A system dynamics model of early-stage transition 

dynamics in the bioproducts industry

Rebecca Hanes, Brian Bush, Emily Newes

Strategic Energy Analysis Center, National Renewable Energy Laboratory

International Symposium on Sustainable Systems and Technology

Buffalo, NY, USA

June 27, 2018

NREL/PR-6A20-71774

This work was authored by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, 

LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by U.S. 

Department of Energy Office of Energy Efficiency and Renewable Energy Bioenergy Technologies Office. The views 

expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The U.S. 

Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. 

Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published 

form of this work, or allow others to do so, for U.S. Government purposes. 
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Technology Development Process

Adapted from Bürer and Wüstenhagen, Energy Policy, 37 (2009)

• How do developerinvestor interactions and 

other factors impact low 

TRL stages of bioproduct 

development? 

• (How) Can the likelihood 

that a bioproduct reaches 

commercial production, 

or is sold as IP, be 

influenced, and by whom?

Technology Readiness Level (TRL): Numeric representation of technology maturity, from 1 

(beginning of applied R&D) through 9 (technology fully developed and at operational scale)
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Bioproduct Transition Dynamics Project

Objectives

Understand the 

environment and drivers 

that impact bioproducts 

industry growth: 

investor decisionmaking, bioproduct

techno-economics, and 

end use factors

Identify synergies 

between the bioproduct 

and biofuel industries

Outcomes

Transparent, analytic 

system dynamics model

of early-stage industrial 

transition dynamics in 

the bioproducts 

industry
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Why System Dynamics Modeling?

Adapted from Sterman, Am J Public Health, 96:3 (2006)
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Introduction to 

System Dynamics

System Dynamics Model

A system of coupled, nonlinear, 

first-order differential or 

integral equations

bbiiiiiiiii + dd = bb rr rr × PPPPPPPPPP PPPP( )

dd + dd = dd rr rr × PPPPPPPPPP PPPP( )

• Flows (births, deaths) are the 

rates of change of stocks

• Stocks (Population) are the 

integrals over time of flows

• Feedback loops (A, B) exist 

among stocks, flows and 

model parameters

• Feedback loops are either 

reinforcing or balancing

• A is reinforcing

• B is balancing

• SD models are based on system structures and capture 

patterns of behavior

PPPPPPPPPP PPPP = �

tt0

tt

bb − dd ( ) dd
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BTD Model Structure

Model structure was derived from...

• Interviews with bioproduct 

industry experts

• Research on investor decisionmaking and innovation processes

• Shared learning models

• End use structure research

Actors include...

• Bioproduct developers 

• Investors

• Bioproduct purchasers

• Government agencies
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Investor Decision Making

Investor requirements 

vary by development 

stage. (Damodaran, 

2009)
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Research Process

0

0.001

0.002

0.003

0.004

0.005

0.006

0.007

0.008

0.009

0.01

0 2 4 6

TRL Gain/dollar spent

TRL

Effectiveness of researching

controls the rate at which TRL is 

gained during research.

Research management effectiveness controls 

how much of each dollar spent is available 

for conversion into TRL gains.
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Piloting and Demoing Process

0

0.001

0.002

0.003

0.004

0.005

4 5 6 7 8 9

TRL gain/operating hour

TRL

Piloting Effectiveness

Demoing Effectiveness

Piloting effectiveness and 

demoing effectiveness (not 

shown in diagram) control the 

rate at which TRL is gained 

during piloting and demoing.

Pilot and demo management effectiveness are 

both analogous to the research management 

effectiveness parameter.
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Sample TRL Path and Events

Researching Piloting Demoing

Failures trigger 

additional research

Pilot-stage failure 

sends project back 

to researching stage

Development 

process completes

Rate of TRL gain 

slows as research 

nears completion

Project stalls while

funds accumulate
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Sensitivity Analysis and 

Model Verification

• Selling price potential

• Selling price

• Size of green premium

• Government policy

• Research cost share

• Capital cost share

• Production incentive

• Developer effectiveness

• Research stage

• 14.8 million simulations

• Assess sensitivity to investor, developer decision-making parameters 

and bioproduct (succinic acid) techno-economics

Investor behavior

• Optimism

• Bioproduct strategic value

• Expected government policy 

continuity

Management effectiveness

• Research stage

• Pilot stage

• Demo stage
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Succinic Acid Techno-Economics

The three pathways differ significantly in their cost structure.

Nth Plant Parameters Lignocellulosic Commodity Sugar Maleic Anhydride (fossil)

Large Small Large Small Large Medium Small

Capacity Ton product/year 286,300 28,630 283,465 28,627 83,00 41,500 20,750

Capital cost USD $1,253M $462M $906M $401M $131M $92.8M $70.9M

Feedstock cost USD/ton $100 $263 $1,500

Fixed 

operating cost USD/year $27.0M $12.8M $21.0M $11.4M $10.8M $8.57M $7.29M

Variable 

operating cost USD/ton product $494 $815 $504 $1,219 $29

Process yield Ton product/ton 

feed 0.409 0.770 1.179

Lifetime Years 30

Feedstock Capital Cost Operating Cost Feedstock Cost

Lignocellulosic High High Low

Commodity Sugar Moderate High Moderate

Maleic Anhydride (fossil) Low Low High
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Results: Highest TRL Reached

• Color indicates TRL at 

end of model run for 

each simulation

• Failure to progress to 

higher TRLs results from 

inability to raise new 

investor funds.








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Results: Success Likelihoods





• The interaction of 

grants and policy 

continuity is more 

impactful than either 

alone

• Bioproduct selling 

price and expected 

green premium are 

good predictors of 

success

Capital grants increase

Expectation of policy continuity increases

Low Average High Low Average High Low Average High
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Conclusions and Next Steps

The Bioproduct Transition Dynamics model captures the 

bioproduct technology development process from basic 

research through commercial production, including 

interactions between developers and investors.

• BTD workshop will be held July 16, 2018 to solicit 

guidance on model logic, enhancements and validation

• BTD model development and validation will continue 

through 2019

• An NREL technical report is planned for release in 

September 2018, with the potential for additional 

publications in the future
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For further information, contact:

Rebecca Hanes, rebecca.hanes@nrel.gov

Brian Bush, brian.bush@nrel.gov

Emily Newes, emily.newes@nrel.gov

Thank you