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Analyzing critical factors influencingeconomic and environmental performanceof biofertilizers management systems
Roozbeh Feiz and Karin Tonderski (Niclas Svensson,Mats Eklund)
Linköping University
Center of excellence for biogas solutions (BRC)
2 22
Biogas Research Center – initially eight projects
Vision Resource-efficient biogas solutions are implemented in many new applications andcontribute to a more sustainable energy supply, improved environmental conditions,and good business opportunities
Challenges More gas fromexisting systems
New substrates New sectors Cooperation forbetter performance
Conditions fordevelopment
Exploratoryprojects
Improvement of thebiogas productionprocess
Systematicassessment offeedstock for anexpanded biogasproduction
Biogas in newindustries
Collaboration toimprove economicand environmentalperformance
Municipalitiesas systembuilders inenergy systems
Developmentprojects
Increased methaneproduction andprocess stability inbiogas reactors
Enzymaticincrease ofsludge digestion
Systems andtechnology foreffective use ofbiofertilizers
3 33
BRC DP 8 Systems and technology for effective useof biofertilizers
1. Assessment model to analyze and learn about criticalfactors affecting economic and environmental performance of
different biofertilizer management approaches
4. Potentialproblems withthe biofertilizer
quality?
3. Barriers anddrivers for use ofbiofertilizers in
agriculture?
2. Possiblities tomaximize the
value of differentfractions?
4 44
Aim and goalsLearn how the organization of biogas production systems influences theirperformance.
• How do different parameters influence the economic and environmentalperformance of biogas production system:
– Scale of the plants• Small scale, large scale
– Location in relation to rural and urban areas• Distance from sources of feedstock
• Distance from biofertilizer market
• Distance from biogas market
– Energy system• Electricity, heat or fuel for transportation
– Handling of digestate• Direct application or further treatment?
– Biofertilizer markets
5 55
Method
• Life cycle assessment (LCA) – for environmental impacts
• Life cycle costing (LCC) – for biogas producers
• Tool development based on existing biogas systems:
– Focus on the system and not only the biogas reactor, include the digestate handlingsystem
– Incorporate expert knowledge
– Probability distributions/intervals, Monte-Carlo simulation
• Mixed method (quantitative and qualitative data):
– LCA/LCC data collection from existing biogas plants
– Reference group
– Interview with actors
6 66
System diagram
Pretreatment
AnaerobicDigester
Flaring
Upgrading
Transportation
Storage,Spreading
AmmoniaStripping
• Injection to natural gas grid• Injection to dedicated pipeline• Convert to CBG, Transport• Convert to LBG, Transport
Dewatering.
To heat/electricityproduction
Re-feed into reactor
To wastewatertreatment
To cropgrowth
Liquid
Fiber/semi-solid(biofertilizer)
feedstocks
Solid(biofertilizer)
Vehicle-grade fuel
Transportation
• Upgrading waste-derived feedstocks• Producing dedicated feedstocks
biogas
biomethane
digestate
Unit of analysis: biogas plant
7 77
Modeling approach
• Uncertainties regarding the parameters:
– Stochastic (Inherent parameter uncertainty)
– Epistemic (knowledge gaps)
• Decrease epistemic uncertainty
– Deal with incomplete/imprecise information
– Using expert knowledge
– Random variables
– Suitable probability distribution
• Learning tool, not over-complicated but will give insights
– Critical parameters regarding the system
– Key indicators
8 88
Process and Simulation
LCA/LCC model
Input variablesSuitable probability distributions
Experts knowledge
Output valuesInput valuesRandomly selected
Monte-Carlo Simulation
Probability distribution(Possibility distribution)
Likely / Unlikely value intervals
9 99
Steps in LCA model (in the Excel tool)
Scenario definition
Process input/outputinventory
Life cycle inventory(LCI)
Life cycle impact
(LCIA)
Life cycle impactassessment (EPD)
(LCIA)
Results
101010
Scenario definition
Digestate treatment steps yes/no
Is any digestate sent for direct land application (DLA)? 1
Is dewatering process (DWR) used? 1
Is any DWR-liqour sent for land application (LA)? 1
Is ammonia stripping or struvite percipitation (SPP) used? 1
Digestate treatment steps, more info percentage effective
Fraction of total unprocessed digestate which is available 90.0% 90.0%
Fraction of digestate which is going for DLA 100.0% 90.0%
Fraction of digestate which is going for DWR 0.0% 9.0%
Fraction of total DWR input which is extracted as semi-solid 15.0% 1.35% 4
Fraction of total DWR input which is left as liquid 85.0% 7.65% 5
Fraction of DWR-liqour which is going for LA 10.0% 0.8% 6
Fraction of DWR-liqour which is going for SPP 90.0% 6.9% 7
Fraction of total SPP input which is extracted as solid 3.0% 0.21%
Fraction of total SPP input which is left as liquid 97.0% 6.7%
What type of DWR technique is used?
What type of SPP technique is used?
Y/N
Y/N
Y/N
Y/N
100%
111111
Scenario process diagram
100%
ADS
100%
100%
100%
9.0%
DWR 10.0% SPP
100% 100.0% 100.0% 100% 90.0% 90.0% 100% 80.0% 72.0% 100% 90.0%
0.0% 10.0% 10.0% 10.0%
0.0% 10.0% 9.0% 7.2%
26.2%
Tota l amount of bi ofertl i zer a s N-ferti l izer a vai la ble to pla nt. (for bi ogas system)
Tota l amount of bi ofertl i zer a s N-ferti l izer a vai la ble to pla nt.
Pl ant growth a nd
a griculture system
1
2
4
5
6
7
8
unprocessed digestate
to DWR
unprocessed digestate availablefor trea tment
tota l produced digestate(unprocessed)
total produced biogas(ra w)
unprocessed digestate
to LA
La nd application (as
biofertilizer i n farming)
l i qour output of DWRto LA
li qour output of DWRl iqour output of DWRto SPP
solid output of SPP to
LA
Feed into ADS
pla nt-available nitrogen (% dry matter)
mi neralN-fertilizer added
DWR semi-solid
121212
Example of LCA result
Scenario 2Scenario 1
131313
Risk/probability for positive economic result
0
0.2
0.4
0.6
0.8
1
-800 -600 -400 -200 0 200 400 600 800
0
0.2
0.4
0.6
0.8
1
-800 -600 -400 -200 0 200 400 600 800
Economic result (1000 SEK)
Probability of positiveeconomic result
Risk
Decreasing epistemic uncertainty
141414
Challenges
• LCA is a time consuming method with many limitations.
• Availablity of first hand data regarding emissions andenvironmental impacts
–Methane emissions from storage or post-spreading ofbiofertilizer.
• Data uncertainty
• Many assumptions when modeling processes
–Spreading
–Storage
151515
Relevance of the model
–Biogas producers & investors
• Risk & business assessment
–Policy makers
• Policy analysis
–Research community
• Technology assessment
–General public and other stakeholders
• Increase knowledge and awareness about biogassystems
161616
Future research
• Develop future-oriented scenarios and extend the model
–New feedstocks
–New digestate handling solutions
• Refine and expand
–Based on knowledge from other biogas research
• Reduce identified epistemic uncertainties
–Critical factors and key indicators