Modelling and dynamic simulation of hybrid moving bed biofilm reactors: Model concepts and application to a pilot plant

Biochemical Engineering Journal 56 (2011) 23–36

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Modelling and dynamic simulation of hybrid moving bed biofilm reactors: Model concepts and application to a pilot plant Giorgio Mannina a,∗ , Daniele Di Trapani a , Gaspare Viviani a , Hallvard Ødegaard b a b

Dipartimento di Ingegneria Civile, Ambientale e Aerospaziale, Università di Palermo, Viale delle Scienze, 90128 Palermo, Italy Department of Hydraulic and Environmental Engineering, Norwegian University of Science and Technology (NTNU) S.P. Andersensvei 5, N-7491 Trondheim, Norway

a r t i c l e

i n f o

Article history: Received 23 November 2010 Received in revised form 13 April 2011 Accepted 25 April 2011 Available online 30 April 2011 Keywords: Biofilm modelling Dynamic simulation Hybrid moving bed biofilm reactors Kinetic parameters Wastewater treatment

a b s t r a c t In the recent years, there has been an increasing interest in the development of hybrid reactors, especially in the up-grading of existing activated sludge plants that are no longer able to comply with concentration limits established by regulatory agencies. In such systems the biomass grows both as suspended flocs and as biofilm. In this way, it is possible to obtain a higher biomass concentration in the reactor, but without any significant increase of the load to the final clarifier. The paper presents the setting-up of a dynamic mathematical model aimed at quantitatively describing the biokinetic processes occurring in a hybrid moving bed biofilm reactor (HMBBR), and, more in general, in integrated fixed-film activated sludge (IFAS) processes, as well as to compare the simulation results with measured data from a HMBBR pilot plant built at the Norwegian University of Science and Technology in Trondheim (Norway). Particularly, the pilot plant consisted of three aerobic tanks in series; the first and third aerobic reactors were pure suspended biomass systems, while the second aerobic reactor was filled with the AnoxKaldnesTM K1 carriers for biofilm development. The mathematical model consists of two connected models for the simulation of both suspended biomass and biofilm. Biochemical conversions are evaluated according to the well known matrix notation used in the Activated Sludge Model No. 1 (ASM1) for both attached and suspended biomass and, in addition to biochemical conversion, the model contains the simulation of particulate detachment from the biofilm into the bulk liquid. The results showed an overall good agreement between measured and simulated data, for both biofilm and suspended biomass, with a good reproduction of dynamic processes in the hybrid moving bed pilot plant, and they are encouraging for further developments. © 2011 Elsevier B.V. All rights reserved.

1. Introduction Recently, due to an increase of the urbanization as well as the even more strict effluent limits imposed at the outlet of municipal wastewater treatment plants (WWTPs) by the Water Framework Directive [1], there has been the necessity to upgrade existing WWTPs. The secondary treatment of a WWTP is usually accomplished by biological processes that can be classified as being either suspended biomass or biofilm processes. The mostly used suspended biomass system is represented by the well-known activated sludge process (AS). Indeed, this process can present some shortcomings when exposed to increased hydraulic and organic loads. To increase the performances of an existing AS system it would be necessary to increase the amount of biomass inside the

∗ Corresponding author. Tel.: +39 09123896556; fax: +39 0916657749. E-mail addresses: [email protected] (G. Mannina), [email protected] (D.D. Trapani), [email protected] (G. Viviani), [email protected] (H. Ødegaard). 1369-703X/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.bej.2011.04.013

aerobic reactor; however, in such a case, the major WWTP limitation is represented by the following unit, i.e. the settling tank. In the last years, the idea to combine the two different processes (biofilm and suspended biomass) by adding carrier media, usually plastic carriers, into the biological reactor for biofilm growth, has been proposed [2]. This kind of system is usually referred as IFAS (Integrated Fixed-film Activated Sludge) process [3]. In these systems, the biomass grows both as suspended flocs and as biofilm. In this way, it is possible to obtain a higher total biomass concentration in the reactor, but without any significant increase of mixed liquor suspended solids to the final clarifier. Therefore, the up-grading of overloaded existing plants, no longer able to meet the effluent limits, can be easily obtained without the construction of new tanks. Furthermore, the increase of the overall sludge age in the system leads to a favourable environment for the growth of nitrifying bacteria [3]. In the last years, many studies have been carried out on IFAS systems, with the aim of investigating the process performances and the interaction between suspended growth and biofilm, as well as to compare different biofilm carriers, obtaining very interesting results and highlighting the effectiveness of

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Nomenclature AF D i k Kx K b  f ix L V Q KL a r S WWTP HRT SRT RAS MLSS MLVSS TN NH4 -N NOx-N DO TSS VSS TCOD FCOD CODsoi ASM AS HYB IFAS HMBBR X Y z m  ˇ   uf kdt kd E  Sav O P

biofilm surface area (m2 ) diffusion coefficient (cm2 d−1 ) stoichiometric conversion factor (gTSS gCOD−1 ) kinetic coefficient (depending on transformation) hydrolysis constant (mgCODp mgCOD−1 ) monod half-saturation coefficient (mg L−1 ) decay rate (d−1 ) coefficient for anoxic growth fraction of inert material ammonia fraction (mgN mg COD−1 ) thickness (m) volume (m3 ) flowrate (m3 d−1 ) oxygen transfer coefficient (h−1 ) conversion rate (g m−3 d−1 ) substrate concentration (mg L−1 ) wastewater treatment plant hydraulic retention time (h) sludge retention time (d) return activated sludge (L/h) mixed liquor suspended solids (mg L−1 ) mixed liquor volatile suspended solids (mg L−1 ) total nitrogen (mg L−1 ) ammonium nitrogen (mg L−1 ) nitrite plus nitrate nitrogen (mg L−1 ) dissolved oxygen (mg L−1 ) total suspended solids (mg L−1 ) volatile suspended solids (mg L−1 ) total COD (mg L−1 ) soluble filtered COD (mg L−1 ) truly soluble COD (mg L−1 ) activated sludge model activated sludge hybrid integrated fixed film activated sludge hybrid moving bed bio-reactor biomass or Particulate matter (mg L−1 ) yield coefficient (mg mg−1 ) coordinate normal to biofilm surface (m) biofilm mean density (kg m−3 ) active fraction dimensionless penetration depth maximum growth rate (d−1 ) stoichiometric coefficient (g g−1 ) advective velocity (m s−1 ) detachment coefficient detachment rate (m s−1 ) efficiency error variance sensitivity model output (mg L−1 ) coefficient (depending on the coefficient)

Subscripts A autotrophic H heterotrophic anoxic heterotrophic growth Anox at attached NH ammonia soluble organic matter S OH heterotrophic saturation for oxygen OA autotrophic saturation for oxygen

NO nitrate nitrogen h hydrolysis a organic nitrogen to ammonia g growth b biomass particulate p I inert ND organic nitrogen substrate type i j species, state variable or coefficient u upper 1 lower B, detached biofilm detached simulated sim meas measured var variated calibrated cal

such systems both for carbon and nitrogen removal (inter alia [4–9,33]). 2. Previous studies on HMBBR mathematical modelling The mathematical description of HMBBR is complex because one must now account for transformation processes occurring in both the suspended biomass and biofilm compartments, and the interaction between these compartments. In this context, modelling and dynamic simulation should become an important tool to better understand the kinetic behaviour of these systems. A wide range of biofilm models is currently available in technical literature [10] and a recent guideline for biofilm model selection has been provided by the International Water Association (IWA) Specialist Group on Biofilm Modelling [11]. In particular, existing models can be differentiated by their underlying assumptions, which primarily depend on the objectives of the modeling effort. Despite some mathematical models offer a good description of the ongoing processes, they can be onerous regarding computational time needed. This aspect, which often leads to lengthy simulation time, prevents a feasible application and put the attention on the simplified ones. Concerning the modelling aspects of hybrid reactors, only few models have been developed in the past to simulate such systems, and mainly for steady state conditions, in order to reduce the computational burden due to the resolution of the first order differential equation involved in such systems [5,12–19]. The steady-state model proposed by Chen et al. [17] was aimed at quantitatively examining the removal of both easily and slowly biodegradable organic substances. In this study, a single organic substance and a flat support medium was considered, assuming the same kinetic parameter values for both suspended and attached growth, in order to simplify the modelling for organic removal by the biofilm. The hydraulic retention time (HRT), the organic loading rate and the ratio of support surface to reactor volume were considered as the most important operation factors. Lee [19] presented a mathematical model to describe a hybrid system, assuming that the sludge was wasted from the aeration tank introducing the concept of fraction of substrate used by suspended biomass at steady-state. The model assumes two growths competing for a rate-limiting substrate in hybrid reactors, and it is based on Monod kinetic expression. According to the author, the critical advantage of this model is represented by the fact that it maintains all the essential concepts for single-growth kinetics but uses only two fundamental parameters of empty-bed hydraulic

G. Mannina et al. / Biochemical Engineering Journal 56 (2011) 23–36

retention time and suspended-biomass solid retention time to predict the competing results. The model proposed by Gebara [5] assumed that the aeration tank was composed of two reactors in series. The author proposed two sub-models for each compartment. This method of design did not give a precise result nor a good estimation of system parameters. Another model was proposed by Pastorelli [18], and it was a simplified model for hybrid moving bed biofilm reactors design in steady-state conditions, considering mass balance equations based on Monod expression, and introducing the concept of attached biomass activity ratio as the fraction of substrate removed by the biofilm. The proposed model only considered the removal of readily biodegradable organic substrate, but it can be useful as a screen for the potential design. Fouad and Bhargava [15] proposed a simple mathematical model based on Monod kinetic expressions, for both suspended and attached growth, and Fickian diffusion law for biofilm. The model aimed at investigating a simple expression for substrate flux into the biofilm. Since the latter was the main parameter in the model and its value changed with HRT, the model is able to accurately describe the hybrid system if an accurate value for the flux is established. However, since the flux has a partial role in hybrid reactors, it causes less divergence compared to pure attached growth models. The model proposed by Sriwiriyarat et al. [16] was able to predict carbonaceous removal, nitrification and denitrification at steadystate conditions in activated sludge systems with integrated sponge media. The rate expressions and substrate fluxes were used to establish the mass balance equations, and the program solves the mass balance equations simultaneously with a system of nonlinear equations, using Newton modified techniques. These model predictions agreed very well with experimental data from pilot studies, except for mixed liquor volatile suspended solid (MLVSS) concentrations and denitrification in aerobic reactors, probably related to sampling or analytical errors in the pilot plant during the studies. Plattes et al. [14] proposed a model for the dynamic simulation of a pilot-scale MBBR, combining the ASM1 equations [20] for biofilm growth and decay with migration and attachment of particulates from the bulk to the biofilm and detachment of biofilm into the bulk liquid. The authors considered the limitations due to diffusional mass transportation as implicitly described by the ASM1 model by means of adaptation of half-saturation constants in the Monod equations. Though the relatively poor fitting of the dynamic simulations with the experimentally observed effluent quality and the short duration of the validation (5.5 days), the model was able to describe the trend of nitrogen forms in the effluent, thus confirming the adaptability of the ASM approach to biofilm processes. More recently, Sin et al. [13] successfully applied ASM1 to predict ammonia removal in a moving-bed sand biofilter for tertiary nitrification, highlighting the effect temperature and influent load on the performances under dynamic conditions. More recently, Boltz et al. [12] proposed a mathematical model for steady-state IFAS and MBBR simulation. The model was an extension of the IWA ASM2d [21] and biofilm modelling techniques presented by Eberl et al. [11]. The model included simultaneous diffusion and Monod-type reaction kinetics inside the biofilm, competition between aerobic autotrophic nitrifiers, non-methanol-degrading facultative heterotrophs, methanoldegrading heterotrophs, slowly biodegradable chemical oxygen demand, and inert for substrate and space inside the biofilm. The assumption of the model is that reaction kinetics are identical for bacteria within suspended biomass and biofilm. The main aim of this model was to provide a useful tool for researchers and practitioners who seek to analyze or design HMBBR and/or IFAS bioreactors.

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3. Aim of the study The main objective of the present study was to develop a mathematical model aimed at quantitatively describing the biokinetic processes occurring in IFAS/HMBBR systems, that can be useful for dynamic simulation in order to improve designing as well as managing such systems. The proposed model is aimed to be easy to implement in terms of computational burden, therefore replacing the two order differential equations typical of some of the already available mathematical models devoted for simulating such systems. On the other hand, the model is aimed at keeping the comprehension of the involved phenomena avoiding the conceptualization of the simulated processes. The mathematical model is made up of two connected models for the simulation of suspended biomass and biofilm, respectively. The model is based on Monod-type biochemical reactions for both suspended biomass and biofilm compartments, which are in competition for electron donor and acceptor; more in detail, concerning biofilm compartment, zero-order conversion rates have been considered. Further, biochemical conversions are developed according to an adaptation of the well-known matrix notation used in the ASM1 model for both biofilm and suspended biomass. In addition to biochemical conversion, the model contains the simulation of particulate detachment from biofilm into the bulk liquid, which actually represents the connection between suspended biomass and biofilm compartments, since detached biofilm is converted to active suspended biomass. On the other hand, expressions derived from analytical solutions have been implemented for simulating the flux of dissolved substrates across the biofilm surface. Further, dissolved substrates can fully or partially penetrate the biofilm (sometime referred as thin or thick biofilm), resulting in different analytical solutions. The model has been evaluated comparing the simulation results with measured data from a HMBBR pilot plant. The results showed a good agreement between measured and simulated data, with a good reproduction of dynamic processes occurring in IFAS/HMBBR pilot plants, and they are encouraging for further developments.

4. Materials and methods 4.1. Description of the pilot plant The pilot plant was built at the Department of Hydraulic and Environmental Engineering of the Norwegian University of Science and Technology (NTNU) in Trondheim, and was fed with municipal wastewater subjected to primary clarification. The pilot consisted of three equivalent volume and dimension containers, of 30 L each, and a final settler; the pilot plant layout is represented in Fig. 1. The first and third aerobic reactors contained only suspended biomass (in the following AS1 and AS2, respectively) while the second aerobic reactor (in the following HYB) was filled with the AnoxKaldnesTM K1 carriers, with a filling fraction of 60% (liquid displacement: 3.5 L), corresponding to a net specific surface area of 300 m2 /m3 in the reactor. Such configuration was chosen in order to limit competition between heterotrophic and autotrophic bacteria for the dissolved oxygen in the AS1 reactor, thus allowing a high organic matter removal in such compartment as well as to enhance the growth of autotrophic bacteria on biofilm in HYB reactor, and the consequent “seeding” effect of nitrifiers from the biofilm to the mixed liquor, that should have a positive effect on the nitrification rate of the suspended biomass. Indeed, from ammonia uptake rate (AUR) batch tests carried out on the suspended biomass, the values of nitrification rates resulted higher respect to the traditional ones of conventional activated sludge processes. Such a result was attributed to the biofilm contribution that once detached from the

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G. Mannina et al. / Biochemical Engineering Journal 56 (2011) 23–36

Activated sludge

Primary Settling tank

IFAS

Activated sludge

Final Settling tank

Inlet

Outlet Waste sludge Return sludge Sludge

C

Air blower Fig. 1. Pilot plant layout.

Table 1 Average influent wastewater characteristics in the experimental campaign. Parameter

1st period 2nd period

TCOD (mg L−1 )

FCOD (mg L−1 )

TBOD7 (mg L−1 )

TN (mg L−1 )

NH4 -N (mg L−1 )

NO3 -N (mg L−1 )

NO2 -N (mg L−1 )

TP (mg L−1 )

TSS (mg L−1 )

185.15 214.50

100 111.75

76 88.5

30.25 36.2

18.42 28.04

0.29 0.19

0.37 0.19

3.84 4.30

93.8 94.7

carriers become suspended active biomass, thus enriching mixed liquor with nitrifying bacteria. Mixing in the reactors was provided by the coarse-bubble aeration system, installed at the bottom of each tank. Special sieve arrangements, to retain the carriers within the aerobic reactors, were placed at the outlet of the second reactor. In order to maintain the sludge age close to the desired value (near to 6 days) during the experimental period, daily sludge waste operations were carried out directly from AS2 reactor, and the amount of waste sludge removed was recorded. The municipal wastewater was pumped from the adjacent sewer system and stored into a load equalization basin (volume: 9 m3 ) in order to secure a quite constant pollutants concentration during the day. Further, the wastewater was pumped into a primary settling tank and then fed to the pilot plant. The experimental campaign was divided in two periods (25 days each), characterised by different inlet flow values. Indeed the experimental campaign started with a feeding flow equal to 19 L/h (corresponding to a 4.5 h HRT), while in the second period it was increased to 25 L/h (corresponding to a 3.5 h HRT), in order to evaluate the pilot plant behaviour with the increased hydraulic and organic load. The pilot plant was started up with inoculum of activated sludge taken from a nearby WWTP, and fresh carriers were added to the HYB reactor. Further, in order to enhance biofilm development on the carriers surface, the pilot plant was continuously fed for about one month before starting the field campaign. Table 1 lists the average influent wastewater characteristics. It can be seen that in the second experimental period, the wastewater concentration was higher than in the previous one; this situation, coupled to the higher hydraulic load, contributed to significantly increase the organic and ammonium load to the pilot plant. In Table 2 the operative conditions during the experimental periods are shown. Grab samples were taken three times a week and analysed for total nitrogen (TN), ammonium nitrogen (NH4 -N), nitrite and

nitrate nitrogen (NOX -N), total COD (TCOD), soluble filtered COD (FCOD), truly soluble COD (CODsol ) and every working day for total suspended solids (TSS) and volatile suspended solids (VSS); all the analysis were carried out according to standard methods [22] except CODsol that was analysed according to the method proposed by Mamais et al. [23]. Data collected from the overall experimental campaign have been used for model application Test on carrier samples were carried out, in order to establish the amount of biomass attached on the carriers. The adopted procedure was a modification of the one proposed by Helness [24]. In particular, for each test, a 20 carriers sample was removed from the HYB reactor. The carriers with biomass were dried in a oven for one night at 105 ◦ C and then weighed. The biofilm was removed from the carriers by putting them in a flask with ultra pure water (20 mL) which was placed in an ultrasound bath for 60 min. After ultrasound treatment, the carriers were rinsed with ultra pure water (2× 10 mL). Then the carriers were cleaned with detergent and additional ultrasound treatment to ensure complete removal of residual biomass. After another night at 105 ◦ C, the carriers were weighed again for gravimetric determination of biomass concentration. 4.2. Model description As discussed above, the model features the dynamic simulation of both suspended biomass and biofilm in a hybrid system. The model was aimed at quantitatively evaluating the role of both biofilm and suspended biomass in their overall contribution toward organic carbon and ammonium removal. Particular attention was paid on the simulation of substrate transport inside the biofilm, as well as on the interaction between suspended biomass and biofilm, governed by detachment rate of biofilm. The model is composed of two models, for the simulation of suspended biomass and biofilm. In particular, the suspended biomass behaviour has been simulated according to the concepts of the wellknown activated sludge model ASM1, considering mass balance

Table 2 Average operational conditions during the experimental campaign. Parameter

1st period 2nd period a

T (◦ C)

pH

Inlet flow (L/h)

RAS (L/h)

SRT (days)

DOa (mg L−1 )

11.56 11.48

7.41 7.25

19.20 25.00

22.08 29.00

5.71 5.73

4.43 3.68

Referred to the HYB reactor.

G. Mannina et al. / Biochemical Engineering Journal 56 (2011) 23–36

equations and Monod-type kinetics for the different substrates [21]. In particular, the following processes have been simulated: • • • • • •

aerobic growth and decay of heterotrophs; anoxic growth of heterotrophs; growth and decay of autotrophs; ammonification of soluble organic nitrogen; hydrolysis of entrapped organic compound; hydrolysis of entrapped organic nitrogen.

The model considers the fate of both dissolved and particulate substances; in particular, the state variables considered herein are: rapidly biodegradable organic matter SS , slowly biodegradable organic matter XS , soluble inert organic matter SI , inert organic matter XI , heterotrophic biomass XB,H , dissolved oxygen SO , ammonium nitrogen SNH , soluble nitrate nitrogen SNO , soluble biodegradable organic nitrogen SND , slowly biodegradable organic nitrogen XND , autotrophic biomass XB,A . All model components have been expressed as COD fractions; the conversion between COD and TSS has been evaluated considering the following equation: MLSS = iSS,XI XI + iSS,XS XS + iSS,BH XB,H + iSS,BA XB,A

dSO = KL a(SO,sat − SO ) dt

In order to study the interaction between the biofilm and the bulk liquid compartment, the dissolved and particulate constituents have been singled out. In fact, while dissolved substances are transferred from the bulk into the biofilm and further transported by means of molecular diffusion, particulate components cannot be transferred within the biofilm, but they can be attached to the biofilm surface or displaced leading to a biofilm loss, i.e. detachment process. Consequently, particular care has been addressed to the detachment process since it plays a central role in the fate of the interactions between the two sub-systems represented by suspended and attached biomass. On the other hand, concerning the attachment process this phenomena was not neglected according to the employed modelling approach proposed by Rauch et al. [25]. Concerning the soluble compounds, they can fully or partially penetrate the biofilm depth. In the former case, when the biofilm is fully penetrated, an analytical solution proposed by Harremöes [26] has been used, in order to simulate the soluble substrate transfer from the bulk liquid into the biofilm. In this situation, under the hypothesis of biofilm with an idealised geometry [25], steady-state conditions and zero-order reaction for bacterial species, the proposed equation to evaluate the substrate Si penetration depth in the biofilm is the following:



(1)

where MLSS is the mixed liquor suspended solids expressed as mg TSS/L, while iSS,XI , iSS,XS , iSS,BH , iSS,BA are the stoichiometric conversion parameters, whose values have been chosen from literature, 0.75 gTSS/gCOD for iSS,XI and iSS,XS , and 0.9 gTSS/gCOD for iSS,BH and iSS,BA , respectively [20]. The oxygen input is simulated according to the following equation [21]: (2)

where SO is the oxygen concentration inside the reactors, KL a is the oxygen transfer coefficient and SO,sat is the oxygen saturation concentration equal to 10.31 mgO2 /L, for the considered operational conditions. Concerning the biofilm model, it is based on a simple dynamic model proposed by Rauch et al. [25] that allowed fast but sufficiently accurate simulation of biofilm dynamics. Indeed, a very detailed mathematical description of the processes in mixedculture biofilms has some drawbacks that can be identified in the computational efforts for solving the resulting set of partial differential equations as well as in a general high model complexity. The underlying concept of the biofilm model is to decouple the modelling of the diffusion process and spatial distribution of bacterial species from the biokinetic reactions. The achievement of such goal was carried out by means of a two step procedure where (i) for each conversion process that is influenced by diffusion mechanism, the active fraction of the biomass within the biofilm is computed by means of a simple analytical solution to the problem, and (ii) all conversions within the biofilm are then calculated as if the biofilm was an ideally mixed reactor, but with only the active fraction of the species contributing [25]. The avoidance of the numerical solution of partial differential equations by using the sequential two-step procedure results in a fairly simple model structure. Regarding the biofilm compartment, two phases are distinguished: the liquid phase, in which the dissolved substances are transferred by means of diffusion phenomena, and the solid matrix, which is characterized by several bacterial species as well as of particulate substrate and inert material. The attached biomass modelling has been worked out considering the system as two connected ideally mixed tank reactors: the first tank representative of the bulk liquid, i.e. the water phase outside of the biofilm, and the second one of the effective biofilm.

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zi =

2Di Si,0 ri

(3)

where zi is the substrate Si penetration depth, Si,0 is the concentration of substrate Si in the bulk liquid, Di is the diffusion coefficient for the i-th substrate Si and ri is the zero-order conversion rate for the substrate Si . The conversion rate ri has been evaluated as follows: ri =



− j Xj ij

(4)

j

where j is the specific growth rate of the species j, Xj is the j-th bacteria species concentration and ij is the stoichiometric coefficient. Eq. (3) is valid only for steady-state conditions; however, such solution has been accepted since, as reported in [27], the characteristic time of biofilm diffusion phenomena is approximately one order of magnitude smaller than the conversion time scale inside the biofilm. As a consequence, the model considers that the substrate profile reaches an equilibrium very rapidly. As for the case of full biofilm penetration, for partial biofilm penetration, the model employs the same solution proposed by Harremöes [26] by introducing the concept of “active fraction” for the biomass species Xj . More specifically, as pointed out by Rauch et al. [25], when there is substrate limitation, the biofilm can be considered as partitioned in an active (upper) part and an inactive (lower) part close to the substratum. This limitation effect can also be expressed by assuming that only a certain fraction of the biomass is active. The active fraction is assumed to be equal to the dimensionless penetration depth of the substrate limiting first, and it is expressed as follows:



z j = ˇi = i = Lf

2Di Si,0 ri Lf2

(5)

where j is the active fraction of the specie Xj , ˇi is the dimensionless penetration depth for substrate Si and Lf is the biofilm thickness. However, the case with a single substrate/single species system represents the simplest one; in a real situation, when there is a multiple substrate/multiple species system, things can immediately become more complex. For instance, a sequential diffusion limitation occurs when a substrate Si is consumed in at least two

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INPUT

MASS BALANCES IN SUSPENDED BIOMASS • Calculate mass of attached biomass BIOFILM

• Calculate biofilm thickness Lf • Calculate biofilm concentration XB,r

CALCULATE THE UPPER PENETRATION DEPTH zu

Is the biofilm fully penetrated?

DIFFUSION LIMITATION

No

Yes φA= φH=1, φH*=0

Oxygen limiting SO

Organic carbon limiting SS

φA=φH=βSO

φH=βSS φH* =0

Case 1: φH*=βSS-βSO

Case 4: φA=βSO

Case 2: φH*=βSNO- βSO

Ammonium limiting SNH

φA=φH=βSNH

Case 6: φH*=0

Case 5: φA=βSNH

Case 3: φH*= βSNH-βSO

Calculate the detachment rate Kd and the biofilm loss

MASS BALANCES

OUTPUT Fig. 2. Model flow-chart showing the computational procedure adopted.

processes and one of these processes is earlier limited by another substrate Sj . In this case, the conversion rate for the substrate Si is not the same throughout the biofilm depth, but two different conversion rates will be established in the upper and in the lower part of biofilm depth. Further, the upper penetration depth is assumed to be equal to the penetration depth zj of the first limiting substrate Sj , while the lower penetration depth can be derived with an analytical solution proposed by Rauch et al. [25]:



zj = and zi =

2Dj Sj,0 rj

  zj2

1−

ri,u ri,l

 +

2Di Si,j ri,l

(6)

where ri,u and ri,l are the conversion rates for the substrate Si , in the upper and in the lower part, respectively.

The proposed model takes into account all the possible situations for dissolved substrate diffusion limitation, in the competition between heterotrophic and autotrophic bacteria, as better outlined in the model flow-chart, represented in Fig. 2. On the other hand, Fig. 3 reports, as an example, two cases of different concentration profiles for an hypothetical idealized biofilm structure, characterized by uniform density M and instantaneous uniform thickness Lf . More in detail, Fig. 3 illustrates substrate limitation in the competition between heterotrophic and autotrophic bacteria for their growth inside the biofilm. Referring to the simulation of the biomass loss, the detachment rate kd has been simulated according to Horn and Hempel [28] approach, considering the advective velocity uf at z = Lf , by which the biofilm surface moves in a perpendicular way to the carrier surface. More in detail, uf is derived from the following expression:

uf =

Lf t

=

Lfk − Lfk−1 t k − t k−1

(7)

G. Mannina et al. / Biochemical Engineering Journal 56 (2011) 23–36

(a) Biofilm

(b)

So Ss SNH

Carrier

Bulk Liquid

29

Biofilm

So Ss SNH

Carrier

Bulk Liquid

So

So

zo

zo

zo

zo

Ss

Ss

zs

zs

zs

zs

SNH

SNH

zNH

zNH

zNH

zNH

z

z

Lf

φH =

zS Lf

φA =

Lf

zo Lf

φH * = 0

φH = φ A =

zo Lf

φ H* =

z S − zo Lf

Fig. 3. Example of sequential diffusion limitation in an idealized 1D biofilm utilizing SS , SO and SNH : case (a) soluble organic is limiting first carbon oxidation process, while sufficient oxygen and ammonium nitrogen are still available; (b) oxygen is rate limiting for both aerobic organic matter removal and nitrification, and in a certain depth anoxic carbon oxidation occurs where: SS : soluble organic matter; SO : dissolved oxygen; SNH : soluble ammonium nitrogen; Lf : biofilm thickness; zo : oxygen penetration depth; zS : soluble organic matter penetration depth; zNH : soluble ammonium nitrogen penetration depth; H : aerobic heterotrophic active fraction; A : autotrophic active ∗ : anoxic heterotrophic active fraction. fraction; H

where Lf is the biofilm thickness variation in t, that is the time step between the time k − 1 and k, respectively. Further, if uf is higher than zero, the rate of biomass detachment kd is equal to the product between the detachment coefficient kdt (set equal to 0.1, according to Horn and Hempel [28]) and uf , otherwise the biofilm loss is zero. In the case of biofilm detachment, the biomass was modelled to be converted into active suspended biomass, XB,detached , according to the following equations: XB,detached = M AF kd

(8)

where M is the mean biofilm density and AF is the biofilm surface inside HYB compartment. A global view of components and processes that constitute the model, mainly based on Petersen matrix, is reported in Table 3; on the other hand, Table 4 reports, as an example, the 12 non steadystate mass balance equations for the components i, referring to the hybrid compartment only, based on the process rates j of the Petersen matrix reported in Table 3. More in detail, for each component Si , the variation term is given by the difference between the influent loading rate minus the effluent loading rate and the reaction terms, based on Monod-type kinetic expressions considering both suspended and attached biomass. In particular, VHYB represents the liquid volume of hybrid compartment, Q is the influent/effluent flowrate, while the considered mass from Eqs. (1)–(12) are respectively: rapidly organic matter SS , dissolved oxygen SO , soluble nitrate nitrogen SNO , soluble ammonium nitrogen SNH , heterotrophic suspended biomass XB,H , autotrophic suspended biomass XB,A , heterotrophic attached biomass XB,Hat , autotrophic attached biomass XB,Aat , slowly biodegradable organic matter XS , inert organic matter XI , soluble

biodegradable organic nitrogen SND , slowly biodegradable organic nitrogen XND . The model equations were solved at the finite difference, choosing a time step of 1 min.

4.3. Model calibration The model calibration has been carried out according to the trial and error method, by optimizing a target function defined by the Nash and Sutcliffe index [29], described by the following expression:

 E=

1−

2 sim 2 meas

 (9)

2 is the variance of the error, defined as the difference where sim 2 is the varibetween the measured and simulated values and meas ance of the observations. In particular, the efficiency target function has been calculated as the sum of each model state variable function taken into account and for different sections of the system.

ETOT =

n 

Ei,j

(10)

j=1

where j represents the model state variables, while i is the reactor considered. More specifically, for the pilot scheme investigated, a total of 14 efficiencies have been considered, and, as discussed above, the parameter estimation has been carried out using the trial and error method.

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G. Mannina et al. / Biochemical Engineering Journal 56 (2011) 23–36

12 = bHat XB,Hat 13 = bAat XB,Aat 14 = kHat XS 15 = khat XS (XND /XS ) 16 = kaat SND XB,Hat

11 =  ˆ Aat XB,Aat A

1 −1

−1

iXBat − fpad iXPat iXBat − fpad iXPat

∗ 10 =  ˆ ∗ ngat XB,Hat Hanox

9 =  ˆ Hat XB,Hat H

The sensitivity analysis has been performed to select the model parameters which affected the simulation results most. Further, its aim was to evaluate the stability of the proposed model. The sensitivity analysis was carried out by identifying all the independent coefficients used to simulate the 14 model outputs and the inputs of the model (3 for MLSS, NH4 -N, NOx -N and DO, 1 for TN and TCOD). 33 model parameters have been selected for the model sensitivity analysis. The initial value of each coefficient was obtained from the calibration procedure, then it was increased by a certain fraction, typically set at 10% [30], giving the resulting output and consequently the parameter sensitivity, as better outlined in the following. A straightforward sensitivity analysis was considered herein, adopting a variation of one parameter per time. In the present study, as first step, a local sensitivity of each coefficient was given for all the outputs used in the model, and then the sum of the local sensitivities was calculated, indicating how sensitive each coefficient was on the system globally. Indeed, the local sensitivity of each coefficient was defined as follows (average value on the basis of the experimental measurements):

−iXB,at −

1

1−YHat YHat

4.57−YAad YAad

−

− 1 1

1 Nmeas



−1

1

S(j,TOT ) =



Ovar,j − Ocal,j Pcal,j Pvar,j − Pcal,j Ocal,j

j=1

(11)



Nmeas

 Ovar,j − Ocal,j

j=1

Ocal,j

10

(12)

1 2 Sj,av(n) n

(13)

n

−1

1

1

Nmeas



Nmeas

Finally, the sum of the local sensitivities of each coefficient j was derived from the following expression, taking into account all the local sensitivities:

−1 −1

1 1

1

1

where j represents the investigated coefficient and n designates the output of the model. Ocal,j is the reference value of the output n, calculated on the basis of the coefficient j value obtained from the calibration step, Ovar,j is the simulation result for the output n, after varying the coefficient j. Pcal,j and Pvar,j represent the default and the increased value of the coefficient j, respectively, while Nmeas is the number of the experimental determinations. Since Pvar,j = (1 + 0.1)·Pcal,j , the sensitivity equation reduces to:

1/YAat

1−Y − 2.86YHat Hat

−iXBat

−iXBat

1

−iXB

−iXB − 1 YA 4.57−YA YA

−

1−Y − 2.86YH H

−iXB 1−Y − Y H H

XB,Hat XB,A XB,H

Sj,av(n) =

Sj,av(n) =

XB,Aat

SO

SNO

SNH

1 YA

1 YAat

1

−1

−1

8 =

XND XS

+ g



K0,H K0,H +S0

KNO +SNO

 SO K +S  OSNOO

XS /XB,H

X +(XS /XB,H )

7 = kh K

iXB − fp iXP iXB − fp iXP

4 = bH XB,H 5 = bA XB,A 6 = ka SND XB,H

XB,A

XB,H

S0 SNH 0,A +S0 KNH +SNH

3 =  ˆAK

2 =  ˆ H

∗

SS S0 0H +S0 KS +SS

1 =  ˆH K

SND

XND

Process rate j

KOH SS SNO KO,H +S0 KS +SS KNO +SNO

g XB,H

XB,H

4.4. Sensitivity analysis

fpad fpad

fp fp

1 − fpad 1 − fpad −1 Hat

Anoxic het.growth

Aerobic aut.growth

Het decay Aut. decay Ammonif.

Hydrolysis org. comp.

Hydrolysis organic N

Biof. aer. het. growth

Biof. anox. het. growth

Biof. aer. aut. growth

Biof. het .decay Biof. aut. decay Biof. hydrolysis org. comp. Biof. hydrolysis org. N Biof. ammonif.

2

3

4 5 6

7

8

9

10

11

12 13 14 15 16

1

−Y

1

−Y1

Aerobic het.growth 1

1

− Y1 H

Hat

−1

1 − fp 1 − fp

XS − Y1 H

SS

Component i

5.1. Model calibration

Process j

Table 3 Petersen matrix of the model, based on the ASM1 assumption.

XI

5. Results and discussion

As aforementioned, the model calibration has been carried out by considering a trial and error procedure. First, the MLSS has been considered as model state variable in the all pilot plant tanks and the parameter values that are related to this state variables most have been assessed. More specifically, the maximum growth rate and the yield coefficient of heterotrophic and autotrophic have been assessed. Thereafter, the COD values have been selected as state variables for the seeking of the model parameters. The nitrogen compounds in the different tanks have been selected following the COD and finally the oxygen concentrations have been selected as model state variables. The parameter values obtained from the calibration step are given in Table 5. In particular, Table 5 reports the calibrated values obtained from the trial and error procedure, together with the typical adopted value as well as the literature range for each model parameter. The obtained values have been compared with typical values reported in the technical literature [20,25,31,32]. The overall model

G. Mannina et al. / Biochemical Engineering Journal 56 (2011) 23–36 Table 4 Mass balance equations referred to HYB section. 1.

VHYB ·

dSS,2

2.

VHYB ·

dSO,2

3.

VHYB ·

dSNO,2

4.

VHYB ·

dSNH,2

5.

VHYB ·

dXB,H,2

VHYB ·

dXB,A,2

VHYB ·

dXB,H,at

VHYB ·

dXB,A,at

6. 7. 8.

dt

= Q (r + 1) · SS,1 − Q (r + 1) · SS,2 + VHYB ·

H

1 YH

· 2 + 3



1−Y

= Q (r + 1) · SNO,1 − Q (r + 1) · SNO,2 + Vaer · − 2.86·YH 2 +



dt





+ VHYB · − Y 1 9 −



Hat

H

1 YA



1−YH YH

dt dt dt dt

9.

VHYB ·

10.

VHYB ·

dXI,2

11.

VHYB ·

dSND,2

VHYB ·

dXND,2

· 1 −



1 YHat

4,57−YA YA

10 + 14



1−Y



1 YA



H,at



3 + VHYB · −

· 3 + VHYB · − 2.86·YH,at · 10 +

= Q (r + 1) · SNH,1 − Q (r + 1) · SNH,2 + VHYB · −iXB · 1 − iXB · 2 − iXB +

dt

dXS,2

12.

− Y1 · 1 −

= Q (r + 1) · SO,1 − Q (r + 1) · SO,2 + VHYB · KL a · (SO,sat − SO,2 ) + VHYB · −

dt



1 YA,at



· 11



1−YH,at YH,at

9 −

4,57−YA,at YA,at



· 3 + 6 + VHYB · −iXB · 9 − iXB · 10 − iXB +

· 11

1 YA





· 11 + 16



= Q (r + 1) · XB,H,1 − Q (r + 1) · XB,H,2 + XB,detached + VHYB · [1 + 2 − 4 ] = Q (r + 1) · XB,A,1 − Q (r + 1) · XB,A,2 + VHYB · [3 − 5 ] = VHYB · (9 + 10 − 12 ) = VHYB · (11 − 13 )

= Q (r + 1) · XS,1 − Q (r + 1) · XS,2 + VHYB · {(1 − fP ) · 4 + (1 − fP )5 − 7 } + VHYB · [(1 − fP,at ) · 12 + (1 − fP,at ) · bA,at · 13 ]

dt dt



31

= Q (r + 1) · XI,1 − Q (r + 1) · XI,2 + VHYB · [fP · 4 + fP · 5 ] + VHYB · (fP,at · 12 + fP,at · 13 )

dt dt

= Q (r + 1) · SND,1 − Q (r + 1) · SND,2 + VHYB ·



= Q (r + 1) · XND,1 − Q (r + 1) · XND,2 + VHYB ·

−6 + 7 ·



XND,2 XS,2



+ VHYB · [15 − 16 ]

(iXB − fp iXP ) · 4 + (iXB − fp iXP ) · 5 − 7 ·

· 13 − 15 }

parameter values are generally consistent with the values coming from previous studies. However, it has to be stressed that due to nature of the employed mathematical model, i.e. characterized by a high number of both model parameters and model state variables, identifiability issues occur (among others [37,38]). To tackle these latter an in-depth analysis would be needed employing dedicated calibration protocols [39]. Such analysis was, however, out of the scope of the paper and are not herein discussed. Concerning

XND,2 XS,2



+ VHYB · {(iXB,at − fp,at iXP,at ) · 12 + (iXB,at − fp,at iXP,at )

the attached biomass, the calibrated values were well in the range of default ones, except for the maximum autotrophic growth rate (A,at ), which was lower, the maximum heterotrophic growth rate (H,at ), and the autotrophic yield coefficient (YA,at ), which were a little bit higher than reference values usually adopted for biofilm; this result can be related to the fact that there probably was a specialization in the biofilm activity, particularly referring to the autotrophic biomass, related to the presence of the first activated

Table 5 Calibrated and typical adopted values for each model parameter, as well as literature range. Symbol

Parameter description

Suspended biomass

A Max. growth rate aut. YA Aut. yield coefficient KNH Sat. coefficient for ammonia

H Max. growth rate het. YH Het. yield coefficient KS Sat. coefficient for organic matter KOH Het. saturation coefficient for oxygen KOA Aut. saturation coefficient for oxygen KNO Sat. coefficient for nitrate KL a Oxygen transfer coefficient bH Het. decay rate bA Aut. decay rate hg Coeff. for anoxic het. growth Hydrolysis rate kh Kx Hydrolysis constant ka Organic N transformation coefficient to ammonia fp Fraction of inert material in biomass ixb Ammonia fraction in biomass ixp Ammonia fraction in particulate products Attached biomass Diffusion coefficient for oxigen DSO DSS Diffusion coefficient for organic matter DNO Diffusion coefficient for nitrate nitrogen DNH Diffusion coefficient for ammonium nitrogen

H,at Max. growth rate het.

A,at Max. growth rate aut.

Anox,at Max. growth rate anoxic YH,at Het. yield coefficient YA,at Aut. yield coefficient bH,at Het. decay rate bA,at Aut. decay rate g,at Coeff. for anoxic het. growth Hydrolysis rate kh,at ka,at Organic N transformation coefficient to ammonia fp,at Fraction of inert material in biomasss Ammonia fraction in biomass iXB,at iXP,at Ammonia fraction in particulate products

Calibrated

Typical

0.40 0.22 – 6.00 – 20.00 – 0.20 – 207 0.45 – – 1.00 0.01 0.04 – – –

0.80 0.24 1.00 3.00 0.67 15.00 0.20 0.40 0.50 – 0.30 0.20 0.80 3.00 0.90 0.05 0.08 0.08 0.06

– – 0.1 – 2.80 0.04 – – 0.22 0.10 0.06 – – – – – –

2.1 0.58 2 1.8 0.80 0.10 2.40 0.65 0.20 0.15 0.04 0.80 0.08 0.05 0.08 0.08 0.06

Literature range

Reference

Units

0.2–1 0.07–0.28 0.4–2 0.6–8 0.38–0.75 5–225 0.015–0.2 0.4–2 0.1–0.5 – 0.05–1.6 0.05–2 – 1.5–4.5 0.015–0.045 0.04–0.12 – 0.03–0.129 0.03–0.09

[20] [20] [20] [20] [20] [20] [20] [20] [20] – [20] [20] [20] [20] [20] [20] [20] [20] [20]

d−1 mgCOD mgNH4 -N−1 mg L−1 1/d mgCOD/mgCOD mg L−1 mg L−1 mg L−1 mg L−1 d−1 d−1 d−1 – mgCODp mgCOD−1 d−1 mgCODp mgCOD−1 L mgCOD−1 d−1 – mgN mgCOD−1 mgN mgCOD−1

– – – – 0.6–8 0.2–1 – 0.380–0.75 0.07–0.28 0.05–1.6 0.05–2 – 1.5–4.5 0.04–0.12 – 0.03–0.129 0.03–0.09

[25] [25] [25] [25] [20] [20] [31] [20] [31] [31] [32] [31] [20] [20] [25] [20] [20]

cm2 d−1 cm2 d−1 cm2 d−1 cm2 d−1 d−1 d−1 d−1 mgCOD mgCOD−1 mgCODmgNH4 -N−1 d−1 d−1 – d−1 L mgCOD−1 d−1 – mgN mgCOD−1 mgN mgCOD−1

32

G. Mannina et al. / Biochemical Engineering Journal 56 (2011) 23–36

3

4

5

MLSSAS1 simulated [g/L]

NH4-NHYB measured [mg/L]

2

R = 0.61 E = 0.34 Np = 34

3

15

10 2

R = 0.72 E = 0.49 0.49 Np = 12

5

2

4 5 simulated d [g/L [g/L]] MLSSHYB simulate

5

10

15

NH4-NAS2 measured [mg/L]

2

R = 0.68 E = 0.36 0.36 Np = 34

3

2

3

4 MLSSAS2 simulated simulated [g/L] [g/L]

R2 = 0.87 E = 0.55 Np = 13

15

R2 = 0.54 E = 0.53 Np = 12

5

5

5

10

R2 = 0.462 E = 0.266 Np = 47

4

25

3

30

6

20

R2 = 0.81 E = 0.59 Np = 8

15

4 5 6 DOHYB simulated [mg/L]

R2 = 0.584 E = 0.302 Np = 47

5 4 3 2 1

15

NH4-NAS2 simulated [mg/L [mg/L]]

20 25 simulated [m [mg/L] g/L] NOx-NAS2 simulated

1

2

3

4

5

6

DOAS2 simulated [mg/L]

55 TNAS2 measured [mg/L]

90 TCODAS2 measured [mg/L]

20

15 0

6

3

15

25

10

4

5

NOX-NHYB simulated [mg/L]

0 2

20

10

15

4

25

20

2

DOAS1 simula simulated ted [mg/L] [mg/L]

6

NH4-NHYB simulated [mg/L]

5

0

10 0

3

2

15 20 NOx-NAS1 simulated [mg/L]

30

20

0

2

MLSSAS2 measured [g/L]

25 35 [mg/L] NH4-NAS1 simulated [mg/L]

2

R = 0.54 0.542 E = 0.305 4 Np = 47

0 10

15

NOx-NAS2 measured [mg/L]

MLSSHYB measured [g/L]

4

R2 = 0.68 E = 0.38 Np = 13 10

5

5

5

DOAS1 measured [mg/L]

R = 0.94 E = 0.75 Np = 16

15

15

DOHYB measured [mg/L]

2

2

DOAS2 measured [mg/L]

2

25

NOX-NHYB measured [mg/L]

R2 = 0.693 E = 0.418 Np = 34

3

NOx-NAS1 measured [mg/L]

NH4-NAS1 measured [mg/L]

MLSSAS1 measured [g/L]

4

6

20

35

5

75

60

R2 = 0.86 0.86 E = 0.57 Np = 5

45

45

35 2

R = 0.92 E = 0.81 Np = 6

25

15

30 30

45

60

75

90

TCODAS2 simulated [mg/L]

15

25 35 45 55 TNAS2 simulated [mg/L]

Fig. 4. Measured versus simulated values for model outputs in both experimental periods.

sludge compartment, where the carbonaceous substances were removed, leading to a high nitrifying activity in the hybrid compartment. Since zero order reaction for attached biomass was assumed, half saturation coefficients were not considered in the calibration step for this kind of growth. Concerning the suspended biomass, the calibrated autotrophic yield coefficient (YA ) resulted in good agreement with the reference one at 20 ◦ C [20], showing a higher nitrification activity than what would be normal in a pure activated sludge reactor working under the same operative conditions; this result can probably be related to the seeding effect due to the biofilm sloughing off from the carriers, which contributed to increase the nitrification ability of the suspended biomass. The half saturation coefficients are of particular interest in view of diffusion mass transport limitations; in particular, the half-saturation coefficient for soluble organic substrate (KS ) was found to be slightly bigger than the

default value adopted for activated sludge systems [32] suggesting that dissolved organic matter diffusion limitations for suspended biomass were not so important. On the other hand, the oxygen half saturation coefficient for suspended autotrophic biomass (KOA ) was found to be smaller than literature values [20]; the suggestion is that autotrophic bacteria in suspended biomass are less sensitive to dissolved oxygen concentration, becoming oxygen-limited only for very low dissolved oxygen concentrations. Such result could likely be related to the carriers presence inside the system, which would be responsible of floc size reduction, thus favouring oxygen transfer towards the inner part of suspended flocs. Indeed, this result should be confirmed in the future with further experimental analysis. Fig. 4 shows the comparison between simulated and measured values, for the 14 model state variables chosen in this study. As can be observed, the model generally is able to reproduce the measured values satisfactorily, showing acceptable correlation coefficients.

G. Mannina et al. / Biochemical Engineering Journal 56 (2011) 23–36

33

Fig. 5. Dynamic model results for MLSS in AS1 (a), HYB (b), AS2 (c), DO in AS1 (d), HYB (e) and AS2 (f) compared with experimental data from both periods.

A comparison between measured and simulated data showed that the simulated data are in good agreement with the measured ones. In particular, the indices that have been used to evaluate the model adaptation to the measured data are the correlation coefficient R2 and the Nash and Sutcliff index [29], as better outlined in the following Table 6. The dynamic simulation results of MLSS, DO, NH4 -N, NOX N, TCOD and TN, compared with measured data, are shown in Figs. 5–7. It can be seen that the simulation results from the proposed mathematical model agree well with experimental data. Concerning the MLSS simulation, the obtained results were really good, except for day 26, when a problem in the RAS system occurred, with a huge loss of biomass, and the available data were probably not enough to well simulate the complexity of the real situation. Model simulation for ammonium nitrogen also showed a good agreement with measured data, except for the first experimental days, when the ammonium concentrations in the reactors were probably too low to allow a good adaptation of the model. Table 6 Goodness of fit indices for the different outputs. R2

E

MLSS NH4 -N NOX -N DO

0.693 0.940 0.68 0.542

0.418 0.75 0.38 0.305

MLSS NH4 -N NOX -N DO

0.607 0.72 0.87 0.462

0.34 0.49 0.551 0.266

MLSS NH4 -N NOX -N DO TCOD TN

0.68 0.54 0.817 0.584 0.86 0.92

0.357 0.532 0.589 0.302 0.579 0.817

Further, the daily variations of NOX -N and DO in the three reactors were well reproduced by the simulation results. Concerning the TCOD and TN simulation results, it has to be stressed that, during the first experimental days in particular, there was a difference between the model and measured values; this situation is probably due to the few data available for these parameters, since TCOD and TN were measured once a week, as control parameters for the pilot plant. In order to pin down the benefit of the HMBBR system, once calibrated the mathematical model, a simulation run has been carried out reducing to zero the contribution of the biofilm. In other words, a system characterized by only suspended biomass has been simulated assuming the same influent as model input and as values of the model parameters for the suspended biomass, the ones coming from the model calibration carried out in the previous step. The model simulations revealed a negligible effect in terms of COD. On the other hand, the variation in terms of ammonium removal was about 20% lower than for the case of the HMBBR system. Therefore, for the analyzed HMBBR plant layout, the main removal contribution is in terms of ammonium removal. However, it has to be stressed that such efficiency values in terms of both nitrogen and carbon removal can be increased considering other plant layout schemes. However, such goals were out of the scope of the present paper and were not detected.

5.2. Sensitivity analysis As discussed above, in order to investigate if the coefficient values determined during the model calibration were influencing the model output, a sensitivity analysis was performed. In Fig. 7, the sum of local sensitivity for each coefficient is reported. Indeed, the parameters that in general have the strongest impact on the model outputs are the heterotrophic yield coefficient (YH ), the maximum autotrophic growth rate (max.A ) and the autotrophic yield coefficient (YA ) for the suspended biomass; other parameters of the suspended biomass, such as ammonium fraction in biomass

34

G. Mannina et al. / Biochemical Engineering Journal 56 (2011) 23–36

Fig. 6. Dynamic model results for NH4 -N in AS1 (a), HYB (b), AS2 (c) and NOX -N in AS1 (d), HYB (e) and AS2 (f), compared with experimental data from both periods.

(ixB ), hydrolysis rate (kh ) and the global transfer coefficient for dissolved oxygen (KL a), have a moderate impact on the model, while other kinetic parameters (max,H , bH and bA ) for the suspended biomass give a low impact on the model output. It has to be stressed that, in general, the coefficients related to the suspended biomass have a higher impact than those related to attached biomass on the model output, and in particular, the heterotrophic yield coefficient YH , was very influential on the MLSS, NH4 -N, NOX -N and DO simulation results. Such a result could be due to the pilot plant configuration characterized by two tanks devoted to the suspended biomass activities and one tank for both attached and suspended biomass. Since the sensitivity coefficients have been calculated as average values respect to the different model state variables, in relative terms the suspended biomass processes have a higher weight respect to the attached one leading to the sums of the sensitivity coefficients globally higher for suspended biomass. Furthermore, it is worthy to observe that the values reported in Fig. 8 indicate an overall sensitivity, as sum of the local sensitivities for each coefficient; further, since the sensitivity analysis for TCOD had a high impact, and the suspended

biomass was more responsible in TCOD removal, this is a possible reason for the obtained results, in terms of overall sensitivity. 5.3. Pilot plant removal efficiencies As previously mentioned, in the first sub-period of the experimental campaign, the influent flow was equal to 19 L/h, corresponding to a 4.5 h HRT, while in the second phase it was decided to increase the flow up to 25 L/h, with a 3.5 h HRT. The increase of the hydraulic load, together with the increase of the wastewater strength, allowed to increase the organic and ammonium loads influent to the pilot plant, which ranged from 88.91 to 128.78 gTCOD/d and from 8.7 to 16.8 gNH4 -N/d for TCOD and NH4 N, respectively. The average removal efficiencies for both TCOD and FCOD were a little bit higher during the first period (77.78 and 65.54%, respectively) compared to the second one (75.65 and 64.34%, respectively), even if the decrease in the removal efficiencies during the second period was not so great. These results confirm the robustness of the system and that a HMBBR process is resilient to organic load variations, thus resulting in a suitable

Fig. 7. Dynamic model results for TCOD (a) and TN (b), in AS2 compared experimental data from both periods.

G. Mannina et al. / Biochemical Engineering Journal 56 (2011) 23–36

35

10 9 8

Sum of local sensitivity

7 6 5 4 3 2 1

fp,at

ng,at

ixp,at

KNO

kh,at

fp

bA

ix,at

ixp

ixb,at

μmmax,H,at,anox

ng

KO,H

KO,A

Ks

μmmax,h

bA,at

YH,at

Kx

YA,at

bH,at

μmmax,A,at

μmmax,H,at

KNH

ka,at

ka

bH

kh

KLa

ixb

μmmax,A

YA

YH

0

Fig. 8. Sums of the local sensitivity of the 14 model state variables.

solution in the up-grading of overloaded activated sludge systems [34]. Concerning the nitrification process, the average nitrification efficiencies were 81.3 and 71% in the first and in the second period, respectively. Despite the temperature value was near to 11.5 ◦ C, the average ammonium removal efficiency during the overall experimental period resulted as high as 76.5%, referring to the simulation results. In particular, during the first period, as aforementioned, the ammonium removal was higher, more than 81%, with a 4.5 h HRT. When the flow was increased to 25 L/h, corresponding to a 3.5 h HRT, a decrease in the nitrification efficiency was recorded, but the removal percentages never decreased down to 71%. This good performance despite the high hydraulic, ammonium and organic loads is probably related to the contemporary growth of the biofilm that reached in the second period a concentration of 11 gTS/m2 (3.5 gTS/L). Indeed, compared to a pure suspended growth process, a hybrid system is less influenced by the temperature decrease [35]; this aspect is related to the fact that the nitrification process is highly influenced by the oxygen diffusion, and, in a biofilm process, diffusion increases when the temperature decreases, as oxygen solubility is higher with lower temperatures [6]. 6. Conclusions A simplified mathematical model has been developed to simulate an IFAS/hybrid moving bed biofilm reactor (HMBBR) process in dynamic condition. The model is composed of two models, for the simulation of suspended and attached biomass. The calibrated coefficients used in the model allowed to well describe the behaviour of the HMBBR pilot system, and the dynamic simulation results, compared with data obtained from a field gathering campaign, were good, except for ammonium, TCOD and TN, with some differences during the first experimental days in particular. The sensitivity analysis suggested that the biological behaviour of a nitrifying HMBBR is strongly influenced by YH , max,A and YA , referring to suspended biomass. However, such a result needs to be confirmed by an in-depth sensitivity analysis such as global sensitivity analysis which is based on Monte Carlo simulations runs (see [36]). Moreover, further developments of the proposed model are required,

considering a longer experimental campaign and a direct evaluation of kinetic parameters of hybrid reactors that probably are different from pure suspended or attached biomass systems.

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