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On-Site Analytical Laboratories to Monitor Process Stability Of Anaerobic Digestion Systems

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On-Site Analytical Laboratories to Monitor Process Stability Of Anaerobic Digestion Systems

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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.

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.

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On-Site Analytical Laboratories to Monitor Process Stability Of Anaerobic Digestion Systems

  1. 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. 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. 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. 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…
  5. 5. 0 0.2 0.4 0.6 0.8 1 0% 20% 40% 60% 80% 100% AA NHV RL NH PAT SK EM CapacityFactor(outof1.0) OnlineEfficiency(%) AD systems Online Efficiency (%) Capacity Factor Performance of AD systems - The case of NYS Gooch et al., 2011 88% average online efficiency
  6. 6. 0 0.2 0.4 0.6 0.8 1 0% 20% 40% 60% 80% 100% AA NHV RL NH PAT SK EM CapacityFactor(outof1.0) OnlineEfficiency(%) AD systems Online Efficiency (%) Capacity Factor Performance of AD systems - The case of NYS 57% average capacity factor Gooch et al., 2011
  7. 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
  8. 8. 0 20 40 60 80 100 1 3 5 7 9 11 13 15 17 19 21 23 25 27 Biogasproduction(ft3/min) Sampling (bi-weekly) AA NHV NH SK EM 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. 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. 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. 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. 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. 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. 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. 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. 16. AD process monitoring labs in NYS
  17. 17. Case study: “ Farm X AD system” 0 100 200 300 400 500 600 0 100 200 300 400 500 600 1/1/2011 2/1/2011 3/1/2011 4/1/2011 5/1/2011 6/1/2011 7/1/2011 8/1/2011 9/1/2011 10/1/2011 11/1/2011 12/1/2011 1/1/2012 2/1/2012 Biogasproduction(ft3/min) Poweroutput(kW) Biogas production Power output Power output Biogas production
  18. 18. 0 200 400 600Poweroutput(kW) CHP Power Output(kW) 6.0 6.5 7.0 7.5 8.0 8.5 9.0 pH EffluentpH 0.0 1.0 2.0 3.0 4.0 5.0 VFA(g/L) EffluentVolatile Fatty Acids (g/L) 30 40 50 60 70 VS(g/L) EffluentVolatile Solids (g/L) 0 200 400 600 Biogas(ft3 /min) AD Biogas Production (ft3/min) Case study: “ Farm X AD system”
  19. 19. Case study: “ Farm X AD system” 0 100 200 300 400 500 600 0 100 200 300 400 500 600 1/1/2011 2/1/2011 3/1/2011 4/1/2011 5/1/2011 6/1/2011 7/1/2011 8/1/2011 9/1/2011 10/1/2011 11/1/2011 12/1/2011 1/1/2012 2/1/2012 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. 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. 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. 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. 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. 24. THANKS! Contact Rodrigo Labatut Cornell University e-mail: labatut@cornell.edu

Notas del editor

  • Let’s take the data from EPA…………………. Now, let’s assume
  • Feed-in tariff: VT = 20c /kWh, CA = 12-14 c/kWh, OR = 0.25-0.41
  • In the absence of feed in tariff, tipping fees are the economic driver of on-farm AD systems
  • Data collected over several months of normal operation was used to create a baseline range for “healthy digester operation”. The baseline was created for each specific AD system given the differences of each system and operation. Parameters that fall outside the baseline’s normal range of variation can potentially be a sign of a digester upset and the cause needs to be investigated. The data collected is helping the digester operators to understand how digesters work. They have learnt about the parameters that need to be monitored for a healthy digester operation and the system variables that can make the digester perform better.
  • Normal expected variation, but
  • two-week downtime period of the AD system results in about $10,000 in tangible economic losses.
  • Normal expected variation, but
  • Perturbation can be originated from changes in AD operating parameters or substrates/feedstock characteristics

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