Bioresource Technology 102 (2011) 4098–4103

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Bioresource Technology

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Real time monitoring of a biogas digester with gas chromatography,near-infrared spectroscopy, and membrane-inlet mass spectrometry

Alastair James Ward a, Emiliano Bruni b, Morten K. Lykkegaard c, Anders Feilberg a,Anders P.S. Adamsen a, Anders P. Jensen b, Allan K. Poulsen c,⇑a Department of Biosystems Engineering, Faculty of Agricultural Sciences, Aarhus Univ., Blichers Allé 20, DK-8830 Tjele, Denmarkb Xergi A/S, Burrehøjvej 43, DK-8830 Tjele, Denmarkc Danish Technological Institute, Centre of Chemistry and Biotechnology, Kongsvang Allé 29, DK-8000 Aarhus, Denmark

a r t i c l e i n f o a b s t r a c t

Article history:Received 9 September 2010Received in revised form 13 December 2010Accepted 14 December 2010Available online 22 December 2010

Keywords:MIMSl-GCNIROn-line monitoringBiogas

0960-8524/$ - see front matter � 2010 Elsevier Ltd. Adoi:10.1016/j.biortech.2010.12.052

⇑ Corresponding author. Tel.: +45 72201824; fax: +E-mail address: akp@dti.dk (A.K. Poulsen).

Four methods of monitoring the anaerobic digestion process were studied at pilot scale. The methodsemployed were Micro Gas Chromatography (l-GC) and Membrane Inlet Mass Spectrometry (MIMS) formeasurements in the gas phase, Near Infrared Spectroscopy (NIRS) and pH in the liquid phase. MicroGas Chromatography accurately measured H2, CH4, H2S, N2 and O2 in the headspace whereas the MIMSaccurately measured CH4, CO2, H2S, reduced organic sulfur compounds and p-cresol, also in the head-space. In the liquid phase, NIRS was found to be suitable for estimating the concentrations of acetate, pro-pionate and total volatile fatty acids (VFA) but the error of prediction was too large for accuratequantification. Both the l-GC and NIRS were low maintenance methods whereas the MIMS required fre-quent cleaning and background measurements.

� 2010 Elsevier Ltd. All rights reserved.

1. Introduction the experience of the plant operator. Therefore there is a need for

Anaerobic digestion of organic wastes or energy crops is aneffective method of producing renewable energy. However, manybiogas plants operate at sub optimal loading rates to ensure astable process (Costa et al., 2009) at the expense of digester pro-ductivity. A major problem is the sensitivity and slow growthrate of the methane producing organisms with a resulting riskof process failure if loading rates vary or are increased rapidly.A reliable method for on-line monitoring of key process variablesand automatic control are necessary for maximum loading whilstmaintaining a stable process (Steyer et al., 1999). A recent surveyof centralized biogas plants in Denmark has shown that poorprocess monitoring was an important factor regarding processimbalances and suboptimal conditions (Nielsen and Angelidaki,2008).

Current practice for agricultural biogas plants is to use a super-visory control and data acquisition (SCADA) system for basic plantoperating procedures only. This typically comprises only tempera-ture and level control and logging of biogas flow rate and morerarely also logging of pH in the digester. The decision making re-lated to the loading and composition of the biomass input includ-ing introduction of new and potentially toxic substrates is based on

ll rights reserved.

45 72201019.

monitoring and control systems at biogas plants treating manureand solid wastes (Boe et al., 2008).

In a complicated biological process such as anaerobic digestion,there are many factors which are indicative of the process state.These factors can be grouped into liquid or gas phase measure-ments. Volatile fatty acids (VFA) have been shown to be of keyimportance as they are formed in excess as a result of processimbalance (Ahring et al., 1995). Individual VFAs such as propionate(Boe et al., 2008; Nielsen et al., 2007), butyrate and isobutyrate(Ahring et al., 1995) have been put forward as being of particularinterest. However, there is no defined threshold inhibitory levelof any VFA species that is valid for all reactors (Angelidaki et al.,1993) therefore VFA monitoring is preferable as an on-line applica-tion to examine relative changes over time.

Gas phase parameters for monitoring the process can be eitherthe measurement of the biogas flow rate or of the gas composition.Gas phase parameters alone have been criticized for being slow torespond to changes in the liquid phase but when combined withother parameters such as pH, biogas flow rate (Steyer et al.,1999) and analysis of H2, CH4 and CO2 (Mathiot et al., 1992) haveproven to be suitable inputs for control systems. Membrane InletMass spectrometry (MIMS) has potential for investigating and on-line monitoring of anaerobic bioreactors (Matz and Lenneman,1996), monitoring of biological process activity with high specific-ity and sensitivity (Lloyd et al., 2002) and also for detecting VFA inthe headspace with pH correction. MIMS was recently applied for

A.J. Ward et al. / Bioresource Technology 102 (2011) 4098–4103 4099

monitoring biofilter efficiency with respect to e.g. VFA, phenols andreduced organic sulfur compounds (Feilberg et al., 2010).

Normal practice for monitoring process parameters is the timeconsuming method of sampling and laboratory analysis. VFAs arenormally measured off-line by gas chromatography (GC). On-linesystems for VFA measurement by GC have been developed (Pindet al., 2003) which use microfiltration of a reactor sample. An on-line VFA measurement system without filtration has also beendemonstrated which acidifies a sample and measures VFA in theheadspace by GC. However, this system resulted in high variationsof sensor response at low VFA concentrations and problems relatedto foaming and material resistance (Boe et al., 2007). NIR is a ro-bust method that can measure VFA in the liquid phase (Hanssonet al., 2003; Holm-Nielsen et al., 2007), and is a promising tech-nique for on-line applications. No commercial NIR systems areyet available for process VFA monitoring.

Gas composition analysis is routinely carried out in laboratoriesby GC (Nakakubo et al., 2008) although electrochemical cells areavailable for on-line hydrogen sulfide or hydrogen measurement(Mathiot et al., 1992) and infrared sensors are available for meth-ane and carbon dioxide (Holubar et al., 2002). However, a l-GCis able to measure gas composition on-line and is more portablethan a regular GC and is therefore more suited to field work (Sado-wska-Rociek et al., 2009) or integration into an on-line processmonitoring system.

The work presented here compares different on-line methodsfor the monitoring of process parameters in both the liquid andgas phases of a pilot scale anaerobic digester. The methods con-sisted of:

� A pH probe connected to data acquisition software.� A near infrared spectrometer equipped with a diffuse reflec-

tance probe situated in a re-circulation loop to monitor VFA inthe liquid phase.� A MIMS to measure various gases in the headspace.� A l-GC, calibrated for H2, CH4, CO2, H2S, N2 and O2 measure-

ments in the headspace.

The process was monitored for a period of 2 months (October –November 2009), during which the organic loading rate (OLR) wasincreased. To our knowledge this is the first time MIMS and l-GCare used for on-line monitoring of biogas reactors.

2. Methods

2.1. Pilot-scale biogas plant

2.1.1. Substrate preparationThe substrate for the biogas process was a mixture of chicken

manure and pig manure in equal proportions (on a wet weight ba-sis) which was further diluted with water at a manure to water ra-tio of 0.35:1 w/w. The manures were kept frozen at �18 �C untiluse. The mixed substrate had TS (total solids) and VS (volatile sol-ids) content of 6.7% and 5.3%, respectively, and TKN (total Kjeldahlnitrogen) and ammoniacal nitrogen (NH4–N) of 3.98 and 1.84 g L�1,respectively. The methane potential of the substrate was272 ± 8 mL CH4 (g VS)�1.

2.1.2. Reactor set-upA pilot-scale plant designed and built by Xergi A/S (Foulum,

Denmark) was used for the experiments. The reactor is shownschematically in Fig. 1. The plant was composed of two stainlesssteel tanks used as substrate storage and as digester, respectively.The total volume of the reactor tank was 300 L, the working vol-ume was 200 L and the headspace volume was 100 L. A vertical

mixer rotating at 2 Hz (installed on both tanks) ensured continu-ous mixing. The tanks were equipped with load cells for controlof feeding and of effluent removal. Eccentric pumps were usedfor feeding and for effluent removal. The removal of the effluentpreceded the feeding by 15 min. The process temperature in thebiogas reactor was maintained at 52 ± 0.5 �C. The OLR was changedin six steps from a starting point of 3.5 g VS (L d)�1 to 4.9, 6.2, 8.6,11.0, 8.2 g VS (L d)�1 and finally to an extreme load of 26.5 g VS(L d)�1 on days 11, 18, 25, 32, 36 and 47, respectively. The changesin OLR were achieved by modifying the hydraulic retention time(HRT). This method prevented problems with pump blockagewhich were expected from a substrate with an elevated dry mattercontent and has previously been shown to be a suitable method ofvarying process parameters for experimental purposes (Ahringet al., 1995; Converti et al., 2009). A controller was used for auto-matic control of temperature, effluent removal, feeding and fordata logging.

2.2. Analytical methods

TS, VS, total nitrogen TKN and ammonium nitrogen NH4–N(Kjeldahl-N method) were measured according to the standardmethods (APHA, 1998). The concentration of VFAs was measuredas described by Kaparaju et al. (2009). The methane concentrationof the biogas was also measured off-line with a gas chromatographequipped with a flame ionization detector (FID). The methane po-tential was determined in batch assays (infusion bottles of 543 mltotal volume, 200 ml anaerobic inoculum from biogas plant digest-ing cow manure, 5 g WW substrate, at 52 �C) as described by Bruniet al. (2010). The methane potential was calculated according toHansen et al. (2004). All gas readings were corrected to STP condi-tions (standard temperature and pressure, 273 K, 101.3 kPa).

2.2.1. SamplingAn eccentric pump re-circulated the content of the reactor

through a loop from the bottom of the reactor back to the top ofthe reactor with a flow rate of 10 L min�1. Liquid sampling foroff-line VFA determination took place from a tap installed alongthe re-circulation loop. The NIR probe was situated at a 90� bendat the highest point of the re-circulation loop adjacent to the liquidsampling point (Fig. 1). The probe was positioned to face down-wards. The pH probe was inserted directly in the content of thebiogas reactor. The biogas production was measured with a flowmeter (Gallus 2100 G 1.6 TCE). The MIMS and l-GC inlets wereconnected to the outlet biogas stream with the l-GC following asilica gel filter (see Fig. 1) to prevent water intake in the GC-columns.

2.2.2. Micro-gas chromatographyMicro-gas chromatography (l-GC, Agilent 3000) was used for

online determination of head space composition. The l-GC wasequipped with two parallel capillary columns containing differentcoatings (MolSieve 5Å PLOT, 10 m � 0.32 mm � 12 lm and PLOTQ, 10 m � 0.32 mm � 10 lm. Both columns were connected tothermal conductivity detectors (TCD) with argon applied as a car-rier gas with a column pressure on 20 psi. Sampling was carriedout every 30 min with each sampling event lasting approximately2 min. Every 14 days, the instrument was calibrated against a cer-tified biogas standard with 1000 ± 20 ppmv H2, 1000 ± 30 ppmvH2S, 40 ± 0.79% CO2 and 50 ± 1.0% CH4 (Air Liquide, DeutschlandGmbH).

2.2.3. Membrane-inlet mass spectrometryFormeasuringawiderrangeofgases, includinglowconcentration

gases (<10 ppmv), a MIMS (Balzers QMG 420) was connected to theoutlet biogas stream, sampling and measuring the headspace biogas

Fig. 1. A schematic presentation of the pilot plant and the monitoring of the process.

4100 A.J. Ward et al. / Bioresource Technology 102 (2011) 4098–4103

online (Fig. 1). The working principle is that a constant sample gasflow (50–60 mL/min) flows along a 50 lm thick silicone membrane(SIL-TEC Sheeting, Technical Products Inc., USA), where volatile com-pounds selectively are absorbed and diffuse across the membraneand evaporate into the vacuum chamber (�10�6 mbar) of the massspectrometer. The membrane inlet system was kept at 50 �C. Thecompounds were ionized by electron impact (70 eV) and separatedaccording to their mass-to-charge ratio (m/z) in the quadrupole massfilter and detected by a secondary electron multiplier.

Gas was sampled every 2 h with automated background mea-surements on a humidified certified biogas standard 60 ± 0.6%CH4, 1 ± 0.01% O2, rest CO2 (Air Liquide, Deutschland GmbH) onceper day. The relatively low sampling frequency was selected in or-der to minimize membrane and ion source fouling.

Additional characterization in terms of linearity and detectionlimits of the MIMS system for measuring VFA was carried out byusing a calibration system based on permeation tubes (VICI, Swit-zerland). The biogas standard was connected to a Dynacal 150 per-meation oven (VICI, Switzerland) containing acetic acid, propanoicacid, and butanoic acid in permeation tubes. The emission rates ofthe permeation ovens were determined gravimetrically. The biogasstandard was humidified with a gas washing bottle and was usedfor further dilutions of the output from the permeation oven. Con-centrations in the range of 0.2–20 ppm ( ± < 10%) were generated.The following masses were used to detect VFA: m/z 60 (acetic acidand butanoic acid), m/z 73 (butanoic acid and propanoic acid), m/z74 (propanoic acid) and m/z 88 (butanoic acid).

2.2.4. Near-infrared spectroscopyAn Antaris FT-NIR instrument (Thermo Scientific) recorded NIR

spectra in the liquid phase via a sapphire windowed diffuse reflec-tance probe, connected to the spectrometer by an optical fibercable. The probe illuminated an area of approximately 0.1 cm2

and was set in a re-circulation loop to ensure constant flow ofmaterial for a more representative measurement (Fig. 1).

The spectra collected were averaged over 100 scans which foreach sampling event took approximately 30 s using a spectral res-olution of 16 cm�1. Sampling was every 10 min. A reference spec-trum, averaged over 200 scans, was periodically acquired usingspectralon, positioned in the probe holder, and as such accountsfor spectral contributions of the probe and optical cabling. Theraw spectra obtained were subsequently divided by this reference

spectrum to yield a reflection value relative to the white-standard.For technical and practical reasons white referencing was only per-formed approximately once every 14 days as with the l-GC.

2.2.5. pHA probe pHix compact (MJK Automation A/S) was used for on-

line pH measurements. The probe was calibrated every 14 daysat pH 4.01, 7.00, 10.00. Manual pH measurements took place onceper day. pH was measured mainly with the purpose of correctingheadspace VFA measurements.

2.2.6. Data treatment and monitoringThe subsequent VFA modeling using chemometrics was based

on manual VFA analysis by GC. Multivariate data analysis for mod-el development was applied to the NIR data using TQ Analyst soft-ware (Thermo Scientific). For detection and elimination of outliers,principal component analysis (PCA) was applied to calculate prin-cipal component (PC) scores together with a leverage diagnosticand visual evaluation of the raw spectra. For computing predictionmodels, partial least squares (PLS) regression was used. Absorptionvalues (A = log(1/R)) were computed from the reflection values (R)and multiplicative scatter correction (MSC) was applied to com-pensate for scattering effects in the heterogeneous slurry. For mod-el validation, segmented cross validation (segment of 10) wasapplied and the quality was controlled and optimized in an itera-tive manner by following (1) the root mean square error of crossvalidation (RMSECV, the average error of the model), (2) the num-ber of optimal principal components (PCs, these determine themodel complexity with fewer being preferred to reduce the riskof model overfitting), (3) the coefficient of determination (R2, theproportion of variability accounted for by the model), and (4)residual prediction deviation (RPD, the ratio of standard deviationto RMSECV). In total, no more than 10% of the spectra were identi-fied as outliers.

2.2.7. Data interfaceIn order to effectively compare the large amount of data from

the different analytical methods, a suitable MATLAB interfacewas developed where relevant time intervals can be picked outand as such compare the different data spot on for elucidatinginteresting correlations.

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3. Results and discussion

During the experiment there were no obvious problems or ma-jor failures with any of the analytical equipment besides a few dayswithout measurements due to a power failure.

3.1. Gas phase

In Fig. 2 is seen a typical concentration profile of H2, N2, CH4,CO2 and H2S measured with the l-GC during a period of 2 days.It is possible to observe feeding events, carried out in the morningand in the evening, which manifest as a rise in the N2 signal and adecrease in the remaining signals. This was a result of ambient airbeing drawn into the reactor during daily replacement of organicmaterial. The detection limit of the l-GC is approximately10 ppmv.

The measurement of H2, H2S, CO2 and CH4 with MIMS – alongwith other gases such as VFAs, reduced organic sulfur compounds(ROS) and p-cresol (4-methylphenol) was also attempted. TheMIMS signals were observed to be dependent on the pressure inthe detection chamber, which was higher when sampling humidbiogas compared to ambient air. This is mainly ascribed to high dif-fusion rates of CH4 and H2O in the silicone membrane. Therefore,all calibrations and baseline measurements were performed usingthe previous mentioned humidified CH4/CO2 biogas standard. Thisgave detection chamber pressures comparable to those observedwhen sampling biogas. Some sensitivity drift was observed andthe data have therefore been normalized by the CO2 signal (m/z44).

The MIMS measurements of CH4 and H2S were in good agree-ment with the measurements obtained by l-GC (Fig. 3).

Feeding events resulted in a major increase in MIMS-signals as-signed to dimethyl sulfide (DMS; m/z 47 + 62) and methanethiol(MT; m/z 47 + 48). During effluent removal, some air was suckedinto the digester from the biogas outlet. This can be observed asa correlation between oxygen in the biogas and m/z 47 (DMS/MT)(Fig. 4). The air that was introduced into the system during theremoval of the effluent diluted the other components of the biogassuch as CH4, CO2, H2 and H2S as shown in Fig. 2 yet the DMS/MTconcentrations apparently increased following effluent removaland feeding. This probably can be attributed to a slight oversatura-tion of DMS in the slurry, which is released during the small

Fig. 2. Concentration in ppmv of gases measured with the l-GC as function of time duringthis figure but was also measured with l-GC. The vertical arrow shows an increase in O

vacuum during the effluent removal. Signals at m/z 107 and 108were observed throughout the measurement period. These signalsare assigned to p-cresol based on the ratio of the signals and thefact that this compound is generally observed to be present in slur-ry systems due to degradation of the amino acid, tyrosine(Hofmann and Hammer, 1999; Oneill and Phillips, 1992).

Due to an unusually high background signal at m/z 2 on the spe-cific instrument used, it was not possible to quantify H2 by MIMS inthis study. Normally, however, mass spectrometry should be suit-able for measuring H2, see e.g. Whitmore et al. (1987).

Additional characterization of MIMS sensitivity towards VFAdemonstrated a high degree of linearity of the method withR2 > 0.995 for all masses monitored. The detection limits estimatedfrom replicate blank measurements were as follows: Acetic acid;50 ppb, Propanoic acid; 20 ppb, Butanoic acid; 20 ppb. The calibra-tion was not performed during the campaign and the response fac-tors were therefore not used to convert corrected signals into gasconcentrations. For comparison, the equilibrium VFA concentra-tions in units of ppb-by-volume, Ceq

g , were calculated from the li-quid phase concentrations by the following formula:

Ceqg ¼

Caq

MW� 1000� 1

1þ 10pH�pka� 1

K298H � expðDHsol

R ð1T � 1298:15ÞÞ

� 109;

where Caq is the measured liquid phase concentration (mg/liter),MW is the molecular weight of the compound, K298

H is the Henry’slaw constant of the compound (M/atm), DHsol is the enthalpy ofsolution, R is the gas constant, and T is the temperature of the reac-tor. Henry’s law constants including temperature dependencesfrom Sander (1999) were used as input values. The correspondingaverage gas phase concentrations (±one standard deviation) were:Acetic acid; 49 ± 23 ppb, propanoic acid; 27 ± 18 ppb and butanoicacid; 8 ± 7 ppb. Consequently, the VFA-signals measured by MIMScould only potentially (assuming equilibrium) have been abovethe detection limits at high concentrations of VFA. MIMS-signalshigher than the detection limit (by a factor of �2) were observedduring periods with high liquid phase concentrations. However,MIMS-signals were not observed to be correlated with liquid phaseVFA-concentrations. This could partly be explained by the signalsbeing close to the detection limit giving rise to a higher relativeuncertainty. In addition, it is possible that impedance in liquid-to-gas exchange prevents the gas concentration from trailing the liquid

a random period of one and a half day (Oct. 17–18th). For clarity, O2 is not shown inLR from 4.9 to 6.2 g VS (L d)�1.

Fig. 3. Concentration developments for (A) H2S and (B) CH4 during a period of 33 days, starting October 17th, measured with l-GC and MIMS. The vertical arrows 1–5 showOLR changes from 4.9 g VS (L d)�1 to 6.2, 8.6, 11.0, 8.2 and 26.5 g VS (L d)�1, respectively.

Fig. 4. Dimethyl sulfide (DMS) and methanethiol (MT) measured with MIMS (B) during a period of 8 days in the latter part of the campaign. The O2 signal measured with l-GC (A) is shown for comparison. The vertical arrow shows an increase in OLR from 8.2 to 26.5 g VS (L d)�1.

4102 A.J. Ward et al. / Bioresource Technology 102 (2011) 4098–4103

phase concentration (mainly because of the pH of the liquid phase ofthe biogas digester). The MIMS was a labour intensive measurementmethod when applied to biogas processes as the instrument re-quired frequent cleaning and background measurements.

Table 1NIR VFA prediction model parameters. N is the number of samples used for thecalibration set, NPC is the number of principal components in the respective models.

VFA species N NPC VFA range(g L�1)

RMSECV(g L�1)

R2 RPD

Acetate 111 11 0–5.4 0.80 0.84 1.83Propionate 110 10 0–3.4 0.57 0.83 1.79Total VFA 113 10 0–9.4 1.53 0.84 1.82

3.2. Liquid phase

The pH of the liquid phase of the biogas digester was between7.54 and 7.89.

In the modeling of acetate, propionate and total VFA content,NIR data from the 2 months period with increased OLR has beenused. The VFA prediction models compare reasonably well withpreviously reported NIR data (Holm-Nielsen et al., 2007; Jacobiet al., 2009) in terms of R2 values (>0.80). However, the RMSECVvalues are large and subsequently the RPD values are low (Table 1),below the recommended minimum of 2.5 suggested by Williams(2001) as being the lower limit for quantitative analysis. Thereason for the relatively poor models could be due to the

measurement technique. The positioning of the probe in the recir-culation loop could perhaps be improved: it has been reported thatthe ideal position for sample collection and therefore probe attach-ment to a pipe is a side valve in a vertical pipe section to improveturbulence (and therefore mixing) and reduce gravitational segre-gation (Holm-Nielsen et al., 2006). In this study the probe wasmounted vertically in a T-piece of tubing which formed a 90� bendin the sample loop, see Fig. 1. Although this position would provide

A.J. Ward et al. / Bioresource Technology 102 (2011) 4098–4103 4103

a good deal of turbulence and therefore a representative sample it ispossible that gas in the pipe or large particles could become trappedin front of the probe window causing erroneous measurements.

4. Conclusion

In this study it was found that l-GC accurately measured H2,CH4, CO2, H2S, N2 and O2. MIMS accurately measured CH4, CO2,H2S, reduced organic sulfur compounds, and p-cresol. HeadspaceVFA measurement was also possible with MIMS but the data wereclose to the detection limits and showed little relation to the liquidphase VFA concentration. NIR gave an approximate indication of li-quid phase VFA concentrations. NIR, l-GC and the pH probe provedto be reliable and low maintenance. MIMS is considered not suit-able for long term online measurements because it required fre-quent cleaning and daily background measurements.

Acknowledgements

Thanks are due to Ejnar Paaske Jensen, Pia Jørgensen, CarstenSiggaard, Adris Georgis Shlimon. Thanks to the lab technicians,Britt Amby Malthesen and Heidi G. Christiansen, for excellentassistance with sampling and analyses and to Energinet.dk, TheForskEL programme, PSO 2008-1-0078 Real time control of biogasreactors for financially supporting the project with €384.358.

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