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CO-DIGESTION AND FOOD WASTE AD: BIOWIN MODELLING TO OPTIMISE
OLR AND HRT ON THE AVONMOUTH FOOD WASTE DIGESTERS
Forgacs G. 1, Smyth M. 1, Law I.2 and Arnot T. C.3 1Aqua Enviro, UK, 2 Wessex Water Enterprises Limited (trading as GENeco), UK, 3 Water Innovation &
Research Centre, UK
Corresponding author email: [email protected]
Abstract
In this study, a model for anaerobic digestion of food waste was developed using BioWin 4.1
simulator software. Substrate characteristics were determined by laboratory analysis and steady
state simulation. Kinetic parameters were identified by running dynamic simulations. Input data for
calibration and validation was collected from a large-scale food waste digestion plant in the UK. The
model predictions showed a high degree of agreement in terms of biogas production, COD and VS
reduction, and ammonia and VFAs concentrations. The model was used to investigate how the
process responds to various changes in the operating conditions including increasing organic loading
rate (OLR) or decreasing HRT. The results show that the current 20-25 days HRT could be
decreased by 10% without risking process stability and reducing the efficiency of the anaerobic
digestion, and hence throughput and biogas production could be increased.
Keywords
Anaerobic digestion; BioWin; food waste; simulation;
Introduction
BioWin is a widely used software tool for simulating waste water treatment processes. It contains an
advanced anaerobic digestion model that integrates the IWA activated sludge models (ASM1, ASM2d
and ASM3) with the ADM1 anaerobic digestion model. This model has been successfully applied for
simulation of anaerobic digestion of sludge. However, to our knowledge, BioWin has yet to be applied
using other substrates as feedstock for anaerobic digestion. In this paper, our aim is to investigate the
capacity of a BioWin model to describe the anaerobic digestion of food waste.
Material and Methods
Process description
The development of the food waste digestion model was based on a full sized industrial food waste
plant in UK. The food waste mainly consists of mixture of kerbside collected food waste, supermarket
food waste past the sell by date, and a smaller amount of merchant industrial food waste. The plant
utilizes 30,000 tons year-1 food waste and generates about 16,000-18,000 m3 day-1 of biogas. The
incoming food waste goes through a pasteurization step (1 h, 70˚C) before the anaerobic digestion
process. The anaerobic digestion stage consists of two 2,400 m3 digesters, which are operated at
mesohilic temperature. Typically, the hydraulic retention time is around 21 days, while the organic
loading is 3.0 - 3.5 kg m-3 day-1. The digester effluent is dewatered; the liquid fraction is used in other
processes, while the solid fraction is sold to farmers as fertilizer. Figure 1 shows a schematic of AD
plant, while Figure 2 and 3 summarizes the substrate quality and operation performance data.
Food Waste AD 2 Effluent
CakeAD 1
Pasteurization
Figure 1: Schematic flow diagram of food waste digestion plant used by BioWin
Table 1: Characteristics of food waste at Avonmouth (Data collected during 2013)
Food waste Minimum Maximum Average
TS (%) 5.53 14.06 8.69
VS/TS (%) 66.67 95.60 88.78
VS (mg L-1) 50,000 113,921 56,979
COD (mg L-1) 84,412 259,000 138,518
VFA (mg L-1) 5,400 24,965 13,436
Table 2: Summary of process efficiency at Avonmouth
Biogas yield (m3 kg-1 VS)
Methane yield
(m3 kg-1 VS)
Methane yield
(m3 kg-1 COD)
VS removal (%)
COD removal (%)
Efficiency based on COD
(%)
0.91 0.57 0.285 77.8 80.3 81.6
Model description
A simulation was developed using BioWin 4.1 (EnviroSim Associates Ltd., Canada), a waste water
treatment process simulator that brings together biological, chemical and physical process models. It
integrates international IWA models including ASM1, ASM2d and ASM3 with the anaerobic digestion
model. The combined BioWin AS/AD model includes 50 state variables and 70 process expressions,
which describe the biological processes occurring in activated sludge and anaerobic digestion
systems, including biological, chemical, and physical processes, several chemical precipitation
reactions, and gas–liquid mass transfer for six gases.
Model development and validation of food waste digestion by BioWin 4.1
The main difference between food waste and sludge digestion are (1) substrate characteristics and
the kinetics of the anaerobic digestion. Substrate parameters including nitrogen, ammonia, chemical
oxygen demand (COD) were analysed at the plant. Other parameters (readily biodegradable fraction,
unbiodegradable fraction, etc.) were defined via steady-state anaerobic digestion simulation. Kinetic
parameters were adjusted using a dynamic model and historical data from the plant from 08/2013-
08/2014. The process model was validated based on prediction of biogas production, ammonia
concentration, and reduction of COD.
Results and Discussion
Food waste characterization
In BioWin the substrate (sludge) is characterized based on biodegradability and COD. Table 2
presents the main characteristics of the raw sludge and food waste determined by steady-state
simulation. The readily biodegradable fraction of food waste is 28 % of COD; this value is 75 % higher
than that of typical raw sludge. According to the simulation the main part of the waste degrades slowly
and only a small amount (16 %) of it does not degrade in the anaerobic digesters. The main part of
the unbiodegradable fraction is particulate which probably comprises bones, eggshells, and entrained
fragments of food packaging.
Table 1: Characteristic of food waste compared to characteristic of typical raw sludge
Variable Name Raw sludge Food waste
Fbs - Readily biodegradable (including Acetate) [gCOD/g of total COD] 0.1600 0.2800
Fac - Acetate [gCOD/g of readily biodegradable COD] 0.1500 0.2500
Fxsp - Non-colloidal slowly biodegradable [gCOD/g of slowly degradable COD] 0.7500 0.9000
Fus - Unbiodegradable soluble [gCOD/g of total COD] 0.0500 0.0300
Fup - Unbiodegradable particulate [gCOD/g of total COD] 0.0130 0.1300
Fna - Ammonia [gNH3-N/gTKN] 0.6600 0.2500
Fnox - Particulate organic nitrogen [gN/g Organic N] 0.5000 0.4000
Fnus - Soluble unbiodegradable TKN [gN/gTKN] 0.0200 0.0400
FupN - N:COD ratio for unbiodegradable part. COD [gN/gCOD] 0.0350 0.0700
Fpo4 - Phosphate [gPO4-P/gTP] 0.5000 0.7500
FupP - P:COD ratio for unbiodegradable part. COD [gP/gCOD] 0.0110 0.0110
Kinetic parameters
Kinetic parameters in the BioWin model are divided into the following categories: Ammonia Oxidising
Biomass (AOB), Nitrite Oxidising Biomass (NOB), ANAMMOX, Ordinary Heterotrophic Organisms
(OHOs), phosphorus accumulating organisms (PAOs), Acetogens, Methanogens, pH and switching
functions. Although, several different microbial groups are involved in the anaerobic digestion
process, the methanogens play the most important role in this process. Therefore, during model
validation, the optimal kinetic parameters of the methanogens were tuned by manual adjustment.
Table 2: Kinetic parameters of methanogens
Name Raw sludge Food waste Arrhenius
Acetoclastic max. spec. growth rate [d-1] 0.3000 0.3400 1.0290
H2-utilizing max. spec. growth rate [d-1] 1.4000 2.2000 1.0290
Acetoclastic substrate half sat. [mgCOD L-1] 100.00 950.00 1.0000
Acetoclastic methanol half sat. [mgCOD L-1] 0.5000 0.5000 1.0000
H2-utilizing CO2 half sat. [mmol L-1] 0.1000 1.0000 1.0000
H2-utilizing substrate half sat. [mgCOD L-1] 0.1000 1.0000 1.0000
H2-utilizing methanol half sat. [mgCOD L-1] 0.5000 0.5000 1.0000
Acetoclastic propionic inhibition [mgCOD L-1] 10000 10000 1.0000
Acetoclastic anaerobic decay rate [d-1] 0.1300 0.1300 1.0290
Acetoclastic aerobic/anoxic decay rate [d-1] 0.6000 0.6000 1.0290
H2-utilizing anaerobic decay rate [d-1] 0.1300 0.1100 1.0290
H2-utilizing aerobic/anoxic decay rate [d-1] 2.8000 2.8000 1.0290
As Table 2 shows, the kinetic parameters for the food waste digestion differ from those for sludge
digestion. It is well known that different methanogens, perhaps even different species of
methanogens, dominate the microbial population during food waste digestion and sludge digestion [1,
2]. Also, several factors can affect the microbial community of food waste digestion including heat-
treatment, nutrient availability and ammonia concentration of the digester [1]. The main difference
found in this study is related to the kinetics of the H2-utilizing methanogens. Hydrogenotrophic
methanogens are a minority in food waste digestion, whilst according to Kim et al., [2], in sewage
sludge digesters hydrogenotrophic methanogens are absolutely dominant.
Validation process
The input data for the simulations came from an industrial food waste plant in the UK and covered the
period of September 2013 to September 2014. The model was validated using the most important
outputs, including biogas production and COD reduction. As Figure 2 shows the model prediction
shows a high degree of agreement with the data measured at the plant. Moreover, the model also
gave excellent results regarding VS reduction, biogas composition, and the ammonia and volatile fatty
acid concentrations (data not shown). In the next stage of this study the model will be used to
investigate process responses to various changes in the operation conditions, including increasing
OLR or decreasing HRT.
Figure 2: Model simulation versus the historical plant data: (A) biogas production, (B)
COD reduction in AD1.
Process optimization
In the first part of the simulation study the OLR was increased by 10, 20 and 30 % respectively. For
this purpose, COD concentration of the influent flow was increased and all other parameters were
unchanged. The second part of the study investigated the effect of shorter HRT. Reducing the volume
of digesters is not realistic, thus for practically reasons, in this set of tests the influent flowrate was
increased by 10, 20 and 30 % mimicking the reduction of HRT. Worth to mention that increasing the
flowrate by 10, 20 and 30 % not only reduces the HRT by 10, 20, 30 %, but increases the organic
loading rate by 10, 20, 30% respectively. The simulation period was one year and data between
09/2013 - 09/2014 was used.
In both cases, biogas production and volatile fatty acids (VFAs) formation were predicted over time as
a response to the changings. The VFAs is an intermediate product in AD processes, and it commonly
used to gain information of the health of the digesters, since accumulation of VFAs is first sign of a
stressed and overloaded digester. Biogas production/productivity was analysed to be sure that the
additional organic load is converted to biogas.
Effect of OLR increase
At 10 % OLR increase the biogas production in AD1 and AD3 is increased by 10.4 ± 1.5 % and 10.2 ±
4.0 % on daily bases (Figure 2). These values showed that biogas productivity based on the load did
not change. However, the simulation predicted VFAs spikes up to 5,500 mg L-1, which showed that
the process was overloaded in some points (Figure 2).
Biogas production AD3
29/08/201430/07/201430/06/201431/05/20141/05/20141/04/20142/03/201431/01/20141/01/20142/12/20132/11/20133/10/20133/09/2013
OF
F G
AS
FL
OW
RA
TE
(D
RY
) (m
3/h
r (f
ield
))
500
400
300
200
100
0
Original OLR
10% OLR increase
AD3 VFAs
29/08/201430/07/201430/06/201431/05/20141/05/20141/04/20142/03/201431/01/20141/01/20142/12/20132/11/20133/10/20133/09/2013
CO
NC
. (m
g/L
)
6,000
5,000
4,000
3,000
2,000
1,000
Original OLR
10% OLR increase
Figure 1: Effect of 10% OLR increase on biogas production and VFA formation.
Increasing the OLR by 20 % had very similar result, in that the biogas productivity did not decrease, since the model predicted 19.1 ± 2.5 % and 20.4 ± 13.1 % more gas from AD1 and AD3 respectively. However, the VFAs profile was high during the whole simulation indicating stressed and overload digesters (Figure 3).
Biogas production AD3
29/08/201430/07/201430/06/201431/05/20141/05/20141/04/20142/03/201431/01/20141/01/20142/12/20132/11/20133/10/20133/09/2013
OF
F G
AS
FL
OW
RA
TE
(D
RY
) (m
3/h
r (f
ield
))
500
400
300
200
100
0
Original OLR
20% OLR increase
AD3 VFAs
29/08/201430/07/201430/06/201431/05/20141/05/20141/04/20142/03/201431/01/20141/01/20142/12/20132/11/20133/10/20133/09/2013
CO
NC
. (m
g/L
)
16,000
15,000
14,000
13,000
12,000
11,000
10,000
9,000
8,000
7,000
6,000
5,000
4,000
3,000
2,000
1,000
Original OLR
20% OLR increase
Figure 2: Effect of 20 % OLR increase on biogas production and VFA formation.
Simulation revealed that increasing the COD concentration of the feed by 30 % resulted in process
failure. Both digesters collapsed and the biogas production is terminated, as the VFAs level increased
to 80,000 mg L-1 (Figure 4).
Biogas production AD3
29/08/201430/07/201430/06/201431/05/20141/05/20141/04/20142/03/201431/01/20141/01/20142/12/20132/11/20133/10/20133/09/2013
OF
F G
AS
FL
OW
RA
TE
(D
RY
) (m
3/h
r (f
ield
))
500
400
300
200
100
0
Original OLR
30% OLR increase
AD3 VFAs
29/08/201430/07/201430/06/201431/05/20141/05/20141/04/20142/03/201431/01/20141/01/20142/12/20132/11/20133/10/20133/09/2013
CO
NC
. (m
g/L
)
85,000
80,000
75,000
70,000
65,000
60,000
55,000
50,000
45,000
40,000
35,000
30,000
25,000
20,000
15,000
10,000
5,000
Original OLR
AD 3 Volatile fatty acids
Figure 4: Effect of 30 % OLR increase on biogas production and VFA formation.
Effect of HRT reduction At 10 % increase in flowrate, couple of small peak appeared in the VFAs profile – see Figure 5.
However, these peaks were relatively small (3,000 mg L-1) and they lasted only couple of days, before
system overcame them. The biogas yield was not affected by the VFA fluctuation, since the biogas
production increased by 9.7 ± 1.9 % and 9.9 ± 2.8 % on daily bases.
Biogas production AD3
29/08/201430/07/201430/06/201431/05/20141/05/20141/04/20142/03/201431/01/20141/01/20142/12/20132/11/20133/10/20133/09/2013
OF
F G
AS
FL
OW
RA
TE
(D
RY
) (m
3/h
r (f
ield
))
500
400
300
200
100
0
Original HRT
-10% HRT
AD3 VFAs
29/08/201430/07/201430/06/201431/05/20141/05/20141/04/20142/03/201431/01/20141/01/20142/12/20132/11/20133/10/20133/09/2013
CO
NC
. (m
g/L
)
10,000
9,000
8,000
7,000
6,000
5,000
4,000
3,000
2,000
1,000
Original HRT-10% HRT
Figure 3: Effect of 10 % increase in the influent flowrate on biogas production and VFA
formation.
At 120 % of the original flowrate, the model predicted 19.5 ± 6.3 % and 19.7 ± 6.7 % improvement in
the biogas production – see Figure 6. During the year-long simulation period, the VFAs concentration
exceeded 7,000 mg L-1 several times, showing that the digesters were under stress. However, the
systems were able to recover.
Biogas production AD3
29/08/201430/07/201430/06/201431/05/20141/05/20141/04/20142/03/201431/01/20141/01/20142/12/20132/11/20133/10/20133/09/2013
OF
F G
AS
FL
OW
RA
TE
(D
RY
) (m
3/h
r (f
ield
))
500
400
300
200
100
0
Original HRT
-20% HRT
AD3 VFAs
29/08/201430/07/201430/06/201431/05/20141/05/20141/04/20142/03/201431/01/20141/01/20142/12/20132/11/20133/10/20133/09/2013
CO
NC
. (m
g/L
)
10,000
9,000
8,000
7,000
6,000
5,000
4,000
3,000
2,000
1,000
Original HRT-20% HRT
Figure 6: Effect of 20 % increase in the influent flowrate on biogas production and VFA
formation.
At 130 % flowrate, the biogas yield was not affected, since the system produced 28.8 ± 11.2 % more
biogas than the base case – see Figure 7. However, according to the VFA profile, the process was
under stress for almost the entire period.
Biogas production AD3
29/08/201430/07/201430/06/201431/05/20141/05/20141/04/20142/03/201431/01/20141/01/20142/12/20132/11/20133/10/20133/09/2013
OF
F G
AS
FL
OW
RA
TE
(D
RY
) (m
3/h
r (f
ield
))
500
400
300
200
100
0
Original HRT
-30% HRT
AD3 VFAs
29/08/201430/07/201430/06/201431/05/20141/05/20141/04/20142/03/201431/01/20141/01/20142/12/20132/11/20133/10/20133/09/2013
CO
NC
. (m
g/L
)
14,000
13,000
12,000
11,000
10,000
9,000
8,000
7,000
6,000
5,000
4,000
3,000
2,000
1,000
Original HRT-30% HRt
Figure 7: Effect of 30 % increase in the influent flowrate on biogas production and VFA
formation.
Conclusions
This study describes the development of a food waste digestion model using a software tool (BioWin
4.1), which was originally developed for simulating wastewater treatment processes. The model was
calibrated and validated based on a data from a large-scale food waste plant in UK. The model
prediction showed a high degree of agreement with the historical plant data for biogas production,
COD and VS reduction, and ammonia and VFAs concentration. Furthermore, the model indicated that
hydraulic retention time of the system could decreased by 10% without risking the process stability.
Acknowledgements
We would like to acknowledge funding from Wessex Water and EPSRC IAA Grant reference number
EP/K503897/1.
References
1. Blasco, L., et al., Dynamics of microbial communities in untreated and autoclaved food waste
anaerobic digesters. Anaerobe, 2014. 29(0): p. 3-9.
2. Kim, J., W. Kim, and C. Lee, Absolute dominance of hydrogenotrophic methanogens in full-scale anaerobic sewage sludge digesters. Journal of Environmental Sciences, 2013. 25(11): p. 2272-2280.