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Author(s)

First Name Middle Name Surname Role Email

Rodrigo A Labatut Grad student Ral32@cornell

.edu

Affiliation

Organization Address Country

Biological & Environmental Engineering,

Cornell University

B-45 Riley Robb Hall, Ithaca, NY

14853

USA

Author(s) – repeat Author and Affiliation boxes as needed--

First Name Middle Name Surname Role Email

Norm R Scott Fellow Nrs5@cornell.

edu

Affiliation

Organization Address Country

Biological & Environmental Engineering,

Cornell University

216 Riley-Robb Hall, Ithaca, NY

14853

USA

Publication Information

Pub ID Pub Date

08 2008 ASABE Annual Meeting Paper


An ASABE Meeting Presentation Paper Number: 08

Experimental and Predicted Methane Yields from the Anaerobic Co-Digestion of Animal Manure with Complex Organic Substrates

Rodrigo A. Labatut

Cornell University, B-45 Riley-Robb Hall, [email protected].

Norm R. Scott

Cornell University, 216 Riley-Robb Hall, [email protected].

Written for presentation at the 2008 ASABE Annual International Meeting

Sponsored by ASABE Rhode Island Convention Center

Providence, Rhode Island June 29 – July 2, 2008

Abstract. Anaerobic co-digestion of animal manure with complex organic substrates can improve the economic viability of farm treatment processes. Co-digestion is a method that increases the buffering capacity of substrate mixtures and adds essential nutrients that can substantially improve methane yields. However, despite the benefits of co-digestion, the overall impact that additional materials have on manure-only fermentation processes is not known.

In this study we investigated the potential of co-digestion of dairy manure with a diverse range of organic substrates to increase methane production over conventional manure-only digestion methods. Selected organic substrates (e.g. food residues, energy crops) of different biodegradability and chemical composition were mixed with dairy manure at varying ratios. The biochemical methane potential (BMP) assay coupled with gas chromatography analyses were used to determine the methane production of the different substrate mixtures under mesophilic conditions.

Results of about 175 individual BMP assays on more than 30 different substrates showed that substrates highly rich in lipids and/or carbohydrates with a high volatile solid content are good candidates for co-digestion with dairy manure, as co-digestates such as used oil and fresh pasta demonstrated. Protein-rich substrates are potentially good candidates; however, substrates such as cheese whey, tend to be highly variable in composition, and thus provide extremely variable results. The substrate options to be


used for co-digestion are unlimited; thus, the ability of four different methods to successfully predict ultimate methane yields from elemental biochemical characteristics of substrates was evaluated. It was determined that substrate degradability is critical to ensure the accuracy of these methods.

Keywords. Co-digestion, methane, dairy manure, BMP, predicted

1. Introduction

Anaerobic digestion has been used to stabilize animal manure, reduce greenhouse gas (GHG) emissions, control odors, promote environmental management of nutrients and produce clean energy. Anaerobic co-digestion of animal manure with complex organic substrates, such as food residues, can improve the economic viability of farm treatment processes. Depending on their chemical composition, the added substrates, i.e. co-digestates, may supply additional constituents that potentially can increase methane yields. Similarly, by processing multiple waste streams in a single facility, co-digestion promotes the development of economies of scale, and facilitates the generation of tipping fees when off-farm residues are received. Furthermore, necessary conditions for waste stabilization are rarely provided by the co-digestates alone – if mono-digestion is intended, some degree of chemical pre-conditioning will be certainly needed. Animal manure can be fairly undegradable due to its high lignin content (Chandler et al., 1980); however, it can provide the necessary environmental conditions (e.g., alkalinity, pH, nutrients) to favor methane production. Synergism can also be promoted with co-digestion, by creating co-metabolic processes from particular substrate mixtures which can increase the overall biodegradability of the individual components (Angelidaki and Ahring, 1997). Co-digestion of organic residues with manure has been investigated since the late 1980s (Alatriste-Mondragon et al., 2006). Studies conducted by Wong (1990) found higher methane yields when swine manure was co-digested with sewage sludge at ratios of 66:33 as compared to the sole digestion of manure or sludge. Wong (1990) concluded that the most important factor determining digestion efficiency was pH. Higher methane yields (2-3 fold) were also reported when cattle manure was co-digested with olive oil mill effluent at 50:50 and 25:75 ratios (Angelidaki et al., 1997; Angelidaki & Ahring, 1997). The authors concluded that the high buffering capacity contained in manure, together with the content of several essential nutrients, make it possible to degrade oil mill effluent without previous dilution and the addition of any external source of alkalinity or nitrogen (Angelidaki et al., 1997; Angelidaki & Ahring, 1997). By co-digesting swine manure with poultry manure at ratios of 100:0, 80:20, 60:40, 40:60, 20:80, and 0:100, Magbanua et al. (2001) observed higher methane yields for any substrate combination as compared to the those obtained from either substrate alone. However, despite the benefits of co-digestion, the highly complex nature and inconsistency of the potential co-digestates makes it difficult, if not impossible to predict the overall impact that additional materials have on manure-only fermentation processes. The objective of this study is to evaluate the potential of the co-digestion of dairy manure with a variety of organic substrates to increase methane production over conventional manure-only digestion processes. Selected organic substrates of different degradability and chemical composition were mixed with dairy manure at various ratios and subjected to the biochemical methane potential (BMP) assay originally described by Owen et al. (1979) to identify suitable substrate characteristics and concentrations that maximize methane production. This study contains the results of about 175 individual BMP assays to determine the methane potential of the mesophilic anaerobic co-digestion of dairy manure with a range of organic substrates, mainly food residues. In addition, given the unlimited diversity of possible substrates, a secondary objective of this study was to evaluate the ability of analytical methods to successfully predict ultimate methane yields from biochemical characteristics of the substrate.

2. Methods 2.1. Biochemical methane potential (BMP) Anaerobic degradation of substrates was tested by placing known quantities of the substrates in 120- and/or 250-mL bottles inoculated with an active anaerobic mixed culture media (i.e. inoculum) obtained from the effluent’s supernatant of an operating anaerobic digester. Additional bottles of inoculum were set up as controls to account for the methane yields created by the inoculums alone. Similarly, a water control bottle was used to correct for internal pressure variations due to external temperature and atmospheric pressure fluctuations. Bottles were gassed-out with a mixture of 70% N2 and 30% CO2 and sealed immediately using rubber septa and aluminum crimp caps. Once sealed, the bottles were placed in an incubator to maintain a constant mesophilic (35±1oC) temperature throughout the digestion period. The biogas production inside the bottles was determined in an indirect manner by using pressure transducers, which were attached to a hypodermic needle that was inserted through the septa, in a procedure comparable to that described by Suflita and Concannon (1995). Pressure measurements were performed continuously over time using a data acquisition (DAQ) system interfaced to a computer and controlled using LabVIEW® (National Instruments Co., Austin, TX) programming code. Likewise, temperature was continuously monitored through a thermocouple connected to the DAQ system. The length of the incubation period was variable for each trial as determined by a significant decrease in the biogas production rate of the individual substrates, i.e. the curve’s plateau. Pressure data gathered by the DAQ system were converted to volume of biogas at standard temperature and pressure (STP) according to the ideal law of gases (PV = nRT). Methane and carbon dioxide content in the biogas was determined by gas chromatography (GC) and the methane yield was calculated. A typical BMP run for a 44-day assay depicting raw pressure built-up (as recorded by the DAQ) and biogas yield (corrected for atmospheric changes and inoculum) inside the bottles is shown in Figure 1. 2.2. Substrates characterization

Previous to testing, all substrates were mixed and blended in order to reduce particle size and to create uniform and representative substrate specimens. Substrates were characterized based on selected physicochemical and biochemical parameters relevant for anaerobic fermentation (Tables 1 and 2). Total solids (TS) and volatile solids (VS) were determined according to Standard Methods, sections 2540B and 2540E, respectively (APHA, 1995). Chemical oxygen demand (COD) was based on the colorimetric dichromate closed reflux method from section 5220D of Standard Methods (APHA, 1995), using HACH digestion vials (HACH Co., Loveland, CO). 10-day biochemical oxygen demand (BOD) was determined using the HACH BODTrakTM (HACH Co., Loveland, CO). Total Kjeldahl nitrogen (TKN) determination was conducted according to the HACH Nessler method 8075 (HACH, 2003). Ammonia-N was determined based on the HACH Salicylate method 10031 (HACH, 2003). Total organic carbon (TOC) determination was conducted according to HACH method 10128 (HACH, 2003). Methane and carbon dioxide content in the biogas was determined with a SRI 8610C (SRI Instruments, Torrance, CA) gas chromatograph (GC) using a Haysep column equipped with a thermal conductivity detector (TCD) and a flame ionization detector (FID). Calibration of GC was conducted periodically with mixtures of methane and carbon dioxide.

2.3. Experimental design More than 30 different substrates (including substrate mixtures) have been analyzed using the BMP assay. In addition to dairy manure, these substrates include a range of substrates, predominantly food residues that are currently being co-digested with dairy manure in operating farm digesters throughout New York State. Thus, a BMP assay bottle may consist of the digestion of a pure substrate (e.g. manure, cheese whey, used oil) or the co-digestion of multiple substrates (e.g. manure/whey, manure/oil) mixed at different ratios. All BMP tests were conducted under mesophilic conditions (35oC). A maximum of 30 BMP assay bottles, including three replicates per different substrate, were tested at a time. Substrate performance was evaluated based on the normalized methane yield (NMY), which is defined here as the total volume of methane yield during digestion divided by the quantity of substrate initially added (VS basis) to the bottle (mL CH4/g VS added).

0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45-20

0

20

40

60

80

100

120

140Biogas raw pressure of the substrate, inoculum, and water bottles

Time (days)

Bio

ga

s r

aw

pre

ssu

re b

uilt-

up

in

sid

e th

e b

ottle

s (

kP

a)

Manure Patt

Manure AA

Whey

MW9010

MW7525

MW5050

MW2575

Dog food

Ice cream

MDFIC502525

Inoculum

Water

0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45-40

-20

0

20

40

60

80

100Biogas yield of the substrates at STP - corrected for atmospheric pressure changes & inoculum biogas yield

Time (days)

Bio

ga

s y

ield

(m

L a

t S

TP

)

Manure Patt

Manure AA

Whey

MW9010

MW7525

MW5050

MW2575

Dog food

Ice cream

MDFIC502525

Inoculum

Figure 1. Typical BMP assay curves for a 44-day run showing biogas raw pressure (kPa) and biogas yield

(mL at STP) inside the bottles1. The plateau phase (lowest biogas yield rate) can be observed after 25 days of residence time.

1 Because the pressure inside the test bottles is measured at 35

oC, when the biogas is converted to STP (0

oC, 1

atm), its volume decreases as described by the ideal law of gases, which explains the “negative biogas yields” at

the beginning of the BMP.

Table 1. Physical and biochemical characterization of the pure substrates analyzed

Pure substrates BOD

(g/kg) COD

(g/kg) TS

(g/kg) VS

(g/kg) TOC

(g/kg) TKN

(g/kg) NH3-N (g/kg)

TON (g/kg)

BOD/COD

BOD/TS

BOD/VS

VS/TS

COD/VS

TOC/TON

Raw manures

Raw manure (Mn) 40.1 108.1 103.5 84.8 23.9 3.3 1.8 1.5 0.38 0.39 0.48 0.82 1.28 15.93

Manure separated liquid (MSL) 33.2 71.0 57.5 40.5 18.8 3.2 1.6 1.6 0.47 0.58 0.82 0.71 1.75 11.75

Food residues

Cheese whey (W) 64.9 128.3 71.4 59.8 39.6 1.2 0.2 1.0 0.53 0.93 1.09 0.84 2.14 39.60

Plain pasta (Ppas) 188.7 934.3 422.6 407.7 298.3 12.3 ND ND 0.20 0.45 0.46 0.97 2.29 ND

Meat pasta (Mpas) 205.8 562.8 381.8 340.6 157.4 ND ND ND 0.37 0.54 0.60 0.89 1.65 ND

Used vegetable oil (O) 600.0 1205.0 991.0 988.8 ND ND ND ND 0.50 0.61 0.61 1.00 1.22 ND

Ice cream (Ic) ND 266.8 113.8 109.1 ND ND ND ND ND ND ND 0.96 2.45 ND

Fresh dog food (Df) ND 530.4 132.2 125.6 ND ND ND ND ND ND ND 0.95 4.22 ND

Cola soda (Col) ND 121.5 93.6 88.7 46.6 0 0 0 ND ND ND 0.95 1.37 ND

Potatoes (Pot) 53.5 261.8 177.4 163.5 ND 4.0 ND 1.5 0.20 0.30 0.33 0.92 1.6 ND

Invasive aquatic plants

Frogbit (Oneida lake) 32.9 49.5 51.8 38.7 ND ND ND ND 0.67 0.64 0.85 0.75 1.28 ND

Water Chestnut (Oneida river) 40.4 46.2 89.0 74.2 ND ND ND ND 0.87 0.45 0.54 0.83 0.62 ND

Eurasian Milfoil (Oneida lake) 26.4 27.8 106.1 66.7 ND ND ND ND 0.95 0.25 0.40 0.63 0.42 ND

Water Celery (Oneida lake) 27.9 33.6 92.9 47.0 ND ND ND ND 0.83 0.30 0.59 0.51 0.71 ND

Chara (Tully lake) 27.9 31.5 148.8 37.7 ND ND ND ND 0.89 0.19 0.74 0.25 0.84 ND

Energy crops and others

Switchgrass (Sg) 88.6 706.7 930.1 904.9 91.1 8.0 ND ND 0.13 0.10 0.10 0.97 0.78 ND

Mouthwash (Mw) ND 160.5 130.2 118.4 58.3 0 ND ND ND ND ND 0.91 1.36 ND

WWTP oil sludge (Oslu) 201.0 600.1 267.2 229.7 ND ND ND ND 0.34 0.75 0.88 0.86 2.61 ND

ND: no data

Table 2. Physical and biochemical characterization of the substrate mixtures analyzed

Substrate mixturesab

BOD

(g/kg) COD

(g/kg) TS

(g/kg) VS

(g/kg) BOD/COD BOD/TS BOD/VS VS/TS COD/VS

Mn:W 90:10 45.5 103.2 83.2 68.4 0.44 0.54 0.66 0.82 1.50

Mn:W 75:25 46.4 100.3 68.5 57.7 0.46 0.68 0.80 0.84 1.74

MSL:W 75:25 28.0 61.9 45.1 33.0 0.45 0.62 0.85 0.73 1.88

Mn:Ppas 90:10 91.8 158.5 132.0 116.7 0.58 0.70 0.79 0.88 1.36

Mn:Ppas 75:25 97.2 293.6 222.5 211.4 0.33 0.44 0.46 0.95 1.39

Mn:Mpas 90:10 70.4 151.2 101.9 89.8 0.47 0.69 0.78 0.88 1.68

Mn:Mpas 75:25 98.8 233.0 148.5 136.9 0.42 0.67 0.72 0.92 1.70

Mn:O 75:25 ND 922.0 263.6 235.4 ND ND ND 0.89 ND

Mn:Df:Ic 50:25:25 ND 317.0 106.9 96.9 ND ND ND 0.91 3.27

Mn:Col 75:25 38.6 122.4 102.7 83.8 0.32 0.38 0.46 0.82 1.46

Mn:Pot 75:25 58.0 122.0 134.4 114.3 0.48 0.43 0.51 0.85 1.07

Mn:Sg 75:25 17.1 413.6 308.0 284.4 0.04 0.06 0.06 0.92 1.45

Mn:Mw 75:25 51.1 168.6 110.5 86.0 0.30 0.46 0.59 0.78 1.96

Mn:Col:Mw 75:12.5:12.5 53.5 140.7 108.8 86.8 0.38 0.49 0.62 0.80 1.62

a Notation as on Table 1;

b numbers represent the respective percent of each substrate in the mixture; ND: no data

3. Results and discussion

3.1. Experimental parameters Experimental parameters to be used for the BMP assay were determined through a series of preliminary trials (data not shown). The main objective was to determine the appropriate substrate concentration and inoculum-to-substrate (I/S) ratios for the assay. A combination of three different substrate concentrations and four different I/S ratios were subjected to the BMP test. It was concluded that for an initial substrate concentration of 1.7 g/L (VS basis), the minimum I/S ratio required was 2. Correspondingly, for an initial substrate concentration of 3.0 g/L (VS basis), the minimum I/S ratio required was 0.5, which ensured the process start-up during the first five days. These results are in agreement with the studies conducted by Owen et al. (1979) and Chynoweth et al. (1993) who suggested I/S ratios of 1 and 2 (VS basis), respectively. An I/S ratio of 2 was used in the majority of our experiments. However, to decrease the error attributed to inoculum-associated methane yield, future BMP trials will be conducted at an I/S ratio of 1. 3.2. Experimental methane yields A summary of the results of all trials is presented in Figure 2. For dairy manure, it is shown that the average normalized methane yield (NMY) from a total of 47 BMP tests was 243±60 mL of CH4 per g VS added. This value compares quite well with that reported by the IPCC (1997) of 240 mL/g VS added, but it is somewhat higher that the values reported by Moller et al. (2004) of 148±41 mL/g VS from batch studies and Morris (1977) of 150 mL/g VS from a CSTR. However, the variability of the results is rather large. In our study, the NMY varied from 127 to 329 mL/g VS, and in the study of Moller et al. (2004) the variability was also high, as shown by the standard deviation of their study (±41 mL/g VS). BMP results of other substrates were evaluated on the basis of their predicted methane yields, because available literature on comparable substrates is limited, and meaningful comparisons are impractical.

648.5 (n=3)

502.3 (n=3)

467.3 (n=3)

423.6 (n=10)

407.3 (n=11)

426.6 (n=3)

380.3 (n=13)

373.1 (n=2)

360.6 (n=3)

353.5 (n=6)

334.5 (n=3)

326.1 (n=6)

296.1 (n=3)

285.6 (n=3)

274.3 (n=3)

261.3 (n=3)

258.0 (n=2)

256.5 (n=3)

252.4 (n=14)

242.7 (n=47)

237.6 (n=6)

235.0 (n=3)

232.1 (n=3)

227.7 (n=3)

224.0 (n=3)

220.1 (n=3)

216.2 (n=3)

207.8 (n=3)

167.7 (n=3)

122.2 (n=3)

0 100 200 300 400 500 600 700 800

Used vegetable oil

Ice cream

Manure:dog food:ice cream 50:25:25

Cheese whey

WWTP oil sludge

Dog food (fresh)

Invasive aquatic plants

Cola (soda)

Manure:oil 75:25

Manure:plain pasta 75:25

Potatoes

Plain pasta

Corn silage

Manure:meat pasta 75:25

Mouthwash

Manure separated liquid

Manure:cola:mouthwash 75:12.5:12.5

Cabbage

Manure:whey 75:25

Raw manure

Manure:whey 90:10

Manure:cola 75:25

Manure:meat pasta 90:10

Manure:potatoes 75:25

Manure:plain pasta 90:10

Manure:mouthwash 75:25

Meat pasta

Manure:switchgrass 75:25

MSL:whey 75:25

Switchgrass

mL CH4 per g TVS added

Figure 2. Summary of the normalized methane yields (values inside the bars) for some 30 pure substrates and substrate mixtures analyzed using the BMP assay. Results for the five species of invasive

aquatic plants presented in Table 1 are compiled together in a single value for this graph. Error bars represent the standard deviation of the NMY for each substrate.

3.3. Methane prediction models The ability of various methods to successfully predict ultimate methane yields from biochemical characteristics of the substrate was evaluated. The observed methane yields of selected substrates subjected to the BMP assay were compared to the yields as predicted by four different methods, described as follows. Method 1. Carbon content

Based on the number of moles of carbon contained in a particular substrate, which theoretically are fully converted to CO2 and CH4 by the end of the anaerobic digestion. The moles of carbon of the substrate are obtained from its empirical formula or chemical composition, i.e. proteins, lipids, carbohydrates, etc. The moles are then converted to gas volume according to the ideal law of gases by the following equation (Eq. 1).

(1)

where, n Number of moles (mol) P Pressure (Pa) V Volume (m3) R Universal gas constant (8.3145 m3 Pa K-1 mol-1) T Temperature (K) Method 2. COD stabilization

Based on the stoichiometric relationship that exists between the COD destroyed during the digestion (or ultimate BOD, BODL) and the methane that is created (Eq. 2).

(2)

The relationship shows that one mole of methane is equivalent to two moles of oxygen. Since one mole of an ideal gas occupies 22.4 L at STP and two moles of oxygen contain 64 g, the theoretical methane production from COD stabilization is 350 mL per g of COD converted. Thus, an estimation of the theoretical ultimate methane production can be obtained from the knowledge of the BODL-to-COD ratio of the waste. Method 3. Buswell Formula

Based on the Buswell Formula that was originally proposed by Symons and Buswell in 1933. The method uses the composition of the substrates, i.e. proteins, lipids, carbohydrates, etc., to calculate the methane yields through a general reaction of carbon stabilization (Eq. 3).

(3)

Method 4. Bioenergetics and Stoichiometry

Based on the thermodynamics of microbiological-mediated reactions and the stoichiometry associated therein, which are described in detail in Bioenergetics and Stoichiometry (Gossett, 2004). The method also uses the composition of the substrates. Depending on the model, the accuracy of the methods will depend to a greater or lesser extent on two key pieces of information: 1) chemical composition of the substrate and 2) anaerobic degradability of the substrate. With the exception of the COD stabilization method, all the other predictive models require knowledge of the chemical compositions of the substrates. A thorough online database with the chemical composition of a wide range of foods is maintained by the Nutrient Data Laboratory (NDL) (http://www.ars.usda.gov/ba/bhnrc/ndl) and it was used to obtain the composition of most of the substrates used in our BMP analyses. Although the exact chemical composition of each and every particular substrate is not always available, it can be inferred with some degree of accuracy from other substrates’ composition, or it can be accurately determined via experimental analysis. In the case of manure, for example, the approximate chemical composition described by Moller et al. (2004) was used in our analysis. By far, the most critical and difficult parameter to estimate is the substrate anaerobic degradability. Up to this date, the only validated and accepted method described in the literature to determine the value of this parameter for a particular substrate is via experimental analysis, e.g. the BMP assay. The more accurate the anaerobic degradability information is, the more precise the predictions of methane yields will be. To visualize this, a comparison of the observed and predicted methane yields of various substrates, assuming that all the organic matter can be degraded via anaerobic process, is shown in table 3. Table 3. Comparison of observed and predicted methane yields for various substrates, assuming that 100% of the substrate can be degraded anaerobically

Predicted NMY

(mL/g VS) Observed NMY

(mL/g VS) Percent

diffa

Percent util

b

C content COD Buswell Bioenergetics BMP

Manure (Man) 542.4 446.3 466.6 382.7 242.7 -123.5% 44.7%

Switchgrass (Sg) 414.0 273.3 414.8 308.0 122.2 -238.7% 29.5%

Cheese whey (W) 437.6 750.6 426.0 120.4 423.6 -3.3% 96.8%

Ice cream (Ic) 619.5 856.1 572.0 344.4 502.3 -23.3% 81.1%

Oil (O) 936.7 426.5 1013.6 970.0 648.5 -44.4% 69.2%

Plain pasta (Pasta) 528.9 802.1 448.3 360.2 326.1 -62.2% 61.7%

Meat pasta (Meatp) 404.9 578.3 515.6 441.1 216.2 -87.3% 53.4%

Man:Sg 75:25 441.6 509.0 453.6 364.0 207.8 -112.5% 47.1%

Man:W 75:25 449.3 608.4 459.5 336.9 252.4 -78.0% 56.2%

Man:W 90:10 508.8 528.0 463.9 365.4 237.6 -114.2% 46.7%

Man:O 75:25 596.2 1370.7 484.3 401.7 360.6 -65.3% 60.5%

Man:Pasta 75:25 534.4 486.1 466.2 382.2 353.5 -51.2% 66.1%

Man:Meatp 75:25 347.3 595.6 468.1 384.5 285.6 -21.6% 82.2% a

Percent difference (with respect to the carbon content method) = 100×(BMP – C content)/BMP b

Percent utilization (with respect to the carbon content method) = 100×BMP/C content

http://www.ars.usda.gov/ba/bhnrc/ndl

As observed in Table 3, in spite of the method used, there is a considerable difference in almost all of the predicted values as compared to the observed BMP results. The only exception is whey, which is explained because of the high degradability of this substrate. As expected, the predicted methane yields are much higher than the observed yields, as the characteristic biodegradability of each substrate is not taken into account, i.e. 100% anaerobic degradability is assumed. The percent of utilization indicates the percent of the observed to the predicted (carbon content) methane yields, and thus, it provides an estimation of the degradability portion of the substrate. Based on this parameter, most degradable substrates appear to be cheese whey, ice cream, oil, and manure with meat pasta. Similarly, another estimation of the specific biodegradability of each substrate can be obtained without the need to run the BMP, i.e. through the use of the BOD/COD or BOD/VS ratio (Tables 1 and 2). These degradability parameters indicate the portion of total substrate (either COD or VS basis) that is supposed to be degradable through biological processes. Table 4 and Figure 3 present a comparison of the observed and predicted methane yields of the same substrates presented in Table 3, but using the BOD/COD or BOD/VS ratio as the biodegradable portion of the predicted values.

Table 4. Comparison of observed and predicted methane yields for various substrates, utilizing BOD/COD and BOD/VS ratios as degradability values

Predicted NMY

(mL/g TVS) at STP Observed NMY

(mL/g TVS) at STP Percent diffa

C content COD Buswell Bioenergetics BMP

Manure (Man) 262.5 169.4 225.8 185.2 242.7 -8.2%

Switchgrass (Sg) 40.5 34.3 40.6 30.1 122.2 66.8%

Cheese whey (W) 437.6 396.3 426.0 120.4 423.6 -3.3%

Ice cream (Ic) 619.5 428.0 572.0 344.4 502.3 -23.3%

Oil (O) 562.0 212.4 608.1 582.0 648.5 13.3%

Plain pasta (Pasta) 244.9 162.0 207.5 166.8 326.1 24.9%

Meat pasta (Meatp) 244.7 211.5 311.6 266.6 216.2 -13.2%

Man:Sg 75:25 26.5 21.1 27.2 21.8 207.8 87.3%

Man:W 75:25 361.2 281.5 369.4 270.9 252.4 -43.1%

Man:W 90:10 335.2 231.0 305.6 240.7 237.6 -41.1%

Man:O 75:25 327.9 616.8 266.3 220.9 360.6 9.1%

Man:Pasta 75:25 245.7 160.9 214.3 175.7 353.5 30.5%

Man:Meatp 75:25 250.6 252.6 337.8 277.5 285.6 12.2% a

Percent difference (with respect to the carbon content method) = 100×(BMP – C content)/BMP

0

100

200

300

400

500

600

700

122.2

207.8 216.2237.6 242.7 252.4 256.5

285.6

326.1 334.5353.5 360.6

423.6

502.3

648.5m

L C

H4

per

g V

S ad

ded

Carbon Buswell Bioenergetics Obs NMY (mL/g)

Figure 3. Observed and predicted methane yields using four methane predictions models Table 4 and Figure 3 show that the inclusion of biodegradability values into the calculations indeed improves the accuracy of the results. However, significant error can still be observed in some predictions as compared to the observed BMP results. In this case, the predicted methane yields are either lower or higher than the observed yields. As discussed before, one source of error comes from the use of theoretical (mostly inferred) substrate compositions, as opposed to using the actual, experimentally-determined individual constituents of the substrate (i.e. protein, carbohydrate, lipid, volatile acids, etc). In fact, the lowest prediction error was obtained for manure, where composition was obtained from a recent study about the proximate composition of dairy manure (Moller et al., 2004). The second factor, contributing most of the error to the calculations, is biodegradability. The portion of substrate that was assumed to be anaerobically degradable came either from the BOD/COD ratio (Method 1) or the BOD/VS ratio (Methods 2 – 4). However, these parameters do not necessarily represent the actual biodegradability that can be potentially achieved under anaerobic conditions, as the degradation of the organic matter determined by the BOD test is in itself aerobic. The ultimate objective of the BMP assay measures the degradability of a given organic substrate under anaerobic conditions. The BOD test can be seen as the aerobic counterpart of the BMP assay, in that, the BOD measures biodegradability of a given organic substrate under aerobic conditions. This is clearly seen in the case of switchgrass, which shows a much lower degradability based on the BOD test than that actually achieved through the BMP test, as demonstrated by the results (Figure 3).

3.4. Effects of co-digestion on methane yields: synergistic and antagonistic substrate mixtures Based on composition, the co-digestion of dairy manure and any other substrate should result in a methane yield equal to the sum of the methane yields of the weighted individual contributions produced by the digestion of manure alone and the co-digestate(s), or the weighted normalized methane yield (WNMY). However, this is not always the case, as co-digestion of individual “pure” substrates can also produce either synergistic or antagonistic effects on certain parameters, e.g. methane yield, VS destruction. Synergism would be seen as an additional methane yield in the co-digestion bottles in addition to the WNMY, or the simple sum of the methane yields of the pure substrates from the mono-digestion bottles. Similarly, evidence of antagonism would be seen as a lower methane yield in the co-digestion bottles as compared to the WNMY. Synergistic effects can come from improving the substrate biodegradability, alkalinity addition, enzymatic contribution, etc. Antagonistic effects can arise from several factors, such as pH inhibition, ammonia toxicity, high volatile acid concentration, etc. Table 4 summarizes this analysis for several co-digestion mixtures of manure with food residue depicting the differences between the methane yields from co-digestion bottles and the WNMYs from mono-digestion bottles. For example, the WNMY of manure co-digested with cheese whey is such that if the individual methane yield of manure is 243 mL/g VS and that of cheese whey is 424 mL/g VS, the co-digestion of 75% manure and 25% cheese whey will result in a WNMY of 288 mL/g VS (243 × 0.75 + 424 × 0.25). However, the co-digestion of manure and cheese whey reveals that the observed methane yield was 252 mL/g VS. Since the negative differential in methane yield is within the standard deviation (SD) of this co-digestion mixture (109 mL/g VS), it is not clear if this difference is indeed the result of an antagonistic effect (Table 5). In fact, the predicted methane yields from the two best models for the co-digestion of manure with cheese whey show values in the range of 365 mL/g VS (Table 4), which, as indicated by the SD, suggests either experimental error or high variability of the substrate source, thereby the probable cause of this difference, i.e. not antagonism. The similarity of the observed NMY and weighted NMY values for the co-digestion of manure with switchgrass and the standard deviation suggest that the co-digestion of these two substrates does not produce either synergistic or antagonistic effects. It is clear, however, that this mixture does not produce good results unless some degree of lignin pretreatment is applied. It is evident that the co-digestion of manure with both plain pasta and meat pasta is synergistic, and methane yields are as 30% higher than the digestion of manure alone. The positive differential suggests that the co-digestion of manure with oil is synergistic; however, it is not clear, because this differential is within the standard deviation of the observed NMY.

Table 5. Observed methane yields from the co-digestion bottles (ONMY) as compared to weighted methane yields, calculated as the sum of the individual contributions of the mono-digestion bottles (WNMY)

ONMY SD WNMY Differential

(mL/g VS at STP) (mL/g VS at STP) (mL/g VS at STP) (ONMY - WNMY)

Substrates

Manure 242.7 60.2 - -

Co-substrates

25% Cola 235.0 118.5 275.3 -40.3

25% Potatoes 227.7 81.1 265.7 -38.0

25% Cheese whey 252.4 109.0 287.9 -35.5

25% Mouthwash 220.1 96.5 250.6 -30.5

10% Plain pasta 224.0 34.7 251.0 -27.0

10% Cheese whey 237.6 69.3 260.8 -23.2

10% Meat pasta 232.1 31.0 240.0 -7.9 12.5% Cola and 12.5% Mouthwash 258.0 42.8 262.9 -4.9

25% Switchgrass 207.8 4.5 212.6 -4.8

25% Used oil 360.6 168.1 344.1 16.4

25% Meat pasta 285.6 29.0 236.1 49.5

25% Plain pasta 353.5 36.6 263.6 89.9 25% Dog food and 25% Ice cream 467.3 39.9 353.6 113.7

4. Conclusions

Co-digestion of organic residues with manure has been investigated since the late 1980s. Manure co-digestion is a method that increases the buffering capacity of substrate mixtures and adds essential nutrients that can substantially improve methane yields. However, despite the benefits of co-digestion, the overall impact that additional materials have on manure-only fermentation processes is not known. This study evaluated the potential of the co-digestion of dairy manure with a variety of organic substrates to increase biogas production over conventional manure-only digestion processes. Selected organic substrates of different degradability and chemical composition were mixed with dairy manure at various ratios and subjected to the biochemical methane potential (BMP) assay under mesophilic conditions to identify suitable substrate characteristics and concentrations that maximize methane production. The results of about 175 individual BMP assays on more than 30 different substrates showed that substrates highly rich in lipids and/or carbohydrates with a high volatile solid content are good candidates for co-digestion with dairy manure, as demonstrated with co-digestates such as used oil and fresh pasta. Protein rich substrates are good candidates; however, composition of these substrates, such as cheese whey, tends to be highly variable, especially in terms of solids content, and therefore provide extremely variable results.

To overcome the unlimited diversity of possible substrates for co-digestion, a secondary objective of this study was to evaluate the ability of analytical methods to successfully predict ultimate methane yields from biochemical characteristics of the substrate. From the four methods used to predict methane, both the carbon content and Buswell were the most accurate. A critical parameter to ensure the accuracy of these models is substrate degradability; this parameter is the most critical and difficult to estimate. The more accurate the anaerobic degradability information is, the more precise the predictions of methane yields will be. Glossary

BMP Biochemical Methane Potential

BOD Biochemical Oxygen Demand

COD Chemical Oxygen Demand

FID Flame Ionization Detector

GC Gas Chromatograph

NMY Normalized Methane Yield

TCD Thermal Conductivity Detector

TKN Total Kjeldahl Nitrogen

TOC Total Organic Carbon

TON Total Organic Nitrogen

TS Total Solids

VS Volatile Solids

References Alatriste-Mondragon, F., Samar, P., Cox, H.H.J., Ahring, B.K., Iranpour, R., 2006. Anaerobic codigestion of

municipal, farm, and industrial organic wastes: A survey of recent literature. Water Environment Research, 78, 607-636.

American Public Health Association/American Water Works Association/Water Pollution Control Federation (APHA/AWWA/WPCF), 1995. STANDARD METHODS FOR THE DETERMINATION OF WATER AND WASTE WATER, 19th ed., APHA, Washington, D.C.

Angelidaki, I., Ahring, B.K., 1997. Codigestion of olive oil mill wastewaters with manure, household waste or sewage sludge. Biodegradation, 8, 221-226.

Angelidaki, I., Ellegaard, L., Ahring, B.K., 1997. Modelling anaerobic codigestion of manure with olive oil mill effluent. Water Science and Technology, 36, 263-270.

Chandler, A.J., Jewell, W.J., Gossett, J.M., van Soest, P.J., Robertson, J.B., 1980. PREDICTING METHANE FERMENTATION BIODEGRADABILITY. In: Proceedings of the Biotechnology and Bioengineering Symposium no. 10, pp. 93–107.

Chynoweth, D.P., Turick, C.E., Owens, J.M., Jerger, D.E., Peck, M.W., 1993. BIOCHEMICAL METHANE POTENTIAL OF BIOMASS AND WASTE FEEDSTOCKS. Biomass & Bioenergy, 5, 95-111.

Gossett, J., 2004. BIOENERGETICS AND STOICHIOMETRY. Class notes, Cornell University, Ithaca, N.Y.

HACH, 2003. DR/2500 Spectrophotometer procedure manual. HACH Company.

IPCC, 1977. GUIDELINES FOR NATIONAL GREENHOUSE GAS INVENTORIES: REFERENCE MANUAL. IPCC Guidelines, 1997.

Magbanua, B.S., Adams, T.T., Johnston, P., 2001. Anaerobic codigestion of hog and poultry waste. Bioresource Technology, 76, 165-168.

Moller, H.B., Sommer, S.G., Ahring, B., 2004. Methane productivity of manure, straw and solid fractions of manure. Biomass & Bioenergy, 26, 485-495.

Morris, G.R., 1976. ANAEROBIC FERMENTATION OF ANIMAL WASTES: A KINETIC AND EMPIRICAL DESIGN EVALUATION. M.S. thesis, Cornell University, Ithaca, N.Y.

Owen, W.F., Stuckey, D.C., Healy, J.B., Young, L.Y., McCarty, P.L., 1979. BIOASSAY FOR MONITORING BIOCHEMICAL METHANE POTENTIAL AND ANAEROBIC TOXICITY. Water Research, 13, 485-492.

Suflita, J.M., Concannon, F., 1995. SCREENING-TESTS FOR ASSESSING THE ANAEROBIC BIODEGRADATION OF POLLUTANT CHEMICALS IN SUBSURFACE ENVIRONMENTS. Journal of Microbiological Methods, 21, 267-281.

Symons, G.E., Buswell, A.M., 1933. THE METHANE FERMENTATION OF CARBOHYDRATES. J. Am. Chem. Soc, 55, 2028–2036.

Wong, M.H., 1990. ANAEROBIC-DIGESTION OF PIG MANURE MIXED WITH SEWAGE-SLUDGE. Biological Wastes, 31, 223-230.

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