monitoring and assessing the performance of ...

39.

SIMPOZIJ AKTUALNI ZADACI MEHANIZACIJE POLJOPRIVREDE

UDC 661.9 Originalni znanstveni rad Original scientific paper

MONITORING AND ASSESSING THE PERFORMANCE OF AGRICULTURAL BIOGAS PLANTS MATHIAS EFFENBERGER1, DJORDJE DJATKOV2 1

Bavarian State Research Center for Agriculture, Institute of Agricultural Engineering and Animal Husbandry, Vöttinger Straße 36, 85354 Freising, Germany. 2 University of Novi Sad, Faculty of Technical Sciences, Chair of Biosystems Engineering, Trg Dositeja Obradovica 6, 21000 Novi Sad, Serbia. ABSTRACT Germany is a leading European country in biogas production with currently around 5,000 agricultural biogas plants in operation. There is a recognized need for assessment, comparison and performance improvement of biogas plants. Prerequisites for a reliable assessment include: 1) Detailed and reliable performance data, 2) defined assessment criteria, and 3) a comprehensive assessment approach. Data from thorough monitoring of ten Bavarian agricultural biogas plants are presented. Various performance figures were derived from the collected data to enable comparison of different biogas plants. Four aspects of a biogas plant performance were considered: 1) Biogas production, 2) biogas utilization, 3) environmental impact, and 4) socio-economic impact. Eight pertinent performance figures were selected and used as assessment criteria. To assess the performance of the ten biogas plants, three different approaches were applied and compared based on their limitations and capabilities. In the first approach, which is based on Data Envelopment Analysis (DEA), it was impossible to involve experts in the assessment. In the second approach, two Multi Criteria Decision Making (MCDM) methods were combined and experts determined criteria weights, i.e. importance of criteria in the assessment. The third approach is based on fuzzy mathematics, which enabled experts to determine criteria weights and qualitative evaluation of biogas plant performance. Also, “weak points” in operation were identified, where the operator of a biogas plant could improve the performance of his installation. When necessary, this approach may be adjusted to technological developments or changes in political and economical framework conditions. It was concluded that the third proposed approach is the most appropriate for the assessment of biogas plants. The approach should be methodologically extended to include more assessment criteria and to improve the reliability by consulting a larger number of experts. Key words: biogas plant, agriculture, performance, monitoring, assessment

39. Symposium "Actual Tasks on Agricultural Engineering", Opatija, Croatia, 2011.

201

M. Effenberger, Dj. Djatkov

INTRODUCTION Germany is a leading European country in biogas production with currently around 1.7 GW of installed electrical capacity distributed over 5,000 biogas installations. Electricity production from biogas accounted for 1.7% of the total electricity demand in 2009 (BMU, 2009). The number of biogas plants has grown rapidly since specific feed-in tariffs for “green” electricity were defined in the Renewable Energy Law (EEG) from 2001 and its amended versions from 2004 and 2009. Biogas production has become an important branch of agriculture, mainly due to continuously growing usage of agricultural raw materials for anaerobic digestion (AD). Amongst others, data on actual performance of biogas plants are available from FNR (2009), Effenberger et al. (2009a) and Schöftner et al. (2006). Assessment of environmental impact of operating biogas plants has been reported by Bachmaier et al. (2009). These studies showed that the performance of biogas plants may be described with manifold performance figures. Therefore, the assessment of “overall” biogas plant efficiency requires multi-criteria methods. To date, such an overall performance assessment of biogas plants has been seldom reported. Madlener et al. (2009) used a combination of Multi Criteria Decision Making (MCDM) and Data Envelopment Analysis (DEA) for assessing biogas plants with respect to economic, environmental and social criteria. In Braun et al. (2007) DEA was used as a benchmarking tool to identify the most efficient biogas plants and nominate them as “Best Biogas Practice”. A shortcoming of these approaches is that the used assessment criteria were primarily chosen to fit the applied method, while no clear definition was provided about the performance indicators of biogas plants. Detailed performance figures for agricultural biogas plants have been presented and discussed by Strobl & Keymer (2006) and Effenberger et al. (2009b). Contemporary agricultural biogas plants are characterized by their diversity in terms of site conditions, capacities, designs, input materials, material and energy flows. Consequently, considerable differences in their performance can occur. Therefore, there is a need for comprehensive assessment of biogas plants in agriculture. The objective of this paper was to present monitoring results of ten agricultural biogas plants and their performance assessment using three different approaches. Further objective is to compare the three approaches, to recognize their limitations and to define tasks for future research. MATERIALS AND METHODS Description of biogas plants Ten contemporary farm-scale biogas plants were monitored over a period of two years (Table 1). The plants reflect the diversity of geographical locations and designs of biogas installations in Bavaria. They represent agricultural biogas plants, since only animal manure and energy crops were used as input materials for biogas production.

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Monitoring and assessing the performance of agricultural biogas plants

Table 1 Basic characteristics of the ten monitored biogas plants (Effenberger et al., 2009a) Plant ID Average daily input1 Proportion of animal manure Type of animal manure Type of energy crops

t•d-1 % (m/m)

A

B

C

D

E

F

G

H

I

J

22.3

20.3

18.9

18.5

17.8

28.2

18.6

10.9

11.4

26.2

8

3

7

0

28

15

28

0

41

38

LCM, SCM, SPM MS, GS, GR, CCS

SCM

LCM

–

CGS, MS, MS, GS, MS, WW, CCM, CCS, GS, WR, CCS CCM GR 2005 2004 2004 2,605 3,676 2,290

SPM

MS

LHM, LCM, SPM LCM, – LCM SCM SCM MS, MK, MS, CCS, MS, GR, GN, MK, MS, GS, CCS, GS, GS, SG, SG, LC MS, SB CCS, GN GN RGS 2002 2005 2004 2005 2001 3,740 1,540 1,778 1,095 3,413

2005 2005 Year of commissioning m3 3,015 2,487 Total digester volume2 Storage of digested collect open collect open collect open collect open open collect residue G G G G G G PI PI G G CGU engine type 329 333 380 420 347 526 280 250 324 380 Rated electrical capacity kW kW 447 232 486 472 432 566 300 262 250 486 Rated thermal capacity 1 ) Animal manure and energy crops, not including added water; 2) Sum of the usable volume of all process stages of the biogas plant excluding digested residue storage; Collect: Gas collection; G: Gas engine; PI: Pilot-injection engine; CCM: Corn-CobMix; CCS: Cereal crop silage; CGS: Clover grass silage; GN: Grain; GR: Green rye crop silage; GS: Grass silage; LC: Lawnclippings; LCM: Liquid cattle manure; LHM: Liquid hog manure; MK: Crushed maize kernels; MS: Maize crop silage; RGS: Rye grass silage; SB: Sugar beats; SCM: Solid cattle manure; SG: Sudan grass silage; SPM: Solid poultry manure; TC: Triticale silage; WR: Winter rye crop silage; WW: Winter wheat crop silage.

Figure 1 General scheme of the process chain for the ten monitored biogas plants A general scheme of the process chain with material and energy flows for the ten biogas plants is given in Fig. 1. As in the vast majority of biogas plants in Germany, biogas

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produced in these plants was utilized in a co-generation unit (CGU) with internal combustion engine, to produce electricity and heat. Electricity was fed into the public electricity network. Own electricity demand of the biogas plants was covered either by network electricity, by own production or by production in a small hydropower plant. A certain amount of heat energy was consumed for heating the digesters. Surplus heat energy was utilized externally for district heating or drying processes. Unutilized heat energy (“offheat”) was wasted in the atmosphere. The digested residue was used as fertilizer. In three cases, the residue was subjected to solid-liquid-separation. The storage tanks for the digested residue were either open or covered and connected to the biogas collection system (Table 1). Monitoring procedures The data required for the assessment were either logged manually by the operator or automatically by upgrading the biogas plants with measuring devices. Additionally, samples of input materials and digester contents were analyzed to calculate material and energy flows. Overall, up to 100 parameters per plant were collected in different intervals. The database for storing the monitored data and software for calculation of performance figures were developed at the Institute for Agricultural Engineering and Animal Husbandry in Freising. Performance figures and assessment criteria After monitoring, numerous performance figures were derived from the collected data. These figures were calculated by combining several working parameters. In most cases, the figures were related to electricity production, which often is the primary objective of biogas production. To describe the overall performance of biogas plants, eight pertinent performance figures were selected and used as assessment criteria (C1-C8, see Table 2). These eight criteria determine four aspects of biogas plant performance, such that each aspect was described by two corresponding assessment criteria: 1) Biogas production (C1, C2), 2) biogas utilization (C3, C4), 3) environmental impact (C5, C6), and 4) socioeconomic efficiency (C7, C8). Assessment approaches Data Envelopment Analysis (DEA) approach. DEA is a method commonly used for assessing the relative efficiency (performance) of a set of ‘units’ that are commonly called decision making units (DMUs). Within the DEA procedure, a linear program is applied and solved for each DMU under evaluation (Charnes et al., 1978). The performance measure takes a value between 0 and 1, where the DMUs (in this case biogas plants) with the “best” performance reach a value of 1. Biogas plants with the “worst” performance reach the lowest value (larger than zero). In this paper, a Super-CCR DEA model was applied that is able to distinguish further between efficient biogas plants, by allowing the measure of relative performance to take a value greater than 1 (Cooper et al., 2006). In order to improve the efficiency of a biogas plant, inputs (Is) should be decreased and outputs (Os) should be increased. Therefore, three assessment criteria were determined as Is (C5, C6 and C8) and five of them as Os for the DEA model (C1, C2, C3, C4 and C7). DEA is a nonparametric approach, since no functional relationships are required between Is and Os.

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General DEA procedures have been discussed in Dyson et al. (2001). For assessing the performance of biogas plants, DEA was applied in Djatkov & Effenberger (2010) and Madlener et al. (2009). Table 2 Description of selected assessment criteria Criterion

Title

Unit

Weight

C1

Relative biogas yield

%

C2

Methane productivity

m3•(m3•d)-1

C3

Utilization ratio of CGU

%

C4

Methane utilization ratio

%

C5

Specific GHG g CO2,eq•kWhel-1 emissions

10.8

C6

Cumulated energy demand

kWh•kWhel-1

2.7

C7

Profit

€•(kWel•a)-1

C8

Labor input

Lh•kWel-1

Formulae

2.8

Mp

UM =

Vd

Ep Et

E p − Ed + Qext Hm

EGHG =

CED =

11.6

=

M

U CGU =

3.2

58.8

Ym Yt

P

2.9

7.2

Yr =

Ee − E a Ep

EDc + EDo + EDr − EDa Ep

P=

E=

I −C Pe

Lo + Lm + Lr Pn

Remark Relative biogas yield (Yr) was computed as the ratio of measured biogas yield (Ym) and theoretical biogas yield (Yt) based on animal feed value analysis (Keymer and Schilcher, 2003). It is an indicator of the microbiological degradation ratio of the input materials. Methane productivity describes the volumetric productivity of the digester and as such is equivalent to specific power output. It is the ratio of methane production rate (Mp) and usable digester volume (Vd). Utilization ratio of the co-generation unit (UCGU) describes the efficiency of utilizing the nominal electrical capacity of the CGU. It is the ratio of total electricity production (Ep) and theoretical electricity production given 100% availability (Et) over a specific time period. Methane utilization ratio is the sum of produced electricity (Ep) minus the electricity demand of the biogas plant (Ed), plus external heat utilization (Qext), related to the net heating value of methane output (Hm). If a pilot injection engine is used, the amount of electricity produced from diesel fuel must be subtracted from Ep. This figure describes the efficiency of utilizing the fuel value of produced methane. Specific greenhouse gas emissions (EGHG) of electricity production from biogas are determined as the difference between emitted (Ee) and avoided amounts of GHG (Ea) from the entire process chain (see Fig. 1), specified to electricity output (Ep). Values include emissions of CO2, CH4 and N2O and were obtained from Bachmaier et al. (2009). Cumulated energy demand (CED) is the sum of energy input from fossil resources for the construction (EDc), operation (EDo) and removal (EDr) of the biogas plant minus substituted heat energy from fossil fuels for external users and avoided energy input for fertilizer production (EDa), related to electricity output (Ep). Values were obtained from Bachmaier et al. (2009). Profit was calculated as the difference between total yearly income (I) and yearly cost (C) of agricultural biogas production, related to average electrical capacity (Pe). Labor input (L) is the sum of human labor hours spent for operation (Lo), maintenance (Lm) and repairs (Lr) of the biogas plant, related to nominal electrical capacity (Pn).

Multi Criteria Decision Making (MCDM) approach. In this approach, two MCDM methods were combined: Analytic Hierarchy Process (AHP) and Simple Additive Weighting (SAW). AHP was originally developed by Thomas Saaty (Saaty, 1980). In the assessment of biogas plants, AHP was used for deriving weights, i.e. relative importance of assessment criteria by consulting an expert panel (Table 2, the fourth column). SAW is a commonly used method to combine multiple assessment criteria with unequal weights. For assessment of biogas plants an overall utility value (Ui) was calculated. Derived criteria weights (wj) were directly applied to criteria values (xij) as presented in Eq. 1 (n is the number of biogas plants to be assessed, while m represents the number of assessment criteria). Biogas plants were ranked in descending order with the best-performing biogas plants having the highest utility value. A detailed description of the MCDM approach is given by Djatkov et al. (2009).

205


m

U i = ∑ w j xij

i = 1...n

(1)

j =1

Fuzzy mathematical approach (FMA). FMA was developed using the theory of fuzzy sets and fuzzy logic (Klir and Yuan, 1995). In this approach, the same criteria weights as in MCDM were used. These weights were firstly fuzzified to construct appropriate fuzzy numbers ( Wi ) on the scale between 0 and 100. Fuzzy numbers were constructed from criteria values as well ( Ci ). Fuzzy weights and fuzzy criteria values were combined in Fuzzy Weighted Average (FWA, Eq. 2), to obtain the overall performance assessment. FMA is a fuzzy extension of traditional methods, e.g. SAW, to combine multiple criteria with unequal weights. Beside determination of criteria weights, experts took part in qualitative determination of efficiency states. Five efficiency states, which are fuzzy numbers, were defined for each criterion and for overall performance (“unacceptable”, “poor”, “acceptable”, “good” and “excellent”). After the assessment, biogas plants were assigned to one of the defined efficiency states. The assignment was facilitated by calculating the Euclidean distance between the fuzzy overall performance value of the individual biogas plants ( R~ ) and the respective fuzzy numbers for the five efficiency states ( S~ ). Biogas plants were assigned to the efficiency state with the shortest Euclidean distance. In order to rank assessed biogas plants, centroid defuzzification method was applied (Eq 3). From the resulting fuzzy number, a crisp value (real number) between 0 and 100 was obtained which represents the overall performance of a biogas plant. n ~ ~ ~ ∑ WiCi R = i=n1 ~ ∑i=1Wi

(2)

100 ~ ~ ∑ 0 R( x) ∗ x d ( R) = x=100 ~ ∑x=0 R(x)

(3)

RESULTS AND DISCUSSION Performance figures for the ten monitored agricultural biogas plants are given in Table 3. Six of these performance figures were selected (C1-C6) and used as assessment criteria (Table 2). Individual economic performance figures are confidential and therefore not reported. Compared to similar studies (FNR, 2009; Schöftner et al., 2006), fewer biogas plants were investigated, but the necessary parameters were collected exhaustively and continually. Therefore, the quality of the data pool is considered a particular strength of this study. Table 4 presents the results of performance assessments using three different approaches. Biogas plants were ranked with respect to calculated overall performance on different scales. In FMA, qualitative assessment is provided with letters in parenthesis. Ten plants were assigned “poor”, “average” or “good” overall performance.

206


Table 3 Performance figures for the ten monitored agricultural biogas plants Parameter Evaluation period Organic loading rate1 Overall HRT

Unit

A

B

C

D

E

F

G

H

I

J

d

639

672

305

865

793

823

609

488

640

609

kg ODM•(m3•d)-1

2.0

1.9

2.0

2.6

2.7

3.0

3.0

1.9

3.1

1.9

d

120

126

131

110

78

141

84

127

96

131

Biogas yield/ODM L•kg ODM-1 696 681 709 737 676 519 714 754 729 700 input Potential biogas L•kg ODM-1 541 480 542 534 537 578 550 530 453 526 yield/ODM input Relative biogas % 129 142 131 138 126 90 130 142 161 133 yield Methane m³•(m³•d)-1 0.79 0.71 0.78 0.98 0.97 0.76 1.0 0.74 1.2 0.65 productivity Electricity -1 1,280 1,277 1,549 1,506 1,191 1,077 1,535 1,648 1,362 1,263 kWh•t ODM yield/ODM input Utilization ratio % 97.8 82.4 93.7 79.3 97.1 91.3 96.1 92.2 58.6 89.0 CHPU Share of own % 9.1 17.4 3.7 5.8 8.3 7.3 4.5 10.3 11.1 6.2 electricity demand2 Engine electrical % 32.4 35.6 39.4 40.4 33.6 40.5 38.4 38.4 34.7 36.6 utilization ratio External heat % 2.8 2.1 n.a. 37.9 50.3 58.0 19.3 39.7 60.0 n.a. utilization ratio3 Methane utilization % 30.4 30.0 65.2 49.4 49.0 58.2 42.2 50.9 42.8 42.4 ratio Specific GHG g CO2,eq•kWhel-1 251 207 16 163 -85 29 177 223 216 164 emissions Cumulated energy -1 kWh•kWhel 0.22 0.24 -0.42 -0.13 -0.62 -0.53 0.24 0.19 -0.22 0.06 demand 1 ) Related to total digester volume (see Table 1); 2) Electricity demand of biogas plant related to electricity output; 3) Related to surplus heat output; FM: Fresh matter; HRT: Hydraulic retention time; n.a.: Not available; ODM: Organic dry matter.

Table 4 Performance assessment for the ten monitored biogas plants using three different approaches Plant DEA MCDM FMA ID Performance Ranking Performance Ranking Performance Ranking A 0.526 10 0.559 9 33.60 (P) 9 B 0.779 7 0.189 10 21.14 (P) 10 C 1.522 3 0.983 3 74.49 (G) 3 D 1.938 2 0.989 2 83.14 (G) 2 E 126.8 1 1.000 1 84.93 (G) 1 F 1.317 4 0.902 4 66.82 (G) 4 G 0.608 9 0.798 6 59.54 (A) 6 H 0.618 8 0.632 7 36.15 (P) 8 I 1.292 5 0.613 8 38.91 (P) 7 J 0.826 6 0.848 5 65.44 (G) 5 P: poor; A: average; G: good.

Since it was impossible to involve experts in the assessment with DEA, results obtained from this approach are considered least reliable. MCDM facilitated a more reliable assessment, since experts determined criteria weights. FMA is deemed most reliable, since experts determined criteria weights and efficiency states. Due to methodological similarity

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in the calculation of overall performance values, ranking orders for MCDM and FMA are almost identical. The advantage of FMA is the possibility for an absolute assessment whereas in DEA and MCDM the assessment is relative among a set of biogas plants. In the latter cases, the best biogas plant attains the highest value on the respective scale, although its performance may not be excellent in absolute terms. This is the case with plant E, which attained the highest value in DEA and MCDM, but only a “good” rating in FMA. In FMA, qualitative assessment of each individual aspect of biogas plant performance is possible as well. For example, if one aspect is assessed as “poor”, it can be identified as a “weak point”. The respective plant should then be compared with others that obtained a better rating with respect to that particular aspect. In this way, biogas plant operators receive a good indication of where they should preferably start to improve the performance of their installation. The FMA approach was developed based on collected data and current state-of-the-art of biogas technology in Germany. However, it can be easily adjusted to assess biogas plants in any other geographical region. CONCLUSIONS The thorough monitoring of ten agricultural biogas plants illustrated manifold performance figures that may be used for performance assessment. So far, eight pertinent figures were selected and used as criteria to assess four aspects of biogas plant performance: Biogas production, biogas utilization, environmental impact and socioeconomic impact. The overall performance of a biogas plant was described by combining these four aspects. In this paper, three different approaches were applied to assess the overall performance of ten biogas plants in agriculture. DEA and MCDM approaches may be used to rank a set of biogas plants. FMA is considered superior to the other approaches, since experts knowledge was included in the assessment and an absolute assessment was provided. We propose this approach for assessing (overall) performance of biogas plants. It can serve for indicating starting points for improvement of existing installations and pre-assessment of biogas plants in planning phase. Further research should focus on developing an approach that is capable of handling compensation between individual criteria. The approach should be methodologically extended to enable consulting a larger number of experts to improve reliability. Furthermore, more assessment criteria might be included, but it should not overburden the assessment. ACKNOWLEDGEMENT This work was funded by the Bavarian State Ministry for Nutrition, Agriculture and Forestry and the DAAD (German Academic Exchange Service).

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