*3.1.2.1 Reactor modeling with anaerobic digestion model 1 (ADM1)*

The combination of CFD and a simple bioprocess was used to model an expanded granular sludge bed (EGSB) reactor [21]. The biokinetics usually interact with each other. ADM1 is the widely applied model for modeling bioprocesses in anaerobic wastewater treatment. When calibrated, ADM1 can be integrated with hydrodynamic models to obtain an integrated model for reactor modeling. An integrated model can be obtained when a granular sludge bed reactor is treated as a connection of virtual CSTRs and PF reactors and by applying the ADM1 model to each of these virtual reactors. An integrated model was used to model an UASB reactor treating traditional Chinese medicine wastewater [22]. In the integrated model, values for nonsensitive parameters were adopted from public reports, while sensitive parameters were calibrated. Similarly, sensitive parameters were calibrated while modeling a UASB reactor treating wastewater from a molasses-based ethanol distillery [23]. In these two studies, the original form of ADM1 was maintained. However, the ADM1 can be extended to be more practicable. The ADM1 can be extended by extending the number of microbial species [7, 24] or by including new soluble fermentable substrates [25].

## *3.1.2.2 Reactor modeling with a bioparticle model*

Strategies for reactor modeling based on a bioparticle model are reported. However, this reactor modeling strategy is case-specific, and relevant road maps of each strategy are not clearly stated [3, 4, 12]. By cross-checking these models, a general model strategy is summarized below:


**75**

Not clear

*loading rate.*

**Table 1.**

*Approaches for Modeling Anaerobic Granule-Based Reactors*

sub-models such as sludge concentration distribution along reactor height and sludge bed expansion at different upflow velocities [4]. Furthermore, the size of a representative granule is manually but carefully selected while applying a bioparticle model. The weakness of reactor modeling with a bioparticle model is that a reactor model cannot be obtained when a bioparticle model cannot be obtained. A bioparticle model has not been convincingly established for complex substrates. Therefore, reactor modeling with a bioparticle model for complex substrate is still

> alkalinity and VFA concentration, T, pH in the influent

> > TKN

HRT

<sup>−</sup>, S2<sup>−</sup>, pH,

<sup>−</sup>, NO2

3 Influent sulfide, nitrate concentration, S/N mole ratio, pH, and HRT

3 Influent COD, HRT, pH, COD loading rate

3 HRT, influent COD,

4 Influent, flow rate,

5 Influent COD, Q,

pH, T, alkalinity, VFA, dilution rate, organic load, TSS

inlet and outlet COD

TKN, effluent VFA and bicarbonate

influent–effluent alkalinity and pH, T

rate, influent alkalinity and pH, effluent pH

> effluent pH, T, alkalinity effluent COD and VFA concentrations

**Inputs Outputs Ref**

COD of effluent [30]

[29]

[31]

[32]

[35]

[37]

[38]

Effluent BOD and COD

Nitrate, nitrite, sulfide acetate

Sulfide, nitrate removal percentage, sulfate and nitrogen production percentage

Effluent COD [33]

COD removal [34]

Effluent COD [27]

Biogas production [36]

Biogas production rate

Biogas and methane production rates

Biogas and methane production rates

**No. of layers**

4 NO3

BP UASB, domestic 3 TSS, VSS, COD,

BP UASB, domestic 3 BOD, COD, NH4–N,

*DOI: http://dx.doi.org/10.5772/intechopen.90201*

difficult.

**Types Reactors and** 

BP EGSB, denitrifying

BP UASB, denitrifying

BP UASB,

BP UASB, cotton

NARX UASB, bagasse

ANFIS EGSB, corn

**wastewater**

sulfide removal

sulfide removal

pharmaceutical

textile

wash

processing

BP UASB, molasses 3 OLR, VFA of effluent,

AMIMO UASB, molasses — OLR, TCOD removal

*Overview of neural networks applying to sludge bed reactor modeling.*

UASB, molasses 3 OLR, influent and

*ANFIS, adaptive neuro-fuzzy inference system; OLR, volumetric organic loading rate; TCOD, volumetric total chemical oxygen demand; AMIMO, multiple inputs and multiple outputs; TSS, total suspended solids; VSS, volatile suspended solids; COD, chemical oxygen demand; VFA, volatile fatty acid; T, temperature; BOD, biological oxygen demand; TKN, total Kjeldahl nitrogen; HRT, hydraulic retention time; Q, reactor flow rate; and OLR, organic* 

The bioparticle model applied here is a diffusion-reaction model rather than an IBM because the implementation of an IBM will encounter a huge computational workload. In addition, this strategy can be enriched by including other

*Approaches for Modeling Anaerobic Granule-Based Reactors DOI: http://dx.doi.org/10.5772/intechopen.90201*

*Bacterial Biofilms*

*3.1.2 Applied biokinetics*

soluble fermentable substrates [25].

the model.

virtual reactor.

lar sludge bed.

*3.1.2.2 Reactor modeling with a bioparticle model*

a general model strategy is summarized below:

The biokinetics regarding wastewater treatment are nicely represented by a series of mathematical equations. Either the RC strategy or the CFD strategy is ready to be combined with the equations to model bioprocesses in a bioreactor.

The combination of CFD and a simple bioprocess was used to model an expanded granular sludge bed (EGSB) reactor [21]. The biokinetics usually interact with each other. ADM1 is the widely applied model for modeling bioprocesses in anaerobic wastewater treatment. When calibrated, ADM1 can be integrated with hydrodynamic models to obtain an integrated model for reactor modeling. An integrated model can be obtained when a granular sludge bed reactor is treated as a connection of virtual CSTRs and PF reactors and by applying the ADM1 model to each of these virtual reactors. An integrated model was used to model an UASB reactor treating traditional Chinese medicine wastewater [22]. In the integrated model, values for nonsensitive parameters were adopted from public reports, while sensitive parameters were calibrated. Similarly, sensitive parameters were calibrated while modeling a UASB reactor treating wastewater from a molasses-based ethanol distillery [23]. In these two studies, the original form of ADM1 was maintained. However, the ADM1 can be extended to be more practicable. The ADM1 can be extended by extending the number of microbial species [7, 24] or by including new

Strategies for reactor modeling based on a bioparticle model are reported. However, this reactor modeling strategy is case-specific, and relevant road maps of each strategy are not clearly stated [3, 4, 12]. By cross-checking these models,

i.First, a representative granular size is assumed and applied to all granules in

ii.Second, a RC strategy is applied to divides a real reactor into a single or a

iii.Then, the number of representative granules can be obtained in each virtual reactor in the model by measuring the total sludge mass in a real reactor and

iv.Fourth, the substrate degradation rates in each virtual reactor are obtained by adding substrate degradation rates of all representative granules in each

v.Finally, the substrate degradation rates in each virtual reactor can be added together to obtain a reactor model that models the operation of a real granu-

The bioparticle model applied here is a diffusion-reaction model rather than an IBM because the implementation of an IBM will encounter a huge computational workload. In addition, this strategy can be enriched by including other

series of virtual reactors, that is, CSTRs and/or PF reactors.

calculating the mass of the representative granule.

*3.1.2.1 Reactor modeling with anaerobic digestion model 1 (ADM1)*

**74**

sub-models such as sludge concentration distribution along reactor height and sludge bed expansion at different upflow velocities [4]. Furthermore, the size of a representative granule is manually but carefully selected while applying a bioparticle model. The weakness of reactor modeling with a bioparticle model is that a reactor model cannot be obtained when a bioparticle model cannot be obtained. A bioparticle model has not been convincingly established for complex substrates. Therefore, reactor modeling with a bioparticle model for complex substrate is still difficult.


*ANFIS, adaptive neuro-fuzzy inference system; OLR, volumetric organic loading rate; TCOD, volumetric total chemical oxygen demand; AMIMO, multiple inputs and multiple outputs; TSS, total suspended solids; VSS, volatile suspended solids; COD, chemical oxygen demand; VFA, volatile fatty acid; T, temperature; BOD, biological oxygen demand; TKN, total Kjeldahl nitrogen; HRT, hydraulic retention time; Q, reactor flow rate; and OLR, organic loading rate.*
