Selection of variables for on-line monitoring, diagnosis, and control of anaerobic digestion processes

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  • 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

    ABSTRACT

    F. Molina

    C. Garca

    E. Roca

    J. M. Lema

    Department of Chemical Engineering,

    Universidad de Santiago de Compostela,

    Spain

    E-mail: carlos.garcia.dieguez@usc.es;

    enrique.roca@usc.es;

    juanmanuel.lema@usc.es

    F. Molina

    Department of Sanitary Engineering,

    Universidad de Antioquia,

    Colombia

    E-mail: fmolina@udea.edu.co

    M. Castellano

    Department of Statistics and O.R,

    Universidad de Santiago de Compostela,

    Spain

    E-mail: maria.castellano@usc.es

    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

    INTRODUCTION

    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

    doi: 10.2166/wst.2009.379

    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

    Pilot plant

    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).

    Synthetic wastewaters

    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

    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.

    Disturbances analysis

    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

    Where

    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

    one variable.

    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

    wastewater.

    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

    wastewater.

    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

    Variable

    Carbohydrate-based

    wastewater

    Protein-based

    wastewater

    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

    QCH4-VFA-PA.

    Disturbance analysis

    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

    organic disturbances.

    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.

    Results integration

    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

    Type of

    disturbance

    Carbohydrate-based

    wastewater

    Protein-based

    wastewater

    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

    H2 concentration

    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).

    CONCLUSIONS

    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

    liquid phase.

    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

    wastewater.

    ACKNOWLEDGEMENTS

    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

    (CTQ2004-07811-C02-01).

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    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

    ConclusionsAcknowledgementsReferences

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