A conceptual, stoichiometry-based model for single-sludge denitrification in recirculating aquaculture systems

Aquaculture 259 (2006) 328 – 341 www.elsevier.com/locate/aqua-online

A conceptual, stoichiometry-based model for single-sludge denitrification in recirculating aquaculture systems Sivan Klas a , Noam Mozes b , Ori Lahav a,⁎ b

a Faculty of Civil and Environmental Engineering, Technion, Haifa 32000, Israel Israel Oceanographic and Limnological Research Ltd., National Center for Mariculture, P.O. Box 1212, Eilat 88112, Israel

Received 17 January 2006; received in revised form 10 May 2006; accepted 25 May 2006

Abstract In the near future, the discharge of nitrate from recirculating aquaculture systems (RAS) to receiving water bodies is expected to be constrained by environmental regulations. Following wastewater treatment terminology, nitrate removal that makes use of the organic solid wastes generated within the RAS as the energy source for denitrification, may be termed ‘single-sludge denitrification’. In this approach, the costs associated with the addition of an external carbon source, the treatment of solid-wastes generated in the RAS, and the supplementation of alkalinity are reduced. The simple and economical operation of such a process can be realized by adopting the conventional activated sludge (AS) methodology. Organic solid-wastes taken from the solids-filter of a RAS growing gilthead seabream were characterized for their chemical and biodegradation properties. The results were used to generate a conceptual model to predict the performance of single-sludge denitrification in RAS. The model was run under typical operational conditions, employing the mean solids retention time (SRT) as the key operational parameter. Results indicated that in order to attain high denitrification rates, the ratio of the flux of organic solids (as COD) supplied to the denitrification reactor to the flux of NO−3 reduced should be between 4.0 and 6.0 g COD (g NO3−N)− 1 and the SRT values should be lower than 10 d. At these conditions, sludge production was estimated to be between 40% and 60% of the solids feed mass (in terms of COD), and NH3−N production as a result of ammonification was estimated to be less than 10% of the NO−3 –N removed. Empirical verification of the model is presented elsewhere. The model can be used as a design tool and for predicting the performance of the process at any operational conditions. © 2006 Elsevier B.V. All rights reserved. Keywords: Denitrification; Recirculating aquaculture systems; Activated sludge; Nitrate removal; Modeling

1. Introduction Aquaculture production has been expanding globally at a rate of over 10% a year since 1984, and production is expected to double by 2020 (SECRU, 2002). Large aquaculture facilities produce large amounts of nutrients and solid wastes. When these wastes are discharged ⁎ Corresponding author. Fax: +972 48228898. E-mail address: [email protected] (O. Lahav). 0044-8486/$ - see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.aquaculture.2006.05.048

untreated to a water body, oxygen depletion and nutrient over-enrichment may occur, leading to an adverse effect on freshwater and coastal ecosystems (Szmant, 2002). As a result, the development and operation of intensive aquaculture facilities are already becoming limited by environmental constraints (van Rijn et al., 2006). The technology of recirculating aquaculture systems (RAS) is advantageous, among other things, because of its low daily effluent discharge, which is typically below 10% of the system's volume. The low make-up water flow rates

S. Klas et al. / Aquaculture 259 (2006) 328–341

result in relatively high nitrate concentrations, which are advantageous in terms of biological nitrate removal. Beyond its apparent environmental importance, the removal of nitrate from RAS waters is also worthwhile from a fish growth perspective because (1) if not removed, nitrate can accumulate to toxic levels to certain fish species (Sauthier et al., 1998); and (2) nitrate removal by denitrification produces alkalinity that can be used to maintain the buffer capacity of the water, reducing the necessity for costly external base addition. Denitrification, the dissimilatory reduction of NO3− or NO2− to N2 gas, is carried out by a variety of heterotrophic and autotrophic bacteria. Despite the fact that heterotrophic denitrifiers exhibit a nearly infinite range for their organic substrates, only a few simple organic substrates (e.g. methanol, acetate, ethanol, glucose) have been intensively studied in relation to denitrification in RAS. According to wastewater treatment nomenclature, when an exogenous electron donor is added the process is termed “tertiary denitrification”. In contrast, when the electron donor is already present in the water, the process has been referred to as “single–sludge” denitrification (Rittman and McCarty, 2001). Tertiary denitrification is appropriate whenever the water to be treated contains NO3− or NO2−, but little or no electron donor. In the singlesludge approach the costs associated with supplementing the exogenous electron donor are minimized. Furthermore, in recirculating fish farms the use of intrinsic solids as the electron donor can reduce the total amount of solidwastes generated in the process. The latter advantage is particularly important in seawater fed RAS, because salty sludge poses a considerable environmental problem. These advantages make the single-sludge denitrification process an attractive method for nitrate removal from RAS effluents. Indeed in the last decade or so a few processes for denitrification in RAS using the intrinsic organic matter have been suggested (Jewell and Cummings, 1990; Knosche, 1994, Arbiv and van Rijn, 1995). Because most of the solids in intensive RAS are in the size range of 5 to 35 μm (Timmons et al., 2002), they must undergo biological hydrolysis (i.e. enzymatic breakdown of large organic molecules to much smaller molecules that can penetrate bacteria's membrane) prior to utilization in the denitrification reaction. Rates of hydrolysis, however, are much lower than typical denitrification rates (Janning et al., 1998). It follows that when organic non-soluble solids are used as the electron source, hydrolysis becomes the rate-limiting step in denitrification. Arbiv and van Rijn (1995) and Aboutboul et al. (1995) suggested a system that consisted of two sequencing reactors: an anaerobic reactor for the hydrolysis and fermentation of the solid

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wastes generated in the RAS to volatile fatty acids (VFA), followed by a denitrification fluidized bed reactor where the VFA were used as the electron and carbon source (see also van Rijn, 1996). More recent publications on the same system (e.g. Shnel et al., 2002; Gelfand et al., 2003) indicate that the bulk removal of nitrate actually takes place during the sludge digestion stage. Therefore, in the current paper a different approach is considered in which both the hydrolysis of the organic matter and the denitrification reaction are carried out together in a single oxidation reactor, under constant anoxic conditions. The basic idea is to apply a relatively long solids retention time (SRT), in the order of days, that is sufficient for the breakdown of both the fish excreta and feed residues, accompanied with a relatively short hydraulic RT (in the order of hours), that is sufficient for nitrate reduction. This approach has, ostensibly, a number of advantages over the two-stage concept, the most important being simpler design and operation: The process is comprised of only one easilycontrolled biological reactor, without the need for an anaerobic step. A schematic configuration of a RAS which includes such a denitrification reactor is presented in Fig. 1. Despite the fact that studies on denitrification reactors within RAS started some 30 years ago, a satisfactory unifying concept for the design and operation of such systems has not been developed to date (van Rijn et al., 2006). More specifically, hardly any data has been presented on the chemical composition, electron donating properties and biodegradability characteristics of the recoverable organic matter generated in RAS. This information is essential for the development of any model that attempts to predict the performance of a denitrification reactor which uses this matter as the energy source. The main goal of the current paper is thus to develop a generic model for single-sludge denitrification in RAS. First, a characterization of the organic matter generated in an experimental RAS is shown. Then, these results are

Fig. 1. RAS with a denitrification reactor.

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combined with the conventional activated sludge modeling methodology to produce a conceptual model for the process. In a follow-up paper (Klas et al., 2006) results from the operation of an experimental single-sludge denitrification reactor, fed with this same, characterized, organic matter are compared with the values predicted by the model presented here. 2. Materials and methods 2.1. Chemical characterization of the intrinsic organic solids An elemental analysis was performed on samples of solids taken from the backwash water of a bead solidsfilter operating as part of a seawater low-head RAS, located at the National Center for Mariculture, Eilat Israel (Mozes et al., 2003). The system, stocked by gilthead seabream (Sparus aurata) at approximately 50 kg m− 3, was fed at a daily rate of about 1.4% (by weight) of the biomass, with a make-up water exchange rate of 2 m3 kg−1 feed. Feed comprised of 45% protein and 20% lipid. Prior to analysis the solids were rinsed by consecutive centrifuge actions (KUBOTA 5100) to remove dissolved salts. Rinsing stopped when the electrical conductivity of the washing solution was lower than 2 dS m− 1. Following salts removal, the solids were dried at 60 °C. P analysis was carried out following solid digestion by a colorimetric measurement, as described in the APHA (1995). Total Solids (TS), Volatile Solids (VS) and COD concentrations were also determined according to the APHA (1995). Elemental analysis to determine C, H and N content in the solids was carried out by two external laboratories. In one lab, the analysis was performed by a “Carlo Erba EA-1108” instrument. In the second lab analysis was done using a “Perkin–Elmer 2400 series II Analyzer”. 2.2. Intrinsic organic matter biodegradability experiments The experiment was conducted twice in a batch fashion. Each time two replicates were monitored in parallel. A 10 liter container was filled with the backwash organic matter (BWOM) taken from the solids-filter of the experimental RAS to make for a concentration of 1500 mg VSS L− 1. No bacterial enrichment was performed. Nitrate was added in excess (600 mg NO3−N L− 1). The container was sealed at all times to prevent oxygen indiffusion and stirred constantly. Samples were taken at expanding intervals for a period of 40 d and analyzed for nitrate, nitrite, ammonia, phosphate, TSS and VSS; all analyses were performed according to the APHA, 1995.

3. Results and discussion 3.1. Derivation of a generic chemical formula to represent the BWOM in the experimental RAS Most of the recoverable organics in RAS are in the form of partially stabilized excreta, uneaten food particles, and bacterial cells (Chen et al., 1997). The hypothesis in the current work was that despite this heterogeneous composition, the organic matter can be represented by a generic chemical formula that would not change much throughout the growth period, provided that operational conditions remain unchanged. Such a representation is common in municipal wastewater treatment methodology where it is used primarily to define the expected biochemical reactions and electron-donating potential of the raw sewage. In order to determine the generic chemical formula that represents the recoverable organic matter in the experimental RAS, an elemental analysis was performed on four BWOM samples. The content of the elements C, H, N and P, as well as the measured VS and COD concentrations, all shown as a percent (mass to mass) of the total solids content of the sample (TS) are shown in Table 1. The results obtained from the four samples were close to each other, as represented by the relatively low standard deviation values. The same thing can be said about the results derived from the two different analyzers used. The average nitrogen percentage that was found (5.9%) agrees well with a previous finding of between 4% and 6% N (by weight) in the sludge of a typical RAS (Chen et al., 1997). In contrast, the measured P content (3.6%) was significantly higher than the range reported by the same author (0.2% to 2%). This discrepancy was not investigated further. However, the P content in the solids is relatively small and thus, even if an error occurred in its determination, the effect on the accuracy regarding the content of the other elements was minor. Using the average VS to TS ratio and the average measured percentage of each element out of the TS (Table 1), the average elemental composition in the organic solids (VS) was determined. It was assumed that the only element that is a part of the organic matter, but not measured directly, was oxygen. The results of this analysis are shown in Table 2 where the symbol n represents the relative amount of each element in the organic matter relative to nitrogen, whose index was set arbitrarily at 1. The results presented in Table 2 were used to compose the generic formula shown in Eq. (1), which in all

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Table 1 Average (std) elemental composition, VS and COD content of the organic solids emanating from the solids filter of the experimental RAS (samples 1a and 1b are a duplicate analyzed separately by two different devices) Sample

1a

1b

2

3

4

Apparatus

Carlo Erba

Perkin–Elmer

Carlo Erba

Perkin–Elmer

Perkin–Elmer

%C %H %N %P VS/TS COD/VS COD/TS

29.7 4.6 4.8 3.8 (1.7) 0.69 (0.01) 1.42 (0.05) 0.99 (0.04)

33.0 (0.1) 4.8 (0.1) 5.1 (0.2) 3.8 (1.7) 0.69 (0.01) 1.42 (0.05) 0.99 (0.04)

34.9 5.5 5.3 3.9 (1.1) 0.74 (0.02) 1.65 (0.06) 1.23 (0.04)

42.0 (0.2) 6.9 (0.1) 7.8 (0.1) 2.5 (0.5) 0.77 (0.05) 1.29 (0.11) 0.99 (0.08)

37.7 (0.03) 6.4 (0.1) 6.3 (0.1) 4.1 (1.0) 0.75 (0.01) 1.41 (0.12) 1.06 (0.09)

likelihood represents the majority of the organic matter that may be recovered from the experimental RAS. ð1Þ

C7 H13:4 O3:5 P0:3 N

It is emphasized that Eq. (1) does not represent any actual organic substance, but is rather a general and averaged representation of the multiple organic substances that are stopped by the solids filter, and produced in it. Moreover, the formula can be considered to be representative of only the experimental RAS from which the organic matter was obtained, and under the operational conditions that were employed at the time of sampling. The model developed here uses the formula presented in Eq. (1) for demonstration purposes, and its generality does not depend on a specific formula. To further validate Eq. (1), direct COD measurements were performed on the BWOM (see Table 1). The measured COD results were compared with the theoretical COD content of the formula given in Eq. (1) in the following way: Since the typical oxidation states of the elements in organic matter are: H = + 1, O = − 2, P = +5 and N = − 3, the average oxidation state of each carbon atom in Eq. (1) is thus − 0.7. Using this average carbon oxidation number and considering that in the COD test

Overall (average) results 35.5 (4.7) 5.6 (1.0) 5.9 (1.2) 3.6 (1.7) 0.74 (0.05) 1.44 (0.15) 1.07 (0.11)

the organic matter is completely oxidized to CO2, the amount of electrons that are transferred for each mole of oxidized carbon is 4 − (− 0.7) = 4.7 electron equivalents per mole. Therefore, the theoretical COD to VS ratio in the organic matter represented by Eq. (1) is:   gCOD     −  COD e molC 32 molO2 ¼ 4:7 d  −  d 0:04 VS gVS molC 4 e molO2

gCOD ¼ 1:50 gVS ð2Þ The theoretical ratio obtained in Eq. (2) agrees well with the empirical ratio of 1.44 g COD (g VS)− 1 that was obtained (see Table 1). Furthermore, both ratios are not far from the COD to VS ratio commonly used for organic solids in municipal raw wastewater (i.e. 1.59 g COD (g VS)− 1), where the general formula used is C10H19O3N (Metcalf and Eddy, 2003) and also to the ratio that is typically used for bacterial cells (C5H7O2N), which gives a value of 1.42 g COD (g VS)− 1 (Metcalf and Eddy, 2003). This cross-correlation adds to the validity of the generic formula derived from the elemental analysis procedure. In the model developed in this paper the empirical ratio of 1.44 g COD (g VS)− 1 was used.

Table 2 Elemental composition (% by weight) of the intrinsic organic matter (average values) Element

% from TS

% from VS

Mole of element (g VS)− 1

n

C H N P O

35.45 5.63 5.87 3.62 –

47.91 7.61 7.93 4.89 31.66

0.04 0.076 0.0056 0.002 0.02

7.0 13.4 1.0 0.3 3.5

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3.2. Stoichiometric prediction of process-parameters The model approach developed in this work was based on the premise that despite the fact that the substrate is mostly particulate, the SRT employed would be long enough to enable the existence of sufficient bacterial mass to allow the degradation and utilization of the entire substrate fraction that is capable of donating electrons. In other words, it is assumed that except for a certain fraction that is very slowly biodegradable (Xsb), all of the organic matter that enters the reactor will be completely utilized. Under such an assumption, it is possible to further assume that kinetic considerations are of less importance and that relevant process-parameters can be predicted using a stoichiometry-based approach. The process-parameters that are predicted by the model are: the fluxes of excess sludge, ammonia, CO2, phosphate and alkalinity produced in the process, as well as the required organic matter to NO3− ratio, and the expected denitrification rate. The model aims at expressing the selected parameters as a function of the mean solids retention time (SRT), a common operational parameter. Accurate prediction of the process parameters calculated by the model would enable a full process description and design, along with a possibility to evaluate the implications of the process on both the RAS itself and the environment. Using Eq. (1) and a commonly accepted formula for bacterial cells (C5H7O2NP0.1) (Metcalf and Eddy, 2003), it is possible to portray the possible oxidation–reduction stoichiometric equations for the denitrification reaction. Three half-cell redox reactions are used to represent heterotrophic bacterial metabolism. These include the oxidation of the organic matter, nitrate reduction (both defined together as catabolism) and cell synthesis (anabolism). The three half-cell reactions are represented in a parametric fashion in Eqs. (3), (4), (5) respectively. Ca Hb Oc Pd Ne þ uH2 O→aCO2 þ eNH3 þ dPO3− 4 þ vHþ þ we−

ð3Þ

Where: CaHbOcPdNe = parametric electron donor formula, u = 4d + 2a − c, v = 8d + 4a − 2c + b − 3e and w = 4a − 3e + 5d − 2c + b. NO−3 þ 6Hþ þ 5e− →0:5N2 þ 3H2 O jNH3 þ f CO2 þ  þ IPO3− 4 þ xH þ ye →Cf Hg Oh Pi Nj þ zH2 O

ð4Þ ð5Þ

Where: x = 8i + 4f − 2h + g − 3j, y = 4f − 3j + 5i − 2h + g, Cf HgOhPiNj = parametric cell formula and z = 2f + 4i − h.

The stoichiometric coefficients in the overall reaction are dependent on the fractions of electrons used for anabolism (cell synthesis) f s, and for catabolism (energy), fe. Multiplying Eqs. (4) and (5) by fe and fs respectively, and also multiplying them so that electrons are cancelled out, the overall denitrification biochemical reaction can be obtained. This procedure is demonstrated in the most general fashion in Eq. (6) which shows a denitrification reaction using parametric substrate and cell formulas (stoichiometric coefficients are normalized by the substrate stoichiometric coefficient). αCa Hb Oc Pd Ne þ βNO−3 þ γHþ → δN2 þ εCO2 þ ζNH3 þ ηH2 O þ θCf Hg Pi Nj þ ιPO3− 4 ð6Þ Where: α = 1, β = (1 − fs) d w / 5, γ = 6 d β + x ˙ θ − v, δ = 0.5 d β, ε = a − f d θ, ζ = e − j d θ, η = 3 d β + z d θ − u, θ = fs w / y, ι = d − i d θ and fs = fraction of electrons used for cell synthesis. Y, the observed bacterial yield, can be defined as the net biomass produced per mass of substrate utilized. Because α = 1, this relation can be simply described as follows: Y ¼ θ=Z ¼ ð f s d w=yÞ=Z

ð7Þ

Where Y is the anoxic observed bacterial yield (g COD (g COD)− 1) and Z is the molecular weight ratio between the substrate and the cells, multiplied by the corresponding COD to VS ratio:   12a þ b þ 16c þ 31d þ 14e 1:44 Z¼ d 12f þ g þ 16h þ 31i þ 14j 1:42

ð8Þ

It is noted that the theoretical COD to VS ratios may also be represented by the parameters a to j. However, for simplicity sake and because COD to VS values are generally in the proximity of 1.42 gCOD gVS− 1, the ratios presented in Eq. (8) (i.e. 1.44 and 1.42) are those of the formula given in Eq. (1) and of the typical cells formula (C5H7O2NP0.1), which are used later on. Combining Eq. (6) with Eqs. (7) and (8) the stoichiometric coefficients of the overall theoretical denitrification reaction can be written as a function of the bacterial yield Y as follows: β = (w − y d Y d Z) / 5, γ = 6 d (w − y d Y d Z) / 5 + x d Y d Z − v, δ = (w − y d Y d Z) / 10, ε = a − f d Y d Z, ζ = e − j d Y d Z, η = 3 d ( w − y d Y d Z) / 5 +z d Y d Z −u, θ =Y d Z and ι =d − i d Y d Z.

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COD=NO−3 N ¼ VS=NO−3 Nd1:44 ¼ 5d1:44dð12a þ b þ 16c þ 31d þ 14eÞ ð12Þ =14dðw−ydY dZÞ

Fig. 2. Predicted COD/N and ammonia production as a function of the bacterial yield.

Using Eq. (1) to represent the substrate and the typical cell formula, the overall theoretical denitrification reaction, as a function of Y only, can be written as follows: C7 H13:4 O3:5 P0:3 N þ ð6:6−6:3Y ÞNO−3

þ ð5:8−6Y ÞHþ →ð3:3−3:2Y ÞN2 þ ð7−7:7Y ÞCO2 þ ð1−1:54Y ÞNH3

ð9Þ

þ ð8:1−6Y ÞH2 O þ ð1:54Y ÞC5 H7 O2 P0:1 N þ ð0:3−0:15Y ÞPO3− 4

To demonstrate the application of this approach, Eq. (10) is presented as a particular example of Eq. (9) for a case where the observed bacterial yield coefficient (Y ) = 0. C7 H13:4 O3:5 P0:3 N þ 6:6NO−3

þ 5:8Hþ →3:3N2 þ 7CO2 þ 8:1H2 O þ NH3 þ

ð10Þ

0:3PO3− 4

Eq. (10) can be used to simulate a process, under which the biomass formation rate, to all intents and purposes, is equal to the rate of biomass decay (this may occur if a very long SRT is employed). It is noteworthy that the maximal yield value reported for heterotrophic denitrifiers is in the proximity of 0.53 g COD (g COD)− 1 (Muller et al., 2003). From the information presented above, a set of equations can be written to allow the estimation of essential process parameter values as a function of Y. All these values, presented as a percentage of the flux of nitrate reduced, are given in Eqs. (11) through (16). VS=NO−3 N ¼ ð12a þ b þ 16c þ 31d þ 14eÞ=14β ð11Þ ¼ 5dð12a þ b þ 16c þ 31d þ 14eÞ =14dðw−ydY dZÞ

NH3 −N=NO−3 −N ¼ ζ=β ¼ 5dðe−jdY dZÞ=ðw−ydY dZÞ

ð13Þ

CO2 −C=NO−3 −N ¼ ε=βd12=14 ¼ 60dða−f dY dZÞ=14dðw−ydY dZÞ

ð14Þ

− PO3− 4 −P=NO3 −N ¼ ι=βd31=14

¼ 155dðd−idY dZÞ=14dðw−ydY dZÞ

ð15Þ

ALK=NO−3 −N ¼ γ=β ¼ 6 þ 5dðxdY dZ−vÞ=ðw−ydY dZÞ þ 5dðe−jdY dZÞ=ðw−ydY dZÞ ð16Þ

To demonstrate the behavior of some of the parameters as a function of Y, the outcome of Eqs. (12), (13), (15) and (16) was plotted and presented in Figs. 2 and 3. The information supplied by either Eq. (11) or Eq. (12) is imperative for process design. The value of the parameter “electron donor to electron acceptor utilization ratio”, that is described by both equations, allows the estimation of the substrate mass (i.e. the mass of BWOM) required for complete removal of a given amount of nitrate. The range of the required COD to NO3−N ratio, which is predicted by Eq. (12) and shown in Fig. 2, conforms well to previous reports which showed that 3.0 to 6.0 g COD (g NO3−N)− 1 were utilized in denitrification units treating RAS waters (van Rijn et al., 2006). The considerable importance of this parameter in RAS stems from the fact that in contrast with the case of municipal wastewater, in RAS the amount of the recoverable organic matter may become

Fig. 3. Predicted PO3− 4 and alkalinity production as a function of the bacterial yield.

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limiting. This means that the available organic matter may be insufficient for reducing the entire nitrate that is produced in the nitrification process. For example, in low head RAS, where solid separation is carried out using a plastic bead filter, Mozes (2003) reported a recovery potential not higher than 4 g COD for each g NO3−N produced in the system. In such a case, according to Fig. 2, the overall denitrification reaction would be compelled to operate at yield values lower than 0.33 in order to comply with the available organic matter. The results derived from Eqs. (13) through (16) have major implications on the water quality in the fish tank, in case effluents from the denitrification reactor are recycled (see option in Fig. 1). Eqs. (13) and (15) are also important from an environmental standpoint, because they show that soluble nutrients (NH3, dissolved phosphorus species) are expected to be released to the aqueous phase under all operational conditions. This observation is somewhat self-contradictory in a process that is aimed at nutrient removal. 3.3. Biodegradability patterns of the organic matter generated in RAS In contrast with the case of municipal raw sewage most of the organic solids in intensive RAS are not present as dissolved matter available for bacterial consumption, but rather as a particulate matter that needs to undergo hydrolysis prior to bacterial consumption. Quantifying the biodegradability properties of the organic matter that accumulates in the solids filter is of a clear importance to any attempt to predict reaction rates in a process that utilizes this matter as the electron donor. The major goal of this part of the work was to define the fraction of the organic matter that is biodegradable (i.e. that has the potential to donate electrons) at the solid retention times

typically applied in intensive AS biological reactors, i.e. the amount which can be degraded in a time period of a few days. To characterize the biodegradability patterns of the organic matter, two sets of batch experiments (two replicates in each experiment) were performed. The results obtained from both experiments were, in essence, similar. Fig. 4 shows the change in time of a number of parameters monitored in one of the biodegradation experiments. Fig. 4 shows that the removal rates of the organic matter and nitrate were proportional, as represented by the similar trend in the slope of the curves of both parameters throughout the experiments. Such a proportional behavior was predictable, as both substances are consumed simultaneously by the bacteria. In all four replicates of this experiment, the denitrification rate, as represented by the slope of the nitrate concentration curve, could be roughly divided into four sections. Each of these sections was considered to represent a different denitrification rate resulting from the oxidation of a different fraction of the substrate, each having its own biodegradability characteristics. Sections one to three in Fig. 4 seem to represent decreasingly readily biodegradable fractions of the organic matter, whereas the fourth section, where only minor nitrate reduction occurred, represented the oxidation of a very slowly biodegradable organic matter. The organic matter that remained at the end of section three was assumed to comprise of mostly original bacterial cells (i.e. bacteria that were introduced into the experimental tank with the intrinsic organic matter), newly generated bacterial cells, and presumably also other very slowly biodegradable organic matter such as cell debris or fish scales. By dividing the amount of the nitrate that was consumed in each section by the total amount of nitrate consumed in sections one to three, the relative amount of each biodegradable fraction in the original solids was approximated. The results derived from the two

Fig. 4. Change in organic matter (represented as VSS) and nitrate concentrations with time in a typical biodegradation batch experiment.

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experiments (two replicates in each experiment) are summarized in Table 3. The substrate fractions presented in Table 3 do not account for the presence of bacterial cells and other very slowly biodegradable fractions that were not oxidized in the biodegradability experiments and thus did not affect the nitrate concentration. Since this fraction (defined as Xsb) was assumed to consist of mostly bacterial cells, the biomass fraction was measured directly by applying the technique presented by Vollertsen and Hvitved-Jacobsen (1999), which uses oxygen uptake rate (OUR) measurements to approximate active biomass concentrations (Xb) in sludge. Based on this method it was estimated that the typical active biomass concentration was approximately 16% of the BWOM (in COD to COD units). It is noted that the concentration of the active biomass obtained in this method does not represent the overall bacterial concentration, although it should be in reasonable agreement with the COD that the total cell biomass represents (Vollertsen et al., 2001). Based on these measurements, the full composition of the intrinsic organic matter, including the bacterial cells fraction was approximated as follows: readily biodegradable matter (Ss) ≅ 4% of total COD content; medium biodegradable matter (Xs1) ≅ 50%; slowly biodegradable COD (Xs2) ≅ 30%; very slowly biodegradable bacterial cells (Xb) ≅ 16%. In the following calculations, the combined concentration of bacterial cells and other very slowly biodegradable fraction (Xsb) was assumed to be equal to the biomass value (Xb). The establishment of the biomass fraction in the initial BWOM allowed the estimation of the overall bacterial yield coefficient in the batch experiments. The initial COD (CODi) was approximately 1.44 times the initial VSS (VSSi) concentration (Table 1): CODi ≅ 1.44 * VSSi = 1.44* 1500= 2160 mg COD L− 1. The organic matter concentration at the end of the experiment was approximately 840 mg VSS L− 1, or 1200 mg COD L− 1. It was assumed that this fraction consisted of both biomass, which came with the original BWOM (16% of 2160= 345 mg COD L− 1), along with new bacterial mass that was generated during the experiment. The yield coefficient was thus calculated as the ratio between the new biomass formed (i.e. final COD concentration minus

335

initial biomass concentration in COD terms), divided by the amount of substrate consumed (i.e. CODi minus initial biomass concentration, in COD terms): Y ≅ð1200−345Þ=ð2160−345Þ ¼ 0:47 mgCOD ðmgCODÞ−1

ð17Þ

Total nitrate removal was approximately 410 mg NO3−N L− 1. The resulting empirical COD to NO3−N utilization ratio was thus 2160/410 = 5.3 g COD (g N)− 1. Inserting the value Y = 0.47 into Eq. (12) results in a theoretical COD to NO3−N ratio of 5.05, i.e. a difference of only 4.7%. This latter calculation supports the results derived from the direct estimation of Xb as well as the overall theoretical approach. It is noted that in the calculation of the empirical COD to NO3−N utilization ratio (Eq. (12)), the 16% biomass fraction is not accounted for, because in Eq. (9), which forms the basis for Eq. (12), no distinction was made between different types of electron donors. This issue is discussed further later on. The most significant finding from the biodegradability experiments was that in all likelihood, over 80% of the organic matter recovered from the solids filter of the experimental RAS would be oxidized biologically at the solid retention times typically applied in AS biological reactors, i.e. between 3 and 15 d (Metcalf and Eddy, 2003). The time for complete oxidation of the biodegradable matter in the batch experiments was 14 to 18 d. However, in these experiments the initial bacterial concentration was very low. In a biological reactor, where bacterial concentrations are expected to be at least one order of magnitude higher, oxidation rates are expected to be proportionally higher and organic matter degradation time — proportionally shorter. 3.4. Development of a single-sludge denitrification model in RAS The theoretical and empirical results discussed thus far were incorporated into a conceptual denitrification activated sludge model. As shown earlier, stoichiometry coefficients and process parameters can all be expressed

Table 3 Biodegradable fractions of the intrinsic organic matter (presented as % of the total organic matter that donated electrons — average of four biodegradation experiment replicates) Section

Description of organic fraction

Symbol

Average % (std)

1 2 3

Readily biodegradable Reasonably biodegradable Slowly biodegradable

Ss Xs1 Xs2

5 (±5) 60 (±8) 35 (±8)

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explicitly as a function of the bacterial yield Y. Therefore, the first aim of the modeling effort was to obtain the value of the apparent yield coefficient at any given operational conditions in an AS type reactor. Once the apparent yield has been determined, the stoichiometry of the reaction can be predicted using Eq. (9). One of the most common and helpful control parameters in the conventional activated sludge process is the mean cell retention time (termed also ‘sludge age’), θc. When θc is used as a control parameter, there is neither a need to know the exact amount of the active bacterial concentration in the system nor the need to evaluate the amount of substrate utilized. The use of θc is based simply on the principle that to control the growth rate of microorganisms and hence their degree of waste stabilization, a specified percentage of the cell mass in the system must be wasted daily. In the classical activated sludge methodology the value of the yield coefficient is often linked to θc by the following expression (Metcalf and Eddy, 1991): Ymax ð18Þ Y ¼ 1 þ kd dhc Where Ymax is the maximum yield coefficient [kg COD cells (kg COD substrate)− 1], kd is the endogenous decay coefficient [d− 1] and θc is the mean cell retention time [d]. Using Eq. (18) it is now possible to express the process parameters depicted by Eqs. (11) through (16) as a function of θc instead of as a function of Y. Therefore, at any given θc (that can be practically attained by wasting a certain amount of sludge daily), a desired performance can be achieved. The next modeling step was thus to link θc with system operational parameters related to the daily fluxes of organic matter. In the conventional activated sludge methodology this is done by performing a mass balance on the biomass in the system. However, this methodology cannot be directly applied to the denitrification process discussed here. The classical activated sludge concept assumes that all of the substrate present in the raw wastewater is soluble, with no biomass or particulate solids inflow (Metcalf and Eddy, 1991). In the more advanced models a component to represents the particulate COD has been added, but its value is still small relative to the soluble COD. In contrast, as shown earlier, most of the recoverable organics in RAS are particulate rather than soluble. Because the differentiation between “live” biomass and “dead” suspended solids is not a trivial task, and because the substrate is particulate rather than soluble, it appeared more practical to perform the mass balance on the total

organic solids rather than on the biomass alone. A mass balance on the organic solids (presented in terms of COD) performed on a control volume of the AS system that is illustrated in Fig. 5, gives: dCODr =dtdVr ¼ FCODin −FCODSl −FCODeff −FCODox þ FCODprod

ð19Þ

Where CODr = the COD concentration of the organic solids in the oxidation tank [kg m− 3], Vr = oxidation tank volume [m3], FCODin = the COD daily organic load input [kg COD d− 1], FCODSl = the daily flux of sludge wasted from the system [kg COD d− 1], FCODox = the daily flux of COD removed by (both anoxic and aerobic) oxidation [kg COD d− 1], FCODeff = the daily flux of COD lost with the effluents [kg COD d− 1] and FCODprod = the daily flux of COD produced as result of (both anoxic and aerobic) cell synthesis [kg COD d− 1]. Because the current model hinges around organic solids (which are not necessarily bacterial cells) rather than biomass, the mean cell retention time θc had to be correlated with its corresponding term, i.e. the mean solids retention time, SRT. The mean cell-retention time (θc) is defined in the conventional AS methodology as the total cell mass present in the oxidation tank, divided by the rate of biomass that leaves it (Metcalf and Eddy, 2003). Under steady state conditions, this conventional definition is mathematically equivalent to the definition of the total cell mass present in the oxidation tank, divided by the rate of the biomass that enters the system daily (in the classical AS methodology, the value of biomass that “enters” the system is the net growth only, because it is assumed that the biomass concentration in the influent is zero). In a similar manner, at steady state conditions (where the sum of all COD fluxes that leave the reactor or are consumed in the reactor is equal to the COD influx), SRT can be defined as the total mass of solids present in the oxidation tank, divided by the flux of solids that enter the system daily: CODr dVr SRT ¼ ð20Þ FCODin Where SRT = the mean solids retention time [d].

Fig. 5. Components of organic-solids mass balance in an activated sludge type denitrification reactor.

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Eq. (20) conveniently allows the determination of the organic load on the reactor for any given SRT. As both SRT and θc are defined in a similar fashion, it was concluded that SRT can be substituted instead of θc into Eq. (18), without producing a significant error: Y ¼

Ymax 1 þ kd dSRT

ð21Þ

337

for a certain, very slowly biodegradable fraction, represented by the parameter Xsb (as defined in Section 3.3). FCODoxN ¼ FCODin ð1−Xsb Þ−FCODox02

ð25Þ

Where: FCODoxN = flux of COD removed via anoxic respiration [kg COD d− 1] and Xsb is the fraction of very slowly biodegradable organic matter [−]. The amount of bacterial cells that is produced as a result of anoxic respiration (FCODprodN) is:

Because most denitrifying bacteria are facultative, i.e. they can utilize both oxygen and nitrate as final electron acceptors in their metabolism (Rittman and McCarty, 2001) a certain portion of the organic matter that will enter the denitrification reactor (represented by FCODin) will be consumed by aerobic respiration and will thus not be available for nitrate reduction. In denitrification of RAS waters, oxygen can penetrate the denitrification reactor in two ways: it can diffuse from the ambient air, and, probably more significantly, it is introduced into the reactor with the influents that come from the oxygen saturated fish tank water. The oxygen flux that enters the reactor with the inflow water was estimated as follows:

The overall flux of bacterial cells (newly formed bacterial sludge) produced daily in the reactor as a result of both anoxic and aerobic oxidation reactions is:

FO2 ¼ DOd Q

FCODprod ¼ FCODprodN þ FCODprodO2

ð22Þ

Where: FO2 = flux of oxygen that enters the reactor with the inflow [kg O2 d− 1], DO = dissolved oxygen concentration in the inflow [kg m− 3], and Q = the inflow rate [m3 d− 1]. For modeling purposes it was assumed that oxygen enters the system only with the inflow water, although in reality certain atmospheric oxygen diffusion is unavoidable. The portion of the organic matter flux (i.e. of FCODin) that is lost as a result of aerobic respiration (FCODox02) was approximated using the anoxic yield coefficient (Y ), as follows: FCODox02 ¼ FO2 =ð1−Y =0:8Þ

ð23Þ

Where: FCODox02 = the flux of FCODin fraction that is lost as a result of oxygen respiration [kg COD d− 1], and 0.8 = a correction factor between anoxic and aerobic respiration yields (Muller et al., 2003). The sludge that is produced as a result of the aerobic respiration (FCODprodO2) is thus: FCODprodO2 ¼ FCODox02 ðY =0:8Þ

ð24Þ

Where FCODprodO2 = flux of sludge produced as a result of aerobic respiration [kg COD d− 1]. In order to quantify the amount of COD that is removed as a result of anoxic respiration alone (FCODoxN), it was assumed that all of the intrinsic organic matter (i.e. FCODin) is biodegradable and can donate electrons, except

FCODprodN ¼ FCODoxN Y

ð26Þ

Where FCODprodN = flux of bacterial biomass produced as a result of anoxic respiration [kg COD d− 1]. The overall flux of solids removed by both anoxic and aerobic oxidation (FCODox) is thus: FCODox ¼ FCODoxN þ FCODinPloss ¼ FCODin ð1−Xsb Þ

ð27Þ

ð28Þ

In order to determine the amount of sludge that has to be wasted daily (FCODsl), it is also necessary to quantify the amount of organic solids that leave the system with the effluents: FCODeff ¼ CODe Q

ð29Þ

Where: FCODeff = the flux of organic solids that leave the system with the effluents [kg COD d− 1] and CODe = concentration of organic solids in the effluent [kg m− 3]. Once CODe has been determined, the daily COD flux of surplus sludge (FCODsl) can be calculated using Eq. (19) and assuming steady state conditions, i.e. dCODr/ dt = 0. It is noted that if FCODeff and FCODox02 are small compared to FCODin (typically a good approximation), the amount of excess sludge can be estimated simply by: FCODsl ¼ FCODin d ðXsb þ Y −Xsb dY Þ

ð30Þ

The volumetric denitrification rate (RD) can now be estimated by dividing the amount of the organic matter available for denitrification (FCODin minus FCODox02) by the appropriate COD to NO3−N ratio (COD/N), derived from Eq. (12): RD ¼

FCODin −FCODox02 d1000 ðCOD=N ÞdVr

ð31Þ

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Where RD is the volumetric denitrification rate [g NO3−N m− 3 d− 1]. The reader is reminded that the COD to NO3−N ratio (Eq. (12)) was obtained using the generic intrinsic solids formula (Eq. (1)), under the assumption that all of it is capable of donating electrons. Therefore, the predicted denitrification rate was calculated by dividing the available amount of electron donor (FCODin minus FCODox02), by the appropriate COD to NO3−N value, as presented by Eq. (31). A certain error might result from this procedure, because not all of the organic matter represented by FCODin (i.e. Eq. (1)) will necessarily donate electrons at the SRT applied (invariably a portion of it would be very slowly biodegradable, i.e. Xsb). A more accurate prediction could have been attained if only the electron-donating fraction of the BWOM was represented by a generic chemical formula. Arriving at such a chemical formula is, however, not practical. To overcome this small inherent inaccuracy, model predictions will have to be calibrated by empirical results. It is emphasized that Xsb was neglected in the denitrification rate estimation due to the reasons mentioned above, however, in the solids mass balance considerations it was not neglected (see Eq. (30)). The flow diagram presented in Fig. 6 shows the overall methodology leading to the calculation of all the discussed process parameters. It is noted that in Fig. 6, for didactic purposes, CODr appears as an input, and FCODin is computed by Eq. (20). In actual life the order is reversed, i.e. FCODin is the input and CODr is computed using the same equation. The volumetric denitrification rate is determined, as shown, primarily by the SRT applied. A second operational parameter that has a significant effect on the volumetric rate is the concentration of the organic solids maintained in the oxidation reactor (CODr). For a given SRT, maintaining a

higher CODr means a higher BWOM flux, and consequently a higher denitrification rate. In order to account for this fact, a parameter termed ‘specific denitrification rate’ (R′D) was introduced. R′D is defined as the volumetric denitrification rate (RD) divided by the organic-solids concentration in the oxidation reactor (CODr): RDV ¼

RD CODr d1000

ð32Þ

Where R′D is the specific denitrification rate [g NO3−N (g COD)− 1 d− 1]. R′D is a more general parameter than RD. Its value does not depend on the organic solids concentration in the oxidation tank and thus it can be used for comparing the performances of different systems, or for comparing the performance of the same system under different SRT and COD concentrations. 3.5. Example of model execution In order to demonstrate the application of the modeling concept, the flow chart presented in Fig. 6 was executed with SRT values varying from 50 to 2 d, using the operational conditions listed in Table 4. In this procedure typical process coefficients at 20 °C were used. For different temperatures, modification of the model is necessary to attain better accuracy, particularly at temperatures below 15 °C and above 25 °C. Fig. 7 shows the change in the theoretical specific denitrification rate and the values of the parameter COD to NO3−N utilization ratio, both as a function of the SRT. Fig. 7 shows that the specific nitrate removal rate is inversely related to the mean solids retention time (SRT), i.e. lowering the SRT results in

Fig. 6. A flow chart of the methodology used for the determination of the desired operational parameters by the denitrification AS model, with required inputs (dashed lines) and relevant equation numbers.

S. Klas et al. / Aquaculture 259 (2006) 328–341

339

Table 4 Operational conditions used for executing the denitrification AS model Parameter Inflow rate Oxidation tank volume Maximal anoxic yield Biomass decay constant (at 20 °C) Total solids concentration in the oxidation tank Stoichiometric indexes of the elements in the substrate Stoichiometric indexes of the elements in bacterial cells Fraction of very slowly biodegradable fraction Solids concentration in effluent Inflow oxygen concentration

Symbol Q Vr Ymax kd CODr

Value

Units 3

24 15 0.53 0.06 10.4

Reference

−1

m d m3 kg COD (kg COD)− 1 d− 1 kg COD m− 3

a = 7, b = 13.4, c = 3.5, d = 0.3, e=1 f = 5, g = 7, h = 2, i = 0.1, j=1 Xsb

0.16

Fraction of FCODin

CODe DO

0.005–0.03 0.007

kg m− 3 kg m− 3

(Muller et al., 2003) (Metcalf and Eddy, 1991)

Eq. (1)

(Metcalf and Eddy, 2003)

higher denitrification rates. Based on this relationship only, the conclusion might be that the system should be operated at very low SRT values, in order to attain the highest possible denitrification rates. This conclusion however, is restricted by three constraints: first, it is expected that at very low SRTs removal rates will in fact drop because biomass will be removed together with the sludge at a higher rate than it would be produced. Second, the ability of the system to operate at very low SRTs is restricted by the efficiency of its own solids separation unit. Third, and most importantly, denitrification that is fueled only by RAS intrinsic organic solids may be restricted by the recoverable amount of this organic matter. This restriction is represented by the COD to NO3−N utilization ratio, depicted in Fig. 7. For example, a RAS that can provide solids separation in a ratio of no more than 4 g COD per g NO3−N produced, allows a minimal SRT of around 10 d, that corresponds to a denitrification rate not higher than 0.02 kg N (kg COD)− 1 d− 1 in order to achieve complete

nitrate removal. This means that a large reactor (in the order of tens of percents out of the entire RAS volume) is required. In contrast, in systems where the recovery of the organic solids is more efficient (e.g. systems that use drum filters) with a COD to NO3−N ratio of, for example, around 5, SRT may be reduced down to 2 d and denitrification rate can be as high as 0.1 kg N (kg COD)− 1 d− 1. Such rates will allow the operation of reactors with a volume that is only a few percents of the entire RAS volume. The specific denitrification rate, R′D, can be expressed as a function of SRT only, by substituting Eqs. (12) and (20) into Eq. (32). However, this will result in a rather complex expression. Therefore, a regression line was obtained for the specific denitrification curve depicted in Fig. 6, and the resulting equation is (R2 = 0.9998): RDV ¼ 0:17ðSRTÞ−0:86

The model-predicted specific denitrification rate term shown in Eq. (33) is very similar to the relationship reported in the literature for denitrification (expressed per suspended solids concentration unit) with raw municipal wastewater as the carbon source (WEF, 1998): RDV ¼ ½0:12dðSRTÞ−0:706 hT −20

Fig. 7. Specific denitrification rate and COD to nitrate utilization ratio, as a function of SRT, as predicted by the model.

ð33Þ

ð34Þ

Where: R′D is the specific denitrification rate [g NO3−N (g SS)− 1 d− 1], θ is a temperature correction factor (1.02 to 1.08) and T is the temperature [°C]. Note that by incorporating a COD to TSS ratio of 0.8 g g− 1 measured in the oxidation reactor of such a system (Klas et al., 2006), the factor 0.17 in Eq. (33) is reduced to 0.14, thus the prediction becomes even more similar to the term presented in Eq. (34). Moreover, the

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range of the specific denitrification rates predicted by the model (0.01 to 0.1 gN mgCOD− 1 d− 1) is almost identical (in equivalent terms) to the range reported (0.05 to 0.1 gN gVSS− 1 d− 1) in the literature for suspended growth systems at different locations around the world, using sanitary sewage and for a temperature range of between 15 and 25 °C (WEF, 1998). Fig. 8 shows the predicted flux of sludge production relative to the daily organic load, and the amount of ammonia produced in the process relative to the flux of nitrate removed, both as a function of the SRT. It is shown that the sludge production is expected to amount to values of between 40% and 60% of FCODin at SRTs of 10 and 2 d respectively. Ammonia production (relative to the removed nitrate flux), is expected to decrease with a decrease in SRT. The latter behavior is a result of a more pronounced bacterial assimilation when the observed yield increases. In any event, it is apparent that the release of NH3 to the aqueous phase is never negligible. This means, on the one hand, that ammonia is expected to be always the nitrogen source for bacterial growth (which is the preferred situation), but on the other hand that a polishing treatment for excess ammonia might be required if the effluent is to be discharged into the environment. Alternatively, the load on the nitrification reactor of the RAS would be increased, in case the effluents of the denitrification reactor are recycled back into the fish production system. Fig. 9 shows the predicted phosphorus and alkalinity production (as a fraction of the removed nitrate flux) as a function of the SRT. It is shown that phosphorus production is expected to be significant (e.g. 13% by mass out of the nitrate-N removed flux, at a SRT of 5 d). This means that the effluents may have to undergo further treatment before they are released into the environment. This may pose a serious problem, as methods for P removal from seawater are not very effective to-date. The alkalinity production is shown to decrease slightly as SRT decreases. This parameter has a special importance when the effluents are

Fig. 8. Sludge and ammonia production as a function of the SRT, as predicted by the model.

Fig. 9. Alkalinity and phosphate production as a function of the SRT, as predicted by the model.

recycled into the fish tank, because the alkalinity flux can be used to minimize and even eliminate the need for external base addition. 4. Summary and conclusions A stoichiometry based, single-sludge denitrification model for the treatment of RAS effluents is presented. The model was developed on the basis of the conventional activated sludge methodology, with adjustments to RAS conditions. The model allows the prediction of principal operational parameters as a function of the mean solids retention time employed. The required inputs of the model are the organic matter generic formula, obtained by performing elemental analysis on solids extracted from the RAS solids filter and an estimation of its non-biodegradable fraction (Xsb). For the specific case studied (low-head system growing gilthead seabream), it was estimated that over 80% of the organic solids were biodegradable. However, the biodegradable fraction may be different at different operational conditions and different rearing systems, thus it is strongly suggested to repeat the procedure described in the paper when considering using the intrinsic matter as an energy source for denitrification. Results from the execution of the model using the characterized solids as the substrate indicated that intensive nitrate removal rates of up to 0.1 kg NO3−N (kg COD)− 1 d− 1 are attainable, accompanied by a COD reduction of around 50%. However, attaining such rates requires COD to NO3− ratios of between 4 and 5 kg COD (kg NO3−N)− 1. In case the system cannot supply such a value (i.e. not enough solids can be recovered per kg of nitrate produced in the system), nitrate removal will be incomplete. If complete nitrate removal is required, SRT will have to be increased (i.e. lower denitrification rates and thus a larger reactor volume), until the COD to N ratio requirement is fulfilled. This may pose a limit on removal rates for systems using only the intrinsic organic matter as the

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