Interdependence, Resilience and Sustainability of Infrastructure Systems for Biofuel DevelopmentPI: Ximing Cai, Co-PIs/SPs: Yanfeng Ouyang, Madhu Khanna, Atul Jain, Gregory

McIsaac, Steven Eckhoff, Imad Al-Qadi, Sivapalan Murugesu, Tze Ling NgUniversity of Illinois at Urbana-Champaign

Stephen GasteyerMichigan State University

• Interdependencies of subsystems– Input/output (e.g. biofuel production

transportation refineries)– Proximity (e.g. refineries & farms,

refineries & water supply)– Common environmental (e.g., climate,

land, water quality etc.) and social factors (community support, institutional settings)

• Infrastructure resilience and sustainability

– The emerging bio-economy will increase the interdependencies among infrastructure systems and interactions among engineered infrastructures, social communities and the natural environment.

– Physical resilience vs. social resilience• 3-D approach to assess infrastructure

sustainability

Biofuel refinery

Water supply Transportation

Human Inputs

Weather/Climate

Natural Resources

Biofuel crop production

Wate

r/was

te wate

r

Discharge

Proximity

Proximity

Supply/demand

Proximity

DischargeIrrigation

Environmental Criteria

Engineering Criteria

Socio-Economic Criteria

RESOURCE EFFICIENCY PERFORMACNE

Natural Environment -Resources

InfrastructureSocioeconomic-Demand-Revenue

EnergyMaterials

ProductsServices

Cost effectiveness and recovery

Minimization of residualResidual

System of Systems (a coupled human‐natural system)

Water Supply

RefineryBiofuel Economics

Biofuel Shipment

+ Facility cost+ Traffic loadTraffic load

Biomass Production

Yields Benefit/Cost

EnvironmentCommunities

Water supply Water supply

Waste water

Research activities and findings

•Biomass production: integrated biophysical‐economic  modeling

• Social impacts

• Environmental impacts

• Infrastructure planning • Integrated analysis on interdependence, sustainability and resilience

ISAM-Land-Surface Model

Energy

Hydrology

Carbon

 and

 Nitrogen

 Cycling

Calculate fluxes of carbon, nitrogen, energy, water, and the dynamical processes that alter these fluxes

• 18 Biome types 0.5 x 0.5 degree resolution

• 30 minutes temporal scale• Season-to-interannual

variability (penology)

Average (2006-2010) Miscanthus Yield (t/ha) With and Without Water Stress

Fraction Yield Change Due to Water Stress

Evapotranspiration (mm/yr)

With Temp. & Water Stresses With Temp. & Without Water Stress

50% Higher Switchgrass Yield

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20

40

60

80

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140

0 100 200 300 400 500 600 700 800 900 1000

Pri

ce o

f B

iom

ass

($

/MT

)

Biomass (MMT)

0

20

40

60

80

100

120

140

0 100 200 300 400 500 600 700 800 900 1000

Pri

ce o

f B

iom

ass

($

/MT

)Biomass (MMT)

Economically viable supply of agricultural biomass and  mix of cellulosic feedstocks at various biomass prices in 2030

Corn Stover Wheat Straw Miscanthus Switchgrass Total Biomass

0.9 B metric dry tons of agricultural biomass  can be produced but at$140 per dry metric ton; Relatively small contribution of switchgrass and wheat straw even if switchgrass yields were to increase by 50%

Miscanthus Wheat Straw

Switchgrass Corn Stover

Regional Pattern and Mix of Feedstock Production at $50/MT

Assessing social impact via community capitals framework

A conceptual framework for understanding assets and interactions of assets within a systems setting…

What ARE the Impacts of Biofuels?• Biofuels are sited in more urban counties with

– Lower commute time– Higher personal income– Lower increase in median income– Higher percent minority– Higher educational attainment– Higher petroleum use 

• There are at least apparent natural capital effects– Effects on air quality– Possible effects on water

• Impacts of siting Include– Increased farm employment– Increased farm income– Increased personal income– Declining farm proprietors– Increased petroleum use

Implications for Social Resiliency• Biofuels DO have returns to individual financial capital – as well as evidence in generation of employment—at least in the farm sector

• It does not, however, change trends in declining numbers of farmers…or use of petroleum…

• Returns also have traditionally accrued to more urban counties – closer to commodity agriculture and petroleum processing facilities. 

• Continuation of demographic and agricultural‐structural and economic trends – no returns fundamentally alter the system in most cases.

BloomingtonSangamon River Basin

St. Louis

Springfield

Kaskaskia River Basin

• Sangamon & Kaskaskia

• The two basins are different in terms of:

— Climate— Crop yields— Urban areas— Land cover

Environmental impact: Two case studies

Shallow Aquifer Subbasin 29: Downstream, 289 km2

0

50

100

150

200

250

300

350

400

450

1999.0 1999.1 1999.2 1999.2 1999.3 1999.4 1999.5 1999.6 1999.7 1999.7 1999.8 1999.9 2000.0

Shal

low

Aqu

ifer S

tora

ge a

t End

of M

onth

(mm

)

SoyCornMiscanthusUrbanCRP

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan

• Soil moisture storage under five different land uses.– Different location within the basin, but similar results.

Sangamon Results

January February March April May June July August September October November December0

1

2

3

4

5

6x 10

8S

trea

mfl

ow

(m

3 /mo

)SubBasin 29, 4th Order Stream

CornSoyMiscanthus

January February March April May June July August September October November December0

0.5

1

1.5

2

2.5x 10

8

Str

eam

flo

w (

m3 /m

o)

CornSoyMiscanthus

Streamflow at subbasin outlet for 1999 (top), 2003 (bottom)

SubBasin 29: Downstream (4th order stream)

Integrated Planning of Biofuel Supply Chain Networks and Multimodal Infrastructure Expansion

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Highway Network

Railroad Network

Passengers O/D

Sink Node

Source Node

Biomass/Biofuel O/DBiomass FlowBiofuel Flow

Biorefinery

• Number/Location of Biorefineries

• Construction CostFacility Location

Transportation Plan

• Shipment Route Choice• Travel Time/Delay

Congestion

Infrastructure Capacity

Expansion

Congestion

Shipment O/D

• Lane Addition/Railroad Expansion

• Infrastructure Cost

Road Capacity

Scenarios of infrastructure planning

1. No transportation infrastructure expansion

2. Scenario 1 + highway expansion:

3. Scenario 2+ intermodal shipments

4. Scenario 3 + railroad expansion

5. Multimodal transportation and railroad

expansion only

16

Numerical Results

17

1. No infrastructure expansion

2. Scenario 1 + highway expansion:

3. Scenario 2+ intermodal shipments

4. Scenario 3 + railroad expansion

5. Multimodal transportation and

railroad expansion only

Biofuel Supply Chain Design under Competitive Agricultural Land Use and Feedstock Market Equilibrium

A new energy supply chain penetrates into the existing system‐ Compete for feedstock supply‐ Balance among facility costs, transportation costs, and market profits

Supply chain design Facility location & 

capacity Supply procurement Product distribution 

Farmer reaction Production level Supply allocation

Bio-refineries New Biofuel Markets

Existing Local Grain Markets

Farmers

Existing Local Grain Markets

Farmers

Food

Feed

1

2

3

.

.

.

1

2

3...

1

2

3...

Illinois Case Study

1) a benchmark scenario with no refinery built, where farmers only ship and sell corn to local markets;

2) a cooperative scenario, where the total supply chain profit is maximized;

3) a noncooperative scenario, where the farmers and the biofuel company maximize their own profits;

4) a hybrid scenario, where the biofuel company first builds refineries strategically based on the cooperative game solutions, but makes the pricing decisions in a noncooperative setting.

Biofuel production generally increases the net social welfare

Substantial impact on food market

Cooperative scenario generates highest social welfare

Research activities and findings

•Biomass production: integrated biophysical‐economic  modeling

• Social impacts• Environmental impacts• Infrastructure planning • Integrated analysis on interdependence, sustainability and resilience

A generic mathematical framework to address interdependence, resiliency and sustainability:

Recovery time Interdependency:

Sustainability:

Resiliency & Sustainability Conceptual Development

The dependency of subsystem i on subsystem j is defined as the change in system i resulted from one unit change in subsystem j.

The resiliency of an infrastructure system is its capability to get back to its operational boundary after being affected by disruptions. Measures of resiliency include functionality degradation, recovery time, recovery speed and adaptability.

System sustainability is its long-term capability to use its limited resources effectively to maintain its functionality and to endure stresses.

Nguyen, Cai and Ouyang (2011), Modeling Infrastructure Interdependencies, Resiliency and Sustainability

A Statistical Definition of Interdependence

22

The interdependence between two systems is measured by the statistical correlation between representative variables from each of the systems (Rinaldi et al. 2001)

System 2 System 2 System 2 System 2 System 2

Correlation = -1 Correlation = -0.5 Correlation = 0 Correlation = 0.5 Correlation = 1

The Pearson product-moment correlation coefficient is the covariance of the representative variables divided by the product of the standard deviations.

Source: Rinaldi, S. M.; J. P. Peerenboom and T. K. Kelly (2001). Identifying, understanding, and analyzing critical infrastructure interdependencies. IEEE Control Systems Magazine, 21(6), 11-25.

Description of Hypothetical Biofuel System 

Demand point

Demand point

Each plot of land is 1,000 ha

Corn ethanol refinery

Demand point

Demand point

Cellulosic ethanol refinery

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Multiple Configurations of Hypothetical Biofuel System

Preliminary Results

25

Reliability-Interdependence Vulnerability-Interdependence

Monte Carlo simulations are carried out for different configurations of a hypothetical biofuel system to estimate the relationships between reliability and interdependence, and vulnerability and interdependence. The results have been normalized to remove the effects of mean and variance.

Optimization Model Overview

LAND USE

TRANSPORT-ATION REFINERIES

WATER QUANTITY

MAX PROFIT

WATER QUALITY

Objective Functiono To maximize the overall profitability of the system

Constraintso Environmental resource and infrastructure

constraints

Decisions variableso Allocation of land to cropso Refinery locations o Refinery capacitieso Traffic flow capacitieso Water supply capacities

Overview of Model

Scope of ModelSub-systemsTransportationRefineriesLand useWater qualityWater supply

Refinery typesCorn ethanolCellulosic ethanol

Transportation RoadRail

ProductsCorn for food Corn for ethanolSoybeans for foodCorn stoverMiscanthusSwitchgrassEthanolDried distillers grainsLignin

Applications of the Integrated Model• Sensitivity analysis to model parameters and assumptions

• Scenario analysis• Analysis of various scenarios of water availability, possible locations of refineries, climate etc.

• In particular, scenarios on system failures for the Insights into how the failure of one sub‐system (or a part of it) might affect other sub‐systems.

• Policy analysis• Strategic changes in water supply, transportation system and feedstock

production • Impact of environmental regulations, climate control policies and

technology advances • The need of the mix of knowledge, resources and social networks to

enable social resiliency • Bottlenecks and areas of possible investment/ expansion

Summary

– Work at both system level and subsystem level provides guidance to crop choice, price choice, refinery and shipment facility location and size, strategies for infrastructure design and management

– The interaction between environmental, social and engineering systems will lead to radically new technology in biomass production, biofuel refinery and shipment and associated infrastructure expansion

– Understanding of the dynamics of a “system of systems” will lead to paradigm shifts in the expansion of interdependent engineering infrastructures