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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)

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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

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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?

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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

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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

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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

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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

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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

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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

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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%

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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

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Example of LCA result

Scenario 2Scenario 1

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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

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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

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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

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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