Post on 08-Jan-2018
description
Converting Farm Waste to Energy A Primer of Anaerobic Digestion of
Dairy Waste
S.J. Grimberg
Dept. Civil Environmental Eng.Clarkson University, Potsdam, NY
U.S. Energy Consumption by SourceUS Primary Energy Consumption
To get 1000 MW electrical:Method Investment needed Photovoltaic 100 km2 @ 10% efficiency (40 sq. miles)
Windmills 6,660 wind turbines @ 150 kW (20 m blades)666 wind turbines @ 1.5 MW (40 m blades)
Biogas 7,143,000 cows 60,000,000 pigs800,000,000 chickens
Bioalcohol 6,200 km2 of sugar beets (2,400 sq. miles) 7,400 km2 of potatoes (2,800 sq. miles) 16,100 km2 of corn (6,200 sq. miles) 272,000 km2 of wheat (104,00 sq. miles)
Bio-oil 24,000 km2 of rapeseed (9,000 sq. miles)
Biomass 30,000 km2 of wood (12,000 sq. miles)U. Mich.-Schwank
Source:Gottfried Besenbruch, General Atomics
Anaerobic Digestion of Manure• Biologically converts organic matter to
biogas– 55-68% CH4, 32-45% CO2, trace H2S – Biogas can be used as a fuel source for
engine generator sets or boilers. • Manure management
– Dairy manure produced in the U.S.: • Approx. 1x109 tons/year• Approx 4 million cows on farms with at
least 300 cows– Translates to 1.5x108 tons
manure /year– This could produce > 400 MW of
electric power– Odor reductions and nutrients– Reduction in fecal coliforms and
pathogens
U.S. Farm Digesters• Mostly single farm digesters
– Simple structures, often lack reliability– Poor treatment efficiency; i.e. high capital cost
http://www.biogas.psu.edu/othertypesmod.htmlhttp://www.nyserda.org/programs/Environment/ag&indwastemgt.asp
Anaerobic Digestion Process Schematic
Gas
Organic Composites
MS AA
HAc, HPr, HBu, HVa, NH3, CO2, LCFA
Acetate H2 CO2
CH4
CO2
CH4
H2
H2O
Liquid
Ac-, Pr-, Bu-, Va-, NH4+, HCO3
-, LCFA-
HCO3-
Gas
GasMicrobes
NH3 NH4+
Lipids Carb. Proteins Inerts
Dec
ayG
rowt
h
Physiochemical Processes
Bio
chem
ical
Pro
cess
es
Dairy Manure Characteristics
Parameter Mean ± standard deviation Total COD (mg/L) 92,736 ± 33,846 %TS 8.78 ± 1.78 %VS 6.18 ± 1.55 pH 7.6 Total VFA (mg/L) 14,251 ± 4,809 Acetate (mg/L) 7,817 ± 3,219 Propionate (mg/L) 3,336 ± 1,102 Isobutyrate(mg/L) 686 ± 145 Butyrate (mg/L) 1,043 ± 421 Isovalerate (mg/L) 658 ± 70 Valerate (mg/L) 708 ± 89
Microgy MicgogyMicgogy
Plug Flow Digester Example
AA Dairy has been meticulous in obtaining data for more than 6 years
• biogas production (total & per cow)• biogas composition (CO2 measurements)• oil consumption (daily)• electricity generated on the farm• electricity sold to the grid• difference between generated & sold is
electricity used on the farm
Odor Issues Defined by VFAs
0
5000
10000
15000
20000
25000
12-May 1-Jun 21-Jun 11-Jul 31-Jul 20-Aug
Con
cent
ratio
n (m
g/L)
Influent
Date
Acetic55%
Propionic23%
Isobutyric5%
Butyric7%
Isovaleric5%
Valeric5%
VFA distribution-
Acetic63%Propionic
37%
Effluent
Influent
Effluent
•VFA reduced in Digester•Stinkier Larger VFA molecules not found in effluent
-500
0
500
1000
1500
2000
2500
6/8/98 12/25/98 7/13/99 1/29/00 8/16/00 3/4/01 9/20/01 4/8/02 10/25/02 5/13/03
Net sold to the grid
Electricity generated and net electricity sold to the grid at AA Dairy per day (1998-2003)
Produced on Farm
-50 point moving average
kWhr
/day
Difference = Energy Used on the Farm
Date
Sheland Farms
Downtime Cost (lost revenue)Farm Load Supply Percentages (as installed)
-10
10
30
50
70
90
110
130
1 21 41 61 81 101 121 141 161Operating Time (Days)
Perc
ent O
f Loa
d (%
)
• By using remote smart grid protocols and monitoring, the uptime could be improved for an additional $12k per year over current operations.
Interesting Possibility: Co-generation w/ Food Wastes
–Benefits to farmerBenefits to farmer» Increased biogas productionIncreased biogas production» Improved gas quality (reduced Improved gas quality (reduced
concentrations of Hconcentrations of H22SS»““Tipping fees” can be substantialTipping fees” can be substantial
–Benefit for Food Waste Generators»Potential Savings for disposal
Need for improvement
• NY State incentives:– Limited to farmers only– Treatment of no more than 25% non farm waste
• Farm digester design largely empirical.– Cannot predict mixed waste performance
• A system model may significantly improve operation and design process.
ADM1: Modeling Anaerobic Digestion
• For any organic material (COD basis)• Potential to simulate mixed substrates
• Continuous flow stirred tank reactor (CSTR)
• Plug flow reactors can be simulated using reactors in series.
• Includes:• 26 mass balances• 19 biochemical kinetic processes• 3 gas liquid transfer kinetic processes
• Requires numerous stoichiometric and kinetic constants
• Readily available data however:• Waste COD• Total VFA• Biogas composition
Approach• Develop a simplified input data set using the laboratory
data (Page et al, 2008)• Develop and validate model parameter set using
laboratory and full scale steady state data using trial and error approach (Page et al, 2008).– With good results for primary parameters but could not predict
reaction intermediates• Develop parameter set to simulate pilot digester
operated at non-steady state using detailed measurements and optimization routines
Pilot plant
Time [days]
0 20 40 60 80 100
Effl
uent
CO
D [g
/L]
0
20
40
60
80
100
Measurements Prediction (Page et al. 2008) Prediction this study
COD Removal
Average Influent COD: 92.7±43.0 g/LAverage Effluent COD: 45.5±16.2 g/L
Biogas Results
Time [days]
0 20 40 60 80 100
Bio
gas
Flow
[m3 /d
]
0.0
0.5
1.0
1.5
2.0
2.5
3.0
Measurements Prediction (Page et al. 2008) Prediction this study
Time [days]
0 20 40 60 80 100
Met
hane
Con
tent
[%]
0
20
40
60
80
100
Measurements Prediction (Page et al. 2008) Prediction this study
Average CH4 content: 57%
Volatile Fatty Acids
Time [days]
20 30 40 50 60 70 80 90
Ace
tate
Con
cent
ratio
n [g
/L]
0
2
4
10
15
20
Measurements Prediction (Page et al.2008) Prediction this study
Time [days]
0 20 40 60 80 100
Pro
prio
nate
Con
cent
ratio
n [g
/L]
0.001
0.01
0.1
1
10
Measurements Prediction (Page et al. 2008) Prediction this study
Conclusions • Anaerobic digestion of farm waste represents a good
opportunity for farmers to generate revenues and reduce energy needs
• Mixing dairy wastes with other wastes improves process economics
• Models needed that can handle mixed waste to optimize design• Calibration of the wastewater model provided encouraging
results. concentration, and ammonia.
Acknowledgements• Students:
– David Page, O’Brian and Gere, Syracuse, NY– Ben Durfee, Stearns and Wheler, LLC– Shawn Jones, SUNY Canton, summer research student Clarkson University– Ying Zhang, Yunghui Deng, MS students Clarkson University– Mark Venczel, Rajiv Narula, PhD students, Clarkson University– Noelle du Juvigny, Institut Polytechnique LaSalle Beauvais, France– Alex Maxwell, summer research student Clarkson University
• Drs. Powers, Pillay, Welsh and Thatcher
• North Harbor Dairy• Drs. Christian Rosen, Darko Vrecko and Ulf Jeppsson (all Lund University) for providing the
Matlab ADM1 code.
• Funding Sources: USDA, DOE, New York Ag and Markets, NYSERDA
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