COD fractionation of tannery wastewaters—Particle size distribution, biodegradability and modeling

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COD fractionation of tannery wastewaters—Particle size distribution, biodegradability and modeling O¨. Karahana,, S. Dogruela, E. Dulekgurgena, D. Orhonb a

Environmental Engineering Department, Faculty of Civil Engineering, Istanbul Technical University, TR-34469 Maslak, Istanbul, Turkey Turkish Academy of Sciences, Piyade Sokak No. 27, 06550, C - ankaya, Ankara, Turkey

b

art i cle info

ab st rac t

Article history:

This study aims to establish the scientific link between particle size distribution (PSD) and

Received 18 December 2006

biodegradability of different COD fractions of tannery wastewater, by means of sequential

Received in revised form

filtration/ultrafiltration, respirometric analysis and model evaluation. PSD profiles were

24 September 2007

determined in physical segregation experiments, using eight membrane discs, each with

Accepted 1 October 2007

different pore sizes between 2 and 1600 nm. Biodegradability-related COD fractionation was

Available online 6 October 2007

determined at each size interval by model simulation and calibration of the corresponding

Keywords: ASM3 COD fractions Modeling Oxygen uptake rate Size distribution Tannery wastewater

oxygen uptake rate (OUR) profiles. Activated Sludge Model No. 3 (ASM3), modified for direct growth on hydrolysis products, was adopted for evaluation. PSD analyses defined a COD fingerprint with two significant portions at the two ends of size distribution, with 60% of the total COD at the particulate range, 25% at the soluble range and the remaining 15% well distributed among the colloidal range. Comparative evaluation of the sequence of OUR profiles yielded values of applicable model coefficients. It also enabled the assessment of size distribution for each major COD fraction, as an original tool for better interpretation of specific biodegradation characteristics of the selected tannery wastewater. Results also revealed a very slowly biodegradable/residual particulate COD component with a significant inhibitory effect. Model-based evaluation of the OUR profiles enabled quantifying the impact of inhibition in terms of changes in rate coefficients for growth, hydrolysis of soluble COD and endogenous decay. & 2007 Elsevier Ltd. All rights reserved.

1.

Introduction

Tannery effluent is a strong wastewater with complex characteristics. It is associated with a high level of organic pollutants commonly characterized with chemical oxygen demand (COD) concentrations above 3000 mg L1, together with significant amounts of inorganic compounds such as trivalent chromium and sulfide, capable of exerting toxic or inhibitory effects on biological treatment (Kabdasli et al., 1993; Orhon et al., 1999a; Carucci et al., 1999). Stringent effluent limitations inevitably prescribe biological treatment for this level of organic content. Consequently, achievement Corresponding author. Tel.: +90 212 285 65 40; fax: +90 212 286 7913.

E-mail address: [email protected] (O. Karahan). 0043-1354/$ - see front matter & 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2007.10.001

of effective removal requires elaborate evaluation of biodegradation characteristics and possible inhibitory effects imposed on the degradation process due to the nature of tannery wastewaters. COD is a useful collective (lump) design parameter reflecting the total organic content (CT) of a wastewater. Yet, it has inherent deficiencies when used alone as it covers not only biodegradable organics but also biologically resistant and refractory compounds and provides no information on significant organic fractions with different biodegradation kinetics. This missing information is apparently vital for the tannery wastewater which embodies a wide spectrum

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of various organics. Previous studies have reported that tannery wastewaters include total biodegradable COD portions (CS) comprising different fractions with markedly distinct biodegradation rates and inert COD fractions play an important role on the extent of treatment performance (Ates et al., 1997; Orhon et al., 1998, 1999b). The recognition of different soluble and particulate COD fractions not only provided a major breakthrough in the conceptual understanding and modeling of activated sludge (Henze et al., 1987) but also introduced respirometry as the main instrument for assessing COD fractions as an integral part of wastewater characterization and modeling (Spanjers and Vanrolleghem, 1995). While the interpretation of the oxygen uptake rate (OUR) profiles enabled distinction of readily and slowly biodegradable COD fractions (Dold et al., 1980; Ekama et al., 1986), and assessment of major model coefficients (Kappeler and Gujer, 1992; Avcioglu et al., 1998), COD fractionation has been primarily based on a single size, that is, the routine filtration size (1600 or 450 nm) used for differentiation of particulate and soluble COD components. Introduction of COD fractions with rapid and slow hydrolysis rates, although giving a clearer picture of the biodegradation profile within the soluble (filterable) range (Henze, 1992; Orhon et al., 1999c), did not include any additional information about particle sizes involved. Research effort has recently been directed towards particle size information for a better understanding of COD fractionation and related biodegradation patterns (Sophonsiri and Morgenroth, 2004; Dogruel et al., 2006; Dulekgurgen et al., 2006). Combining physical particle size distribution of COD by means of sequential filtration/ultrafiltration with parallel OUR experiments and model evaluation is the next step for a better interpretation of COD fractions and biodegradability. Integration of these two approaches would also provide useful information for optimization of available treatment techniques indicating which size fraction is more important within the overall organic content and how the biodegradation characteristics of COD fractions differ in terms of treatment requirements. In this framework, the objective of the study was to determine the size distribution profiles of major COD fractions in tannery wastewater. This goal was accomplished by means of sequential filtration/ultrafiltration, together with parallel respirometry and modeling for exploring the relationship between physical segregation and biodegradability. Accordingly, the raw wastewater and the permeates from each sequential filtration/ultrafiltration step were subjected to respirometric tests and the resulting OUR profiles were evaluated by model simulations to assess the respective COD fractions and biodegradation characteristics. Tannery wastewater was selected mainly because it has been well studied and intensely utilized for testing and exploring new mechanisms such as dual hydrolysis phenomenon (Orhon et al., 1999b). In this study, the complexity of the tannery wastewater also required modification of the basic structure of Activated Sludge Model No. 3 (ASM3) for better interpretation and calibration of the OUR profiles.

2.

Materials and methods

2.1.

Study area

The tannery wastewater investigated in this study was taken from Corlu Leather Tanning Industrial District, located at the western part of Turkey. The district houses more than 100 large tanneries with a production capacity representing a major fraction of the sheepskin processing in Turkey.

2.2.

Conventional characterization

Conventional characterization of the raw tannery wastewater was carried out on a grab sample, collected from the equalization tank of the WWTP of the above-mentioned industrial district. The relevant analyses were performed in duplicate in accordance with the Standard Methods (APHA et al., 1998). COD, which was the key parameter in this study, was also measured in duplicate according to the International Standard ISO 6060 (International Organization for Standardization, 1986). For spectrophotometric color determination, optical density values after filtering the raw wastewater through 450 nm membrane (at original pH) were recorded at three different dominant wavelengths of 436, 525 and 620 nm, in accordance with the German Legislation (Anhang 38, 1993, updated online in 2004). Total suspended solids (TSS) and volatile suspended solids (VSS) measurements were conducted after filtering the sample from Millipore AP40 glass fiber filters with an effective pore size of approximately 1200–1600 nm.

2.3.

Sequential filtration/ultrafiltration

A continuously stirred cell with a volumetric capacity of 400 mL (Amicon, Model 8400) was used as the filtration/ ultrafiltration unit, and was operated under positive pressure (0.6–1.2 atm; N2 as the inert gas) during the experiments. Operational parameters, such as temperature and working pressure, applied during the sequential filtration/ultrafiltration experiments were the same as those reported in a previous study by Dulekgurgen et al. (2006) and within the ranges recommended by the manufacturers. No pH adjustment was needed since the original pH of the raw wastewater (Table 1) was within the range applicable for sequential filtration/ultrafiltration experiments (Dulekgurgen et al., 2006). Conventional disposable filters with pore sizes of 1200–1600 nm (Millipore AP40, glass fiber), 450 nm (Durapores HV, polyvinylidene fluoride (PVDF)), and 220 nm (Durapores GV, PVDF) (Millipore Corp., Bedford, MA 01730) were used for sequential filtration. Permeate from the final filtration step was successively passed through ultrafiltration membrane discs with nominal molecular weight cut-off (MWCO) values of 100, 30, 10, 3, and 1 kDa (PL series, Millipore, MA). In order to provide consistency among the different size units of ultrafiltration and filtration, the nominal MWCO values given in kDa units were approximated to the corresponding particle size values defined in nm, by using the approach described by Cheryan (1986) and McGregor (1986). No interference due to interaction between the filters and the tannery wastewater

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Table 1 – Conventional wastewater characterization Parameters

COD total (mg L1) COD filtered through 1600 nm (mg L1) COD filtered through 450 nm (mg L1) TSS (mg L1) VSS (mg L1) VSS/SS (%) TKN (mg L1) NH3-N (mg L1) TP (mg L1) pH Alkalinity (mg CaCO3 L1) Chloride (mg Cl L1) Color (filtered through 450 nm) l ¼ 436 nm l ¼ 525 nm l ¼ 620 nm

Ates et al. (1997)

This study

Mean

Range

4947 1770

2513–8781 1284–3125

3100 1240

–

–

1195

2239 1131 51 214 95 9 8.4 665 7601

1000–4740 650–1540 208–220 56–136 3–22 6.4–10.1 259–1132 6150–9060

1940 1135 59 130 54 13 8.3 1010 4150

– – –

– – –

0.178 0.123 0.090

was expected since the filter materials were chemically compatible with a wide range of solvents and were reported to have no adsorptive capacity for soluble organics. To prevent fouling and other similar effects, the procedures recommended by the manufacturer for cleaning, testing and conditioning of the stirred-cell and ultrafiltration membrane discs were performed. Moreover, to avoid any undesired filtering effect due to formation of a cake on top of the filtering media and to avoid premature polarization, the height of the vortex formed during mixing was kept at 1/3rd of the total height of the sample column at all times, as recommended by the manufacturer. The special design of the magnetic stirrer enabled continuous mixing in suspension without touching the filter membranes/discs. In addition to 100 mL permeate for COD measurements in duplicate, approximately 500 mL permeate of each filtering step was secured for respirometric tests. Thus, filtration was started with an initial volume of 5 L raw wastewater. For a better interpretation of the results, the particle size values were grouped into operational size categories being particulate (remaining above AP40 glass fiber filter, including the settleable (4105 nm) and most of the supracolloidal (103–105 nm) substances), colloidal (in the range of 2 nm (1 kDa membrane) to 1600 nm (AP40 glass fiber filter)) and soluble (filtered through 1 kDa ultrafiltration membrane (o2 nm)) fractions.

2.4.

Respirometry and modeling

A series of respirometric analyses were conducted with the raw tannery wastewater and with each aliquot collected from consecutive filtration/ultrafiltration steps. To secure the nutritional requirements and to provide the necessary buffer capacity during the respirometric tests, two nutrient solu-

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tions were added right before the start of respirometric test in each case. The nutrient solutions were prepared in accordance to the recipes given by O’Connor (1972), and 10 mL from each was added per 1000 mg COD present in the sample. Nitrification inhibitor (Formula 2533–35, Hach Company) was added in each case (0.16 g per 300 mL), in order to prevent any interference from the nitrification process to the OUR assessments. Respirometric tests were performed using a PC-connected respirometer (Manotherm RA-1000). The respirometer was inline with a 3-L aerated reactor and a 750-mL measurement chamber, where the dissolved oxygen consumption in the mixed liquor was continuously monitored. The aerated reactor was seeded with biomass obtained from the WWTP, which was also sampled for the raw tannery wastewater investigated in this study. The endogenous respiration level of the sludge was determined at the beginning of each respirometric test. The amount of wastewater/filtrate added was adjusted such that an average initial food-to-microorganism (F/M) ratio of 0.15 gCOD (gCOD)1 was obtained in all runs. All the experiments were carried out at a constant temperature of 20 1C and aeration was supplied continuously to maintain a dissolved oxygen concentration above 6 mg L1, at all times. OUR data were collected online. pH was 7.0–8.0 during all experiments owing to the buffering capacity provided by the addition of the nutrient solutions. The readily biodegradable COD (SS), like all other biodegradable COD fractions, is calculated using the amount of oxygen utilized, in other words the area under the OUR curve was used for the estimation of biodegradable COD. The procedure to define the readily biodegradable COD (SS) using respirometry was first suggested by Ekama et al. (1986). Its reliability was tested extensively and the procedure was improved by Cokgor et al. (1998). OUR data obtained for raw wastewater and filtrates were used in the modeling studies for determination of the COD fractions and the kinetic and stoichiometric coefficients of the applied mechanistic model. Aquasim software (Reichert et al., 1998) was used to perform the model simulations.

3.

Results and discussion

3.1.

Conventional characterization

Conventional characterization results for raw wastewater taken from the equalization tank of the WWTP serving the Corlu Leather Tanning Industrial District is given in Table 1, together with the mean values and the ranges determined in a previous study (Ates et al., 1997), in which the same wastewater source was monitored for more than a year. As can be seen from the table, the investigated wastewater might be classified as a strong wastewater in terms of total COD content. Other parameters with significant values are nitrogen species, alkalinity, chloride and pH. The sampled wastewater is determined to be deficient in total phosphorus, which is a typical trait for tannery wastewaters. Values of selected parameters determined in this study fall within the range of the general characteristics outlined by the abovementioned, long-term survey, though they are closer to the lower end. This difference might be attributed to the nature

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and timing of sampling in this study (grab sampling during wet season), as well as to the seasonal process variations.

3.2.

PSD-based COD fractionation

No quiescent settling was applied prior to the experiments, thus the total COD value (3100 mg L1), reflects the sum of settleable-, supracolloidal-, colloidal- and soluble-COD fractions. The COD values measured at the aliquots collected after each filtration step can be addressed as cumulative values since each of them corresponds to the total COD below the designated filter size. The difference between two successively measured COD values is defined as differential COD and corresponds to the COD content originating only from the size range specified by the two consecutive filter sizes. PSD-based COD fractionation of the raw tannery wastewater, in terms of differential COD values, is visually presented in Fig. 1. As apparent from the figure, the investigated wastewater has a fairly simple COD fingerprint with two significant portions at the two ends of size distribution—the particulate and the soluble ranges. 60% of the total COD originates from the particulate fraction, while 25% comes from the soluble portion. The remaining 15% is well distributed among the colloidal range. This fingerprint is predictable, since raw tannery wastewaters are generally characterized with high particulate matter (contributing to particulate COD fraction) and high soluble organic content (contributing to soluble COD fraction), both originating from the raw organic materials (sheepskin, etc.) processed during production.

have a soluble rapidly hydrolysable COD fraction (SH) and a particulate slowly hydrolysable COD fraction (XS) (Orhon et al., 1999b, c). As an initial step, model simulation and calibration of the OUR profiles were carried out with ASM3 proposed by Gujer et al. (2000) and with the simultaneous storage and direct growth model proposed by Karahan et al. (2006). The simulation and calibration exercise was first started with models previously suggested in the literature, i.e., ASM3 and simultaneous storage

ASM3-The original model 45 OURdata

40

OUR (mg L-1 h-1)

1086

35

OURmodel.total

30

OURend.dec.

25

OURstorage

20

OURend.resp.Xsto

15

OURgrowthXsto

10 5 0 0

2

4

6

Time (h)

3.3.

Respirometric analyses and modeling studies

3.3.1.

Conceptual framework for modeling

ASM3-including simultaneous direct growth on and storage of SS 45 OURdata

40

OURmodel.total

35 OUR (mg L-1 h-1)

Tannery wastewaters have been characterized to include a readily biodegradable COD fraction (SS), which contains a considerable amount of volatile fatty acids (VFAs). VFAs can be stored as poly-hydroxy-alkanoates by bacteria under dynamic conditions and recent studies have shown that substrate storage phenomena play an important role on the biochemical transformations involved in the biological treatment of tannery effluents (Dizdaroglu-Risvanoglu et al., 2007). It has been previously stated that tannery wastewaters also

OURend.dec.

30

OURstorage

25

OURend.resp.Xsto 20 OURgrowthXsto 15 OURgrowthSS

10 5 2000

0

Differential COD (mg L-1)

0

2

4

6

Time (h) 1500

1000

500

0 <2

nm

nm

nm

nm

nm

nm

0

nm

20

45

00

nm

16

0

nm

3

2-

5

3-

8

5-

-2

0-

0-

60

13

8-

13

22

45

>1

Fig. 1 – COD fingerprint of the raw tannery wastewater.

Fig. 2 – Simulation results for tannery wastewater (filtered through 450 nm) with (a) the original ASM3 model and (b) simultaneous storage and direct growth model (Karahan et al., 2006). OURdata: Experimental OUR data; OURmodel.total: Total OUR estimated with the corresponding model; OURend.dec: OUR estimated for endogenous decay; OURstorage: OUR estimated for storage; OURend.respXsto: OUR estimated for endogenous respiration of stored polymers; OURgrowthXsto: OUR estimated for growth on stored polymers; OURgrowthSs: OUR estimated for direct growth on readily biodegradable substrate.

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and direct growth model proposed by Karahan et al. (2006). The calibration approach involved the use of calculated COD fractions using the process yields reported by DizdarogluRisvanoglu et al. (2007) for a similar tannery wastewater from a different district. Model calibrations were carried out in an iterative manner, i.e. a change in one parameter for the best fit of a particular OUR curve was applied to the simulations performed for all six different OUR profiles. However, the results of the best fits obtained with both models were not able to describe the observed OUR curves with two local maxima, indicating two distinct biochemical processes (Fig. 2). Although it was not the initial intention of this study, an amended mechanistic model was designed by necessity, in order to simulate the biodegradation of different COD fractions of the tannery effluent under investigation. The matrix presentation of the amended model is given in Table 2. The schematic representation of the conversions of different COD fraction in view of the proposed model structure is given in Fig. 3. The first row in the table includes the model components and the last column defines the rates for each

process involved in the model. The amended model suggests that the readily biodegradable COD fraction (SS) is utilized to be converted to storage polymers (XSTO). It is assumed that SS fraction is first converted to the storage polymers, and growth on these polymers starts after the total depletion of all readily biodegradable COD in the bulk solution. This sequence is obtained with the use of the switch function KS/(KS+SS) in the process rate expression of growth on XSTO as given in Table 2. The model also involves two hydrolysis processes for rapidly and slowly hydrolysable COD fractions (SH and XS). Hydrolysis products of both processes are labeled as hydrolyzed easily biodegradable COD (SShyd) and this portion of COD is directly used for biomass growth according to the model. This assumption is acceptable based on the fact that hydrolysis processes are rate-limiting processes and generate easily utilizable products at moderate rates, which cause the activated sludge culture to establish a balanced growth mechanism (Karahan et al., 2006). The endogenous decay of heterotrophic biomass and the decay of storage polymers in the model are defined as similar to those proposed in ASM3 (Gujer et al., 2000).

Table 2 – Matrix presentation of the amended model for degradation of tannery wastewater Component process

SO O2

SI COD

SS COD

Rapid hydrolysis Slow hydrolysis Aerobic storage of SS Direct growth on SShyd Growth on XSTO Endogenous respiration Respiration of XSTO



ð1YSTO Þ YSTO

SShyd COD

XI COD

1

1

kSH

1

ðXS =XH Þ XH kXS ½K þðX XS S =XH Þ

1

XH COD

1 1

1/YH1

1 fSI

XSTO COD

Rate

1

ð1YH2 Þ YH2

(1fSIfI)

XS COD

1/YSTO

ð1Y Þ  Y H1 H1



SH COD

fI

1/YH2

1

ðSH =XH Þ

½KSH þðSH =XH Þ

XH

SS SO XH S þSS KO þSO SShyd SO mH1 K XH Shyd þSShyd KO þSO

kSTO K

mH2 ½K

ðXSTO =XH Þ SO KS STO þðXSTO =XH Þ KO þSO KS þSS SO bH K þS XH O O

1

Fig. 3 – Schematic representation for conversion processes of different COD fractions.

bSTO K

SO O þSO

XSTO

XH

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As shown in Fig. 4, this approach enables successful model simulation of the two distinctive OUR responses obtained in the system.

3.3.2.

aliquot from 2 nm filter and was carried out for bigger pore sizes considering that the same COD fractions have to be present in those permeates. The obtained COD fractions were then used as data for model simulations, which gave consistent results with the same parameter set for all six OUR profiles. Thus, the stoichiometric estimations of COD fractions were justified by model simulations. The readily biodegradable fraction (SS) of tannery effluent was found as 230 mg L1 corresponding to 7% of the total COD. The rapidly hydrolysable fraction (SH) was 615 mg L1 (20%), and the soluble inert fraction (SI) was 395 mg L1 (13%). The value of soluble inert COD (SI) was found to be 13% of total COD and this result is in agreement with the reported range of 9–14% (Orhon et al., 1999d).

Process stoichiometry and kinetics for tannery effluents

Values for storage yield (YSTO), heterotrophic yield for direct growth (YH1), and secondary heterotrophic growth yield on stored material (YH2) used for simulations in this study were chosen as 0.83, 0.68, and 0.79 gCOD (gCOD)1, respectively. Instead of using default ASM3 values, the above-mentioned stoichiometric values were adopted from the study by Dizdaroglu-Risvanoglu et al. (2007), which was conducted on tannery effluents from a different source (Tuzla Organized District of Tannery Industries). Model calibration results for raw wastewater and filtered samples are summarized in Table 3. Values of the kinetic coefficients obtained by model simulations given in Table 3 also agree with those previously reported for tannery wastewaters by Dizdaroglu-Risvanoglu et al. (2007). The simulation results for six respirometric tests conducted with wastewater samples obtained from successive filtration/ultrafiltration are shown in Fig. 5. The simulation result for the wastewater sample filtered through 450 nm was already given in Fig. 4.

3.3.3.

Table 3 – Kinetic coefficients determined for raw and filtered wastewater samples Kinetic coefficients bH bSTO kSTO KS kSH KSH kXS KXS mH1 KShyd mH2 KSTO

COD fractions based on PSD

Model simulation and calibration of OUR profiles require correct assessment of the COD fractions as respective model components. Accordingly, the overall COD fractions estimated by combining physical segregation based PSD and biodegradability based modeling studies are given in Table 4. This estimation procedure involves the calculation of biodegradable COD for each OUR profile obtained for each permeate, as described by Karahan et al. (2002). The procedure was started by calculating the COD fractions for

Units

Filtered samples

Raw sample

d1 d1 d1 mg COD L1 d1 gCOD (gCOD)1 d1 gCOD (gCOD)1 d1 mg COD L1 d1 gCOD (gCOD)1

0.10 0.10 3 12 2 0.01 – – 2 20 2 0.2

0.08 0.08 3 12 1.8 0.01 1.7 0.05 1.8 20 2 0.2

ASM3-including dual hydrolysis, storage of SS and direct growth on hydrolysis products 45 OURdata

OUR (mg L-1 h-1)

40

OURmodel.total

35

OURend.dec.

30

OURstorage OURend.resp.Xsto

25

OURgrowthXsto

20

OURgrowthSShyd

15 10 5 0 0

2

4

6

8

10

12

14

Time (h) Fig. 4 – Simulation results for tannery wastewater (filtered through 450 nm) with ASM3 amended to include dual hydrolysis and direct growth on hydrolysis products. OURdata: Experimental OUR data; OURmodel.total: Total OUR estimated with the adopted model; OURend.dec: OUR estimated for endogenous decay; OURstorage: OUR estimated for storage; OURend.respXsto: OUR estimated for endogenous respiration of stored polymers; OURgrowthXsto: OUR estimated for growth on stored polymers; OURgrowthSshyd: OUR estimated for direct growth on hydrolyzed slowly biodegradable substrate.

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Part icle Size < 3 nm

Part icle Size < 5 nm

30

30 OURdata

OURdata 25

OURmodel OUR (mg L -1 h -1)

OUR (mg L -1 h -1)

25 20 15 10 5

OURmodel

20 15 10 5

0

0 0

2

4

6

8

10

12

14

0

2

4

6

8

10

Part icle Size < 8 nm

14

Part icle Size < 13 nm 35

30 OURdata 25

OURdata 30

OURmodel OUR (mg L -1 h -1)

OUR (mg L -1 h -1)

12

Ti me (h)

Ti me (h)

20 15 10 5

OURmodel

25 20 15 10 5

0

0 0

2

4

8

6

10

12

0

14

2

4

6

Ti me (h)

8

10

12

14

Ti me (h)

Part icle Size < 1600 nm 40 OURdata

OUR (mg L -1 h -1)

35

OURmodel

30 25 20 15 10 5 0 0

2

4

6

8

10

12

14

Ti me (h)

Fig. 5 – Simulation results obtained for filtrates collected after each filtration/ultrafiltration step.

Calibration of the OUR profiles of the filtrates after each filtration/ultrafiltration step provided, as given in Table 4, related COD fractionation for all major soluble components. The value of each COD component also indicated the cumulative level associated with all the lower sizes. This way, the adopted approach enabled verification of applicable COD fractionation by model simulation and yielded a particle size distribution of all major COD fractions. The confidence intervals related to the calculation of COD fractions were determined by the experimental errors involved in COD measurements (75 mgCOD L1) and those of OUR measurements (70.5 mg OUR L1 h1). In this context, an overall error

of 75% was predicted to be involved in the calculation of biodegradable COD fractions, corresponding to a maximum error of 15 mgCOD L1 for SS, 30 mgCOD L1 for SH and 15 mgCOD L1 for XS, which would be acceptable in terms of the scope of this study. Both COD fractions and modeling were performed using the same yield coefficients. As given in Table 4, the COD fraction referred to as the readily biodegradable fraction (SS) was entirely at the soluble range (o2 nm). The majority (83%) of the COD portion defined as the rapidly hydrolysable fraction (SH) and accepted as soluble in the model was actually at the soluble range, and only a minor portion (17%) of SH was at the colloidal range

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220 5 95 25 65 780 105 0 0 0 0 740 0 0 0 0 0 0 0 0 0 0 0 0 115 5 95 25 65 40 105 0 0 0 0 510 0 0 0 0 0 230 13–220 8–13 5–8 3–5 2–3 o2 970 965 870 845 780 740 740 740 740 740 0 0 0 0 0 0 0 0 0 0 230 230 230 230 230 13 8 5 3 2

CT: COD measured after each filtration step (cumulative values). CT: differential COD values (at each size category) seen in Fig. 1. b

a

41600 450–1600 220–450 1240 1195 1190 845 845 845 0 0 0 0 0 0 395 350 345 615 615 615 230 230 230 1200–1600 450 220

3100 1085 1620 240 395 615

Total Filtration AP40 filter HV filter GV filter Ultrafiltration 100 kDa 30 kDa 10 kDa 3 kDa 1 kDa

230

230 225 130 105 42

240 0 0 0 45 5 0 0 0 0 0 0

SS CTa CS XI XS SI SH SS

510 510 510 510 510

240 0 0 1620 0 0

CS XI XS SI SH

Differential COD (mg L1) Size category (nm) Cumulative COD (mg L1) Particle size (nm) Separation technique

Table 4 – Size distribution of the COD fractions of the tannery wastewater obtained by sequential filtration/ultrafiltration, respirometric analyses and modeling

CTb

1860 45 5

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(13–220 nm). Interestingly, only 11% of the COD fraction addressed as the soluble inert fraction (SI) in the model was at the soluble range, whereas the rest (89%) was determined to be distributed over the entire colloidal range (2–1600 nm). The particle size distribution mentioned above is found to be quite wastewater specific, as it differs, for example, from that of a textile wastewater where the majority of SI was located below 2 nm (Dulekgurgen et al., 2006). The area under the OUR curve obtained in the respirometric test conducted with raw tannery wastewater (Fig. 6) can be used to estimate the total amount of biodegradable substrate (CS) utilized during the test. The evaluation initially presumed that all biodegradable substrate was consumed during the test. The amount of dissolved oxygen consumed was relatively low and could only be justified with an overall yield coefficient of 0.79 gCOD (gCOD)1. However, the repeated evaluation indicated that around 20–22% of the slowly biodegradable COD remained unutilized after the test readjusting the yield coefficient to 0.64–0.66 gCOD (gCOD)1, which compares well with the adopted values for other tests. Accordingly, the CS fraction was estimated as 1085 mg L1 with an average net yield value of 0.79 gCOD (gCOD)1, the slowly hydrolysable COD fraction (XS) was found as 240 mg L1, and the particulate inert fraction (XI) as 1620 mg L1. In the literature, XI was reported to range between 7% and 19% of the initial total COD content of tannery wastewater samples (Orhon et al., 1999d). Comparing the values for XI and XS obtained via modeling in this study to those given in the literature, it is apparent that biodegradation of XS was hindered, and a significant portion of that COD fraction appeared to be inert. Calibration of the model using the respirometric data of the raw wastewater also revealed rapid hydrolysis and direct growth rates being 10% lower than the values obtained for the filtered portions (Table 3). The endogenous decay of heterotrophic biomass and stored polymers were also 20% slower for the biomass fed with raw wastewater sample. In fact, a 10–20% inhibition is, in general, not so important, but when reflected on the OUR profile as illustrated in Fig. 6, it induces a significant difference which may be interpreted in terms of rates of different processes. These results showed that the particulate portion of the tannery effluent imposed inhibitory effects on the biochemical reactions and confirmed the use of respirometry as the most useful tool for the assessment of inhibitory effects as suggested in previous studies (Insel et al., 2006). This inhibition was observed as a decrease in the amount of biodegradable COD (CS), namely only 20% of the slowly hydrolysable COD (XS) could be degraded during the test. Inhibition was also detected as a decrease in the rates of hydrolysis, growth and decay related processes, indicating a non-competitive inhibition where a portion of the available enzymes is irreversibly blocked by the inhibitor. This inhibition, among other possible complex organic compounds, can be mostly attributed to the presence of trivalent chromium, which is reported to be of particulate nature, probably entrapped in the particulate organic matter (Ates et al., 1997). These inhibitory effects can clearly be seen in Fig. 6, where OUR responses of the system were simulated for both the real case, when the process is inhibited, and for the

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Verification of Inhibition via Modeling 45 OURdata

40

OUR (mg L -1 h -1)

35

OURmodel (inhibited) OURmodel (no inhibition)

30 25 20 15 10 5 0 0

2

4

6

8

10

12

14

Time (h) Fig. 6 – Simulation result obtained for raw wastewater.

non-inhibitory conditions, when no inhibition was imposed on the system, namely using the same set of parameters obtained for the filtered effluents. The results on the raw wastewater have been included in this study for comparison only and due to the merit of the filtration/ultrafiltration, which practically removes and eliminates inhibitors beyond a given size range, general biodegradation kinetics could be applied.

4.

Conclusions

and therefore removable by physical entrapment and adsorption. (iv) Joint evaluation of particle size distribution and respirometric analysis of COD fractions for significant size intervals offers a significant potential, as demonstrated for tannery wastewater in this study, for providing a new insight for the biodegradation pattern of complex wastewaters. Similar studies are recommended for different wastewaters and also on process effluents, particularly for the assessment and particle size distribution of residual soluble microbial products.

In the light of the results presented in the preceding sections, the concluding remarks of the study may be outlined as follows:

R E F E R E N C E S

(i) The particle size analysis yielded a specific COD fingerprint for the tannery wastewater, with two significant portions at the two ends of size distribution: 60% of the total COD was at the particulate range, 25% was at the soluble range and the remaining 15% was well distributed among the colloidal range. (ii) The results also showed that respirometry could be used as a useful complement of particle size distribution by ultrafiltration for the assessment of biodegradation characteristics of COD fractions within each size interval. The OUR profiles associated with different size ranges between 2 and 1600 nm could be calibrated with the same set of stoichiometric and kinetic coefficients. Calibration also verified and confirmed the validity of the amended model defined as a modified version of ASM3, including direct growth on hydrolysis products. (iii) The combined analysis of filtration/ultrafiltration and OUR modeling yielded not only the particle size distribution of all major COD fractions, but also the extent of their biodegradability. In particular, this approach indicated that, while the majority of SH was in the soluble range (o 2 nm) for the tannery wastewater selected in the study, SI was well distributed among the colloidal range

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