modelling of kinetics of microbial degradation of simulated leachate from tobacco dust waste

Chemical Papers 67 (9) 1138–1145 (2013)DOI: 10.2478/s11696-012-0287-3

ORIGINAL PAPER

Modelling of kinetics of microbial degradation of simulated leachatefrom tobacco dust waste‡

aIvana Cosic*, aMarija Vukovic, bZoran Gomzi, aFelicita Briški

aDepartment of Industrial Ecology, bDepartment of Reaction Engineering and Catalysis, Faculty of Chemical Engineering and

Technology, University of Zagreb, Marulicev trg 19, 10000 Zagreb, Croatia

Received 14 June 2012; Revised 5 October 2012; Accepted 10 October 2012

This paper presents a kinetic analysis of the biodegradation of organic pollutants in a batch biore-actor and investigates the kinetic properties of activated sludge using different mathematical models.The treatment was conducted for different initial concentrations of leachate from 500 mg dm−3 to5000 mg dm−3 and initial concentrations of activated sludge from 1.84 g dm−3 to 6.62 g dm−3 over48 h. Four different kinetic models were applied to the data. The kinetic analysis was performedwith the traditional Monod model, the modified Monod model with endogenous metabolism, theHaldane model, and the Haldane model extended to include endogenous metabolic consumptionand known as the Endo-Haldane model. Kinetic parameters for each model were determined usingdifferential analysis and the Nelder–Mead method of non-linear regression. The lowest deviationsand very good matches with the experimental data were achieved using the Endo-Haldane model.This indicated that this model best described the process of biodegradation of leachate from to-bacco waste composting. This is due to this model incorporating the effects both of inhibition andendogenous metabolism.c© 2012 Institute of Chemistry, Slovak Academy of Sciences

Keywords: activated sludge, biodegradation, kinetic parameters, leachate, tobacco waste

Introduction

In cigarette manufacture, large quantities of to-bacco waste are produced annually, the disposalof which represents a serious ecological problem.Tobacco-related processes can produce solid or liquidwastes. The uncontrolled disposal of tobacco wastecan be a serious threat to the environment and en-dangers public health. The major problem is the con-tinuing growth of tobacco products accompanied bythe increasing level of various tobacco wastes contain-ing toxic and hazardous compounds, especially nico-tine. Nicotine is highly soluble in water; consequently,this toxic compound can be transferred from the solidphase to an aqueous solution through efficient perco-lation. It may also be leached from the wastes andmay permeate into ground waters and surface waters

(Piotrowska-Cyplik et al., 2009; Tyrrel et al., 2008;Wang et al., 2009; Zhong et al., 2010).With characteristics such as high chemical oxygen

demand (COD), total organic carbon (TOC), colour,and potential toxicity, the wastewater has become afurther aspect of the solid waste problem (Veli et al.,2008). The COD value in the leachate and the to-bacco industry wastewater is estimated to be from1.0 g dm−3 to 70.9 g dm−3. Only a limited numberof studies concerning the treatability and toxicity oftobacco wastewaters have been carried out. The bio-logical processes for the treatment of tobacco wastew-aters are aerobic or anaerobic biodegradation (Renouet al., 2008; Sponza, 2001; Wang et al., 2009). Formany years, conventional biological treatments andphysical–chemical methods have been regarded as themost appropriate technologies for the manipulation

*Corresponding author, e-mail: [email protected]‡Presented at the 39th International Conference of the Slovak Society of Chemical Engineering, Tatranské Matliare,21–25 May 2012.

I. Cosic et al./Chemical Papers 67 (9) 1138–1145 (2013) 1139

and management of high-strength effluents such aslandfill leachate. An activated sludge process is com-monly used for biodegrading organic contaminants inwastewater using a mixed population of microorgan-isms (Bae et al., 1999; Celis et al., 2008; Dollerer& Wilderer, 1996). Decomposition of the substratereduces its toxicity. Biological treatment is a viablemethod of nicotine removal and numerous studieshave demonstrated nicotine degradation by microbes(Briški et al., 2012; Sponza, 2001; Wang et al., 2004).There are many mathematical models which can

be applied in biological wastewater treatment. Math-ematical modelling can be helpful in understandingthe behaviour of the biological process and predict-ing the concentrations of organic matter in the sys-tem (Casey et al., 1997; Tsuneda et al., 2002a; Slezaket al., 2012). The model can be used to predict theinfluence of wastewater composition and changes inthe operational parameters on the effluent wastewaterquality (Derco et al., 2011).The purpose of this work was to study the kinetic

properties of the activated sludge process by removalof the pollutant organic matter, represented by the de-crease in the chemical oxygen demand (COD). Variouskinetic models were successfully applied, including theMonod model, the modified Monod model with en-dogenous metabolism, the Endo-Haldane model andthe Endo-Haldane model with inhibition, in order toevaluate the kinetic parameters of each model.

Experimental

Materials and methods

The activated sludge sample was collected fromthe Wastewater Treatment Plant (WWTP) in Za-greb, ZOV, Croatia. The sludge sample from theWWTP was collected from the aeration tank, cen-trifuged (Sigma 3K15, Germany) at 5411g at 0◦C for10 min. Fresh activated sludge was centrifuged un-der the above conditions to remove residual organicmatter from the WWTP and for dewatering. The ini-tial activated sludge concentrations were 1.84 g dm−3,4.06 g dm−3, and 6.62 g dm−3, respectively.The leachate used in the research was prepared

from a solid sample of tobacco waste, Hrvatski duhanid.d., Virovitica, Croatia, to European standard: EN12457-4:2002 (European Committee for Standardiza-tion, 2002). The starting concentration of the preparedleachate was 29660 mg of COD per dm−3. For the setof experiments, the leachate sample was diluted to ini-tial concentrations in mg of COD per dm3: 500, 1000,1500, 3000, and 5000, and denoted as S1, S2, S3, S4,and S5, respectively.Batch biodegradation experiments were conducted

in 500 cm3 conical flasks using 250 cm3 of dilutedleachate inoculated with 3.8 g (experiment A), 11.3 g(experiment B), and 15.0 g (experiment C) of the cen-

trifuged activated sludge. Samples were collected ev-ery 6 h for determination of the chemical oxygen de-mand (COD), mixed liquor suspended solids (MLSS),mixed liquor volatile suspended solids (MVLSS), pH,and dissolved oxygen (DO) values. All experimentswere performed at (25 ± 2)◦C and carried out un-der aerobic conditions agitated on a rotary shaker at160 min−1 for 48 h. Duration of the experiments waschosen on the basis of the final time needed for com-pleting the reaction when a state of equilibrium wasattained.MLSS as well as MVLSS were determined gravi-

metrically and COD was determined by the dichro-mate method using Standard Methods (APHA-AWWA-WEF, 1999). Dissolved oxygen (DO) and pHvalue were measured using an oxygen meter and pHmeter (WTW Multi 340i, Germany). All determi-nations were the averages of measurements acquiredfrom duplicated samples.

Kinetic analysis

The modelling was conducted using the assump-tion that COD (S) represents the limiting substrateconcentration and MLVSS (X) represents the amountof the active biomass. Performing the substrate andbiomass balance on the batch reactor at constant vol-ume yields the equation for biomass growth rate, rXwhich is duly described by the following first order ki-netic equation (Beltran et al., 2008; Beltran de Here-dia et al., 2005; Tsuneda et al., 2002b):

rX =dXdt= µX (1)

where X is the biomass concentration, t is the time,and µ is the specific growth rate of biomass. At thesame time as the production of the biomass, the sub-strate is degraded and the equation for the substratedegradation rate, rS, is:

rS = −dSdt=

µX

Y(2)

where S is the limiting substrate concentration and Yis the overall yield coefficient. The substrate degrada-tion rate can also be expressed by the following equa-tion:

rS = −dSdt= qX (3)

where q is the specific substrate degradation rate, thesingle parameter which characterises the degradationprocess.Re-arranging Eqs. (1)–(3), the equation for bio-

mass growth rate can be presented by the expressions:

rX = qXY (4)

rX = rSY (5)

1140 I. Cosic et al./Chemical Papers 67 (9) 1138–1145 (2013)

There are several expressions which relate the spe-cific rates (µ and q) to the substrate concentration. Inbioprocess modelling, limiting substrate concentrationwhich may occur during the process, may be mod-elled by various kinetic models. Due to their relia-bility and simplicity, the Monod model, the modifiedMonod model with endogenous metabolism, the Hal-dane model, and the Endo-Haldane model are com-monly used.Most frequently applied is the Monod model, which

describes the dynamic behaviour of the process, i.e.shows the relationship between the specific growthrate of the biomass and the limiting nutrient (sub-strate):

µ =µmaxS

KS + S(6)

in this traditional Monod model, µmax represents themaximum specific growth rate and KS is the Monodsaturation constant (i.e. substrate concentration athalf µmax).At the end of the process, when most of the or-

ganic matter from the leachate is removed, due to thelack of substrate, a weaker cell population becomesfood for the healthier one. Due to biomass decay, theendogenous or decay coefficient, kd, must be incorpo-rated in the original Monod model. This coefficientcorresponds to the endogenous metabolism which in-volves reactions in cells that consume cell substances(Okpokwasili & Nweke, 2005; Tsuneda et al., 2002b;Vukovic et al., 2006)

µ =µmaxS

KS + S− kd (7)

When a substrate inhibits its own biodegradation,the Monod model is inadequate and must be modifiedby incorporating the inhibition constant Ki (Okpok-wasili & Nweke, 2005):

µ =µmaxS

KS + S + S2/Ki

(8)

This is the Haldane model which takes into ac-count the inhibition constant (Ki), which is a measureof sensitivity to inhibition by inhibitory substances.However, some authors have proposed that the declinein cell population, i.e. biomass decay, after completeconsumption of the substrate, should be taken intoaccount (Gnanapragasam et al., 2011; Tsuneda et al.,2002b). Therefore, after attaching the coefficient ofmicrobial decay, kd, in the expression of the Haldanemodel, the equation assumes the following form:

µ =µmaxS

KS + S + S2/Ki

− kd (9)

This is the modified Haldane model, subsequentlyreferred to as the Endo-Haldane model. This model is

frequently used because of its ability to account for theeffect of inhibition at high concentrations, and of celldeath and/or maintenance metabolism at low concen-trations. The inhibition constant corresponds to thehighest substrate concentration at which the specificgrowth rate equals one-half of the maximum specificgrowth rate without inhibition (Okpokwasili & Nweke,2005; Tsuneda et al., 2002b).

Results and discussion

Response of control variables

In a biological treatment process, sludge concen-tration is an important factor in ensuring the bio-logical treatment capacity. An adequate sludge con-centration ensures good performance in pollutant re-moval (Huang et al., 2001). MLSS is an indirectmeasure of sludge concentration which is commonlyused to characterise the biological mass in the ac-tivated sludge. MLVSS is a measure of the amountof volatile suspended solids found in a sample ofMLSS. Variations in the concentration of MLVSS in-dicate a change in the amount of biomass share.The presence of microorganisms plays an importantrole in the process of biodegradation (Vukovic et al.,2006).Table 1 demonstrates changes to the control

variables in the aerobic biodegradation process un-der batch conditions with different initial concentra-tions of leachate (S1–S5) and activated sludge over48 h. The average values of the concentration ofMLVSS/(g dm−3) ranged within the limits of 1.27–1.55, 2.50–2.81, and 4.25–4.69 for experiments A, B,and C, respectively. These results show that there wereno significant changes in the amount of viable sludgeover the whole experimental period for all the exper-iments, especially in experiments B and C. This canbe explained by a probable inhibition of the activatedsludge by toxic products generated during degradationof the leachate from the tobacco dust waste under thebatch conditions. The environmental factor that influ-ences the rates and limits microbial growth, hence theprocess of biodegradation, is the pH value. The pHvalue decreased slightly by increasing the initial con-centration of leachate and ranged within the optimallimits which correspond to the biological activity ofsludge (Bitton, 2005).In aerobic bioprocesses, control of the dissolved

oxygen level plays an important role. The dissolvedoxygen concentration in the activated sludge processshould be sufficiently high to supply enough oxygento microorganisms in the sludge for them to degradethe organic matter (Holenda et al., 2008). During theexperiments, the average values of DO concentration(Table 1) decreased with an increase in the initialconcentrations of leachate (COD). This is due to thefact that, at a higher initial concentration of leachate,


Table1.ResultsofcontrolvariablesintheaerobicbiodegradationprocessforallexperimentsfromexperimentsAtoC

MLVSS/(gdm

−3)

pH

DO/(mgdm

−3)

Exp.

S1

S2

S3

S4

S5

S1

S2

S3

S4

S5

S1

S2

S3

S4

S5

A1.27

±0.121.31

±0.171.36

±0.201.43

±0.191.55

±0.218.18

±0.118.03

±0.067.97

±0.067.82

±0.067.67

±0.035.94

±1.214.72

±1.143.89

±0.922.01

±0.741.05

±0.35

B2.73

±0.102.79

±0.162.81

±0.192.39

±0.112.50

±0.118.08

±0.138.00

±0.137.91

±0.157.76

±0.177.53

±0.205.03

±1.184.29

±0.983.41

±0.791.62

±0.680.98

±0.55

C4.38

±0.154.25

±0.164.54

±0.104.69

±0.184.56

±0.177.92

±0.247.89

±0.197.85

±0.187.77

±0.217.69

±0.244.17

±1.273.78

±1.192.69

±1.241.15

±1.100.51

±0.41

Fig. 1. COD removal from leachates: S1 (�), S2 ( ), S3 (�),S4 (•), and S5 (+) over 48 h and comparison of exper-imental data with values obtained using Monod model(· · ·) and Endo-Haldane model (—) for experiment A.

Fig. 2. COD removal from leachates: S1 (�), S2 ( ), S3 (�),S4 (•), and S5 (+) over 48 h and comparison of exper-imental data with values obtained using Monod model(· · ·) and Endo-Haldane model (—) for experiment B.

much more oxygen is needed for substrate degradation(Casey et al., 1997).

Model selection and model parameters estima-tion

From the values obtained for COD removal, kineticanalysis was performed using four different models.COD removal is one of the main indicators, which isused to assess the efficiency of the degradation processof the organic pollutant from the leachate. Figs. 1–3demonstrate that COD values decreased for each con-centration compared with the initial value of the un-treated leachate. The decrease in initial concentration


Fig. 3. COD removal from leachates: S1 (�), S2 ( ), S3 (�),S4 (•), and S5 (+) over 48 h and comparison of exper-imental data with values obtained using Monod model(· · ·) and Endo-Haldane model (—) for experiment C.

of the leachate resulted in an increase in biodegra-dation efficiency up to values of 73.6–82.7 %, 84.2–87.2 %, and 77.2–83.6 % for experiments A, B, and C,respectively.Figs. 1–3 show comparisons of the Monod and

Endo-Haldane model with experimental data. It isclear that the Endo-Haldane model gives a better cor-relation than the Monod model. The kinetic analysiswas performed using the traditional Monod model, themodified Monod model with endogenous metabolism,the Haldane model, and the Endo-Haldane modelwhich takes into consideration the effects of inhibi-tion and biomass decay. Due to mathematical similar-ity between the Monod and Haldane models, as wellas between the modified Monod model and the Endo-

Haldane model, Figs. 1–3 show only comparisons ofexperimental data with the values obtained using theMonod model and the Endo-Haldane model. Kineticparameters were estimated in all of the above modelsand for all experiments, A, B, and C.The kinetic parameters in the equations (Eqs. (6)–

(9)) were estimated using differential analysis and theNelder–Mead method of non-linear regression. Dif-ferential equations (Eqs. (6)–(9)) describing the sys-tem were solved numerically using the Runge–Kuttamethod. According to the procedure as described, Ta-bles 2–4 show the values calculated for kinetic param-eters evaluated in the kinetic models, as well as valuesobtained for the sum of the squares of residuals andfor the statistical Fisher–Snedecor F-test.

Model comparison

The criteria for choosing the objective functionwere the sum of squares of residuals and the Fisher–Snedecor F-test between the calculated and averageexperimental values of the COD (S). Values obtainedfor the sum of squares of residuals do not afford suf-ficient information because different models can havea different number of parameters (Zwietering et al.,1990). Therefore, data-fits obtained using the vari-ous models were compared statistically by use of theFisher–Snedecor F-test.The Fisher–Snedecor F-test renders it possible to

compare the experimental data with the values ob-tained using the different types of models referred toabove. The statistical F-test was conducted using theassumption that the null hypothesis is (Mendenhall,1964):

H0: s21 = s22 (10)

Table 2. Kinetic parameters evaluated for different initial concentrations of leachate from S1 to S5 in experiment A

µmax/h−1 KS/(mg dm−3) Ki · 102/(mg dm−3)Model

S1 S2 S3 S4 S5 S1 S2 S3 S4 S5 S1 S2 S3 S4 S5

Monod model 1.26 3.51 1.28 1.27 1.36 28.51 17.70 19.23 18.40 17.81 – – – – –Monod model with 1.84 3.42 3.50 2.01 4.22 24.16 23.72 32.15 18.85 28.25 – – – – –endogenous metabolismHaldane model 1.31 1.62 3.41 0.98 8.95 29.35 17.95 55.44 13.92 14.43 7.73 0.12 18.7 3.73 6.33Endo-Haldane model 3.82 6.17 1.59 2.29 8.09 50.40 41.55 13.05 19.41 55.96 8.39 0.23 5.34 2.90 11.15

kd · 102/h−1 S.E. · 102 F-testModel


Monod model – – – – – 2.63 3.52 2.77 2.28 3.47 0.60 0.59 0.66 0.75 0.57Monod model with 0.79 2.60 2.77 4.86 0.15 1.71 1.21 0.99 0.92 1.31 0.85 0.99 0.98 0.98 0.96endogenous metabolismHaldane model – – – – – 2.71 3.93 2.75 2.58 3.52 0.60 0.59 0.66 0.75 0.57Endo-Haldane model 0.78 2.65 3.13 5.53 14.7 1.70 1.18 0.97 0.85 1.36 0.87 0.97 0.98 0.98 0.95


Table 3. Kinetic parameters evaluated for different initial concentrations of leachate from S1 to S5 in experiment B







Table 4. Kinetic parameters evaluated for different initial concentrations of leachate from S1 to S5 in experiment C







and the alternative hypothesis is:

H1: s21 �= s22 (11)

where s21 and s22 are the variances we were comparing.The test statistic for comparing variances is given bythe equation (Bronstein et al., 2004):

F =s21s22

(12)

If the two functions have the same variances, thens21 and s22, variances of the samples derived from thesefunctions, are close in value and F is close to 1. On theother hand, if the variances are very different, then s21and s22 tend to be very different. Using the assump-tion that s21 is the smaller variance than s22, the F-testvalues ranged between 0 and 1. Therefore, if F is closeto 1, the evidence is in favour of the null hypothesisthat the two variances are equal.

From Tables 2–4 it can be seen that the mod-els given by Eqs. (7) and (9) describe the process ofthe degradation of organic matter from the leachatemuch better than the models defined by Eqs. (6) and(8). These models take into account both endogenousmetabolism and substrate inhibition. At the end of theprocess, there is a clear reduction in the amount of or-ganic matter and the activity of the aerobic microbes.One particular cell population becomes food for thehealthier one, which causes the changes in the micro-bial community and inhibition of the process. Also,the inhibition effect may be caused by a high initialconcentration of the leachate, because it is not possibleto predict the initial trend of substrate consumption.Therefore, the inhibition of substrate degradation, aswell as endogenous metabolism, should also be takeninto account.This may also be confirmed by the fact that the

presence of the microbial endogenous metabolism al-


lows the models to predict the constant substrate con-centration attained at the end of the process, whereasthe classical Monod model, which does not have thisterm, predicts zero concentration at the end of theprocess. The presence of endogenous metabolism andsubstrate inhibition the Endo-Haldane model enablesit to predict the initial trend of substrate consumptionrate in both high and low concentration experimentsand the constant substrate concentration attained atthe end of the process, hence this model gives the bestfit to the experimental data (Tsuneda et al., 2002b).The values of kinetic constants obtained in this

study, except for the values of µmax, are very closeto the values of kinetic coefficients reported in theliterature (Okpokwasili & Nweke, 2005; Tsuneda etal., 2002a). The maximum specific growth rate (µmax)should be in the range of 0.131–0.363 h−1 for mixedmicrobial cultures. The discrepancy in these valueshas been attributed to cell type and culture environ-ments (Nuhoglu & Yalcin, 2005). The values obtainedfor the kinetic constant for endogenous metabolismare similar to the value reported by Beltran de Here-dia et al. (2005) and Slezak et al. (2012). The high-est values obtained for KS, were in the range corre-sponding to typical values for the activated sludgeprocess (Al-Malack, 2006; Mardani et al., 2011). Thismeans that the microorganisms had a good affinity tosubstrate degradation (Okpokwasili & Nweke, 2005).The rate of substrate degradation is proportional tothe amount of MLVSS and, by increasing the initialamount of MLSS, the final conversion and rate of sub-strate degradation are increasing. The values of kineticconstants in Tables 2–4 are not significantly influencedby the initial values of MLSS.Results obtained statistically by the F-test and

standard error confirm that the Endo-Haldane modelbest fits the experimental data.

Conclusions

The analysis of the biodegradation of organic mat-ter present in leachate and comparison of the exper-imental values with the values obtained by four dif-ferential mathematical models were studied. COD re-moval efficiency rates ranging between 73.6–82.7 %,84.2–87.2 %, and 77.2–83.6 % were achieved after 48 hin experiments A, B, and C, respectively. These resultsconfirmed that the leachate was biologically treatablein all the experiments conducted. On the basis of thefit of experimental data to each model and the val-ues obtained for standard error, as well as the val-ues obtained by the statistic F-test, it was found thatthe Endo-Haldane model, which incorporated the in-hibition effect and endogenous metabolism, providedthe best description of the degradation process in allthe experimental trials. However, the Monod modelwith endogenous metabolism also described the pro-cess very well with negligible differences between the

values obtained for kinetic parameters, F-test, andstandard errors. The results of kinetic studies can beuseful in operating the existing activated sludge sys-tem efficiency and can be applied in novel design stud-ies for industrial purposes.

Acknowledgements. This work was financially supported bythe Ministry of Science, Education, and Sports of the Republicof Croatia under Research Project no.125-1251963-1968.

Symbols

COD chemical oxygen demand mg dm−3

F Fisher–Snedecor distributionKi inhibition constant mg dm−3

KS substrate saturation constant mg dm−3

kd endogenous metabolism kinetic constant h−1

MLSS mixed liquor suspended solids g dm−3

MLVSSmixed liquor volatile suspendedsolids g dm−3

rS substrate degradation rate mg dm−3 h−1

rX biomass growth rate g dm−3 h−1

S substrate concentration mg dm−3

s2 variancet time hX biomass concentration g dm−3

Y overall yield coefficient g g−1

µ specific growth rate of biomass h−1

µmax maximum specific growth rate h−1

References

Al-Malack, M. H. (2006). Determination of biokinetic coef-ficients of an immersed membrane bioreactor. Journal ofMembrane Science, 271, 47–58. DOI: 10.1016/j.memsci.2005.07.008.

APHA-AWWA-WEF (1999). Standard methods for the exami-nation of water and wastewater (20th ed.). Washington, DC,USA: American Public Health Association.

Bae, B. U., Jung, E. S., Kim, Y. R., & Shin, H. S. (1999). Treat-ment of landfill leachate using activated sludge process andelectron-beam radiation. Water Resources, 33, 2669–2673.DOI: 10.1016/s0043-1354(98)00488-6.

Beltran, J., Gonzalez, T., & Garcia, J. (2008). Kinetics of thebiodegradation of green table olive wastewaters by aerobicand anaerobic treatments. Journal of Hazardous Materials,154, 839–845. DOI: 10.1016/j.jhazmat.2007.10.102.

Beltran de Heredia, J., Torregrosa, J., Dominguez, J. R., &Partido, E. (2005). Degradation of wine distillery wastewa-ters by the combination of aerobic biological treatment withchemical oxidation by Fenton’s reagent. Water Science &Technology, 51(1), 167–174.

Bitton, G. (2005). Wastewater microbiology (3rd ed., pp. 211–259). Hoboken, NJ, USA: Wiley.

Briški, F., Kopčic, N., Cosic, I., Kučic, D., & Vukovic, M.(2012). Biodegradation of tobacco waste by composting: Ge-netic identification of nicotine-degrading bacteria and kineticanalysis of transformations in leachate. Chemical Papers, 66,1103–1110. DOI: 10.2478/s11696-012-0234-3.

Bronstein, I. N., Semendjajev, K. A., Musiol, G., & Muhlig, H.(2004). Mathematical handbook (pp. 764, 794–797). Zagreb,Croatia: Golden Marketing-Tehnička Knjiga. (in Croatian)


Casey, T. J. (1997). Unit treatment processes in water andwaste engineering. New York, NY, USA: Wiley.

Celis, E., Elefsiniotis, P., & Singhal, N. (2008). Biodegradationof agricultural herbicides in sequencing batch reactors underaerobic and anaerobic conditions. Water Research, 42, 3218–3224. DOI: 10.1016/j.watres.2008.04.008.

Derco, J., Černochová, L., Krcho, L., & Lalai, A. (2011). Dy-namic simulations of waste water treatment plant opera-tion. Chemical Papers, 65, 813–821, DOI: 10.2478/s11696-011-0076-4.

Dollerer, J., & Wilderer, P. A. (1996). Biological treatment ofleachates from hazardous waste landfills using SBBR tech-nology. Water Science and Technology, 34, 437–444. DOI:10.1016/s0273-1223(96)00776-7.

European Committee for Standardization (2002). Europeanstandard: Characterization of waste – Leaching – Compli-ance test for leaching of granular waste materials and sludges– Part 4: One-stage batch test at a liquid to solids ratio of 10l/kg for materials with particle size below 10 mm (withoutor with size reduction). EN 12457-4. Brussels, Belgium.

Gnanapragasam, G., Senthilkumar, M., Arutchelvan, V., Ve-layutham, T., & Nagarajan, S. (2011). Bio-kinetic analy-sis on treatment of textile dye wastewater using anaerobicbatch reactor. Bioresource Technology, 102, 627–632. DOI:10.1016/j.biortech.2010.08.012.

Holenda, B., Domokos, E., Rédey, A., & Fazakas, J. (2008).Dissolved oxygen control of the activated sludge wastew-ater treatment process using model predictive control.Computers & Chemical Engineering, 32, 1270–1278. DOI:10.1016/j.compchemeng.2007.06.008.

Huang, X., Gui, P., & Qian, Y. (2001). Effect of sludge re-tention time on microbial behaviour in a submerged mem-brane bioreactor. Process Biochemistry, 36, 1001–1006. DOI:10.1016/s0032-9592(01)00135-2.

Mardani, Sh., Mirbagheri, A., Amin., M. M., & Ghasemian, M.(2011). Determination of biokinetic coefficients for activatedsludge processes on municipal wastewater. Iranian Journal ofEnvironmental Health Science & Engineering, 8(1), 25–34.

Mendenhall, W. (1964). Introduction to statistics (pp. 37–38,160–165). Belmont, CA, USA: Wadsworth Publishing Com-pany.

Nuhoglu, A., & Yalcin, B. (2005). Modelling of phenol removalin a batch reactor. Process Biochemistry, 40, 1233–1239.DOI: 10.1016/j.procbio.2004.04.003.

Okpokwasili, G. C., & Nweke, C. O. (2005). Microbial growthand substrate utilization kinetics. African Journal of Bio-technology, 5, 305–317.

Piotrowska-Cyplik, A., Olejnik, A., Cyplik, P., Dach, J., &Czarnecki, Z. (2009). The kinetics of nicotine degradation,enzyme activities and genotoxic potential in the characteri-zation of tobacco waste composting. Bioresource Technology,100, 5037–5044. DOI: 10.1016/j.biortech.2009.05.053.

Renou, S., Givaudan, J. G., Poulain, S., Dirassouyan, F., &Moulin, P. (2008). Landfill leachate treatment: Review andopportunity. Journal of Hazardous Materials, 150, 468–493.DOI: 10.1016/j.jhazmat.2007.09.077.

Slezak, R., Krzystek, L., & Ledakowicz, S. (2012). Mathemat-ical model of aerobic stabilization of old landfills. ChemicalPapers, 66, 543–549. DOI: 10.2478/s11696-012-0133-7.

Sponza, D. T. (2001). Toxicity studies in a tobacco industrybiological treatment plant.Water, Air, & Soil Pollution, 134,137–164. DOI: 10.1023/a:1014111616875.

Tsuneda, S., Auresenia, J., Inoue, Y., Hashimoto, Y., & Hirata,A. (2002a). Kinetic model for dynamic response of three-phase fluidized bed biofilm reactor for wastewater treat-ment. Biochemical Engineering Journal, 10, 31–37. DOI:10.1016/s1369-703x(01)00152-8.

Tsuneda, S., Auresenia, J., Morise, T., & Hirata, A. (2002b).Dynamic modeling and simulation of a three-phase fluidizedbed batch process for wastewater treatment. Process Bio-chemistry, 38, 599–604. DOI: 10.1016/s0032-9592(02)00184-x.

Tyrrel, S. F., Seymour, I., & Harris, J. A. (2008). Bioremedi-ation of leachate from a green waste composting facility us-ing waste-derived filter media. Bioresource Technology, 99,7657–7664. DOI: 10.1016/j.biortech.2008.01.079.

Veli, S., Ozturk, T., & Dimoglo, A. (2008). Treatment of munic-ipal solid wastes leachate by means of chemical- and electro-coagulation. Separation and Purification Technology, 6, 82–88. DOI: 10.1016/j.seppur.2007.09.026.

Vukovic, M., Briški, F., Matošic, M., & Mijatovic, I. (2006).Analysis of the activated sludge process in an MBR understarvation conditions. Chemical Engineering & Technology,29, 357–363. DOI: 10.1002/ceat.200500314.

Wang, S. N., Xu, P., Tang, H. Z., Meng, J., Liu, X. L., Huang,J., Chen, H., Du, Y., & Blankespoor, H. D. (2004). Biodegra-dation and detoxification of nicotine in tobacco solid wasteby a Pseudomonas sp. Biotechnology Letters, 26, 1493–1496.DOI: 10.1023/b:bile.0000044450.16235.65.

Wang, M. Z., Yang, G. Q., Min, H., Lv, Z. M., & Jia, X. Y.(2009). Bioaugmentation with the nicotine-degrading bac-terium Pseudomonas sp. HF-1 in a sequencing batch reactortreating tobacco wastewater: Degradation study and analy-sis of its mechanisms. Water Research, 43, 4187–4196. DOI:10.1016/j.watres.2009.07.012.

Zhong, W. H., Zhu, C. J., Shu, M., Sun, K. D., Zhao, L., Wang,C., Ye, Z. J., & Chen, J. M. (2010). Degradation of nico-tine in tobacco waste extract by newly isolated Pseudomonassp. ZUTSKD. Bioresource Technology, 101, 6935–6941. DOI:10.1016/j.biortech.2010.03.142.

Zwietering, M. H., Jongenburger, I., Rombouts, M., & van’tRiet, K. (1990). Modeling of the bacterial growth curve. Ap-plied and Environmental Microbiology, 56, 1875–1881.

modelling of kinetics of microbial degradation of simulated leachate from tobacco dust waste

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