Selection of variables for on-line monitoring, diagnosis, and control of anaerobic digestion processes
Post on 07-Mar-2017
Selection of variables for on-line monitoring, diagnosis,
and control of anaerobic digestion processes
F. Molina, M. Castellano, C. Garca, E. Roca and J. M. Lema
J. M. Lema
Department of Chemical Engineering,
Universidad de Santiago de Compostela,
Department of Sanitary Engineering,
Universidad de Antioquia,
Department of Statistics and O.R,
Universidad de Santiago de Compostela,
This work aims to systematize the study of indicators for two types of wastewaters:
carbohydrate-based and protein-based synthetic wastewaters. Characterization of steady
states and dynamic response analysis against disturbances were carried out using both
a factorial discriminant analysis (FDA) and a phenomenological analysis, respectively.
This research seeks reconciling both sets of indicators in order to optimize resources and
provide a minimal cost in instrumentation for its implementation at industrial scale. According
to the results of this research, the best indicators for the two types of wastewaters, considering
both process steady states and organic load perturbations are: Biogas flow rate or Methane
flow rate, and Hydrogen concentration in the biogas; Volatile fatty acids and Partial alkalinity
in the liquid phase.
Key words | anaerobic digestion, FDA, process state identification, selection of variables
Anaerobic wastewater treatment is a very complex process,
which involves biochemical transformations as well as
physicochemical processes. To avoid destabilization and
to keep a stable state, the process requires the application of
both a monitoring and a control system. The complexity and
cost of this system depend on the number of variables to be
monitored on-line. The development of a monitoring
system, diagnosis, and control (MD&C) for anaerobic
digestion require an accurately selection of variables
and/or indicators, which give valuable information about
the metabolic status of the process (Castellano et al. 2007).
These variables should fulfill the following key features:
high sensitivity, fast response, and low cost of monitoring
(Switzembaum et al. 1990; Mathiot et al. 1992). Many
researchers have studied and proposed different variables
and groups of variables as indicators of process stability for
anaerobic digestion in presence and/or in absence of
disturbances. Table 1 shows a summary of some of these
proposals. However, there is not a final conclusion about
which is the variable or group of variables that make it
possible to identify the state of the process, in an
appropriate way, and which allows an early detection of
disturbances. Meanwhile, in the proposals presented in
Table 1, only a few variables were compared at the same
time, and a high diversity of wastewater was studied using
heuristic criteria rather than statistical criteria for the
selection of variables.
In order to improve the selection of process indicators,
Castellano et al. (2007) determined the minimum number of
monitored variables for process state identification, using
factorial discriminant analysis (FDA) for winery waste-
water. When only one variable for process state identifi-
cation is used, Castellano and collaborators found that
hydrogen concentration in the gas phase seems to be the
best, since it has a high discriminatory ability among
the process states studied.
One of the purposes of this work is to find out if FDA
classification performance for winery wastewater can be
615 Q IWA Publishing 2009 Water Science & TechnologyWST | 60.3 | 2009
extended to more chemically complex wastewaters or it
would be necessary to use more sophisticated non-linear
non-symmetric discriminant techniques. So using a similar
strategy, this research aims to systematize the study of
indicators for two types of wastewaters: carbohydrate-based
and protein-based synthetic wastewaters. In addition, the
process states were characterized by evaluating the dynamic
response against disturbances. Finally, this research seeks
reconciling both sets of indicators (steady states and
dynamic states) in order to optimize resources and provide
a minimal cost in instrumentation for their implementation
at industrial scale.
MATERIALS AND METHODS
Experiments were conducted in a hybrid USBF (UASB AF) pilot plant with an overall volume of 1.15 m3. The on-
line monitoring system consisted of feeding and recycling
flow-meters (ABB-COPAXE and Siemens-7ME2531), input
and output reactor pH (Cole Parmer) and temperature
(Pt 100); biogas (QBiogas) flow-meter (Brooks-3240);
infrared gas analyzer (Siemens-Ultramat 22P) for measure-
ment of CH4 and CO concentrations in the gas phase;
electrochemical H2 gas analyzer (Sensotrans-Sensotox
420); Total Organic and Inorganic Carbon (TOC/TIC) in
the influent and effluent were determined on-line by catalyst
combustion oxidation and non dispersive infra red (NDIR)
CO2 detection (Shimadzu-TOC 4100); reactor top pressure
(Phead) was measured using a Siemens TMF1563 sensor.
A titrimetric AnaSensew analyzer (Ruiz-Filippi et al. 2005;
Molina et al. 2009) was used for determining both volatile
fatty acids (VFA) and partial alkalinity (PA) every 15
minutes during experimentation. The signals from the
sensors were recorded every 5 seconds. Owing to the high-
stability of the signal, the large time scale of the process, and
hydraulic retention time (HRT) of 24 h, an average moving
window of 15 min was selected for filtering these on-line
signals. A more detailed description of the pilot plant and
monitoring system has been given in Garcia et al. (2007) and
Molina et al. (2007).
Both hydrolyzed protein and dextrin solution have been
used as models of rich proteins wastewater and rich
Table 1 | Summary of proposed variables and indicators used for the identification of states in anaerobic digestion
Phase Variable(s) Kind of waste or synthetic wastewater Reference
Biomass (Solid) Acetotrophic methanogenesis Municipal sludge Conklin et al. (2008)
Physiological and morphologicalcharacteristics
Ethanol plus detergent andsolvent addition
Costa et al. (2009)
Liquid H2 Glucose, protein, and lipids Bjornsson et al. (2001)
Bicarbonate alkalinity Ice cream Hawkes et al. (1994)
Partial alkalinity Municipal sludge Jantsch & Mattiasson (2004)
VFA Manure Ahring et al. (1995)
Manure plus ammoniumchloride addition
Nakakubo et al. (2008)
Gas H2 Vinasses Moletta (1989)
Butyric acid Slater et al. (1990)
VFA and glucose Jones et al. (1992)
Synthetic bakers yeast Guwy et al. (1997)
H2 and CO Sludge plus heavy metals addition Hickey & Switzenbaum(1990, 1991)
H2, CH4 and CO2 Vinasses Mathiot et al. (1992)
H2 and QBiogas PET manufacture Huang et al. (2000)
616 F. Molina et al. | Variables for monitoring and control of anaerobic digestion processes Water Science & TechnologyWST | 60.3 | 2009
carbohydrates wastewater, respectively. Macro-nutrients
(nitrogen and phosphorus), alkalinity, and micro-nutrients
were added to carbohydrate-based wastewater, reaching a
final COD/Bicarbonate/N/P ratio of 1000/600/7/1. Based
on the fact that protein-based wastewater had been
contributed with nitrogen and alkalinity, phosphorus and
micronutrients were added. In both wastewaters, micro-
nutrients were added according to the proportions
suggested by Angelidaki & Sanders (2004) for testing
specific methanogenic activity (SMA).
Experimental conditions for both steady states and dyna-
mical states were 37 ^ 28C, feed flow rate of 48.5 L h21,
recirculation flow rate of 200 L h21, and a 24 h hydraulic
retention time (HRT). Organic loading rate (OLR) has kept
constant at least during 5 days before each dynamical
disturbance experiment. Organic overloads (pulses or steps)
were carried out by increasing the concentration of COD in
the feeding and maintaining the feed flow rate constant.
Table 2 shows the summary of steady states for each
type of wastewater. For protein-based wastewater in some
steady states, mixtures of protein and ethanol in order to
increase the activity of methanogenic biomass were used.
Pulse-type disturbances were intended to explore the
dynamical response of the different variables to a big
organic overload in a short time (approximately 10% of
HRT). In contrast, step-type disturbances provide infor-
mation on the dynamic adjustment of the system, in
presence of a sustained increased in the OLR. Pulse-type
and Step-type disturbances were made by increasing of
COD previous value from 5 to 10 times and 1.5 to 3 times,
respectively. Table 3 shows the OLR increment used to
provoke pulse-type and step-type disturbances for both
carbohydrate-based and protein-based-wastewaters.
Multivariate analysis of steady states
The classification between different steady states was carried
out using FDA. There are two main kind of classification
tools, the supervised classification methods and the unsu-
pervised classification methods (e.g. cluster methods)
(Hand 1981). The first kind is employed when the groups
are well-known and a well-classified data set is available,
and the second group allow to work when the either
available data can not be classified or the number of groups
are not known. FDA is a supervised discrimination tool.
FDA uses a data set (represented by the matrix X), well
classified in g known groups. The belonging to a group can
be represented in different ways. The most common
approach uses a vector Y that take from 1 to g. When a
data Xj is classified in the group h, Yj h. In this work, datawere a priori classified in the different steady states using
first Principal Component Analysis (PCA), in order to
reduce the dimension of the problem, and a linear
regression on a mobile window for checking the steady
state in first and second principal components.
Table 3 | OLR (kgCODm23 d21) changes for provoked pulse-type and step-typedisturbances
Disturbance Carbohydrate-based wastewater Protein-based wastewater
Pulse 1 545 420
Pulse 2 3.535 320
Step 1 57.5 46
Step 2 3.56 36
Table 2 | Summary of steady states for each type of wastewater
Carbohydrate-based wastewater Protein-based wastewater
Steady state OLR (kgCODm23 d21) Composition (Dextrin %) Steady state OLR (kgCODm23d21) Composition (Protein/Ethanol %)
1 3.5 100 1 2 100/0
2 5 100 2 3 67/33
3 7.5 100 3 4 50/50
4 4 100/0
5 6 67/33
6 6 67/33
617 F. Molina et al. | Variables for monitoring and control of anaerobic digestion processes Water Science & TechnologyWST | 60.3 | 2009
The FDA is a linear tool, so it seeks linear combination
of variables, from a geometry approach, hyper-planes (Pena
2002), where the projection of the original data set appears,
as much as possible, separated in the same way as original
groups do. FDA provides a classification of the data using
this geometrical separation between groups. The selection
of the hyper-planes or factors is made in order to minimize
the probability of miss-classification. The analytical
framework used for factors determination involves the
study of some variance and covariance matrix, in order to
minimize the Mahalanobis distances into each group and
maximize the same distance among different groups.
Geometrically the projections into hyper-planes try to
separate the data of different groups and at the same time
try to make the data of the same group be as closer as possible.
FDA provides no only the separation factor but also a
probability function of belonging to each group, so it can
be used to classified new data, not a priori classified. So it
provides a decision rule for assigning a new case to one
class, and its accuracy can be estimated using the original
data using cross-validation.
In this research several FDA analysis were carried out
in order to minimize the number of variables needed to
differentiate steady states. FDA procedure was applied on
whole data belonging to stationary states using the complete
set of monitored variables and also process state classifi-
cation capability of each variable independently and all the
variables combinations were evaluated by FDA.
A phenomenological analysis of dynamic responses facing
organic overloads (pulses or steps) was done taking into
account the normalized slope of response (Equation 1), the
excitation level (Equation 2), and the time to maximum
value. An example of a pulse disturbance can be seen in
Figure 1, this figure shows the parameters used to evaluate
the dynamical systems response.
Sn S=Vi 1
Le Vf 2 Vi=Vi 2
Sn: Normalized slope of response (h21)
Le: Excitation level
S: Slope of response
Vi: Initial value of response
Vf: Final value of response
The Excitation level expresses the sensitivity of process
when occurs an increase of OLR. The Normalized slope as a
result of slope of response divided by the initial value of the
variable includes both sensitivity and speed of response; for
this reason, it is used as a leading indicator of the systems
response to disturbances.
RESULTS AND DISCUSSION
Steady states analysis
In a first approach, FDA was applied using all variables
for evaluating the maximum capability of classification for
steady states. A 99.5% and 100% of correct classification
for the data in different steady states was obtained
with carbohydrate-based and protein-based wastewaters,
respectively. Figure 2 presents the territorial map for
different steady states discriminated by FDA for both
wastewaters. As it can be seen in the Figure 2A, the first
discriminant function (DF1) properly separates the different
steady states for carbohydrate-based wastewater. In con-
trast to the protein-based wastewater, the situation was
more complex (see Figure 2B). This situation required that
the first and the second discriminant functions in order to
achieve a complete separation of different steady states.
In a second step, the minimum number of variables,
which allows for the steady states properly being classified
Figure 1 | Example of pulse disturbance: OLR (- - -) and QBiogas ().
618 F. Molina et al. | Variables for monitoring and control of anaerobic digestion processes Water Science & TechnologyWST | 60.3 | 2009
was determined. In this case, FDA was applied to a single
variable and to combinations of variables. Table 4 shows
the percentage of properly classification applying FDA to
As it can be seen in Table 4, for carbohydrate-based
wastewater the best classification was achieved through
the variables of the gas phase: Phead, QBiogas, and QCH4.
Phead and QBiogas variables have a good correlation
(correlation coefficient-R 2 of 0.98), thus one of this
variables without significant loss of information can be
excluded. QCH4 variable is the product of QBiogas
and %CH4 variables. Bearing in mind, that methane is
the main end product of anaerobic digestion; QCH4
variable is very useful because it offers information on the
activity of methanogenic biomass. The biggest hit in the
classification of steady states by gas phase variables was
related to the rapid degradation of the carbohydrate-based
The substrate protein presented a worse classification of
the steady states when a single variable was used. The two
variables which perform the best classification were: TOCi
and TICi, related to the characteristics of the influent
wastewater. The following response variables which best
classify the steady states were: AP, TOCe, AGV and QCH4.
The minor success in the classification of steady states for
the protein-based wastewater using a single variable was
related to: the wastewater complexity, the mixture of
protein and ethanol in some of these experiments, the
largest number of steady states studied, and the narrowest
rank of OLR applied.
For both types of wastewater, H2 concentration on
biogas is the variable which most poorly classifies the
different steady states. This result differs from those reported
by Castellano et al. (2007) who worked with an ethanol-
based wastewater, finding that H2 concentration in the gas
phase presented one of the biggest classification capacities.
This result may be related to both the acidification degree
variability of carbohydrate-based wastewater before feeding
the reactor and with the complexity of protein-based
In order to improve the capacity of steady states
classification, FDA was applied to combination of two or
three variables. For the carbohydrate-based wastewater,
Figure 2 | Territorial map for steady states discriminated by FDA using all variables. (A) carbohydrate-based wastewater: centroids (A), and steady states: 1 (W), 2 (B) y 3 (K).(B) protein-based wastewater: centroids (A), and steady states: 1 (W), 2 (X), 3 (B), 4 (K), 5 (O) y 6 (S).
Table 4 | Percentage of successful classification for steady states using a singlevariable
PHead 89.9 58.3
%CH4 74.5 53.4
H2 53.6 46.7
QBiogas 91.4 59.0
QCH4 91.7 65.8
TOCi 89.4 78.8
TICi 85.1 82.6
TOCe 72.9 70.3
pHe 63.6 51.3
VFA 81.5 65.2
PA 81.4 73.9
619 F. Molina et al. | Variables for monitoring and control of anaerobic digestion processes Water Science & TechnologyWST | 60.3 | 2009
a 95% of success in classification of the steady states was
obtained using a two-variable combination, thus resulting in
five two-variable combinations: QBiogas-pHe, QCH4-pHe,
QCH4-VFA, QCH4-PA and VFA-PA. In opposition, a three-
variable combination was needed to ensure a correct
classification of the steady states (over 95%) for protein-
based wastewater, thus resulting in three three-variable
combinations: QBiogas-VFA-PA, Phead -VFA-PA and
Table 5 presents some results obtained with carbohydrate-
based wastewater for normalized slope response as the
main parameter. In Table 5, it can be noticed that for
the carbohydrate-based wastewater, the best indicators
for identifying the disturbance caused by a pulse overload
are CO and H2 in the gas phase and VFA in the liquid
phase; this behavior is typical in a reactor that has suffered a
organic overload (Hickey & Switzenbaum 1991; Jones et al.
1992; Ahring et al. 1995; Huang et al. 2000). For step-type
disturbance, the best indicators are H2 in gas phase
and VFA in liquid phase, respectively. For both type of
disturbances, pulse and step, pHe in liquid phase and %CH4
in gas phase are the worst indicators for the detection of
Table 6 shows the best indicators for each type of
wastewater and type of disturbance. For carbohydrate-
based wastewater the best indicators of organic disturbances
were both CO and H2 concentrations in the gas phase, this
result is similar to that found by Hickey & Switzenbaum
(1991), these authors found a clear relation between the CO
concentration in gas phase with the VFA accumulation in
liquid phase; in the liquid phase the best indicator was VFA
concentration, this fact is related with VFA accumulation
due to an imbalance between acidogenic and methanogenic
bio-processes (Ahring et al. 1995). On the opposite for
protein-based wastewater, the best indicator of organic
disturbances was VFA concentration in liquid phase,
followed by H2 concentration in gas phase.
FDA technique combined with a phenomenological study
of the response to disturbances allows identifying the best
useful indicators to early diagnose of different process
states. However, other technical factors would be con-
sidered in order to determine the appropriate instrumenta-
tion for this process. In this sense, the variables should have
three main characteristics: low response delay, high
sensibility and low cost of both, sensor itself and its
operation-maintenance requirements. In addition, there
are other factors that probably will influence slightly on
the results, such as sampling location or sampling rate/
interval. Finally, according to the results of this research,
Table 6 | Best indicators for the identification of organic disturbances in both types ofwastewaters
Pulse CO, H2, VFA VFA, H2, QCH4,QBiogas
Step H2, QBiogas,QCH4, VFA
H2, VFA, QCH4,QBiogas
Table 5 | Results for normalized slope response (h21) as main parameter for theidentification of organic disturbances with carbohydrate-based wastewater
Type of disturbance
Phase Variable Pulse Step
Gas %CH4 0.10 0.02
H2 1.74 0.24
CO 3.10 N.D.
Qbiogas 0.84 0.05
QCH4 0.78 0.05
Liquid TOCe 0.20 0.02
pHe 0.02 0.01
VFA 1.27 0.10
PA 0.04 0.02
N.D. Not Detected.
Table 7 | Levels of instrumentation for on-line monitoring, diagnosis and control ofanaerobic digestion of carbohydrate-based and protein-based wastewaters
Phase Basic Necessary Advisable
Gas QBiogasor QCH4
Liquid pHe, temperatures,feed and recyclingflowrates
VFA and PA
620 F. Molina et al. | Variables for monitoring and control of anaerobic digestion processes Water Science & TechnologyWST | 60.3 | 2009
and considering the monitoring cost, three levels of
instrumentation are recommended for the two types of
wastewater (see Table 7).
According to the results of this research, the best indicators
for the two types of wastewaters, considering both steady
states and dynamic states are: Qbiogas or QCH4 and H2
concentration in the gas phase; VFA and PA in the
For both carbohydrate-based wastewater and protein-
based wastewater, H2 concentration on biogas is the variable
which most poorly classifies the different steady states. But
for detection disturbances, H2 concentration on biogas is a
useful variable in both synthetic wastewaters. Nevertheless,
for carbohydrate-based wastewater, the H2 concentration
in the gas phase depends on the acidification degree of
substrate before feeding it into the reactor; this fact can cause
errors in the diagnosis of the state of the process.
The carbon monoxide concentration in the gas phase is
less useful for the detection of disturbances, as it was
sensible to pulse disturbances only for carbohydrate-based
This research was supported by the Xunta de Galicia
through the project SEDDAN (PGIDIT04TAM265006PR),
Spanish National R&D Program, and European Regional
Development Fund (ERDF) through the project ANACOM
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622 F. Molina et al. | Variables for monitoring and control of anaerobic digestion processes Water Science & TechnologyWST | 60.3 | 2009
Selection of variables for on-line monitoring, diagnosis, and control of anaerobic digestion processes&?tpacr=1;IntroductionMaterials and methodsPilot plantSynthetic wastewatersExperimental conditionsMultivariate analysis of steady statesDisturbances analysis
Results and discussionSteady states analysisDisturbance analysisResults integration