Proceedings available at: http://www.extension.org/67739 The anaerobic digestion of complex materials is a highly dynamic, multi-step process, where physicochemical and biochemical reactions take place in sequential and parallel ways. The stability of the process depends on a delicate balance between the formation and consumption of products. When the concentration of a particular substance reaches the homeostatic equilibrium of certain organism or group of organisms, such balanced is disrupted, and the process becomes upset. If measures to correct the source of the problem are not taken, substrate stabilization and biogas production will progressively decrease, and eventually stop. Recovery of a digester can take several weeks to months, during which, energy generation and waste treatment are not possible, resulting in increased operational costs for the facility. To detect process perturbations and prevent major digester upsets, periodic monitoring is essential.

1. ON-SITE ANALYTICAL LABORATORIES TO MONITOR
PROCESS STABILITY OF ANAEROBIC DIGESTION SYSTEMS
From Waste to Worth: Spreading Science & Solutions
Denver, Colorado ∙ April 1 – 5, 2013
Rodrigo Labatut, Ph.D.
Postdoctoral Associate
Biological & Environmental Engineering
Cornell University

2. Overview of anaerobic digestion (AD) in the U.S.
o 186 on-farm anaerobic digesters in the U.S. (EPA, March 2012)
Wisconsin: 28
New York: 25
Pennsylvania: 23

3. Increasing number of on-farm AD operations co-digesting
manure with food wastes
Increased biomethane yields
Increased revenue by generated tipping fees
Increased project feasibility
Overview of anaerobic digestion (AD) in the U.S.

4. Performance of anaerobic digestion systems
Up to 1998, failure rates were at (Lusk, 1998):
• 63% Plug-flow reactors
• 70% Continuously-stirred tank reactors
2013
Better design and engineering numbers likely to be lower
BUT, inadequate system management and control persists…
Consequences (AD, CHP)
• Inconsistency
• Underperformance
• Short-term failure
Examples in MI, OH, NY…

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AA NHV RL NH PAT SK EM
CapacityFactor(outof1.0)
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Online Efficiency (%) Capacity Factor
Performance of AD systems - The case of NYS
Gooch et al., 2011
88% average online efficiency

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OnlineEfficiency(%)
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Online Efficiency (%) Capacity Factor
Performance of AD systems - The case of NYS
57% average capacity factor
Gooch et al., 2011

7. Performance of AD systems - The case of NYS
Reasons for low CHP performance:
1. Decreased/unstable biogas production
2. Decreased/unstable biomethane content in biogas
3. Downtime of CHP unit due to AD system failure
4. Decreased efficiency of CHP system
5. Over-dimensioning of CHP system
6. Downtime of both AD and CHP systems due to maintenance

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Sampling (bi-weekly)
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Responsibilities: operate, maintain, and monitor both AD and CHP systems in
addition to his/her daily farm-related activities.
Nearly all active on-farm AD systems in NYS are operated by a farm worker, who
usually has no previous experience or training in AD!
Performance of AD systems - The case of NYS
Gooch et al., 2011

9. Implications of low AD system performance/failure
1. Decreased energy generation
Data from US EPA (2012) from 157 operating AD systems with CHP
units in the U.S.
Total of 83,738 kW electrical capacity

10. Implications of low AD system performance/failure
1. Decreased energy generation
83,738 kW electrical capacity
In a well-operated AD system with a CF = 0.9, this translates into:
• 660 GWh of total energy produced per year, an equivalent to
power 57,428 U.S. households for an entire year
• $33 million in revenues, if sold to a utility company in NYS
($0.05/kWh)
BUT, with a CF = 0.57 an AD system will:
• Power 21,057 less households
• Produce $12 million less in revenue

11. 2. Co-substrates
In co-digestion operations, if AD system failure occurs:
• NO tipping fees if farm cannot receive external substrates
Tipping fees are the economic driver of most on-farm AD
systems in the US!
• If contract obligates farm to receive substrates, then where to
store them?
If stored in an open lagoon, odor and greenhouse gases are
no contained
Implications of low AD system performance/failure

12. Operator training and AD monitoring labs in NYS
Manure Management Program at Cornell University
(NYSERDA founded project)
Goals:
1. To train and support a workforce of AD operators and technicians in
NYS
2. To implement analytical labs on selected on-farm AD systems to
monitor key process parameters
3. To improve performance, detect process upsets more efficiently,
and prevent system failure

13. Key process indicators to prevent digester upsets
• Retention time
• Balanced feed
• Adequate nutrients
• Right environmental
conditions
2-3 days 22 days
Digesters are like cows!
Yes Yes
Yes Yes
Yes Yes
High quality
/production milk
High quality
/production biogas
 Result

14. Parameter Determination method
pH pH meter/single-junction electrode
Temperature pH meter/thermocouple
Total alkalinity (ALK) Titration of sample with sulfuric acid 0.1 N to pH 4.0
Volatile fatty acids (VFA) Distillation of sample and titration of distillate with
sodium hydroxide 0.1 N to pH 8.3
VFA/ALK Ratio Titration method (adapted from Kapp, 1984)
Total solids (TS) Drying sample in gravity convection oven at 105oC
overnight (> 8 h)
Total volatile solids (VS) Ashing sample in muffle furnace at 550oC for 1 h
Methane content By difference of carbon dioxide content, measured
using sensidyne tubes
Total ammonia-nitrogen
(TAN)
Ion meter/ion selective electrode
AD process monitoring labs in NYS

15. Parameter Determination method
pH pH meter/single-junction electrode
Temperature pH meter/thermocouple
Total alkalinity (ALK) Titration of sample with sulfuric acid 0.1 N to pH 4.0
Volatile fatty acids (VFA) Distillation of sample and titration of distillate with
sodium hydroxide 0.1 N to pH 8.3
VFA/ALK Ratio Titration method (adapted from Kapp ,1984)
Total solids (TS) Drying sample in gravity convection oven at 105oC
overnight (> 8 h)
Total volatile solids (VS) Ashing sample in muffle furnace at 550oC for 1 h
Methane content By difference of carbon dioxide content, measured
using sensidyne tubes
Total ammonia-nitrogen
(TAN)
Ion meter/ion selective electrode
AD process monitoring labs in NYS

16. AD process monitoring labs in NYS

17. Case study: “ Farm X AD system”
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Biogasproduction(ft3/min)
Poweroutput(kW)
Biogas production
Power output
Power output
Biogas production

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AD Biogas Production (ft3/min)
Case study: “ Farm X AD system”

19. Case study: “ Farm X AD system”
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Biogasproduction(ft3/min)
Poweroutput(kW)
Biogas production
Power output
Power output
Biogas production
• Plug-flow/CSTR AD system
• Need to find the correct sampling place, after VFAs spike
(hydrolysis/fermentation stages)

20. Digester operational parameters
• Organic loading rate (OLR)
• Loading frequency
• Temperature
• Mixing frequency/speed
Substrate/feedstock characteristics
• Solids content (TS, VS)
• Co-digestion ratio
• Co-substrate chemical strength
Process
perturbation
Digester upset
AD system
failure
• Steady increase VFA concentrations,
or VFA/ALK ratio
• Increase H2 partial pressure
• High VFA (i.e. acetate, propionate)
• High H2 concentrations
• Lower pH (sour digester)
• Decreased biogas production
• Decreased methane content
• Decreased VS stabilization
• Biogas production stopped
• AD system failure
• CHP system down
Relativetime
Anatomy of an AD process perturbation

21. Conclusions
• Study in NYS: <60% of electric energy potential due to poor AD
performance and system failure
 Inadequate management and process control to blame
• Well-trained and qualified personnel to operate and monitor
AD systems the process is essential
 Prevent digester upsets and potential system failures
 Efficient organic waste stabilization and stable biogas
production

22. Conclusions
• Monitoring labs installed on selected farm-based AD
systems in NYS
 Monitor key process parameters and detect process
upsets more efficiently
• Measured process parameters (i.e. VFA, VFA/ALK ratio) are
good indicators of process upsets
• Potential to identify and correct the source of the problem
before system failure occurs

23. Acknowledgements
The authors would like to acknowledge the following farms for their
willingness to participate in this project:
• Sunnyside
• Roach
• Sheland
• Synergy
• SUNY Morrisville
Special thanks to the lab operators!
• Don Kulis
• Gary Mutchler
• Doug Shelmadine and Sons
• Randy Mastin
• Ben Ballard and his students
New York State Energy Research and Development Authority (NYSERDA) for
funding in support of this work

24. THANKS!
Contact
Rodrigo Labatut
Cornell University
e-mail: labatut@cornell.edu