Introduction to the Bioproduct 

Transition Dynamics Model

Rebecca Hanes, Brian Bush, Emily Newes

Webinar

June 21, 2018

NREL/PR-6A20-71773

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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Overview

• During the workshop, we’ll be soliciting feedback on validity 

of our Bioproduct Transition Dynamics model and on ways to 

improve the model.

• This webinar provides background information on the BTD 

project and model to enable more in-depth discussions during 

the workshop.

How does this webinar relate to the July 16 workshop?

• Knowledge of the BTD project motivations, objectives and 

outcomes

• A high-level understanding of the BTD model assumptions, 

structure, logic and use cases

What are we hoping you’ll gain from this webinar?
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BETO has been developing a 

broad understanding of 

different conversion processes 

that produce bioproducts and 

the associated end use 

attributes.

However, there is currently not 

much understanding around 

how investment decisions are 

made and the possible 

successful scenarios for 

advancing the bioproducts and 

biofuels industries.

Bioproduct Transition Dynamics Project

Adapted from Bürer and Wüstenhagen, Energy Policy, 37 (2009)
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Bioproduct Transition Dynamics Project

Outcomes

• Transparent, analytic system

dynamics model

• Method for exploring

transition dynamics during

early industry development

as a function of:

o Investor decision-making

o Bioproduct technoeconomics

o End use factors

Goal

• Develop an analysis capability

• To achieve deeper

understanding of the

environment and drivers that

impact the growth of the

bioproducts industry

• In order to support BETO

bioenergy strategy

development

• How do developer-investor interactions and other factors

impact low-TRL stages of bioproduct development?

• (How) Can the likelihood that a bioproduct development

project succeeds be influenced, and by whom?
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Bioproduct Transition Dynamics Project

This project builds upon existing work that has been funded by BETO: 

System 

Dynamics 

Model

Data from Existing 

BETO Projects

AGILE 

Biomanufacturing

Clean Energy 

Manufacturing 

Analysis Center

Techno-Economic 

Assessments Core R&D Analysis 

Projects

Market 

Assessment of 

Chemicals from 

Biomass

• Industrial assessment of chemicals from 

biomass 

o Laid out the existing end use capacity for 

chemicals from biomass and potential for 

expansion. 

• Techno-economic assessments

o The Clean Energy Manufacturing Analysis 

Center (CEMAC) bioproducts task 

o Current BETO-funded work considers 

coproduction of biofuels and chemicals 

from biomass.

• Research & Development (R&D) projects 

o Current BETO-funded analysis work 

considers coproduction of biofuels and 

chemicals from biomass.

o On-going efforts in the AGILE 

biomanufacturing project

These projects are information-rich and lay the foundation 

for exploring possible future scenarios and the connections 

between bioproducts and biofuels. 
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Why System Dynamics Modeling?

Adapted from Sterman, Am J Public Health, 96:3 (2006)
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System Dynamics Example

• 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

o Loop A is reinforcing

o Loop B is balancing

System dynamics models are often developed as stock-and-flow 

diagrams, in which stocks and flows may represent physical or 

non-physical quantities.
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Bioproduct Transition Dynamics 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 

(industry, academia)

• Investors (seed, venture)

• Purchasers (firms)

• Government agencies
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Investor Decision Making

Input data quantifies 

how investor 

requirements vary by 

development stage. 

(Damodaran, 2009)
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Developer Decision Making

When funds are close to depleted, 

the spending rate is reduced, slowing 

development work, until more funds 

are received or the project fails.

Bioproduct developers spend money on researching, piloting and 

demoing as funds become available.
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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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Shared Learning

• Commercial-scale

bioproduct production

creates shared learning

that benefits the biofuels

industry

• Learning is accounted for

on a unit operation level

• Only unit operations in

common between the

bioproduct and biofuel

processes benefit.

Unit 

Operations

Biofuel 

Process

Bioproduct 

Process

Learning 

Rate

LC biomass 

processing 1 1 0.2

Enzymatic 

hydrolysis 1 1 0.2

Biological 

upgrading 0 1 0.2

Catalytic 

upgrading 1 0 0.2

Extraction, 

purification 

and 

finishing

1 0 0.2

• 1: Indicates a unit operation shared between

the biofuel and bioproduct processes

• 0: Indicates a unit operation that does not

appear in one or both processes
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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

• 14.9 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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Results: Ranking Factors by Impact

• Factors explored 

during the sensitivity 

analysis are ranked by 

their impact on the 

success likelihood

• Research management 

effectiveness was 

found to be the most 

impactful factor of 

those explored

3% Success 58% Success 99% Success

Selling Price < $1750/ton

$1750/ton < Selling Price < $3250/ton

$3250/ton > Selling Price

13% Success

42% Success

62% Success

79% Success

36% Success

90% Success

Green Premium < 37.5% Green Premium > 37.5%

Expected Continuity of

Government Policy < 37.5%

Expected Continuity of

Government Policy > 37.5%

Government Capital

Cost Share < 25%

Government Capital

Cost Share > 25%

14,580,000 

Simulations

0% Success

77% Success

Research Management Effectiveness < 70%

Research Management Effectiveness > 70%
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Wrap-up 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

• An NREL technical report will be released in FY18, with the 

potential for additional publications

• BTD development, including implementing suggestions 

from the workshop, and model validation will continue in 

FY19.
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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