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University of Natural Resources and Life Sciences, Vienna Institute for Sustainable Economic Development The greenhouse gas mitigation potential of green biorefineries in Austria Stefan Hltinger, Mathias Kirchner, Johannes Schmidt &

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University of Natural Resources and Life Sciences, Vienna Institute for Sustainable Economic Development

The greenhouse gas mitigation potential

  • f green biorefineries in Austria

Stefan Höltinger, Mathias Kirchner, Johannes Schmidt & Erwin Schmid University of Natural Resources and Life Sciences, Vienna

24 August 2016, (LTU) Luleå, Sweden

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University of Natural Resources and Life Sciences, Vienna Institute for Sustainable Economic Development

Outline

  • Introduction
  • Biorefinery
  • Motivation for biorefinery research
  • Drivers for green biorefinery development in Austria
  • Data and methodology
  • Integrated modelling approach
  • Spatially explicit data
  • LCA approach
  • Results and outlook
  • Profitability of different green biorefinery concepts
  • GHG mitigation potential

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University of Natural Resources and Life Sciences, Vienna Institute for Sustainable Economic Development

Definition and classification

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IEA Task 42 Classification of Biorefineries 1

  • Raw materials (agricultural-, forest- and aquatic biomass, biogenic residuals and

waste materials)

  • Intermediates (Platform) (starch, proteins, fibres, press juice, biogas, syngas)
  • Processes (mechanical, thermochemical, chemical and biotechnological)
  • Products (food, feed, chemicals, materials, fuels, electricity, heat)

„Biorefining is the sustainable processing of biomass into a spectrum of marketable bio-based products and bioenergy.”

IEA - Task 42 Biorefineries

1 Cherubini et al. (2009). Toward a common classification approach for biorefinery systems. Biofpr.

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University of Natural Resources and Life Sciences, Vienna Institute for Sustainable Economic Development

Motivation for GBR research

  • Biorefineries are promoted for
  • Mitigating climate change 1
  • Replacing fossil resources by renewable raw materials or waste 2
  • Increasing economic efficiency and sustainability of energy technologies 3
  • Drivers for the green biorefinery concept in Austria
  • Oversupply of grassland biomass due to changes

in agricultural policies and structures

  • Alternative utilization for grassland biomass to preserve cultural

landscape

  • Employment opportunities for rural areas

1 EC (2008). 20 20 by 2020 - Europe's climate change opportunity 2 EC (2011). A resource-efficient Europe - Flagship initiative under the Europe 2020 Strategy” 3 EC (2009). “Investing in the Development of Low Carbon Technologies (SET-Plan)”

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University of Natural Resources and Life Sciences, Vienna Institute for Sustainable Economic Development

Objectives

  • Spatially explicit, techno-economic
  • ptimization model (BioResume) to assess
  • economic feasibility of various green

biorefinery (GBR) concepts

  • determine key parameters that affect the

profitability

  • GHG mitigation potential
  • Impact of different policy support schemes

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University of Natural Resources and Life Sciences, Vienna Institute for Sustainable Economic Development

Green Biorefinery (GBR) concepts

  • Assessment of GBR concepts and biogas (CHP)

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Feedstocks and products

GBR - Concept Feedstock Press juice Press cake GBR_fibres grass silage feed proteins, biogas CHP fibres for technical applications GBR_amino_acids grass silage amino acids, lactic acid Biogas CHP Biogas grass silage Biogas CHP

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University of Natural Resources and Life Sciences, Vienna Institute for Sustainable Economic Development

Integrated modelling framework

EPIC

Biophysical process simulation model

PASMA[grid]

Austrian agricultural and forestry sector model Simulates biophysical processes such as crop yields and nutrient cycles at 1x1 km Derives economically optimal production

BioResume

Biorefinery supply chain optimization model Maximizes profits along the biorefinery supply chain by selecting optimal. plant locations and capacities

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University of Natural Resources and Life Sciences, Vienna Institute for Sustainable Economic Development

PASMA[grid]

  • Biomass supply
  • Spatially explicit biomass supply

curves at 1x1 km resolution

  • competition with livestock,

bioenergy and food production

  • aggregated to 20x20 km supply

regions for MIP model

  • Direct and indirect soil

emissions

  • GHG emissions for different

management intensities (fertilizer inputs)

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Annual grass silage supply per 20x20 km supply region at a feedstock price of 100 Euro per t dm

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University of Natural Resources and Life Sciences, Vienna Institute for Sustainable Economic Development

BioResume – Input data

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Spatially explicit data Techno-economic and LCA data

  • Biomass supply and soil GHG

emissions of different management intensities (1x1 km)

  • 174 supply regions (20x20 km)
  • 79 potential sites
  • Road network dataset
  • Annualized capital costs
  • Operating costs
  • Energy inputs and costs
  • Transportation costs and GHG

emissions

  • Product yields and prices
  • GHG emissions of reference

products

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University of Natural Resources and Life Sciences, Vienna Institute for Sustainable Economic Development

Life cycle GHG emissions of GBRs

  • Scope
  • Ghg mitigation potential of utilizing one t dm grass silage in different

green biorefinery systems compared to energetic utilization in biogas plants

  • Functional unit – input orientated
  • 1 t dm biomass input
  • System boundaries
  • GHG emissions from cradle to factory gate
  • Cultivation and harvest (soil emissions and machinery), biomass

transport and processing in GBR

  • Reference system

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System boundaries and reference systems

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University of Natural Resources and Life Sciences, Vienna Institute for Sustainable Economic Development

Sensitivity and uncertainty analysis

  • Sensitivity analysis
  • Monte-Carlo simulation with 500 model runs with feasible ranges for
  • product yields and prices
  • Life cycle GHG emissions of biorefineries
  • Ghg emissions of substituted products
  • Uncertainty analysis
  • Impact of single model parameters on model results uncertainty

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University of Natural Resources and Life Sciences, Vienna Institute for Sustainable Economic Development

Results – Supply chain design

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  • Optimal capacities for

biorefineries are on average about 6 times larger than for biogas plants

  • 8-14 GBRs instead of 30-35

biogas plants to optimally utilize the biomass potential

  • Average biomass

transportation distances increase from 30 km up to 45-50 km

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University of Natural Resources and Life Sciences, Vienna Institute for Sustainable Economic Development

Results - Profitability

  • All concepts are economically

feasible under current policy support schemes

  • GBR_amino acids and

GBR_fibres are not economically feasible in 6% and 1% of the simulation runs for, respectively

  • Biogas lower profitability, but

also lower uncertainty due to guaranteed feed-in tariff

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University of Natural Resources and Life Sciences, Vienna Institute for Sustainable Economic Development

Results – GHG emissions

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University of Natural Resources and Life Sciences, Vienna Institute for Sustainable Economic Development

Results – GHG mitigation

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Effect of abolishing feed-tariffs for bioenergy

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University of Natural Resources and Life Sciences, Vienna Institute for Sustainable Economic Development

Conclusions

  • Green biorefineries can offer a profitable utilization

pathway for grass silage in Austria

  • biogas plants rely on the current policy support schemes

(feed-in tariffs)

  • Profitability of green biorefineries is very sensitive to
  • market prices of key products (organic acids and technical fibres)
  • the development of separation and downstream costs
  • upscaling costs from pilot- to industrial scale
  • The GHG mitigation potential per t dm biomass input is in a

similar range than the pure energetic utilization in biogas plants

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University of Natural Resources and Life Sciences, Vienna Institute for Sustainable Economic Development

Outlook

  • Limited LCA approach
  • only GHG emissions covered
  • Non renewable energy inputs
  • Land use implications
  • Demand restrictions biorefinery products
  • Limitation for overall mitigation potential

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University of Natural Resources and Life Sciences, Vienna Institute for Sustainable Economic Development

Thank you!

  • 08. 10. 2013

Stefan Höltinger 21

Thank you for your interest

University of Natural Resources and Life Sciences, Vienna Department of Economics and Social Sciences Institute for Sustainable Economic Development Stefan Höltinger, Mathias Kirchner, Johannes Schmidt, Erwin Schmid Feistmantelstraße 4, A-1180 Vienna Tel.: +43 1 47654-73119 stefan.hoeltinger@boku.ac.at , http://www.wiso.boku.ac.at/inwe/

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University of Natural Resources and Life Sciences, Vienna Institute for Sustainable Economic Development

Ghg credits for substituted products

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product unit min max insulation boards kg CO2 eq kg-1 1.03 1.72 feed protein concentrate kg CO2 eq kg-1 0.73 0.90 lactic acid kg CO2 eq kg-1 0.40 1.20 amino acid mixture kg CO2 eq kg-1 8.40 11.38 electricity kg CO2 eq kWh-1 0.28 surplus heat kg CO2 eq kWh-1 0.24

  • Key factors
  • Choice of reference products
  • Multiple feedstocks and production routes
  • Product yields
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Ghg emission green biorefineries

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unit min max soil emissions kg CO2 eq t dm-1 10 50 cultivation and harvest kg CO2 eq t dm-1 31 36 transport kg CO2 eq tkm-1 0.2 0.2 processing kg CO2 eq t dm-1 220 275

  • Key factors
  • Fertilizer intensity
  • Average field size and machinery used
  • Transport distance and means of transport
  • Biorefinery process design and energy sources
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Product yields and prices

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GBR concept and products Press juice (kg dm t-1 dm biomass) Content (kg t-1 dm juice) Process yield (%) Product purity (%) Product output (kg t-1 dm biomass) min max min max min max min max GBR_fibres feed protein 100 200 120 280 45 60 50 10.8 67.2 GBR_proteins feed protein 100 200 110 250 45 60 50 9.9 60.0 white protein 100 200 10 30 45 60 90 0.5 4.0 lactic acid 100 200 330 450 90 100 88 33.8 102.3 GBR_amino_acids amino acid mixture 200 300 110 230 55 80 90 13.4 61.3 lactic acid 200 300 200 365 87 93 88 39.5 115.7 Product Price ranges (€ per kg or kWh) min max Feed proteins (50% cp content) 0.15 0.48 White proteins (90% cp content) 5.00 10.00 Amino acid mixture 2.50 5.00 Lactic acid (food grade 88%) 0.60 1.10 Short natural fibres 0.40 0.60 Electricity (feed-in tariff) 0.10 0.15 Heat (process energy) 0.02 0.03

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University of Natural Resources and Life Sciences, Vienna Institute for Sustainable Economic Development

BioResume – Objective Function

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University of Natural Resources and Life Sciences, Vienna Institute for Sustainable Economic Development

BioResume - Constraints

  • Transport limited by biomass supply
  • Production limited by supply and capacity
  • Maximum one plant per site (Binary)

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BioResume - Nomenclature

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University of Natural Resources and Life Sciences, Vienna Institute for Sustainable Economic Development

GBR_Amino_Acids

  • Simplified process overview GBR demo plant Utzenaich (AT)

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University of Natural Resources and Life Sciences, Vienna Institute for Sustainable Economic Development

GBR_Proteins

  • Process overview GBR Havelland (GER)

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GBR_Fibres

  • Process overview GBR-Brensbach (GER)

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How does PAMSA[grid] work?

  • Objective:
  • Maximize the sum of gross margins of land use and production choices
  • under the consideration of
  • resource endowments
  • technical constraints
  • economic and political conditions
  • Assumptions (simplified):
  • Farmers maximize the gross margins of ther production possibilities
  • Production possibilities are constrained due to:
  • Land endowment, housing capacities, nutrient and feed demand, regulation…
  • Observed activities in the past
  • Typical regional farm (no farm type model)
  • Given the objective and assumptions PASMA[grid] derives economically

efficient land use and production choices, e.g.:

  • How much (e.g. bread wheat in t and ha)
  • Where (e.g. how much ha on what 1 km grid)
  • How (e.g. fertilizer intensity, soil management)
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PASMA[grid]?

  • PASMA (Positive Agricultural and Forestry Sector Model Austria) is an

economic bottom-up modell representing the agricultural and forestry sector in detail with regard to:

  • Structure (e.g. farm size)
  • Production (e.g. land use, livestock, management measures)
  • Policies (e.g. agri-environmental payments, payments for less-favoured areas)
  • Bottom-up = Consideration of detailed production technologies and

resource endowments (vs. supply elasticities)

  • PASMA[grid] is a spatial variant of PASMA representing land use at 1km
  • Allows for a better representation of Austria‘s heterogeneous agricultural landscape
  • Specificially designed to be integtrated in spatially explicit integrated modelling

frameworks

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University of Natural Resources and Life Sciences, Vienna Institute for Sustainable Economic Development

PASMA[grid] model structure

Output: regional producer surplus, ‘optimal’ model activities, external impacts such as GHG emissions or fertilizer balances Input: prices & costs, policy payments, yields, nutrient & feed requirements, regional endowments, observed land use activities …

Model Equations

Objective function max regional producer surplus [for each NUTS3] Model constraints Endowments (e.g. land, livestock housing) Feed balances (e.g. concentrated feed, fodder) Fertilizer balances (e.g. manure, nutrient needs) Product balances (e.g. imports, sales) Mixes for observed:

  • land use types

[spatial HRU level]

  • crop and livestock activities [NUTS3 level]

Model Activities

Land use [spatial HRU level] Land use type (e.g. cropland, grassland) Cultivar (e.g. wheat, corn, alfalfa, conifer) Management intensity (e.g. low, medium, high, organic) Soil management (e.g. conventional or reduced tillage, winter cover crops) Livestock [NUTS3 level] Livestock type (e.g. dairy cattle, fattening pigs) Management intensity (i.e. conventional, organic) Housing system (e.g. loose housing, deep litter) Sales (e.g. cash crops, meat, milk) Manure Fodder Crops

Intra-regional trade (e.g. fodder crops & livestock) Imports (e.g. mineral fertilizer, feedstock)

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PASMA[grid] -GHG emissions from agricultural soils

  • GHG emissions for different intensities (fertilizer inputs)
  • Follows calculations of the Austrian Inventory Report and IPCC

Guidelines and accounts for:

  • Direct soil emissions
  • Synthetic fertilizer
  • Application of animal manure
  • Biological fixation (N fixation by legume crops)
  • Crop residues (N input to soils)
  • Sewage sludge (N losses according to CORINAIR)
  • Indirect soil emissions
  • Atmospheric depositon
  • Nitrogen leaching losses
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System boundaries and reference systems

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Results

  • Specific revenues

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Results

  • Specific costs

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Results

  • Sensitivity of GBR profits

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Results

  • Uncertainty analysis

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Results

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  • Number of plants
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Results - Feedstock cost

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  • Feedstock cost

per t dm input