage



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•

Synthetic domestic sewage and biomass mixture with atotal volume of 2L was analyzed in order to observe thesoluble COD removal with time under aerobic conditions.



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• NOTCOD removal,

• Oxygen uptake rate (OUR) was determined



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• Concentration of around 500 mg/L which is mainly

composed of readily and slowly biodegradable COD part

(Ss + Xs).

• The batch system was operated at steady-state with a

sludge age of 10 days.



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• Before the OUR measurements, the wastewater in the

reactor was diluted in order to be able to be used as

oxygen source by microorganisms hence preventingthe oxygen limitations.



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• 1 ml of sludge was taken from the reactor and 250ml of dry biomass was added to the system.

• 5 g of a nitrification inhibitor was added to prevent the oxygen utilization due tonitrification.



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• After observing the endogenous OUR level, 27 ml peptone was added to mixed liquor as carbonsource.

• Then 1723 ml of tap water was used to make the

total volume 2L. During the experiment, the oxygen in the

reactor was kept around 5mgO2/L that is sufficient for heterotrophic activity.



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Model and

Wastewater

Selection• The model parameters and initial state variables wereselected based on the general activated sludge model.

State variables So, Ss, Xs, Xh

Parameters kh, Kx, µH, Ks, YH, f

Table1. The state and parameter vectors of the selected model



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• Inert fraction of endogenous biomass, f andheterotrophic yield coefficient, YH were

assumed as 0.2 and 0.6 cell COD/COD, respectively.

• The model parameters and initial state variables wereestimated in order to find the best fit of degradation model

on experimental OUR data.



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Parameters

Ss Xs Xh So Rate

Growth -1/YH 1 -(1-

YH)/YH

µH*(Ss/(Ks+Ss))/Xh

Hydrolysis 1 -1 kh*((Xs/Xh)/(Xs+

(Xs/Xh)))*Xh

Decay -1 (1-f) kd*Xh

Parameter, ML-3 COD COD Cell

COD

O2

Table 2. Process kinetics and stoichiometry for heterotrophic bacterial growth in an aerobic environment



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Identifiability

AnalysisThe parameter estimation is based on;

• the maximization or minimization of a goodness-of-fitcriterion

• Least Squares Methods.



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• Process kinetics and stoichiometric data should be noted

that the growth process is limited by

hydrolysis therefore their parameters may not be

estimated simultaneously.



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Parameters of growth and hydrolysis are foundseparately;

1. Model estimation was done for theparameters of growth process.

2. They were assumed to be known and the same

estimations were done for the parametersof hydrolysis.



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• the inert fraction of endogenous biomass

•

f • YH; heterotrophic yield coefficient,

were assumed to be known.



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• overall rate constant for hydrolysis (day−1),

•

kh; half saturation coefficient (mg COD l−1),

• Ks; overall saturation coefficient for hydrolysis (mg cell

COD)−1),

•

Kx

• µH; maximum heterotrophic growth rate (day−1),

were estimated by the AQUASIM computer

program.



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SIMULATION

RES ULTS ANDDISCUSSION• Calculate the model parameters providing the best fit on yourexperimental data using parameter estimation method.Estimate the parameter subset of mueH, KS, kh, kx, SS, XS.

Set the others to their default values suggested in literature.



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Kh Ks Kx Ss mue Xs

4.925382 8.659017 0.6874981 75.434734 4.6608553 434.4768



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• Find the standard deviations of your selected modelparameters and initial state variables (substrate

concentrations, active biomass) together with calculatedcorrelation matrix. Provide appropriate explanations of the parameter estimation correlation matrix.



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• Estimated correlation matrix of the parameters:

Kh

Ks

Kx

Ss

Mue

Xs

Kh

1

0.54661408

0.97783491

0.69569517

0.72383042

-0.93273769

Ks

0.54661408

1

0.68787891

0.81593719

0.90770715

-0.54800857

Kx

0.97783491

0.68787891

1

0.72016554

0.71075787

-0.88613633

Ss

0.6969517

0.81593719 0.72016554

1

0.51917409

-0.59431076

Mue

0.72383042

0.90770715

0.71075787

0.51917409

1

-0.48255053

Xs

-0.93273769

-0.54800857

-0.88613633

-0.59431076

-0.48255053

1



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• Estimated standard errors of the parameters:

It can be easily seen that the most correlated matrices are Kh

and Kx with the value of 0.97783491, also the leastcorrelated matrices are Xs and mue with the value of 0.48255053.

Kh Ks Kx Ss mue Xs

1.883098 3.3062908 1.4692623 3.2503086 0.20638784 3.5664478



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• Conduct sensitivity analyses for the most correlatedparameters provided in the correlation matrix (R2>0.8)



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• Kh-Kx (R2=0.97783491)



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• It can be observed that the parameters Kh and Kx have

opposite affects to each other. They both affect thehydrolysis process.

dXs / dt = ( ( Xs / Xh ) / ( Kx + Xs / Xh) ) * Kh * Xh

•

Their affects are close to each other , but theaffect of Kh is a little bit more than the other parameter,



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• The correlation matrices values of these

parameters are close to 1.

• Kh increases the growth rate and oxygen utilization rate

decreases. Kx is reciprocal of Kh so it has the inverse

effect of Kx and it increases the oxygen utilization rate.



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• Xs-Kh (R2=-0.93273769)



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• It can be observed that the parameters Kh and Xs have

close affects to each other. They both affect the

hydrolysis process.

dXs / dt = ( ( Xs / Xh ) / ( Kx + Xs / Xh) ) * Kh * Xh

• Their sensitivity lines are close to each other, but the affect

of Xs is a little bit more than the other parameter . The reason of this can be explained with the

Contois growth kinetics model.



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• If Xs >> Xh , dXs / dt = - Kh * Xh

•

If Xs << Xh, dXs / dt = ( Kh / Kx ) * Xs



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•

The effect and sensitivityof the

Xsparameter

can be more than the Kh parameter .


parameters are very close to 1..

• Xs and Ks parameters increase the hydrolysisrate, so the energy is not used and oxygen consumption

rate is not decreased. Accordingly the sensitivity lines go

up.



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• mue-Ks (R2=0.90770715



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• mue and Ks have opposite affects to each other. They both affect the growth process.

dSs / dt = mue * ( ( Ss / ( Ks + Ss ) ) * Xh

• mue is multiplied by the reciprocal of the Ks, so they

have opposite affects to each other. Their affects are close

to each other, but the affect of mue is more thanthe other parameter, because mue is not divided just by

Ks.



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parameters are very close to 1.

• Mue parameter increases the growth rate, and if

the growth rate increases, oxygen consumption increases

too. Oxygen utilization rate decreases with

time. On the other hand Ks is the reciprocal of the mue

so that it has the inverse effect.



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• Xs-Kx (R2=-0.88613633)



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• Xs and Kx have opposite affects to each other .

They both affect the hydrolysis process.

dXs / dt = ( ( Xs / Xh ) / ( Kx + Xs / Xh) ) * Kh *Xh

• Xs is divided by Kx, so they have opposite affects to

each other. The affect of Xs is a little bit more than the

other parameter , because Xs is not only divided by Kx.



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• Xs and Kx parameters both affect thehydrolysis rate. When Xs increases,hydrolysis increases and no oxygen is used, so theoxygen utilization rate does not decrease.

On the other hand Kx has the negative effect,

because it is the reciprocal of the Xs in hydrolysisequation.



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• Ss-Ks (R2=0.815937109)



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• Ss and Ks have similar affects to each other. They bothaffect the growth process.

dSs / dt = mue * ( ( Ss / ( Ks + Ss ) ) * Xh

• Their affects are close to each other , but the

affect of Ss is more than the other parameter , because Ss is not only divided by Ks.



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• When Ss increases growth rate increases and

oxygen utilization rate decreases, but Ks is

reciprocal of Ss so it has the inverse effect.



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• Conduct OUR simulation profiles with 3 differentinitial biomass concentrations (XH) by interpretting

the simulation outputs.



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• Xh = 500 g COD/L

Xh concentration is decreased oxygen utilization rate decreased.



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• Xh = 1000 g COD/L

Oxygen consumption rate increased more. Increasing the Xhvalue oxygen consumption rate can be increased if it is comparedto the previous graph.



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• Xh = 2500 g COD/L

Xh concentration is more than to be estimated in the aquasim analysis. Oxygenutilization rate is maximum, because the Xh concentration is maximum.

In conclusion, the Oxygen consumption rate increases with theinitial Xh concentration.



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• Calculate the biodegradable COD fractions in the feedwater.



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Ss

(Readily biodegradable COD)

Xs

(Slowly biodegradable COD)

75.434734

434.4768

Total COD = 500 mg COD/LCS : Total biodegradable CODCS = SS + XS



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• In this experiment SI and XI are not consumed,

so there is no inert part. Total COD is equal to the

total biodegradable COD.

• 75.434734 + 434.4768 = 509.911534 ~ 510 mg COD/L

•

SS : %85• XS : %15



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• Compare your findings with the literature parametersvalues suggested for domestic wastewater.



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• The expected total COD after aquasim analysis was

500 mg COD/L, but it was calculated as

approximately 510 mg COD/L, so some

incorrections can be made in the analysis.

• When the peptone mixture wastewater sample wascompared to the domestic sewage, it can be easilyobserved that they have different COD fractions. Our

peptone water sample does not have inert COD; however the results were so close to each other.



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Parameters

Estimated values in Aquasim

Literature Data

Mue (maximum growth rate) 4.6608553 6

Ks (half saturation constant) 8.659017 4.0

Kh (hydrolysis rate) 4.925382 4.30

Kx (rapidly hydrolysis half saturation rate) 0.6874981 0.03

Kd (decay rate) - 0.19

Yh (heterotrophic yield coefficient) - 0.67

fe - 0.2

After the Aquasim analysis the estimated values of the parameterswere determined and they were close to the literature data. Literature data

was the expected data whose retention time is 10 days.



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WORKS CITED•

D. Orhon et al., Validity of Monod kinetics at differentsludge ages – Peptone biodegradation under aerobicconditions, Bioresource Technology, 100 (2009) 5678– 5686.

•

E.U. Cokgor and D. Okutman, EnvironmentalBiotechnology Lecture Notes ‘Characterization of wastewaters and sludges’, Istanbul, Turkey, 2012.

• E.U. Cokgor et al., Respirometric evaluation of a mixture

of organic chemicals with different biodegradationkinetics, Journal of Hazardous Materials, 161 (2009) 35– 41.

• G. Insel, D. Orhon, P.A. Vanrolleghem, Identification andmodelling of aerobic hydrolysis – application of optimalexperimental design, Journal of Chemical Technology and



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• Skopp, J.; Jawson ,M. D. ; Doran, J. W. Steady-StateAerobic Function of Soil Water Content. Web.

• <http://ddr.nal.usda.gov/dspace/bitstream/10113/17205/1/I ND91018421.pdf>

• Sozen S., Ubay Cokgor E., Orhon D., Henze M.,Respirometric analysis of activated sludge behavior--II.

Heterotrophic growth under aerobic and anoxic conditions,Water Research, Volume 32, Issue 2, February 1998, Pages476-488.

• Waul, C., Arvin, E., Schmidt, JE. 2008. Model description

and kinetic parameter analysis of MTBE biodegradation ina packed bed reactor, Water Research 42(12), 3122 - 3134.

• Guclu Insel, Orhon Derin, Vanrolleghem Peter A."Identification and Modelling of Aerobic Hydrolysis – Application of Optimal Experimental Design " (2003):

a batch respirometric analysis (1)

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