multi-objective cascade controller for an anaerobic digester
TRANSCRIPT
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Process Biochemistry 46 (2011) 900–909
Contents lists available at ScienceDirect
Process Biochemistry
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ulti-objective cascade controller for an anaerobic digester
arlos García-Diégueza,∗, Francisco Molinab, Enrique Rocaa
USC - University of Santiago de Compostela, Department of Chemical Engineering/School of Engineering, Rua Lope Gomez de Marzoa s/n, 15782 Santiago de Compostela, SpainFaculty of Engineering, University of Antioquia, A.A. 1226, Medellin, Colombia
r t i c l e i n f o
rticle history:eceived 3 September 2010eceived in revised form 9 December 2010ccepted 25 December 2010
eywords:naerobic digestioniogasSBF reactor
a b s t r a c t
In this work, a new multi-objective control strategy based on the concentration of volatile fatty acids(VFAs) in the effluent and the methane flow rate (Qch4) has been proposed for an upflow sludge bed-filter(USBF) reactor, which is used in the anaerobic treatment of winery wastewater. The approach presentedhere is novel due to the following reasons: (i) it considers two operational objectives, i.e., control of theeffluent quality and control of the maximum production rate of methane; (ii) it takes advantage of thedifference between the dynamics of the liquid and gas phases using variables from both phases. Thecontrol system is based on a cascade control strategy with a reference signal for the methane flow rate.The control system computes the feed flow rate for adjusting the organic load applied to the reactor. The
ascade controlontrol systemsptimisationastewater treatment
performance of the proposed control scheme is illustrated through numerical simulations and parameteroptimisation using the Anaerobic Digestion Model no. 1 (ADM1) with regards to influent disturbances.Moreover, the controller has been validated in the closed-loop control of a 1.15 m3 USBF reactor treatingwastewater containing ethanol, which emulates winery effluents under different operational scenarios:restart-up, long duration organic overload, long duration organic underload and successive organic dis-
stemed hi
turbances. The control sytested, and it demonstrat
. Introduction
Biochemical processes are difficult to control because micro-rganisms are highly sensitive to changes in environmentalariables and are unable to fully influence the cells’ internal envi-onment by manipulating the external environment in which theyive. In general, biological systems are known to be highly variablend difficult to measure, and no reliable biological law is availableor their measurement [1]. Some of the factors that contribute tohe difficulty in controlling anaerobic wastewater treatment (AWT)ystems are the following: (i) the nonlinear dynamic behaviour ofhe models; (ii) the different levels of complexity presented by thexisting models and the unpredictable variation of the parameters,hat are partly influenced by biomass adaptation; (iii) the unpre-ictable load disturbances in the inlet stream due to changes in the
nlet flow rate and composition; and (iv) the lack of reliable sensorso measure intracellular activities [2,3].
In recent years, there have been significant advances in the
evelopment of sensors, which have led to the availability of on-lineensors for measurement/monitoring of key biological variablesuch as volatile fatty acids (VFAs), chemical oxygen demand (COD)nd total organic carbon (TOC) [4–8]. However, COD and TOC∗ Corresponding author. Tel.: +34 981 563100x16772.E-mail address: [email protected] (C. García-Diéguez).
359-5113/$ – see front matter © 2011 Elsevier Ltd. All rights reserved.oi:10.1016/j.procbio.2010.12.015
supplied adequate control action in response to the different disturbancesgh reliability in achieving the desired set-point.
© 2011 Elsevier Ltd. All rights reserved.
analysers are expensive and are usually recognised as fragile mea-surement devices [9]. Furthermore, their maintenance is costlycompared to VFA titrimetric sensors [7,8].
The aim of an anaerobic reactor controller is to maintain condi-tions of stability in a bioreactor against possible changes in influentcharacteristics (flow rate or composition) and to attain adequateeffluent quality and maximise the methane productivity of theAWT plant [10]. Controllers can be designed for the managementof the process under the following different operational scenar-ios: organic overload, hydraulic overload, toxic effects of inhibitorcompounds, thermal shock and restart-ups [11].
Robust control of anaerobic processes is crucial for avoidingpossible instability due to disturbances. Consequently, importantresearch efforts have been focused on the development of differentfeedback control strategies for AWT processes. In the last few years,several control feedback structures have been developed to over-come the difficulty in controlling AWT processes. Researchers havereported a simple on/off control algorithm that uses the alkalinityconsumption and the feed flow rate as the process state variableand the manipulated variable, respectively [12]. A combination ofthe on/off and neural networks algorithm to control bicarbonate
was proposed by Guwy et al. [13]. Other researchers have usedPID (proportional–integral–derivative) controllers for regulatingAWT processes [14–17] because this type of controller can be eas-ily implemented in wide variety of plants. However the tuning ofthese controllers is based on heuristic rules. Anaerobic digestion is
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complex and nonlinear process. Therefore, traditional and linearontrollers (e.g., on/off, PID, etc.) may fail in certain ranges unlesshey have been properly adapted and their parameters have beenrecisely calibrated to handle specific situations.
Rule-based expert systems have also been reported for theupervision and control of AWT systems [18–22]. Most of theecently published work about expert systems for AWT controls based on fuzzy logic [23–27]. Moreover, fuzzy structures haveeen used to develop control systems from expertise of operatorsnd phenomenological information [23,28–30] to derive gain-cheduling schemes for tuning on-line control parameters and toanage process uncertainties [10]. With this aim and due to the
nitial difficulty in modelling AWT systems, neural networks haveeen applied to develop controllers using data from AWT plants13,31,32].
Adaptive controllers for AWT plants [28,33–39] have beeneveloped to account for the nonlinearities and transient featuresf the anaerobic digestion process. The drawback of such strate-ies is that complete knowledge of the parameter structure of theystem is required, which can be difficult to obtain when dealingith bioprocesses such as AWT. In addition, adaptive controllerssually respond more aggressively against disturbances than lin-ar controllers, and therefore, strong variations may occur in theanipulated variable, which is usually the feed flow rate [16].Interval controllers have been successfully applied to the AWT
f distillery vinasses to solve the problem of COD and VFA regula-ion [40]. The use of this type of controller permits management ofhe relatively large uncertainty in some key process variables. Nev-rtheless, its performance is strongly dependent on the definitionf the uncertainty intervals. Another interesting approach of thenterval-based control application, which has not yet been experi-
entally validated, is the regulation of the influent COD acting onhe dilution rate [41].
A variant in the feedback control topology is cascade control,hich consists of two or more feedback control loops. A few cascade
pplications can be found in the literature. Alvarez-Ramirez et al.15] have shown a direct feedback control system using only PIDontrollers in a cascade configuration. This system allows the regu-ation of an anaerobic digester working at a low COD concentration.iu et al. [10,42,43] developed a cascade controller embedded into auzzy rule-based supervisory system. This approach demonstratedhe ability to achieve high productivity conditions for the produc-ion of methane by the AWT reactor.
Control strategies have often been tested for different reactoronfigurations, different scales and under different operating con-itions, which makes it difficult to compare their performance.owever, it has been recognised that a suitable combination ofirect and indirect feedback control provides the best control strat-gy [15]. Direct feedback control acts at a regulatory level whereasndirect feedback control acts at a supervisory level (e.g., fuzzyogic). An ideal control system must fulfil different characteristicst the regulatory level for simultaneously ensuring the followingriteria: (a) high effluent quality; (b) maximum methane produc-ion; (c) general system stability; and (d) applicability to differentypes of wastewater [23].
In this work, a new robust multi-objective controller has beeneveloped and used for the control of an upflow sludge bed-filterUSBF) reactor. Methane flow rate has been used as the inner con-rol loop variable and VFA has been chosen as the external controloop variable. The final control variable is the variation (increaser decrease) in the feed flow rate. The proposed scheme has been
esigned to deal with modelling errors, and its structure allowshe attenuation of disturbances in the COD concentration in thenlet stream. Validation of the control system has been performedsing simulations with ADM1 (Anaerobic Digestion Model no. 1)nd closed-loop control of a pilot-scale USBF reactor. The approachchemistry 46 (2011) 900–909 901
is novel as it considers the main operational criteria in the samecontrol structure. Moreover, this controller takes advantage of thedifferent dynamics of the liquid phase and gas phase through acascade structure with a reference signal.
2. Reactor, model description and problem statement
2.1. Anaerobic reactor
Experiments for the closed-loop validation of the cascade con-troller were conducted in a USBF, pilot-scale reactor (UpflowAnaerobic Sludge Blanket—UASB zone + Anaerobic Filter—AF zone)[26] with an approximate liquid volume of 1.15 m3 (Fig. 1). Thereactor temperature was controlled at 37 ± 2 ◦C using a singleon/off loop. The on-line measurement devices that were availableincluded feed and recycling electromagnetic flow-meters (ABB,COPA-XE and Siemens, 7ME2531), input and output reactor pHmeters (Cole Parmer) and thermometers (Pt-100), a biogas flow-meter (Brooks, 3240), an infrared gas analyser (Siemens, Ultramat22P) for the measurement of CH4 and CO concentrations in the gasphase and an electrochemical hydrogen gas analyser (Sensotrans,Sensotox 420). On-line TOC and total inorganic carbon (TIC) weredetermined by catalyst combustion oxidation and non-dispersiveinfrared (NDIR) CO2 detection (Shimadzu, 4100). All data weremonitored on-line with the sensors and recorded at 15-min inter-vals. Detailed descriptions of the equipment and data acquisitionsystem have been reported elsewhere [23,44].
During the experiments, the biomass inside the reactor showedspecific methanogenic activity of 0.66 ± 0.18 kg COD kg VSS−1 d−1.The average total biomass observed in the reactor was 18.6 ± 1.5 kgVSS, corresponding to an average VSS concentration of 16.9 ± 1.4 kgVSS/m3 [45]. These values corresponded to a maximum organicloading rate of 12 kg COD/m3 d.
Synthetic wastewater, containing ethanol which emulateswinery effluents, was used in the experiments. The influent com-position consisted of dilute white wine, nutrients and sodiumbicarbonate. The wine was diluted in situ using a static mixer justbefore it entered the reactor to avoid pre-acidification in the feedingtank. Nutrients and sodium bicarbonate were added to maintain aCOD/bicarbonate/N/P ratio of 1000/400/7/1, which is required forbiomass growth, and to maintain an adequate buffering capacityinside the reactor [46].
A titrimetric AnaSense analyser [7,8] was used to determinethe following operational reactor parameters: VFA (volatile fattyacids), bicarbonate, and partial and total alkalinity. After thedetermination of these parameters, the IA/TA ratio (intermediatealkalinity/total alkalinity ratio) was calculated (IA corresponds tothe difference between total and partial alkalinity).
2.2. Anaerobic Digestion Model no. 1 (ADM1)
ADM1 [48] is a complex model of the multi-step anaerobicprocess transformations. This tool is adequate for predictions ofanaerobic wastewater treatment processes with sufficient accu-racy for use in process development, optimisation, and control.It is a standard benchmark for developing operational strategiesand evaluating controllers [16]. ADM1 incorporates processes suchas the hydrolysis of particulates, acidogenesis, acetogenesis andmethanogenesis, and it includes 26 dynamic state concentrationvariables, 19 biochemical kinetic processes, 3 gas–liquid transfer
kinetic processes, and 8 implicit algebraic variables per liquid ves-sel. A modified version of the ADM1 toolkit was used in this study.The modified version incorporates an extension to ethanol degra-dation pathways through an additional group of ethanol degradersand a new state variable for ethanol concentration, which is cali-
902 C. García-Diéguez et al. / Process Biochemistry 46 (2011) 900–909
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ig. 1. USBF reactor and cascade control scheme. USBF—upflow sludge bed filter reM—liquid measurements.
rated for a USBF reactor [49]. The extension for ethanol considershe hydrogen and acetate pathways and accounts for the VFAs path-ays (propionate and butyrate) through stoichiometry. Thus, VFAs
re suitably predicted by the model, including any possible over-oad or transitions between steady states.
. Controller design
.1. Controller objectives
Previously, many authors have used the Haldane kinetic modelFig. 2) to describe the behaviour of anaerobic digesters [50–52].aldane kinetic model also provides a simple method of explainingontroller objectives.
Haldane kinetics have an obvious equilibrium point (biomass
ashout) and two operational regions, i.e., a stable operating regionnd an unstable operating region (see Fig. 2), which have differentynamic properties in terms of stability [50]. In practice, the riskf destabilisation of an AWT process can be avoided by operatingn the stable region or far below the maximum reactor capacity
ig. 2. Haldane kinetic model for an anaerobic digester. (- · - · - ·) Methane flow rateetpoint. (···) Operational point with the objective of fulfilling environmental regu-ations.
GFC—gas phase controller; LFC—liquid phase controller; GM—gas measurements;
(optimum methane flow rate production), i.e.,√
KI · KS (KI is theinhibition kinetic constant and KS is half saturation constant) forthe Haldane kinetic model [10].
Normally, AWT plants attempt to fulfil environmental regula-tions. Therefore, the process requires a controller, which keeps thesystem stable at a fixed set-point. However, when AWT plants seekto maximise methane production, an optimal or suboptimal controlstrategy must be proposed. In practice, the risk of system overloadcan be reduced by operating with a security margin that is belowthe maximum reactor capacity. In any case, the main objective ofany controller for AWT plants is to keep the system in the stableregion [53].
As a consequence, the development of a controller that is ableto integrate both objectives, i.e., the emission level set-point andthe maximum methane flow rate, and is able to robustly regulatethe process in the same single structure is of great interest to suchwastewater treatment systems. Different control structures couldbe considered for developing the controller. However, a cascadeframework has been used in our study.
3.2. Selection of control variables
The first step during the development of a control system isthe selection of a group of process variables, which can provideinformation about the metabolic state of the process. Differentcombinations of variables were studied to establish the most appro-priate combination for maximum control of regulation in a cascadestructure. In this sense, when two of the variables for state iden-tification in the anaerobic process are considered with winerywastewater, several combinations of variable pairs are able to pro-vide accomplish a complete classification of states [54]. A similarstudy [55] with other types of wastewaters has showed that anappropriate combination of variables in the gas and the liquidphases can be used to develop a monitoring, diagnosis and control
(MD&C) system. Furthermore, variables in the liquid phase presenthigher response times than gas phase variables. Therefore, accord-ing to this criterion an inner control loop with a secondary variablein the liquid phase does not seem appropriate. Besides, the use of acascade control structure has been proposed to reduce the adverse
C. García-Diéguez et al. / Process Bio
Table 1Evaluation criteria for inner-loop variables suitability. (Y—yes; N—no or notappreciable; N/A—not applicable; A—advanced instrumentation; O—in occasions;S—similar).
Criterion Qch4 %CH4 Qgas %CO2 CO H2
Secondary variable is available A A Y O A AIndicates a key disturbance Y O Y N N YCausal relationship with the Y N Y N N N
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controlled variableSecondary dynamic faster than
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ffects of measurement delays and exploit the faster response timesf gas phase variables (e.g., biogas flow rate or methane flow rate)ompared to liquid phase variables.
Methane flow rate (Qch4) was chosen as the inner-loop variablefter accounting for the technical, economical and dynamic processesponse criteria. A combination of this variable with the VFA con-entration allows the detection of any potential imbalance in theWT process.
The selected inner-loop variable determines the performancef cascade structures because the cancellation of the error in thenner-loop affects the cascade efficiency. A suitable internal vari-ble must fulfil a series of criteria or requirements such as thoseisted above for the considered variables. These criteria establish
ranking amongst the variables under consideration in terms ofheir adequacy for the development of a cascade controller for anWT plant. The methane flow rate (Qch4) and the biogas flow ratere considered the best inner-loop variables, as shown in Table 1.
A feature of the cascade control structure is that it can bewitched to operate as a single loop that seeks a suboptimal valuef Qch4. This is easily achieved by turning off the external controloop and by providing an external reference trajectory to the slaveoop.
.3. Methane productivity – inner control loop
The scheme for the inner control loop corresponding to theet-point of methane productivity is illustrated in Fig. 3. In thisase, the controller works as an auto-setting control system with aethane reference signal (Qch4ref). Note that Qch4ref provides the
et-point of the inner controller, and it is changed to achieve opti-um methane productivity. In other words, the inner controlleras designed to maintain the process near the operational point
orresponding to maximum methane production (Qch4max) whenhe output loop was disconnected, and thereby push the systemo higher organic loading rate (OLR), which avoided working in aangerous and unstable operational zone (see Fig. 2).
The inner controller was implemented as a PID controller. There-ore, the equation of this controller can be expressed as follows:∫
in(t) = Qin(t − 1) + KPs · eQch4(t) + KIs eQch4(t)dt+ KDsd(eQch4(t))
dt(1)
Fig. 3. Block diagram of the methane productivity controller (inner-loop).
chemistry 46 (2011) 900–909 903
where Q(t) represents the current reactor feed flow rate or the con-troller output, Qin(t − 1) is the initial value of the feed flow rate, KPs
is the proportional gain, KIs is the integral gain, KDs is the deriva-tive gain, and eQch4 is the error between the actual value of Qch4and the reference signal (i.e., eQch4(t) = Qch4ref(t) − Qch4(t)). Theproblem of controlling the output of a system to track a prescribedreference in the presence of model uncertainties and input distur-bances is of great interest in the control of bioreactors.
The methane flow rate was measured at intervals of 5 s and wasfiltered using a moving window of 15 min. Identical time intervalswere considered for modifying the action of the controller. Thesetime intervals are considered appropriate when the scale of indus-trial anaerobic bioreactors and the slow dynamics of this processare taken into account.
3.4. Tracking methane reference
In anaerobic digestion, if one is primarily interested in theamount of methane generated, the total methane production dur-ing the transition period between two steady states can be used asan appropriate measure of the system’s performance, which in turncan be maximised (see Eq. (2) – optimal control approach).
J(Qin(t)) =∫ tf
0
Qch4(t) · dt (2)
However, optimal control is a very sensitive technique for theproposed model. It requires complete knowledge of the processmodel, including an analytical expression for each specific rate inthe system. In biotechnology, particularly in the field of anaerobicdigestion, this assumption is never fulfilled in practice; an opti-mal profile is generally calculated using a model that describes theprocess correctly from a qualitative viewpoint. Therefore, it is veryuseful to construct suboptimal strategies that do not suffer fromthe above difficulties.
Based on a two-step anaerobic model structure provided byBernard et al. [51], it is possible to express the methane produc-tion rate (qM) as a function of VFA concentration and methanogenicbiomass concentration.
qM = kM · �maxVFA
VFA + Ks + (VFA2/KI)Xmet (3)
where kM represents the yield coefficient for methane productiondetermined from previous experimental data calibration, �max isthe maximum specific growth rate for methanogenic bacteria, Xmet
is the methanogenic biomass concentration, and Ks and KI are thekinetic parameters for Haldane’s kinetic expression (half saturationconstant and inhibition constant, respectively).
Consider that the nonlinear adaptive parameter �1 is definedaccording to Eq. (4):
�1 = qM
VFA
(Ks + VFA +
(VFA2
KI
))(4)
where qM is expressed in kg mol/m3 d and therefore needs to beconverted into units of m3/d through the ideal gas law Eq. (5),assuming a constant temperature (T) of 37 ◦C and a pressure equiv-alent to 1 atm. The same temperature and pressure conditions areused in the remaining equations in this manuscript.
Qch4 = qM · RT
P(5)
where VFA* is the concentration of the volatile fatty acids at theoptimum methane flow rate production (see Eq. (6) and Fig. 2):
VFA∗ =√
KI · KS (6)
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Then, the suboptimal methane flow rate can be computed fromq. (7) assuming a 10% margin of security beyond the beginning ofhe unstable zone (see Fig. 2). Eq. (7) provides the reference sig-al, in this case the set-point, for the first control objective (i.e., toaximise methane production).
ch∗4ref = 0.9 · �1
√KI√
KI + 2√
Ks
· R · T
P(7)
A disadvantage of this strategy is the dependency of kineticarameters on the control law, which can be solved by developingmechanism that changes the parameters of the control law with
ime to account for biomass changes (i.e., biomass adaptation). Anption to the adaptation mechanism may be to approximate thetable zone in Haldane’s model (see Fig. 2) with a straight line (Eq.8)).
M = kmet · VFA (8)
Evidently, if the slope of this line (methanogenic adaptationlope – kmet) increases with time, we can conclude that biomassdaptation has occurred. On the other hand, a decrease in the slopef the methanogenic adaptation implies a loss of biomass, the deathf biomass, the entry of a toxic substance into the reactor or anyther operational circumstance that diminishes biomass activity.n both cases the parameters need to be varied as per the mea-urements. Thus, the adaptive mechanism consists of frequentlyhecking the methanogenic adaptation slope and comparing it withrevious values. A switching mechanism (Eq. (9)) has been pro-osed in this study. If the absolute value of the difference betweenhe methanogenic adaptation slope and its average is greater than.5, then the parameters are modified to account for biomassdaptation. However, if the difference between the methanogenicdaptation slopes is small (i.e., less than 0.5), then the parame-ers are allowed to remain at their previous values. The values arehecked with the average kmet2 values after 12 h of operation, whichs sufficient considering the duplication time for methanogenicacteria (approximately 7 days). kmet is initially established fromaldane’s model (Fig. 2) and then it is progressively updated usingq. (10).
if |kmet 1 − kmet 2| > 0.5 then modify parameters to account
for biomass adaptation
if |kmet 1 − kmet 2| > 0.5 then maintain parameter values
(9)
If biomass adaptation is detected, then measurement of botharameters, the methane flow rate and the VFA concentration, isecessary for computing a new slope as shown in Eq. (10).
met = Qch4t
VFAt· P
R · T(10)
Finally, with the new slope of methanogenic adaptation, assum-ng that biomass adaptation occurs only by increasing substrateffinity (i.e., Ks decreases), then Ks can be adapted as shown in Eq.11). It is very important to ensure that Ks remains positive (i.e., ifs does not remain positive, then it must retain its previous value).
s = �1 − K1 · Kmet
kmet(11)
While controlling the concentration of organic matter in theffluent (i.e., second controller objective), the methane reference
ignal is established as a function of VFA, methanogenic biomassoncentration (Xmet) and some model parameters (see Eq. (12)).ch4ref = kM · �max · VFA
VFA + Ks + (VFA2/KI)· Xmet · R · T
P(12)
chemistry 46 (2011) 900–909
A simple mass balance observer (Eq. (15)) can be derived fromthe mass balance of the methanogenic biomass (Eq. (13)) and themethane productivity equation (Eq. (14)). This observer permits theestimation of the methanogenic biomass concentration (Xmet). It iseasy to demonstrate that the observer converges asymptoticallywhen ˛·D > 0, which is always true except when D is equal to zero(i.e., the reactor is shut down) [56].
˙Xmet = rM − ¯ DXmet (13)
Qch4 = kM · rM · R · T
P(14)
˙Xmet = Qch4
kM· P
R · T− ¯ DXmet (15)
where D is the dilution rate computed by dividing the feed flow ratevalue by the volume of the reactor and ˛ describes the deviation ofa completely stirred tank reactor (CSTR) from the ideal behaviour,which allows the simplification of the solid retention model. Thevalue of ˛ is 0.005, and it is estimated using the average of the solidretention time (SRT) and the average of the dilution rate (Eq. (16)).These values were estimated from experimental data of the USBFreactor.
¯ = 1
SRT ·D(16)
3.5. VFA control – external control loop
A bioreactor with a single loop performs sufficiently well whenthe dynamics is fast, dead time is small and the disturbances aresmall and slow, as in the case of the methane productivity con-troller. However, a single loop for controlling VFA is insufficientbecause it lacks the characteristics described above. In this case, acascade control structure may be considered as a good alternative.The cascade structure proposed for this task is shown in Fig. 4.
The feed flow rate (Qin) is constrained by a saturation func-tion (according to the physical restrictions on the manipulatedvariables). In practice, the flow rate that is physically applied tothe anaerobic reactor should be positive and must have an upperbound. The minimum feed flow rate (Q) was zero, and the max-imum feed flow rate (Q ) was established using the minimumhydraulic retention time (HRT) criterion of 9 h for preventing unde-sired washout of biomass. Under this criterion, the operationallimits of the pumps must be respected.
sat(Qin) =
⎧⎪⎨⎪⎩
Q, Qin ≥ Q
Qin, Q < Qin < Q
Q, Qin ≤ Q
(17)
In addition, variation in the feed flow rate (�Qin) was limitedto 0.012 m3/d for each 15 min interval to avoid extreme behavioursuch as oscillations in operational zones that were further awayfrom the desired operating conditions. Due to the integral action ofthe inner controller, the saturation of the control input could poten-tially induce undesirable phenomena such as reset windup, whichcould lead to large overshoots and high settling times. Therefore,an anti-reset windup scheme was added to the control structureby introducing static gain feedback of the difference between thecomputed control signal and the actual saturated control action.
The master controller was also implemented as a PID controllerwith the following equation:
Qch4sp(t) = KPm · eVFA(t) + KIm
∫eVFA(t)dt
+ KDmd(eVFA(t))
dt(18)
C. García-Diéguez et al. / Process Biochemistry 46 (2011) 900–909 905
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COD/m3 d) over a large period of time to ensure that a steady statewas reached. The influent COD concentration used in the simula-tions was 8–12 kg COD/m3. This is a typical concentration range forindustrial winery wastewater [51,57]. The inlet inorganic carbon
Fig. 4. Block diagram of the cascade controller. Note that the reference si
here eVFA can be calculated through the following expression:
VFA(t) = spVFA(t) − VFA(t) (19)
The inner loop controller equation is the same as that used in sin-le loop control (i.e., to maximise methane production). However,he error in measurement/calculation of the methane flow rateeQch4) is modified by an appropriate reference signal (Qch4ref):
Qch4(t) = Qch4 sp(t) + Qch4ref(t) − Qch4(t) (20)
When the second control objective is pursued (i.e., VFA regula-ion), then the Qch4ref value allows to modify the error (Eq. (20))n the inner loop eQch4, because it is necessary to improve theehaviour of the control system when the process is near to theFA set-point. On the contrary, whether Qch4ref was not includedn inverse behaviour could take place when the error on VFA (eVFA)as small. Moreover, Qch4ref accelerates the controller responsehen eVFA is high. Therefore, it helps to reduce restart-up time
nd conveniently decreasing the feed flow rate during organic over-oads.
Due to the inherent uncertainty in the VFA measurement, aechanism avoiding the excessive oscillations in the control action
round to the desired set-point was incorporated. This mechanismonsists of introducing an uncertainty band equivalent to 5% of theet-point value.
To obtain controller settings, the system was tuned througharameter optimisation. These parameters were computed usingDM1, and an integrated square error (ISE) was used as the crite-ion for optimisation. The parameters (see Table 2) were found toe very similar for the range of concentrations studied, i.e., fromto 16 kg COD/m3 for CODin, which is a characteristic of wineryastewater [51]. Observe that derivative and integral values of the
ID controllers were negligible.
. Validation
Taking into account the performance of the reactor at equilib-ium, any possible imbalance in the process can be attributed to theharacteristic changes of the influent (e.g., a pronounced change ints organic matter content known as organic overload or underload,ariation in pH, variation in feed flow rate and presence of toxicubstances), sudden changes in the operational environment (e.g.,xcessive temperature fluctuations) and excessive loss of microbialiomass.
Therefore, to validate controller performance, numerical sim-lations were carried out with the ADM1 virtual plant, and pilotlant experiments were performed under three different operat-
ng conditions: organic overload, organic underload and restart-upf the plant after a short period of rest (2–3 weeks) for the anaerobicigester described previously.
.1. Validation using ADM1
The cascade control system that was developed in this studyas tested using data generated by the ADM1 simulation. Three
ch4 modifies the set-point to the inner controller in the cascade scheme.
different operational scenarios were studied: automatic restart-up,organic overload and organic underload. The simulations are shownin Fig. 5. The initial condition (i.e., the initial state of the reactor)for the simulations was obtained for a medium value of OLR (8 kg
Fig. 5. Validations by means of simulations with ADM1. (a) Restart, (b) organic over-load, (c) organic underload. VFA—volatile fatty acids concentration; eVFA—externalcontroller volatile fatty acids error; Qin—feed flow rate; and eQch4—inner controllermethane flow rate error.
906 C. García-Diéguez et al. / Process Biochemistry 46 (2011) 900–909
Table 2Parameters of the control system.
Parameters Inner controller Parameters External controller
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4
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Note that at time around 1.8 and 3.4 days a rapid change onVFA had occurred. In these cases the calibration frequency of theAnaSense® was of 4 days, lesser than in the rest of the experiment.Thus, it is recommended to recalibrate the sensor with at a higherfrequency (e.g., each 2 days) in order to avoid this drawback.
KPs 0.0172 (m3/m3 CH4)KIs 7.2 × 10−7 (m3/m3 CH4 d)KDs 0.0006 (m3 d/m3 CH4)
oncentration, inlet inorganic nitrogen concentration, cations andnions concentrations in the ADM1 were estimated considering theame bicarbonate and nutrients ratio that was used in the pilotlant experiments (COD/bicarbonate/N/P ratio of 1000/400/7/1)58].
AWT plants are often restarted after a short period of rest inertain industrial applications such as the treatment of winery andistillery wastewaters where wine production is seasonal [16]. This
s also true for the treatment of wastewater from other agro-foodndustries where the collection of the agricultural products is a sea-onal activity. Automatic control systems can help minimise theime required to restart-up the AWT plants. The cascade controllersee Fig. 5a) restart-up the AWT plant in 3 days with a very smallnitial feed flow rate (0.12 m3/d) and a CODin of 10 kg COD/m3.owever, the desired set-point is achieved only after two days.
As shown in Fig. 5b (organic overload), the controller was ableo maintain the process in a stable condition after a severe increasef CODin from 10 kg COD/m3 to 15 kg COD/m3, which correspondso a 50% increase in the organic load. Without automatic control,his disturbance would cause the treatment system to fail, becauset exceeds the treatment capacity of the reactor. When it occurs,FAs accumulation produces stronger inhibitory effects [5]. Sim-
larly, the controller responded to a 30% decrease of the organicoad by increasing the feed flow rate, thereby accurately handlinghe organic underload. In this case, a minimum variation of the VFAoncentration would be detected. In all the simulations, the pH wasaintained close to a constant neutral value. Also, the IA/TA ratioas lower than 0.2 (results not shown here), which is considered
o be adequately lower than the recommended limit of 0.3 [59].These simulations demonstrate that the anaerobic digestion
rocess is accurately regulated through cascade control, which cane designed and implemented with minimal effort.
A few other researchers have successfully used cascaded config-rations for controlling anaerobic digesters. Alvarez-Ramirez et al.15] have shown that direct feedback control using linear con-rollers in a cascaded configuration has potential application in theptimal operation of AWT plants. However, these authors set theirontrol variables as VFA and COD, which belong to the liquid phasend would delay the response of the controller system. Anothernteresting application using the cascade control of pH and theow rate of biogas was developed by Liu et al. [10,42,43]. In thesetudies, good control performances were achieved in a closed-looponfiguration during operation of anaerobic reactors at lab. How-ver, the use of pH as an inner loop variable requires precise controlver the addition of the source of alkalinity, especially at industrialcales to attain a fast response. Moreover, this controller requires aensitive pH-meter, which is not feasible at industrial scales. Suchontrollers are only designed to achieve maximum methane pro-uctivity. Therefore, they are unable to regulate the concentrationf organic matter in the reactor effluent.
.2. Closed-loop validation at pilot scale
The cascade controller was experimentally validated using a
SBF pilot scale reactor working in a closed loop. To evaluatets behaviour, the control system was tested under both restart-p conditions and different disturbances (i.e., long duration ofrganic underload, long duration of organic overload and consecu-ive overload and underload). The controller was roughly tuned in
KPm 0.38 (m3 CH4 L/mgAc d)KIm 0KDm 0
simulation using the modified ADM1 incorporating ethanol degra-dation. The same parameters were used in both simulations andduring the different experimental tests. However, it is recom-mended to periodically recalibrate the controller parameters beforeusing it, because important changes on biomass concentration andactivity may occur after long periods of operation that may affectthe controller performance. It is also very important to take intoaccount the type of wastewater to be treated. This control approachhas been validated with winery wastewater, but it should workwith other similar types of wastewater.
4.3. Restart-up operation
As previously mentioned, the restart-up of AWT plants is ofinterest to the treatment of winery wastewater because wine pro-duction is a seasonal activity, and digesters must often be restarted[16,57,60].
Two restart-up experiments were performed in this study. Theresults presented in Fig. 6 correspond to two different VFA set-point(spVFA) values. Fig. 6 shows a restart-up situation where the inputCOD (CODin) concentration was initially fixed at 8 kg COD/m3 fora set-point value of 120 mgAc/L. In this first experiment, the con-troller only needed three days to restart-up the process after a shortstop of 3 weeks, and produced 2.7 m3/d of high quality methane(in agreement with the stoichiometry for this residue) containingclose to 71% of methane. The final OLR achieved was nearly 8.35 kgCOD/m3 d and the pH was close to the neutral value.
The objective of the second restart-up experiment was to testthe robustness of the controller design using a different VFA set-point (550 mgAc/L) while retaining the controller parameters thatwere established for the previous experiment. Fig. 7 shows theresponse of the reactor during restart-up after a short stop of 1month with an approximate CODin concentration of 10 kg COD/m3.The main criterion used for these stops was the seasonal behaviourin the winery industry [11].
Fig. 6. Experimental closed-loop control validation of the control system during arestart of the USBF reactor (spVFA 120 mgAc/L).
C. García-Diéguez et al. / Process Biochemistry 46 (2011) 900–909 907
Fr
4
boocot(c
adcocomtmcu
acCt
Fl
ig. 7. Experimental closed-loop control validation of the control system during aestart of the USBF reactor (spVFA 550 mgAc/L).
.4. Organic underload and overload
The robustness of the controller was also tested with distur-ances in CODin. In this test, the influence on the process variablesf a change in the CODin value was investigated over a long periodf time. In this case, the objective was to analyse the response of theontrol system when faced with a sudden underload or a suddenverload during the AWT process. The underload condition wasested by diluting the influent to 50% of its initial concentrationunderload of −50%) over 4 days and then returning it to its initialoncentration (8 kg COD/m3).
As shown in Fig. 8, under conditions of underload, the controllerdjusted the applied load by increasing the feed flow rate (Qin). Thisisturbance did not induce noticeable changes in the output VFAoncentration. The applied underload resulted in a sudden decreasef the methane flow rate and only a slight decrease in VFA con-entration. This behaviour can be attributed to the faster responsef the VFA concentration (liquid phase variable) compared to theethane flow rate (gas phase variable). The performance of the con-
roller was found to be similar to the behaviour predicted by theodified ADM1 simulations (see Fig. 5c). The disturbance was over-
ome in approximately one day, and the process remained stablentil day 13.5, when an organic overload was applied.
When the inlet COD concentration returned to 8 kg COD/m3,
t first, the methane flow rate and VFA concentration increasedonsiderably. However, in this case, the overload corresponds to aOD concentration increase of 100%. Thus, the controller responseo this overload condition was simply to decrease the input flowig. 8. Experimental closed-loop control validation of the control system during aong duration organic underload and overload (spVFA 120 mgAc/L).
Fig. 9. Experimental closed-loop control validation of the control system during along duration organic underload and overload (spVFA 550 mgAc/L).
rate back to the initial value before the overload was applied. Fig. 8also shows the delay in the response time of VFA during overload.
Although the controller response was appropriated in each casethe set point error could be improved using a better adaptivemechanism of the controller parameters. In this sense, the use ofnonlinear control techniques could be of interest due to the non-linear nature of the anaerobic wastewater treatment process.
Fig. 9 shows another similar experiment that was performedwith a higher VFA set-point (spVFA 550 mgAc/L). First, the concen-tration of organic matter was decreased by 50%, and then an organicoverload corresponding to a 100% CODin concentration increasewas applied. With the help of the control system, the process recov-ered from the instability induced by both disturbances. In thiscase, the VFA concentration decreased during organic underloadand increased significantly during overload, because the treatmentsystem was close to its maximum methanogenic capacity. Manage-ment of overloads is riskier than underload management, becausethe increase of VFA induced during an organic overload can drivethe treatment process towards an unstable operating zone, as canbe seen in Fig. 2. During an underload, however, only a part of thetreatment capacity is wasted. Nevertheless, the controller avoidsacidification, and thereby, process failure. The maximum capacitywas estimated for an OLR of 12 kg COD/m3 d. This can be observedby comparing the methane flow rates during organic overload inboth experiments (Figs. 8 and 9). The effect of the disturbancesremained for some time even after the perturbations had beenremoved.
In general during long-term COD changes, the controller wasable to maintain the stability of the process for the completeduration of both experiments. Furthermore, other relevant pro-cess variables such as pH and alkalinity ratio (IA/TA) (data notshown) were maintained within acceptable limits. The measuredpH was near neutral with a slight decrease during overload. On theother hand, the IA/TA ratio remained lower than 0.3, which is therecommended value [59]. This value was only slightly surpassedduring organic overload in the second experiment. The addition ofalkalinity must be regulated with a simple control system. Severalpromising controllers shown in the literature [61] could potentiallyhelp optimise the addition of alkalinity. Other interesting issueabout this type of process is the entrance of a toxic in the feed-ing line. But, there exist a great variety of toxic compounds witha different grade of affectation on anaerobic biomass depending ofthe toxic compound nature and its concentration in the wastew-
ater. In the experimental tests showed here the wastewater usedwas synthetic and therefore the effect of a toxic must be establishedin a further research. However, if the effect of a toxic on anaerobictreatment process is known, it is possible to establish how the con-
908 C. García-Diéguez et al. / Process Bio
Fcdr
teo
4
scsiVsd
ai8dmec
Aittammo
ig. 10. Experimental closed-loop control validation of the control system duringonsecutive variations of CODin (spVFA 120 mgAc/L). (a) Influent chemical oxygenemand (CODin), organic loading rate (OLR). (b) Methane flow rate (Qch4), feed flowate (Qin), volatile fatty acids concentration (VFA).
roller would act. If the controller is unable to manage the toxicantffects will be necessary to introduce some changes in its structurer use a supervisory control.
.5. Successive variations
The practical application of the control law was also demon-trated in an additional experiment consisting of successive CODhanges without permitting the process to reach stable or steady-tate conditions. The variations in OLR, CODin, methane flow rate,nfluent COD, feed flow rate (i.e., the control action), spVFA andFA are shown in Fig. 10. The remaining variables are not pre-ented because the key aspects of the control action are sufficientlyemonstrated with these variables.
In this experiment, two successive organic overloads werepplied to the reactor. Initially, the inlet COD concentration wasncreased from 6 to 8 kg COD/m3, and then another increase from
to 12 kg COD/m3 was induced. Then, an underload was induced,ecreasing CODin to 8 kg COD/m3. Fig. 10 depicts the variation of theanipulated variable (Qin) with time, such that the control strat-
gy adjusted the loading rate in accordance with changes in theoncentration of the influent.
The increasing availability of reliable on-line sensors, likenaSense®, and the increasing of the available knowledge of biolog-
cal phenomena involved in anaerobic digestion processes allowedhe application of a cascade structure to propose a controller able
o consider together the quality of the effluent (VFA regulation)nd the maximisation of the methane production objectives. VFAeasure could be changed by other variable related to organicatter content in the wastewater, as for example the chemicalxygen demand although with a high cost. In this respect, the con-
chemistry 46 (2011) 900–909
troller system presented in this paper had advantages over othercontrollers, and it was widely validated by simulations and on apilot-scale USBF reactor of 1.15 m3. In addition, the relative lowcost of the measurement devices required, make this controllervery interesting for industrial application.
5. Conclusions
A new multi-objective cascade control system was developedfor the regulation of anaerobic wastewater treatment. It was vali-dated using simulations performed with a virtual ADM1 plant in aclosed-loop anaerobic reactor. The controller demonstrated excel-lent performance even under conditions of severe disturbance byproviding an adequate control action to achieve the desired set-point and manage sudden changes in the influent concentration. Itwas also able to restart-up the AWT plant in a short period of time.
The main drawback of the proposed cascade scheme is that anexcessively large VFA set-point can introduce additional nonlinear-ities in the system due to inhibition effects. However, in this case,the appropriate control action can be employed using an accuratealkalinity control system. Both control modes (cascade and sin-gle loop) are compatible with promising alkalinity monitoring andcontrol systems that are currently available, such as the controllerdeveloped by [61], which permits the control of carbon dioxideconcentration in the gas phase (i.e., biogas quality).
From a practical point of view, the resulting control schemepresents the following advantages: (i) tuning of the controller iseasy to achieve due to its linear structure and the fact that only a fewparameters must be considered (i.e., PID slave parameters, PID mas-ter parameters, and a few kinetic parameters); (ii) the requirementsfor practical implementation are easily satisfied; (iii) it requires theleast amount of prior knowledge of the system, which increases therange of situations to which it can be applied; and (iv) the relativelylow cost of the measurement devices required. As a consequence,the resulting scheme can be potentially used in actual industrialapplications.
Further closed-loop tests of the influence of temperature maybe of interest to ensure the robustness of the controller in an indus-trial environment. However, in this case a supervising system maysense a failure in the temperature control system and deactivatethe cascaded controller as a preventive measure.
Acknowledgments
The authors wish to acknowledge the Spanish National R&DProgram and European Regional Development Fund (ERDF) for theProject ANACOM CTQ2004-07811-C02-01, and the Spanish FPI pro-gram for the grant BES-2005-10878.
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