A simplified method is presented for estimating chemical oxygen demand (COD) fractions. Its effectiveness was assessed by comparing different values found in literature. This case study considered two main sanitary sewer collection systems in Bogotá, Colombia. The results showed important differences in COD fractions between the sewer collection systems and the reference data. The method presented demonstrated advantages over those currently in place, the paper provides detailed explanations of the steps required and establishes a unified interpretation of the results obtained.

INTRODUCTION

Modeling has provided valuable insight into wastewater treatment plant (WWTP) optimization. When applied to activated sludge (AS) processes, modeling outcomes not only reduce operating costs but can also improve effluent quality (Vanrolleghem et al. 2003). In this paper, focus is placed on Activated Sludge Models (ASMs), which are based on chemical oxygen demand (COD) balances.

Some experimental methods are suitable for determining COD fractions. However, the lack of standard procedures for COD fractionation has led to a proliferation of experimental methods for their determination. As a consequence, this could lead to significant differences in the values obtained (Gillot & Choubert 2010). Regardless of the method used, COD fractions are not generally defined by physical separation. Sometimes, e.g. when evaluating soluble and particulate biodegradable fractions, a biokinetic criterion is used; in other words, a dynamic response in AS processes (Wentzel et al. 1995) is studied. Alternative test methods, especially biological ones, have been used widely (Ekama et al. 1986; Sollfrank & Gujer 1991; Spérandio & Roustan 1998; Xu & Hasselblad 1996). In addition, physico-chemical separation techniques, such as filtration and coagulation, often serve to determine COD fractions (Dold et al. 1986; Ekama et al. 1986; Mamais et al. 1993). The COD fractions and nomenclature adopted in this study are presented in Figure 1. In this paper, standardized notations in wastewater treatment modeling, as proposed by Corominas et al. (2010) are adopted.
Figure 1

COD fractions and nomenclature.

Figure 1

COD fractions and nomenclature.

According to Henze (2000), Henze & Gujer (1987), Makinia (2010), Choubert et al. (2013) and Rieger et al. (2013) the COD fractions presented in Figure 1 have the following characteristics:

  • Soluble Unbiodegradable (SU): cannot be removed by any biological treatment process or waste AS, so it reaches the effluent unaltered. Its value limits the minimum soluble effluent COD concentration.

  • Particulate Unbiodegradable (XU): accumulates in the sludge impacting sludge production, mixed liquor suspended solids concentration and clarifier performance.

  • Soluble Biodegradable (SB): can be metabolized readily by microorganisms, i.e. transported to cells, and oxidized or converted into storage products or biomass. SB affects denitrification, anoxic tank size and P removal.

  • Particulate Biodegradable (XB): is often not metabolized immediately by microorganisms. Before being taken up by heterotrophic biomass, XB requires hydrolysis. In wastewater treatment modeling, ‘hydrolysis’ is understood as the conversion of slowly biodegradable substrate into a more readily biodegradable form. Like SB, XB is relevant for carbonaceous biological oxygen demand.

Because colloidal particles (0.001 to 1 μm) cannot be removed by sedimentation, chemical methods must be used (Metcalf & Eddy, Inc. (2013)). The ‘Water Environment Research Foundation’ (WERF) and the ‘Foundation for Applied Water Research’ (STOWA) influent fractionation methods contain a specific flocculation step to discriminate between colloidal and soluble fractions (Roeleveld & Van Loosdrecht 2002; Melcer 2003). Colloidal COD quantification is important in relation to plant-wide energy recovery and carbon footprint. Chemically enhanced primary treatment (CEPT) acts to improve removal of particulate COD (pCOD) and convert colloidal COD to particulate form allowing its removal. Primary settling increases the solid fraction of COD processed in anaerobic digestion. In consequence, biogas production increases when CEPT is used, with an associated positive effect in energy recovery. If, as consequence of coagulant addition, the soluble substrate, necessary for denitrification, is reduced, carbon must be added. Because of this, the energy cost savings by enhancing primary sedimentation would be reduced by the costs of both the coagulants and the supplemental carbon (Gori et al. 2013).

Experimental methods for COD fractionation

Ekama et al. (1986) proposed meticulous methods for estimating XU, SB and SU. They estimated XU first by calibrating the parameter of a steady state model as done in Marais & Ekama (1976). Second, SB is estimated by one of three different methods depending on reactor operating conditions (flow-through AS, aerobic batch and anoxic batch). Third, SU is calculated using filtered effluent COD for aerobic reactors operated with sludge age exceeding 5 days. Finally, the XB could be estimated by calculating the difference between the total COD and the previously estimated fractions. Wentzel et al. (1995) calculated the SB fraction and heterotrophic active biomass using bioassays to determine the oxygen utilization rate (OUR) through the application of respirometry tests. In this case OUR should be registered over about 20 hours.

Another common approach features respirometry tests under varying conditions, high and low ratios between wastewater COD and biomass (measured as volatile suspended solids, VSS). Spérandio et al. (2001) discuss this approach and the calibration of a mathematical model. The model they proposed represented a simplified form of ASM focused on the aerobic growth and death of heterotrophs, hydrolysis and adsorption. Their method established bCOD, XB, and active heterotrophic biomass.

Hulsbeek et al. (2002) developed a procedure combining the biochemical oxygen demand (BOD10) test and physico-chemical tests. Using their procedure, the following fractions can be determined: (I) bCOD is the sum of SB and XB. As BOD5 does not represent bCOD, bCOD is based on daily BOD values observed during the BOD10 test. (II) The method to determine SB is based on a physical separation, which involves pre-flocculation of the sample with Zn(OH)2 or other flocculants, followed by filtration (0.1 μm). As the results obtained are almost the same, using filtration or flocculants, either is acceptable. Ultimately, since both SB and SU pass through the filter, the concentration of SU must be determined independently and then subtracted from the soluble COD to determine SB. (III) SU is calculated based on the concentration of inert COD in the WWTP's effluent. As for SB, the soluble fraction is separated by filtration or flocculation. Unbiodegradable COD and XU are calculated as the difference between the total COD and other COD fractions.

Rational procedures for the calculation of COD fractions

An example of an alternative approach using parametric assumptions for estimating COD fractions has been proposed by Gori et al. (2011). In this procedure, COD fractions are calculated from commonly measured water quality parameters like BOD5, COD, and VSS. Additional information is also needed regarding WWTP particulate removal in primary sedimentation. Two, more uncommon but easily measured, water quality parameters are also required: the soluble non-biodegradable COD and pCOD. The ratio pCOD/VSS is a relevant input in this method, a relationship between pCOD and VSS between 1.07 and 1.87 was adopted, and is the basis for estimating the particulate and soluble COD fractions. BOD5 is the only input required to determine the biodegradable (bCOD = 1.6 * BOD5). Other COD fractions as SS, XB and XU are calculated by subtraction from values determined above (Gori et al. 2011).

The literature abounds in methods for estimating wastewater COD fractions. The examples presented attest to the diversity available, i.e. alternatives to combinations of physical and bioassay methods. Sometimes, the authors fail to provide clear directions for implementing their methods, which has occasionally led to subjective data interpretation. The latter is pertinent to interpreting changes in slope and in the definition of areas associated with respirometry tests. The lack of a standard set of conditions and the subjectivity of data interpretation hinder the implementation of the procedures discussed (Spérandio et al. 2001).

In this paper, a method is presented for estimating COD fractions. It is based on dedicated lab-scale experimental methods for wastewater characterization. It consists primarily of bioassays and physico-chemical methods intended to produce a simple, readily interpretable alternative to the most frequently used methods. Evaluation of the proposed method's effectiveness involved a case study of COD fractions in domestic wastewater in Bogotá, Colombia, at two different sites. The results obtained were compared to reference values reported in the scientific literature.

METHODS

Wastewater source

The Salitre facility is the only WWTP in the Colombian capital, Bogotá. The influent enters the CEPT through a pumping station. The plant's capacity is not adequate to treat all of the wastewater discharged to the city's sanitary sewer system, so some of the influent is treated and some dumped (untreated) into the Bogotá River. There are many theories about the sedimentation and biological degradation occurring in the channel, and their effect on influent wastewater characteristics. Because of the WWTP's inability to treat the city's wastewater, an upgrade of Salitre WWTP and a second WWTP dubbed Canoas are planned. The design includes an AS process but many have doubts about the ability of such processes to treat Bogotá's wastewater. This has spurred research, one research line entailed constructing an AS pilot plant, known as Gibraltar, using a feed with characteristics reflecting those of the influent for Canoas' projected WWTP. There are still questions surrounding the actual quality and variability of the Canoas influent.

Case study

Two sources of domestic wastewater, Salitre and Gibraltar, were chosen for COD fractionation. The first comprises raw sewage from Salitre WWTP, the second is the raw sewage flowing into the Gibraltar experimental plant. Six experiments were conducted: four using Gibraltar samples and two using Salitre samples. Samples were kept in refrigerators for an average of 12 hours at below 4 °C and analyzed within 24 hours. AS from the aerobic reactor of the AS pilot plant was collected for the respirometry tests.

Respirometry

Respirometry is used to measure and interpret the biological oxygen consumption rate under well-defined conditions. As oxygen consumption is directly associated with biomass growth and substrate removal, respirometry helps monitor, model and control the AS process (Vanrolleghem 2002), and enabled tracking of OUR.

The bioreactor used (BioFlo 115, New Brunswick) comprises a 3-liter aerated tank with continuous stirring. Dissolved oxygen (DO) concentration, pH and temperature were monitored and controlled in the vessel, and a heating element maintained constant temperature. A pH controller and two chemical dosing pumps fed acid and base solutions into the bioreactor for pH stability. A computer equipped with bioprocessing software (BioCommand, New Brunswick) supervised the entire process, for further control, and collected data. OUR can be seen in the DO concentration graph's decreasing slope, as conditions were controlled and the air supply intermittent. The main components of the bioreactor are shown in Figure 2.
Figure 2

Bioreactor component diagram. 1. Bioreactor vessel, 2. Console, 3. Computer, 4. Acid, 5. Base, 6. Peristaltic pumps, 7. Air diffuser, 8. Temperature sensor, 9. Stirrer, 10. DO sensor, 11. pH sensor, 12. Heating element.

Figure 2

Bioreactor component diagram. 1. Bioreactor vessel, 2. Console, 3. Computer, 4. Acid, 5. Base, 6. Peristaltic pumps, 7. Air diffuser, 8. Temperature sensor, 9. Stirrer, 10. DO sensor, 11. pH sensor, 12. Heating element.

Estimating COD fractions

Figure 3 comprises a flowchart of the procedures used to determine COD fractions in wastewater. A primary benefit of the proposed method is its simplicity.
Figure 3

Flowchart of COD fraction calculation.

Figure 3

Flowchart of COD fraction calculation.

Step 1: sample collection and laboratory analysis

The first step involves collecting sufficient volumes of untreated wastewater and AS to fill the bioreactor vessel and provide samples for laboratory analysis – see Table 1.

Table 1

Initial laboratory testing

Sample Analysis Purpose 
AS VSS FM ratio 
Wastewater Total COD FM ratio 
From BOD1 to BOD10 XB fraction 
Effluent Soluble COD (coagulated and filtered at 0.45 μm) SU fraction 
Sample Analysis Purpose 
AS VSS FM ratio 
Wastewater Total COD FM ratio 
From BOD1 to BOD10 XB fraction 
Effluent Soluble COD (coagulated and filtered at 0.45 μm) SU fraction 

Step 2: sludge aeration

The AS used in the experiment must be aerated, as it facilitates the endogenous phase and ensures residual substrate removal. Köhler (2008) says that at least 6 hours should be allotted for aeration.

If wastewater comes from an AS process, sludge samples should be collected from the same system. Otherwise, if AS comes from a different system, allowance must be made for sludge acclimatization. The AS is required to be acclimatized to the wastewater. During this acclimatization phase, the microorganisms have to adjust to their new surrounding environment before the assessment of COD fractions.

Step 3: bioreactor preparation

Once the bioreactor vessel and related elements are sterilized, installation must be carried out with great care; proper calibration of the DO and pH sensors proves particularly important. Likewise, optimal kinetics cannot be achieved for the microorganisms unless the temperature and pH are constant. For the former, Köhler (2008) suggests 25 °C. For the latter, the pH of the mixed liquor must be approximately 7 during testing to ensure the correct environmental conditions for the microorganisms' metabolic processes. Thus, an acid or base (H2SO4 or NaOH, 1 M) should be added automatically when pH deviates from the target value.

Step 4: food/microorganism ratio

Food/microorganism (F/M) ratio is based upon the ratio of food fed to the microorganisms each day to the mass of microorganisms held under aeration. It is a simple calculation, using the results from the influent COD test and the mixed liquor volatile suspended solids (MLVSS) test. The former (food) is the product of WW COD concentration and its volume, whereas the latter (microorganism) is the product of the MLVSS concentration and its volume in the bioreactor vessel. Ekama et al. (1986), and Melcer (2003) argue that a 0.6 F/M ratio is ideal for producing easily interpretable respirograms.

Step 5: nitrification inhibition

Nitrification during respirometry tests results in modifications to the respirogram, thus complicating it and the COD fraction interpretation. To reduce the rate at which ammonium is converted to nitrate, Ekama et al. (1986), and Melcer (2003) propose the use of a nitrification inhibitor – e.g., N-Allylthiourea in concentrations of 5 mg/l.

Step 6: respirometry

The details of this step depend on the respirometer and related equipment used. The goal is to supply air pulses to maintain DO and use a mechanical stirrer to assure a homogeneous mix, preferably controlled automatically. DO concentrations must be recorded at small time intervals throughout the test. For this work, DO was recorded at one-minute intervals. The air supply must not fluctuate until the target concentration is reached in the mixed liquor, after which, no more air is supplied and oxygen is used by the microorganisms. On completion, a graph – a respirogram – like that in Figure 4 (upper part) is made.
Figure 4

DO series and respirogram.

Figure 4

DO series and respirogram.

Three days are needed to complete this step, though respirograms (Figure 4) should be made before this to confirm that the endogenous phase has been reached. The latter is identified by the OUR series displaying an asymptote roughly horizontal to the origin and with two significant drops in the curve (e.g. the lower section of Figure 4). Once this phase has been confirmed, the experiment is finished. The whole process can take up to 7 days.

Step 7: respirogram interpretation

The magnitude of decreasing slopes in the DO concentration graph (Figure 4, upper) represents OUR. Graphing these slopes against time produces a respirogram (Figure 4, lower) whose values represent the organic matter degradation phases.

The curve's two horizontal asymptotes establish well-defined areas (Figure 4, lower). The first, between the black OUR curve and the green line, expresses the readily biodegradable chemical oxygen demand (SB). The second, between the black OUR curve and the red line, indicates biodegradable particulate chemical oxygen demand (XB) (Dold et al. 1986; Ekama et al. 1986; Melcer 2003). The method explained here requires only calculation of the SB COD (first area).

Processing respirograms does not need specialized computer tools; any program that plots the negative slopes associated with DO series attained via respirometry tests can be used (e.g. Microsoft Excel). However, for the data obtained, the code in Appendix 1 is useful. It enables estimation of OUR based on the oxygen consumption slopes and is written in the programming language ‘Python’ (‘Respirogram.py’). A Python interpreter and respective console (e.g. PyCharm) must be used. In order to guarantee a unified interpretation of the results use of the proposed code is recommended.

The method presented also showed advantages over those currently in place, the paper provides detailed explanations of the steps required for the method and establishes a unified interpretation of the results obtained.

Step 8: estimating soluble unbiodegradable COD (SU)

In practical terms, 90% of typical urban effluent COD can be considered SU (Siegrist & Tschui 1992). Hence, estimation of the SU fraction involves Equation (1) proposed by Hulsbeek et al. (2002): 
formula
1
where CODeff, sol is the soluble COD content of a water sample from the WWTP final effluent, coagulated and filtered using a 0.45 μm nylon membrane filter. The sample must be collected from the same plant as that used for wastewater characterization.

Step 9: estimating soluble biodegradable COD (SB)

To determine the SB, the amount of oxygen consumed degrading rapidly biodegradable COD (RB area) must be calculated before applying Equation (2) (Ekama et al. 1986): 
formula
2
where is the COD oxygen consumption. As reported by Ekama et al. (1986), the result should close to 3. ΔO is readily biodegradable chemical oxygen demand (equivalent to the RB area); Vml and VWW are the mixed liquor and wastewater volumes, respectively.

Step 10: biodegradable particulate COD (XB) estimation

This step requires calculation of the XB as outlined by Roeleveld & Van Loosdrecht (2002): SB is subtracted from the bCOD, with bCOD estimated by analyzing the BOD curve constructed from the daily BOD readings collected over a 10-day period (Step 1). To calculate bCOD, the Python code ‘TotalCOD.py’ has been used successfully (see Appendix 2). The proposed code is recommended to guarantee a unified interpretation of the results.

Step 11: unbiodegradable particulate (XU) estimation

Finally, the XU COD value can be determined either directly or indirectly. It can be calculated directly by measuring the residual COD after a long-term BOD test (Lesouef et al. 1992; Vanrolleghem et al. 2003). It is found indirectly by subtracting the sum of the other fractions from the total COD. The indirect method is expressed in Equation (3), which is recommended. 
formula
3

RESULTS AND DISCUSSION

Table 2 shows the results from the tests performed using the method described. In Table 3 those results are compared to typical values noted in the literature for the same fractions.

Table 2

COD fractions (Gibraltar and Salitre)

  Total COD COD efl, sol Volume of wastewater bCOD RB area SU SB XB XU COD fractions
 
WWTP mg/L mg/L mg/L mg-O2/L mg/L mg/L mg/L mg/L fSU fSB fXB fXU 
Gibraltar 441 29 1.6 300 42.7 26 160 140 115 0.06 0.36 0.32 0.26 
Gibraltar 685 50 1.6 419 64.9 45 251 168 221 0.07 0.37 0.24 0.32 
Gibraltar 403 30 1.6 280 60.0 27 225 55 96 0.07 0.56 0.14 0.24 
Gibraltar 590 30 1.6 478 59.6 27 224 255 85 0.05 0.38 0.43 0.14 
Salitre 360 10 1.9 342 28.7 89 253 0.02 0.25 0.70 0.03 
Salitre 575 111 1.9 461 39.2 100 122 339 14 0.17 0.21 0.59 0.02 
  Total COD COD efl, sol Volume of wastewater bCOD RB area SU SB XB XU COD fractions
 
WWTP mg/L mg/L mg/L mg-O2/L mg/L mg/L mg/L mg/L fSU fSB fXB fXU 
Gibraltar 441 29 1.6 300 42.7 26 160 140 115 0.06 0.36 0.32 0.26 
Gibraltar 685 50 1.6 419 64.9 45 251 168 221 0.07 0.37 0.24 0.32 
Gibraltar 403 30 1.6 280 60.0 27 225 55 96 0.07 0.56 0.14 0.24 
Gibraltar 590 30 1.6 478 59.6 27 224 255 85 0.05 0.38 0.43 0.14 
Salitre 360 10 1.9 342 28.7 89 253 0.02 0.25 0.70 0.03 
Salitre 575 111 1.9 461 39.2 100 122 339 14 0.17 0.21 0.59 0.02 
Table 3

COD fractions comparison

  Average values
 
Roeleveld & Van Loosdrecht (2002) 
 
Hulsbeek et al. (2002)  Gori et al. (2011) Applied to Gibraltar
 
COD fraction Gibraltar Salitre Minimum Average Maximum Unsettled wastewater Scenario A Scenario B 
fSU 0.06 0.10 0.03 0.06 0.10 0.05 0.147 0.147 
fSB 0.42 0.23 0.09 0.26 0.42 0.20 0.314 0.314 
fXB 0.28 0.65 0.10 0.28 0.48 0.323 0.536 
fXU 0.24 0.03 0.23 0.39 0.50 0.13-0.22 0.216 0.004 
pCOD/VSS – – – – – – 2.3 2.3 
bCOD – – – – – – 1.2 * BOD5 1.6 * BOD5 
  Average values
 
Roeleveld & Van Loosdrecht (2002) 
 
Hulsbeek et al. (2002)  Gori et al. (2011) Applied to Gibraltar
 
COD fraction Gibraltar Salitre Minimum Average Maximum Unsettled wastewater Scenario A Scenario B 
fSU 0.06 0.10 0.03 0.06 0.10 0.05 0.147 0.147 
fSB 0.42 0.23 0.09 0.26 0.42 0.20 0.314 0.314 
fXB 0.28 0.65 0.10 0.28 0.48 0.323 0.536 
fXU 0.24 0.03 0.23 0.39 0.50 0.13-0.22 0.216 0.004 
pCOD/VSS – – – – – – 2.3 2.3 
bCOD – – – – – – 1.2 * BOD5 1.6 * BOD5 

Fraction comparison

For influent wastewater in Gibraltar, the largest proportional COD fraction is SB. At Salitre, the largest COD fraction is XB. The results for the total biodegradable fraction (SB + XB) in the wastewaters analyzed show that, on average, this component comprises 70% of total COD at Gibraltar and 88% at Salitre. The main difference arises from the XU fractions, which were 24% (Gibraltar) and 3% (Salitre) on average, respectively (Table 2).

COD fraction comparison

The comparison demonstrates that, in terms of averages, the proposed method yielded values similar to those reported in the reference literature. The matches were not exact as some reflected reference maxima and minima. Some even fell outside the reference value range.

The atypical values may simply be related to particularities of the wastewaters studied. On the one hand, Gibraltar's SU and XB fractions were close to the average reference values. On the other, the proposed method's SB values for Gibraltar were close to the maximum reference values while its XU values were close to the minima reported in the literature. At Salitre, the SB and SU results are consistent with the reference literature averages, while its XB exceeds the maximum reference values and XU is below their minima.

In the AS process, the selector concept entails the selective growth of floc forming organisms at the initial stage of the biological process by providing a high F/M ratio at controlled DO levels. Selector design is based on biokinetic mechanisms that result in SB uptake by the floc forming bacteria under aerobic or anoxic conditions. The wastewater at Gibraltar has characteristics favorable for selectors; with selectors the oxygen demand should be proportionally less for wastewaters with characteristics like Gibraltar's, especially as selectors favor the use of SB without oxygen, in turn reducing the COD load on the aerobic reactor.

In the rational procedure the pCOD/VSS ratio has been defined as being in the range 1.07 to 1.87. The data available for Gibraltar yield a pCOD/VSS ratio of 2.3. The difference between pCOD and VSS explains the huge variability in XU.

CONCLUSIONS

The method discussed here enables COD fractionation. Step-by-step directions for COD fractionation are provided. The method is oriented towards providing uniformly interpretable results. Some problems associated with other methods used for COD fractionation are obviated, in particular their limited applicability stemming from subjective data interpretation.

This method does have limitations, however. These include the time required for bioassays to determine COD fractions (up to 7 days) and the need for a 10-day BOD test. Furthermore, this method requires expensive materials and equipment (e.g. a bioreactor). In light of this there is a clear need to develop methods with shorter response times. Given that water quality variability often has diurnal patterns, the unambiguous explanation of testing procedures is critical, especially for ASMs. Among other things, the method presented here does not offer enough temporal resolution to capture COD fraction variability, which is an issue that needs to be dealt with.

ACKNOWLEDGMENTS

This study was funded by the Pontificia Universidad Javeriana as part of Project 4541. Additionally, COLCIENCIAS (Colombian Administrative Department of Science, Technology and Innovation) has provided financial support for the Doctoral Studies of the corresponding author.

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