Waste treatment and biogas quality in small-scale agricultural digesters

Available online at www.sciencedirect.com

Bioresource Technology 99 (2008) 5881–5890

Waste treatment and biogas quality in small-scale agricultural digesters Stephanie Lansing a

a,*

, Rau´l Botero Botero b, Jay F. Martin

a

Department of Food, Agricultural and Biological Engineering, The Ohio State University, 590 Woody Hayes Drive, Columbus, OH 43210-1057, United States b EARTH University, Apartado Postal 4442 – 1000, San Jose, Costa Rica Received 22 June 2006; received in revised form 7 September 2007; accepted 25 September 2007 Available online 26 November 2007

Abstract Seven low-cost digesters in Costa Rica were studied to determine the potential of these systems to treat animal wastewater and produce renewable energy. The effluent water has a significantly lower oxygen demand (COD decreased from 2968 mg/L to 472 mg/L) and higher dissolved nutrient concentration (NH4-N increased by 78.3% to 82.2 mg/L) than the influent water, which increases the usefulness of the effluent as an organic fertilizer and decreases its organic loading on surface waters. On average, methane constituted 66% of the produced biogas, which is consistent with industrial digesters. Through principle component analysis, COD, turbidity, NH4-N, TKN, and pH were determined to be the most useful parameters to characterize wastewater. The results suggest that the systems have the ability to withstand fluctuations in the influent water quality. This study revealed that small-scale agricultural digesters can produce methane at concentrations useful for cooking, while improving the quality of the livestock wastewater. Ó 2007 Elsevier Ltd. All rights reserved. Keywords: Anaerobic digestion; Manure; Nitrogen; Renewable energy; Water quality

1. Introduction In developing countries, water pollution and access to energy resources present challenges to human health, environmental health, and economic development. Small-scale, economically feasible technologies that combine wastewater treatment and energy production can simultaneously protect the surrounding water resources and enhance energy availability. In developing countries, animal rearing creates a resource that can be used in anaerobic digesters to produce methane gas for cooking. In this study, seven agricultural digesters in Costa Rica that used excreta from bovines and swine were studied to determine the extent of wastewater treatment and the quality of the produced gas. The variability between these digesters was studied to determine if management styles and wastewater strength had an effect on wastewater treatment and the concentration of methane in the produced biogas. Biogas produced *

Corresponding author. Tel.: +1 918 749 1282; fax: +1 614 292 9448. E-mail address: [email protected] (S. Lansing).

0960-8524/$ - see front matter Ó 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.biortech.2007.09.090

by the digester systems in this study was used to meet the farmers’ energy needs for cooking. Biogas is derived through anaerobic digestion of biomass, such as animal wastes, municipal wastewater, and landfill waste. Anaerobic digestion is a microbially mediated biochemical degradation of complex organic material into simple organics and dissolved nutrients. Digesters are physical structures that facilitate anaerobic digestion by providing an anaerobic environment for the organisms responsible for digestion. Processing livestock manure through anaerobic digesters captures methane, which can be used as an energy source while reducing emissions of this greenhouse gas. Currently, digesters are concentrated in developing countries, with over 5 million household digesters constructed in China and India alone (Huttunen and Lampinen, 2005). Digesters built around the world vary in their design complexity, construction materials, and costs. In developed countries, digesters often are concrete stirred tank reactors (CSTRs), in which a portion of the produced biogas is utilized to heat the digester (Berglund and Bo¨rjesson, 2006).

5882

S. Lansing et al. / Bioresource Technology 99 (2008) 5881–5890

In developing countries, many of the digesters do not have mixing components, do not require continuous monitoring, and are adaptable to any tropical climate (Chara´ et al., 1999; Ong et al., 2000). The Taiwanese-model digesters are simple, flow-through reactors consisting of a tubular polyethylene bag, PVC piping, and plastic hosing to transport the biogas from the digester (Fig. 1) (Chara´ et al., 1999; Botero and Preston, 1987; An and Preston, 1999). The construction, materials, and labor costs for construction of a Taiwanesemodel digester can vary from $34 USD in Vietnam (An et al., 1997) to $150 USD in Costa Rica. Biogas from Taiwanese-model digesters has been successfully collected and used for cooking, eliminating the need to buy propane or use firewood. By using biogas in place of burning biomass, indoor air quality is dramatically improved and deforestation is reduced (Chara´ et al., 1999). The biogas from digesters is composed of 50–70% methane, 30–40% carbon dioxide, and trace amounts of other gases,

Fig. 1. Taiwanese-model digesters are flow-through reactors consisting of a tubular polyethylene bag, PVC piping, and plastic hosing. The biogas is transported from the digester to the kitchen/cooking area, where it is utilized for up to 12 h of cooking per day. The length of the digester can vary from 10 to 20 m, with a 5 m circumference. The wastewater to biogas ratio is approximately 1:3 (Botero and Preston, 1987).

Table 1 Relative composition of biogas in a Taiwanese-model anaerobic digester (Botero and Preston, 1987) Gas component

Percentage in digester (%)

Methane (CH4) Carbon dioxide (CO2) Hydrogen (H2) Nitrogen (N2) Carbon monoxide (CO) Oxygen (O2) Hydrogen sulfide (H2S)

50–70 30–40 1.0 0.5 0.1 0.1 0.1

as detailed in Table 1 (Botero and Preston, 1987; Erickson et al., 2004; Erickson and Fung, 1998). Previous studies have shown that small-scale digesters reduce the amount of organic matter and solids in the animal wastewater by 55–90% (Chen and Shyu, 1996; Pedraza et al., 2001; Botero and Herna´ndez, 2005). In addition, the digester effluent has little odor and an increased concentration of dissolved nutrients, which provides farmers with an improved organic fertilizer (Parsons, 1984; Botero and Preston, 1987: Chara´ et al., 1999; Sophea and Preston, 2001; Sophin and Preston, 2001; Thy and Preston, 2003). The majority of digester studies have been conducted on technologically advanced, lab-scale digesters (Griffin et al., 1998; Lettinga et al., 1999; Sterling et al., 2001; Collins et al., 2003; Erickson et al., 2004; McMahon et al., 2004). There has been a lack of experimental studies on Taiwanese-model digesters because of their development as a practical, easy to implement technology for utilization in tropical countries (Botero and Herna´ndez, 2005). Studies have shown that the Taiwanese model-digesters can produce biogas with methane concentrations above 60%, with temperatures at or below the mesophilic range (20–40 °C) (An and Preston, 1999; Botero and Preston, 1987). Most previous studies have concentrated on the general success of a single digester (Gowda, 1995; An et al., 1997; Moog et al., 1997; Chara´ et al., 1999; Sophea and Preston, 2001; Sophin and Preston, 2001; Esquivel et al., 2002; Botero and Herna´ndez, 2005). Rigorous water quality analyses of the influent and effluent wastewater have rarely been used on small-scale systems due their concentration in remote areas of developing countries. This study strives to statistically access the ability of multiple small-scale digesters to treat animal wastewater and obtain biogas with high methane levels. In this study, twelve influent and effluent wastewater parameters were analyzed to determine statistically significant trends. The study was conducted on seven digester sites to assess variability within these systems, which were identical in construction materials, but differed in digester length, wastewater management styles, wastewater source, and hydrologic loading (Table 2). The objectives of this study were to determine the following: (1) significant wastewater characteristics in the treatment process that should be monitored in the future; (2) the variability of water quality parameters and methane concentration between different digesters.

Table 2 The characteristics for each of the seven digesters studied Farm

Elevation (m)

Waste source

Washes per day

Washing time (min)

Digester volume (m3)

Digester retention time (days)

1 2 3 4 5 6 7

50 50 50 70 250 350 250

65 pigs 30 cows 40 pigs 12 pigs 7 pigs 5 pigs 3 pigs

2 2 2 1 2 3 2

45 20 30 20 10 5 15

70 60 40 40 40 25 25

11.1 19.3 16.2 44.4 51.3 91.4 55.6

S. Lansing et al. / Bioresource Technology 99 (2008) 5881–5890

2. Methods 2.1. Study site The seven digesters studied were located in the Limon Province of Costa Rica (10°N, 83°W). Four digesters were located on small-production farms and three digesters were located at EARTH University, an international undergraduate university for the study of sustainable agriculture. The digesters analyzed in this study were all Taiwanese-model plastic digesters (Fig. 1). The digester varied in elevation from 350 m (Farm 6) to 50 m (Farms 1, 2 and 3) (Table 2). All digesters used animal wastewater, with the majority using swine manure (Table 2). Manure was washed directly from the stalls into the digester. The frequency and amount of wastewater used in each digester varied between the seven farms, with an average stall-washing time of 15 min, 2 times a day. The retention time in each digester varied from 11 to 91 days depending on the amount of water used for stall washing and the digester volume (Table 2). 2.2. Analytical methods Wastewater influent and effluent samples were collected from each digester from July, 2005 to October, 2005. Temperature, pH, dissolved oxygen (DO), and conductivity measurements were taken on-site using a hand-held 556 MPS YSI probe. Four site visits were made to each farm. Three influent and three effluent wastewater samples were collected from each digester during each site visit. In total, at least twelve influent and twelve effluent samples were taken from each digester. All wastewater analyses were conducted at EARTH University’s Soil and Water Laboratory. Each sample was analyzed according to standard methods (APHA, 1989) for the following: biochemical oxygen demand (BOD5), chemical oxygen demand (COD), turbidity, total suspended solids (TSS), ammonium-nitrogen (NH4-N), nitrate-nitrite (NOx-N), and orthophosphate (PO4-P). In addition, samples collected during the final two site visits were analyzed for total kjeldahl nitrogen (TKN) (APHA, 1989). An IR-30M hydrocarbon meter (Environmental Sensors Co.) was used to detect on-site methane (CH4) concentrations, and a Z-900 hydrogen sulfide (H2S) meter (Environmental Sensors Co.) was used to detect on-site H2S concentrations. TM

2.3. Statistical analysis A principle component analysis (PCA) was conducted to determine: (1) wastewater variables that were altered during the digestion process, (2) examine redundancy of variables, and (3) identify the most important variables to include in future analyses. PCA is a mathematical procedure that reduces data dimensionality by transforming correlated variables into uncorrelated variables called prin-

5883

ciple components. Each principal component accounts for as much of the data variability as possible, with the first component retaining the characteristics that contribute the most to the variability (Jolliffe, 2002). Multivariate analysis of variances (MANOVA) was conducted to determine if there were significance differences in the overall dataset. Individual analysis of variance (ANOVA) and subsequent Tukey–Kramer multiple comparison analyses determined which wastewater variables were significantly different between the seven farm digesters. A multiple linear regression (MLR) was preformed to compare the CH4 concentration to the wastewater variables that were determined to be significant from the PCA analysis. All statistical analyses were conducted using SASÓ (SAS Institute, Inc) or PC-ORDÓ software (McCune and Medford, 1999). Ten of the twelve environmental variables collected during the study period were used in the statistical analyses (temperature, pH, conductivity, COD, BOD5, turbidity, TSS, NH4-N, PO4-P, and TKN). The high concentrations of solids in digester influent samples interfered with the DO and NOx-N analyses (APHA, 1989), and thus, these results were not used in the statistical analyses. The following environmental variables were log-transformed in order to meet the assumptions of the PCA and MANOVA: conductivity, turbidity, COD, BOD5, TSS, PO4-P, and TKN. Before transformation, these variables had high skewness (2.9–0.58), high kurtosis (11.1–0.69), were not normal, or did not have homogeneity of variances. After transformation, the skewness (0.79–0.05), kurtosis (2.39–0.12), normality, and the homogeneity of variances improved, with COD, BOD5, PO4-P, and TKN having homogeneity of variance at the 0.05 significance level.

3. Results 3.1. Water quality improvements During the digestion process, all of the organic matter and solid variables showed significant decreases (ANOVA p-values <0.001) (Tables 3 and 4). The average COD of the influent wastewater decreased 84.1% from 2970 mg/L to 472 mg/L, and the BOD5 decreased 79.4% to 96.2 mg/ L. The average turbidity decreased 90.5% from 1820 NTU to 172 NTU, and the TSS concentration decreased 85.6% L to 319 mg/L (Table 3). The average TKN concentration decreased 45.7% from 306 mg/L to 166 mg/L (pvalue < 0.001). Dissolved nutrient (PO4-P, NH4-N) concentrations and conductivity increased as the wastewater moved through the digester (Table 3), with the NH4-N concentration increasing 78.3% to 82.2 mg/L (p-value < 0.001). The DO concentration slightly increased in the digester, but both the average influent (0.21 mg/L) and effluent (0.54 mg/L) DO concentrations were anaerobic. The average pH decreased from 7.34 to 6.64 (p-value < 0.001).

5884

S. Lansing et al. / Bioresource Technology 99 (2008) 5881–5890

Table 3 Average influent and effluent data ± SE (n) for Taiwanese-model digesters (Farms 1–7)

Temperature (°C) Conductivity (mS/cm) pH DO (mg/L) BOD (mg/L) COD (mg/L) Turbidity (NTU) TSS (mg/L) NOx-N (mg/L) PO4-P (mg/L) NH4-N (mg/L) TKN (mg/L)

Average influent

Average effluent

Percent decrease/increase

26.2 ± 0.2 (74) 1.59 ± 0.1 (74) 7.34 ± 0.1 (74) 0.21 ± 0.1 (73) 467 ± 40 (74) 2970 ± 260 (73) 1820 ± 200 (73) 2210 ± 223 (72) 0.92 ± 0.2 (71) 13.3 ± 1.7 (74) 46.1 ± 5.1 (72) 306 ± 28 (35)

26.1 ± 0.1 (80) 1.73 ± 0.1 (80) 6.64 ± 0.04 (80) 0.54 ± 0.1 (80) 96.2 ± 11 (80) 472 ± 40 (79) 172 ± 15 (80) 319 ± 56 (80) 0.18 ± 0.03 (80) 15.4 ± 1.4 (80) 82.2 ± 5.0 (79) 166 ± 13 (37)

0.4% decrease 8.8% increase 9.5% decrease 157% increase 79.4% decrease 84.1% decrease 90.5% decrease 85.6% decrease 80.4% decrease 15.8% increase 78.3% increase 45.7% decrease

Table 4 Individual ANOVA and MANOVA results for the differences between all influent and effluent values and the differences between individual farm influent and effluent values are given below

Temperature (°C) Log(Conductivity) pH Log(BOD5) Log(COD) Log(Turbidity) Log(TSS) Log(PO4-P) NH4-N (mg/L) Log(TKN) MANOVA

Influent/effluent

By farm: influent

By farm: effluent

p-value (degree of freedom) F-stat

p-value (degree of freedom) F-stat

p-value (degree of freedom) F-stat

0.672 (1,152) 0.18 0.003 (1,152) 8.92 <0.001 (1,152) 77.1 <0.001 (1,152) 129 <0.001 (1,150) 197 <0.001 (1,151) 272 <0.001 (1,150) 156 0.070 (1,152) 3.34 <0.001 (1,149) 25.7 <0.001 (1,70) 25.5 <0.001 (10,61) 54.2 Wilks’ k = 0.10

<0.001 (6,28) 13.1 0.004 (6,28) 4.12 <0.001 (6,28) 24.0 <0.001 (6,28) 5.91 <0.001 (6,28) 10.1 0.005 (6,28) 3.95 0.010 (6,28) 3.52 <0.001 (6,28) 10.7 0.007 (6,28) 3.74 0.222 (6,28) 1.48 <0.001 (60,104) 5.63 Wilks’ k < 0.001

<0.001 (6,30) 5.85 <0.001 (6,30) 199 <0.001 (6,30) 19.9 0.001 (6,30) 5.63 0.017 (6,30) 3.12 0.005 (6,30) 4.01 <0.001 (6,30) 6.48 <0.001 (6,30) 26.7 <0.001 (6,30) 72.7 0.023 (6,30) 2.92 <0.001 (60,115) 9.61 Wilks’ k < 0.001

The p-value (degrees of freedom) and F-statistics are given for each ANOVA, and the p-value (degrees of freedom), approximate F, and Wilks’ k are given for each MANOVA.

All the collected influent and effluent data from the digesters at Farms 1 through 7 were used in the PCA and MANOVA analyses. Axis 1 and axis 2 of the PCA analysis had eigenvalues (3.85 and 2.59, respectively) above the broken stick values (2.93 and 1.93, respectively). Axis 1 explained 39% of the variance, and axis 2 explained 25%. Together the two axes cumulatively explained 64% of the variance (Fig. 2). The PCA analysis shows that log(BOD5), log(COD), log(TSS), and log(turbidity) are highly correlated in multivariate space and have a high positive influence on the location of axis 1. NH4-N, log(conductivity) and log(PO4-P) are highly correlated and have a high positive influence on the location of axis 2 and a negative influence on the location of axis 1. The wastewater variables that measure organic matter and solids (BOD5, COD, TSS, and turbidity) were located opposite of the variables that measure dissolved species (NH4-N, conductivity and PO4P) in multivariate space. Log(TKN) influences the location of both axis 1 and axis 2, but is not clustered in multivariate space with either the organic matter/solids or the dissolved species. All of the environmental variables included in the PCA influenced the location of the two axes, and thus, all the

Fig. 2. Axis one and axis two of the digester principle component analysis (PCA) for influent and effluent data from Farms 1–7. Axis one has an eigenvalue of 3.85, and the percentage of variance explained is 39%. Axis two has an eigenvalue of 2.59, and the percentage of variance explained is 25%.

S. Lansing et al. / Bioresource Technology 99 (2008) 5881–5890

variables were used in the MANOVA analyses. The MANOVA shows significant differences between farm digester influent and effluent data (p-value < 0.001; Wilks k = 0.10) (Table 4). The individual ANOVAs show significant differences between influent and effluent values of all the wastewater parameters at the 0.05 significance level, except temperature and log(PO4-P) (Table 4). The individual ANOVAs with the largest F-statistics, and therefore the largest contrast between influent and effluent concentrations are log(turbidity) (F-stat = 272) and log(COD) (Fstat = 197). 3.2. Water quality comparisons between individual farms The data from each individual farm was compared to examine differences between influent and effluent data across the seven farms. For each wastewater variable, the effluent and influent data from each individual farm were averaged (Table 5), and these average influent and effluent

5885

values were used in the PCA analysis comparing individual farm digester influent and effluent water (Fig. 3). Axis 1 and axis 2 had eigenvalues (5.66 and 2.18, respectively) above the broken stick values (2.93 and 1.93, respectively). Axis 1 explained 57% of the variance, and axis 2 explained an additional 21% of the variance. Together axis 1 and 2 explained 78% of the variance. The farm digester effluent values have a positive influence on the location of axis 1, and the influent values have a negative influence on axis 1. The effluent values are more tightly correlated in multivariate space along axis 1 than the influent values. Farm 2, which is the only farm digester that utilized cow manure as a wastewater source for the entire study, had the highest positive influence on axis 2. The influent and effluent data from the digester on Farm 3 negatively influenced the location of axis 2. Farm 3 had a low retention time (16 days), a high number of pigs (40), and solid separation prior to the digester entrance (Fig. 3 and Table 2).

Table 5 Average influent and effluent data ± SE (n) for Taiwanese-model digesters at Farms 1–7

Influent temp. (°C) Effluent temp. (°C) Influent cond (mS/cm) Effluent cond (mS/cm) Influent pH Effluent pH Influent BOD5 (mg/L) Effluent BOD5 (mg/L) Influent COD (mg/L) Effluent COD (mg/L) Influent turb. (NTU) Effluent turb. (NTU) Influent TSS (mg/L) Effluent TSS (mg/L) Influent PO4-P (mg/L) Effluent PO4-P (mg/L) Influent NH4N (mg/L) Effluent NH4N (mg/L) Influent TKN (mg/L) Effluent TKN (mg/L) *H

Farm 1

Farm 2

Farm 3

Farm 4

Farm 5

Farm 6

Farm 7

27.6 ± 0.3 (12) *H

28.4 ± 0.3 (12) *H

26.3 ± 0.3 (11)

23.8 ± 0.2 (11)

25.5 ± 0.2 (12)

25.7 ± 0.4 (10)

25.6 ± 0.4 (6)

27.2 ± 0.2 (12) *H

27.8 ± 0.3 (12) *H

25.3 ± 0.1 (12)

25.2 ± 0.1 (12)

25.2 ± 0.2 (12)

25.8 ± 0.4 (8)

26.1 ± 0.2 (12)

1.0 ± 0.2 (12)

1.3 ± 0.2 (12)

3.0 ± 0.2 (11) *H

2.4 ± 0.4 (11)

0.8 ± 0.1 (12)

1.0 ± 0.2 (10)

1.9 ± 0.7 (6)

1.5 ± 0.02 (12)

0.9 ± 0.01 (12) *L

2.9 ± 0.1 (12) *H

1.6 ± 0.1 (12)

1.9 ± 0.1 (12)

2.1 ± 0.2 (8)

1.34 ± 0.1 (12)

8.4 ± 0.1 (12) *H 7.1 ± 0.03 (12) *H 156 ± 25 (12) *L

7.2 ± 0.1 (12) 6.2 ± 0.1 (12) *L 373 ± 72 (12)

6.8 ± 0.04 (11) 6.8 ± 0.04 (12) 551 ± 74 (11)

7.3 ± 0.2 (11) 6.5 ± 0.03 (12) 887 ± 138 (11)

7.1 ± 0.1 (12) 6.7 ± 0.1 (12) 471 ± 91 (12)

7.3 ± 0.1 (10) 6.9 ± 0.1 (8) 275 ± 45 (10)

7.2 ± 0.1 (6) 6.4 ± 0.03 (12) 668 ± 62 (6)

115 ± 14 (12)

116 ± 12 (12)

87.3 ± 10 (12)

55.7 ± 8 (12)

126 ± 68 (12)

60.4 ± 10 (8)

102 ± 24 (12)

3580 ± 650 (10)

4220 ± 410 (11) 3600 ± 810 (12)

957 ± 160 (12) *L 3220 ± 630 (12) 418 ± 29 (12)

714 ± 83 (12)

294 ± 28 (12)

550 ± 121 (12)

923 ± 201 (12)

2100 ± 730 (12)

1620 ± 450 (11)

279 ± 37 (12)

107 ± 15 (12)

195 ± 33 (12)

2260 ± 660 (12)

2710 ± 610 (11)

3030 ± 410 (9)

35.8 ± 10 (12) *L

241 ± 58 (12)

83.9 ± 16 (12)

8.61 ± 1.7 (12)

2.72 ± 2.2 (12) *L

17.5 ± 1.7 (12)

194 ± 37 (12)

4680 ± 1050 (6)

302 ± 69 (8)

802 ± 159 (11)

561 ± 147 (9)

3590 ± 530 (6)

59.2 ± 10 (12)

153 ± 17 (8)

313 ± 57 (12)

3450 ± 720 (12)

1070 ± 230 (10)

2260 ± 390 (6)

964 ± 251 (12)

230 ± 76 (12)

411 ± 204 (8)

296 ± 92 (12)

32.8 ± 4.4 (11)

6.97 ± 1.1 (11)

17.7 ± 4.6 (12)

3.78 ± 1.0 (10)

27.0 ± 6.0 (6)

1.51 ± 1.5 (12) *L

26.4 ± 2.8 (12)

10.5 ± 1.1 (12)

8.11 ± 1.1 (12) *L 30.3 ± 5.2 (8)

18.6 ± 3.3 (12)

60.8 ± 14 (12)

20.5 ± 2.6 (12)

106 ± 11 (11) *H

45.6 ± 13 (9)

14.2 ± 2.5 (12)

37.1 ± 12 (10)

37.8 ± 13 (6)

84.5 ± 3.0 (12)

18.5 ± 1.8 (12) *L

129 ± 7.9 (12)

82.9 ± 3.9 (12)

96.7 ± 6.9 (12)

149 ± 8.6 (7) *H 42.0 ± 4.0 (12) *L

286 ± 46 (11)

249 ± 21 (12)

490 ± 37 (2)

288 ± 4.9 (3)

306 ± 54 (3)

160 (1)

551 ± 230 (3)

199 ± 26 (11)

108 ± 10 (12)

200 ± 5.5 (2)

219 ± 6.1 (3)

152 ± 29 (2)

228 (1)

177 ± 46 (6)

92.6 ± 5 (12) 769 ± 135 (12)

2040 ± 310 (11) 2470 ± 560 (12)

1310 ± 160 (10)

and *L indicate the values are significantly higher or lower, respectively, than at least five of the other six farms based on Tukey–Kramer analyses.

5886

S. Lansing et al. / Bioresource Technology 99 (2008) 5881–5890 Table 6 Average percentage of methane (CH4) and hydrogen sulfide (H2S) in the produced biogas are given for the digesters at Farms 1–7

Farm Farm Farm Farm Farm Farm Farm

1 2 3 4 5 6 7

Percent methane (CH4)

Hydrogen sulfide (H2S) in ppm [%]

68.8% ± 11 (2) 61.7% ± 24 (2) 72.5% ± 0.4 (2) 68.4% ± 3 (2) 67.9% ± 0.5 (2) 63.9% ± 0.7 (2) 61.4% ± 0.7 (3)

84.2 ± 0.1 (2) [0.0084%] 4.8 ± 3.4 (2) [0.0048%] 84.4 ± 0.4 (2) [0.0084%] 84.3 ± 0.1 (2) [0.0084%] 0.37 ± 0.3 (2) [0.00004%] 83.6 ± 0.6 (2) [0.0084%] 42.5 ± 42 (3) [0.0043%]

The CH4 data are given in percentage ± SE (n), and the H2S data are given in ppm ± SE (n), with the percent H2S in brackets.

Fig. 3. Axis one and axis two of the digester principle component analysis (PCA) for influent and effluent data from Farms 1–7. Axis one has an eigenvalue of 5.66, and the percentage of variance explained is 57%. Axis two has an eigenvalue of 2.18, and the percentage of variance explained is 21%.

Statistical analyses (MANOVA and ANOVA) revealed that there were significant differences between the influent and effluent data from the seven farm digesters. MANOVA analyses showed significant differences between both the influent data (p-value < 0.001; Wilks k < 0.001) and the effluent data (p-value < 0.001; Wilks k < 0.001) when compared across individual farm digesters (Table 4). The individual ANOVAs show significant differences at the 0.05 significance level between the influent data from each farm digester for all environmental parameters, except log(TKN), and significant differences between farm effluent data for all environmental parameters. The individual ANOVAs with the largest F-statistics were pH (24.0) for influent analysis and log(conductivity) (198) for effluent analysis. Subsequent Tukey–Kramer multiple comparison analyses on the individual ANOVAs are detailed in Table 5. 3.3. Biogas quality – methane (CH4) concentration The average methane concentration from the seven digesters over the study period was 66.3% (663,000 ppm). Hydrogen sulfide levels at all seven farms were below 0.01% (100 ppm) (Table 6). The highest CH4 concentration was at Farm 3 (72.5%), which used swine manure and had the highest TSS and COD loading rates (167 mg/L-day and 221 mg/L-day, respectively) (Tables 2, 5 and 6). The lowest CH4 concentrations were found at Farm 2 (61.7%), which used dairy manure, but had the second highest TSS and COD loading rates (117 mg/L-day and 167 mg/L-day, respectively), and Farm 7 (61.4%) which used swine manure, and had the second lowest TSS loading rate (40.6 mg/L-day).

Statistical analyses did not reveal any significant relationship between influent water quality and the percentage of CH4 in the biogas (MLR p-value = 0.308; VIFs < 2.0). Based on the PCA results and the variance inflation factors (VIF), the variables included in the MLR were log(turbidity), NH4-N, temperature, and pH. The MLR analysis between CH4 and influent log(TKN) and log(NH4-N) was also not significant (p-value = 0.283; VIFs = 1.2). The MLR was conducted with average influent wastewater variable values and CH4 concentrations from the digesters at Farms 1–7. Biogas production data is not presented here due to problems getting reliable data with standard gas meters. The small-scale digesters in this study did not have sufficient pressure to allow for the biogas to pass through the meters. Without meter interference, the farmers were able to cook using the produced biogas for an average of six hours a day. 4. Discussion The digesters in this study consistently reduced organic matter and solids by 79% to 91%, with COD decreasing from 2968 mg/L to 472 mg/L. TKN decreased by 46% to 166 mg/L. Mineralization occurred during the digestion process, increasing the average NH4-N concentration by 78.3% to 82.2 mg/L. The effluent water had a significantly lower oxygen demand and higher dissolved nutrient concentration compared to the influent water, which increases its usefulness as an organic fertilizer and decreases its organic loading. Enough biogas was produced during the digestion process to allow for an average of 6 h of cooking per day. The concentration of CH4 in the biogas averaged 66%. The concentration of methane in the biogas from the plug-flow, Taiwanese-model digesters analyzed in this study were at levels comparable to the high-tech, completely stirred digester favored in developed countries (Chen and Shyu, 1996; Kayhanian and Rich, 1995; Parsons, 1984). This study revealed that small-scale agricultural digesters can produce methane at concentrations useful for cooking, while improving the quality of the livestock wastewater. Further studies need to be conducted on

S. Lansing et al. / Bioresource Technology 99 (2008) 5881–5890

biogas production to fully understand the Taiwanesemodel digestion systems. 4.1. Reductions in organic matter and solids The digesters analyzed in this study had high, statistically significant reductions of organic matter and solids (Tables 3 and 4). Seventy-nine to 91% of the influent concentrations of BOD5, COD, TSS, and turbidity were removed, which is greater than or approximately equal to values reported for other Taiwanese-model digesters (Chara´ et al., 1999; Esquivel et al., 2002; Pedraza et al., 2001). Based on the PCA analysis, turbidity and COD have the greatest influence on the data in multivariate space and have the least amount of variability among the collected effluent samples when compared to BOD5 and TSS (Fig. 2 and Table 3). There is a large equipment cost associated with conducting COD and turbidity tests, but the simplicity of these test yields a lower likelihood of human error (APHA, 1989; Crites and Tchobanoglous, 1998). This study showed that these four variables (COD, BOD5, TSS, turbidity) are highly correlated and duplication may not be necessary, unless environmental regulations specifically require them. It has been recommended in the 2007 Protocol for Quantifying and Reporting the Performance of Anaerobic Digestion Systems for Livestock Manures that COD and total solids analyses be used in future digesters studies (USDA, 2007). 4.2. Nitrogen cycling in taiwanese-model digesters TKN is a measure of both total organic nitrogen and NH4-N in wastewater. The TKN decreased by an average of 45% in the digesters studied. The total nitrogen levels were likely reduced through microbial uptake and some settling of solids within the digester. The PCA analysis showed that TKN was positively correlated with organic matter and negatively correlated with NH4-N in multivariate space (Fig. 2). As the organic matter decreased in the digestion process, the TKN also decreased, but at a slower rate because the organic nitrogen was converted to NH4-N through mineralization (Crites and Tchobanoglous, 1998). Denitrification also likely occurred, as the average NOxN concentration decreased by 80.4% (from 0.92 to 0.18 mg/ L), but the loss of NOx-N is not reflected in the TKN analyses (Table 3). The Anammox process is an unlikely explanation for the total nitrogen removal due to the low NOx-N concentration. In addition, previous studies have shown that the microorganisms responsible for the Anammox process are less competitive than denitrifying organisms in anaerobic digesters (Dong and Tollner, 2003). Nitrification, converting the NH4 to NOx, is unlikely to have occurred during the digestion process due to the low DO levels in the digester (>0.6 mg/L). Settling of solids might have also contributed to the decrease in TKN levels. Taiwanese-model digesters are flow-through digesters, and therefore the contents of the

5887

digesters are not regularly emptied, as practiced in CSTRs. The digesters in this study were 1–8 years old and none had failed previously due to solid accumulation. Although it is difficult to understand mass balances in flow-through digestion systems, the 2007 Protocol for Quantifying and Reporting the Performance of Anaerobic Digestion Systems for Livestock Manures suggests monitoring fixed solids (FS) and total phosphorus (TP) to determine the amount of solid accumulation in digesters, since FS and TP are not transformed in the digestion process (USDA, 2007). In future Taiwanese-model digesters studies, a better understanding of solid accumulation, and thus nitrogen cycling, could be gained using FS and TP data. In most previous Taiwanese-model digester studies, TKN was not directly measured because of high cost and error rates (Peters et al., 2003), but reporting TKN values could provide insights that are not revealed by testing COD alone. Farms 1 and 3 had the highest TKN loadings (25.3 and 30.2 mg/L-day, respectively), the lowest COD/ TKN ratios (3.3 and 7.3, respectively) and the highest CH4 concentration in the biogas (68.8% and 72.5%, respectively). The higher TKN loadings at Farm 1 and 3 could have also led to higher ammonification rates, which would increase the pH levels of wastewater (Sterling et al., 2001) and contribute to the increased methane quality at these digesters (Table 5). Farm 1 had an effluent pH of 7.2, which was significantly higher than the pH levels at the other farms. Farm 3 also had a high effluent pH (6.8). The optimal pH is 6.4–7.6, with maximum methane quality occurring above pH 7 (An and Preston, 1999; Parsons, 1984). The pH dropped an average of 0.7 units during the digestion process due to the production of fatty acids during digestion (Botero and Herna´ndez, 2005), but the higher NH4-N mineralization at Farms 1 and 3 could have kept the pH near optimal levels and led to the greater percentage of CH4 in the biogas. Moderate concentrations of NH4-N can increase the pH and increase CH4 production, but when NH4-N concentrations increase above a certain threshold, the activity of methanogens can be inhibited. Methane production inhibition has occurred with concentrations of NH4-N above 4920 mg/L, with 100% inhibition with NH4-N concentrations above 8000 mg/L (Sterling et al., 2001; Sung and Liu, 2003). The CH4 inhibition observed in these previous studies occurred at NH4-N concentrations considerably higher than the influent and effluent values found in this study. Farm 6 had the highest concentrations of NH4-N in the effluent (149 mg/L) and had a moderate percentage of CH4 in the produced biogas (63.9%). Farm 2 had the lowest (18.5 mg/L) NH4-N effluent concentration and a low percentage of CH4 in the biogas (61.7%). In this study, NH4-N concentration did not have a significant affect on methane quality (p-value = 0.104). Further studies need to be conducted in order to determine if there is a relationship between TKN, NH4-N, pH, CH4, and biogas production.

5888

S. Lansing et al. / Bioresource Technology 99 (2008) 5881–5890

4.3. Increases in dissolved nutrients during digestion NH4-N, conductivity, and PO4-P were highly correlated in the PCA (Fig. 2). All farm digesters in this study had an increase in NH4-N during digestion (Table 5). Conductivity increased in some digesters but decreased in others (Table 5). Conductivity is useful as a general parameter of the strength of dissolved species in wastewater, but the dominant species contributing to the conductivity are not identified. PO4-P influent concentrations were not statistically different from effluent concentrations. Nitrogen is taken up by the microorganisms more readily than phosphorus, and thus, the amount of nitrogen in the wastewater source is more important than phosphorus to gauge microbial community stability (Tchobanoglous et al., 2003). NOx-N concentrations in the digester studied were always less than 1.0 mg/L, which was expected due to the low DO levels (>0.6 mg/L). It is recommended that NH4-N be monitored in future digester studies, while PO4-P, conductivity, and NOx-N analyses were found to be less important in characterizing the digester effluent. The NH4-N concentration in the effluent determines the usefulness of the digester effluent as an organic fertilizer, and affects its potential impact on the quality of receiving waters. Through this study, it was determined that the effluent from Farm 6 would be more suitable to use as an organic fertilizer due to the high NH4-N content, while the effluent from Farm 2 (the bovine farm) would not be as useful as fertilizer, but would have less effect on aquatic life if discharged into nearby waters.

4.4. Methane quality In this study, there were variations in the wastewater influent concentrations (Table 5), the management styles, and the elevations of the digesters (Table 2), but all the digesters were able to achieve CH4 concentrations greater than 60%, with an average concentration of 66%. While there might have been differences in the quantity of biogas produced, there was low variability in the CH4 concentrations (61.4–72.5%) in the biogas when compared to the high variability of the wastewater influent concentrations (Tables 5 and 6). There was less variation in effluent water quality when compared to influent water quality in the PCA, which suggests that these digesters had the ability to withstand fluctuations in influent water quality (Fig. 3). In order to run a generator fueled with biogas, the CH4 concentration needs to be greater than 50% and the H2S concentration needs to be less than 1%. All of the digesters in this study meet these minimum criteria to power a generator. The ability of a farm manager to use a digester in combination with a generator depends on the digester size, the daily production of biogas, and ability to store biogas when the generator is not in use. The digesters in this study may have the potential to be upgraded for electricity generation based on the percentage of methane produced, but

capital costs to purchase the generator might negate the economic value of these simple systems and complicate their minimalist management. Further studies need to be conducted to determine if enough biogas is produced to provide an economic benefit.

5. Conclusions The digesters in this study were effective at generating a high quality biogas to meet the farmers’ cooking needs. Organic matter and solids concentrations were consistently reduced in the effluent waters and NH4-N concentrations were increased. The methane production in these digesters creates a number of indirect environmental and societal benefits, including (1) a reduction in deforestation associated with firewood collection, (2) less hours devoted to firewood collection, (3) eliminating the need to purchase propane for cooking, (4) less organic matter in effluent waters, (5) an organic liquid fertilizer, and (6) a reduction in greenhouse gas emissions to the atmosphere. Through statistical and analytical analyses, the most important wastewater parameters that should be used for characterization of the influent and effluent from Taiwanese-model digester were determined. COD, turbidity, NH4-N, pH, and TKN should be utilized in future studies for proper characterization of the digester wastewater. In addition, the 2007 Protocol for Quantifying and Reporting the Performance of Anaerobic Digestion Systems for Livestock Manures recommends analyzing TP, FS, and total solids (USDA, 2007). There were significant variations among the wastewater influent and effluent values of the seven different digesters analyzed in the study, but these variations did not significantly influence the methane concentrations, but might have influenced the quantity of biogas produced. The digester effluent concentrations exhibited less variation than the influent concentrations, which suggests that these systems have the ability to withstand fluctuations in influent wastewater quality. The ability of the various small-scale digesters in this study to treat a wide range of wastewater quality with varying management styles consistently is a testament to the design of these simple digesters. This research is very timely in that the majority of investigations on digesters have concentrated on large-scale systems, when in fact, the overwhelming majority of digesters are small-scale systems located in the developing world. There is still much to be learned from these simple systems. Further studies need to be conducted on biogas production in Taiwanese-model digesters to access their ability to generate electricity and compare their methane production rate to the 0.3496 m3 per kg of COD destroyed seen in large-scale digesters (USDA, 2007). Additionally, economic, environmental, and social analyses need to be conducted to determine if combining these digesters with electrical generators would be beneficial for the rural, small-scale farmers.

S. Lansing et al. / Bioresource Technology 99 (2008) 5881–5890

Disclaimer ‘‘This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.’’

Acknowledgements This material is based upon work supported by the Department of Energy [National Nuclear Security Administration] under award number (DE-FG02-04ER63834), and the Ohio State University’s Targeted Investments in Excellence ‘Carbon-Water-Climate’ Project. We would like to thank the Department of Energy (DOE) and the Ohio State University for funding this research, and the laboratory and DOE staff at EARTH University for their assistance in the research, including Carlos Herna´ndez, Jane Yeomens, Jorge Vinicio Murillo Rojas, Marianela Castro Valverde, Herbert Arrieta, Carlos Araya, Luis Emilio Pineda, and Gerardo Ceden˜o. We also wish to thank the student workers at the Ohio State University and EARTH University, including Melanie Miller, Blanca Rivas, Marco Gu¨ilcapi, and Joaquı´n A. Vı´quez. We would also like to thank our partners at Central State University for their assistance, including Sritharan Subramania and Bryan Smith. Additional thanks are also extended to Richard Fortner, David Hansen, and Pat Rigby for their administrative guidance.

References An, B.X., Preston, T.R., 1999. Gas production from pig manure fed at different loading rates to polyethylene tubular biodigesters. Livestock Research for Rural Development 11 . An, B.X., Preston, T.R., Dolberg, F., 1997. The introduction of low-cost polyethylene tube biodigesters on small-scale farms in Vietnam. Livestock Research for Rural Development 9 . APHA, 1989. Standard Methods for the Examination of Water and Wastewater. American Health Association, Washington, DC. Berglund, M., Bo¨rjesson, P., 2006. Assessment of energy performance in the life-cycle of biogas production. Biomass and Bioenergy 30, 254– 266.

5889

Botero, R., Herna´ndez, C., 2005. Manejo productivo de excretas en sistemas ganaderos tropicales. In: Revista INFOHOLSTEIN, Asociacio´n de Criadores de Ganado Holstein de Costa Rica, San Jose, Costa Rica. Botero, R., Preston, T.R., 1987. Biodigestor de bajo costo para la produccio´n de combustible y fertilizante a partir de excretas. Manual para su instalacio´n, operacio´n y utilizacio´n, Centro para la Investigacio´n en Sistemas Sostenibles de Produccio´n Agropecuaria (CIPAV), Cali, Colombia. Chara´, J., Pedraza, G., Conde, N., 1999. The productive water decontamination system: a tool for protecting water resources in the tropics. Livestock Research for Rural Development 11 . Chen, T.H., Shyu, W.H., 1996. Performance of four types of anaerobic reactors in treating very dilute dairy wastewater. Biomass and Bioenergy 11, 431–440. Collins, G., Woods, A., McHugh, S., Carton, M.W., O’Flaherty, V., 2003. Microbial community structure and methanogenic activity during start-up of psychrophilic anaerobic digesters treating synthetic industrial wastewaters. FEMS Microbiology Ecology 46, 159–170. Crites, R., Tchobanoglous, G., 1998. Small and Decentralized Wastewater Management Systems. McGraw-Hill Companies, Inc., USA. Dong, X., Tollner, E.W., 2003. Evaluation of Anammox and denitrification during anaerobic digestion of poultry manure. Bioresource Technology 86, 139–145. Erickson, L.E., Fayet, E., Kakumanu, B.K., Davis, L.C., 2004. Anaerobic digestion. In: Carcass Disposal: A Comprehensive Review by the National Agricultural Security Center Consortium USDA APHIS Cooperative Agreement Project: Carcass Disposal Working Group. National Agricultural Biosecurity Center, Kansas State University, Manhattan, KS (Chapter 7). Erickson, L.E., Fung, D.Y.C., 1998. Handbook on Anaerobic Fermentation. Marcel Dekker, New York. Esquivel, R.R., Me´ndiz y Cazern, M.D., Preston, T.R., Pedraza, G., 2002. Apectos importantes al introducir biodigestores en explotaciones lecheras a pequen˜a escala. Livestock Research for Rural Development 14 . Gowda, M.C., 1995. Rural wastewater management in a southern Indian village – a case study. Bioresource Technology 53, 157–164. Griffin, M.E., McMahon, K.D., Mackie, R.I., Paskin, L., 1998. Methanogenic population dynamics during start-up of anaerobic digesters treating municipal solid waste and biosolids. Biotechnology and Bioengineering 57, 342–355. Huttunen, S., Lampinen, A. (Eds.), 2005. Bioenergy Technology Evaluation and Potential in Costa Rica. University of Jyva¨skyla¨ Printing House, Jyva¨skyla¨, Finland. Jolliffe, I.T., 2002. Principal Component Analysis. second ed.. Springer, New York. Kayhanian, M., Rich, D., 1995. Pilot-scale high solids thermophilic anaerobic digestion of municipal solid waste with an emphasis on nutrient requirements. Biomass and Bioenergy 8, 433–444. Lettinga, G., Rebac, S., Parshima, S., Nozhevnikova, A., van Lier, J.B., Stams, A.J.M., 1999. High-rate anaerobic treatment of wastewater at low temperatures. Applied and Environmental Microbiology 65, 1696– 1702. McCune, Medford, 1999. Multivariate Analysis of Ecological Data, Version 4.20. MJM Software, Gleneden Beach, Oregon, USA. McMahon, K.D., Zheng, D., Stams, A.J.M., Mackie, R.I., Paskin, L., 2004. Microbial population dynamics during start-up and overload conditions of anaerobic digesters treatment municipal solid waste and sewage sludge. Biotechnology and Bioengineering 87, 823–834. Moog, F.A., Avilla, H.F., Agpaoa, E.V., Valenzuela, F.G., Concepcion, F.C., 1997. Promotion and utilization of polyethylene biodigester in smallhold farming systems in the Philippines. Livestock Research for Rural Development 9 . Ong, H.K., Greenfield, P.F., Pullammanappallil, P.C., 2000. An operational strategy for improved biomethanation of cattle-manure slurry in an unmixed, single-stage, digester. Bioresource Technology 73, 87–89.

5890

S. Lansing et al. / Bioresource Technology 99 (2008) 5881–5890

Parsons, R.A., 1984. On-farm Biogas Production. Northeast Regional Agricultural Engineering Service, Ithaca, New York. Pedraza, G.X., Chara´, J., Conde, N., Giraldo, S., Giraldo, L., 2001. Evaluacio´n de los biodigestores en geomembrana (PVC) y pla´stico de invernadero en clima medio para el tratamiento de aquas residuals de origin porcino. Livestock Research for Rural Development 14 . Peters, J., Combs, S., Hoskins, B., Jarman, J., Kovar, J., Watson, M., Wolf, A., Wolf, N., 2003. Recommended Methods of Manure Analysis. Cooperative Extension Publishing, Madison, WI. Sophea, K., Preston, T.R., 2001. Comparison of biodigester effluent and urea as fertilizer for water spinach vegetable. Livestock Research for Rural Development 13 . Sophin, P., Preston, T.R., 2001. Effect of processing pig manure in a biodigester as an input for ponds growing fish in polyculture. Livestock Research for Rural Development 13 .

Sterling Jr., M.C., Lacey, R.E., Engler, C.R., Ricke, S.C., 2001. Effects of ammonia nitrogen on H2 and CH4 production during anaerobic digestion of dairy cattle manure. Bioresource Technology 77, 9–18. Sung, S., Liu, T., 2003. Ammonia inhibition on thermophilic anaerobic digestion. Chemosphere 53, 43–52. Tchobanoglous, G., Burton, F.L., Stensel, H.D., 2003. Wastewater Engineering: Treatment and Reuse. McGraw Hill, New York, NY. Thy, S., Preston, T.R., 2003. Effluent from biodigesters with different retention times for primary production and feed of Tilapia (Oreochromis niloticus). Livestock Research for Rural Development 15 . USDA Rural Development, Association of State Energy Research and Technology Transfer Institutions (ASERTTI), and USEPA AgSTAR Program. 2007. A Protocol for Quantifying and Reporting the Performance of Anaerobic Digestion Systems for Livestock Manures. .