Biomass Conversion in the Biomass Scenario Model

From Open Energy Information


Download & Use the Biomass Scenario Model (Public)

About The Biomass Scenario Model

Supply Chain in the Biomass Scenario Model

  • Biomass Conversion

The Biomass Scenario Model (BSM) tracks the simulated deployment of biofuels conversion technologies according to industry development of the technologies and the reaction of the investment community.

How Biomass Conversion is Modeled in the Biomass Scenario Model

The BSM is used to develop insight around simulated investments in commercial biofuels plants. It’s built on the premise that plant construction depends on the progression of the industry along learning curves that determine modification to techno-economics-based maturation of the technology and industry. Investment in plants is assumed to occur when the net present value compares favorably to other investments. The Reinforcing Feedback Loop illustration represents the operation of plants that contribute to industrial learning, which reduces the modeled risk of the next potential investment in a commercial plant.

The BSM tracks the simulated deployment of biofuel technologies over time by representing investment in construction of commercial plants. The diagram, Stock and Flow of the Buildout and Retirement of Plants or Plant Capacity, illustrates the primary stock and flow structures used by the BSM to track the number and capacity of commercial plants constructed and in operation.

Example alt text
Stock and Flow of the Buildout and Retirement of Plants or Plant Capacity, Source: National Renewable Energy Laboratory

A key question for modeling biofuel industry development is how to model the transition of pre-commercial technologies to a fully mature commercial industry. The key inputs that the BSM uses to model such a transition:

• What is the current state of the industry?

• What is the theoretical future potential of a technology?

• What determines the paths from current to future conditions?

Technology Pathways

The BSM represents various technology pathways for a variety of feedstocks/fuels.

For each feedstock/fuel and technology pathway, the BSM uses estimated techno-economic conditions based on data for future commercial plant techno-economics published in peer-reviewed design reports. These estimates include:

• Biofuel yield

• Max throughput capacity

• Fixed capital costs

• Operations costs

• Co-product revenue

• Total products

• Gasoline blendstock

• Diesel blendstock

• Jet blend stock

• Others.

Depending on the maturity of a technology at the time an investment decision is simulated in the model, five key Nth plant techno-economic performance attributes are modified.

Performance Attribute Multiplier Factors
Scale: Large-Scale Pioneer plant before completing integrated pilot Large-Scale Pioneer plant after completing integrated pilot Large-Scale Pioneer plant after completing demonstration
Nth plant attribute from techno economics Unit of Attribute Unitless Multiplier for Attribute Unitless Multiplier for Attribute Unitless Multiplier for Attribute
Process Yield Gal/dry ton 0.5 0.75 0.85
Process Yield Dry ton/day 0.8 0.8 0.8
Feedstock throughput capacity Million $ 2 1.5 1.25
Fixed capital investment cost growth % 7.5 5 0.25
%Equity % 0 0 0.5
Source: National Renewable Energy Laboratory

Questions the Conversion Module Can Answer

The conversion module of the BSM answers a variety of questions. Among them:

• How likely is the biofuels supply chain to develop naturally?

• What points of leverage exist to accelerate the adoption of biofuels?

• How do tipping-point dynamics affect the dominance of a particular biofuels pathway?

• What are the implications of precommercial investments in pilot and demonstration facilities?

A Sampling of Biomass Scenario Model Conversion Results

A few biomass conversion analysis results are shown to illustrate the capability.

BSM models shows that there are four keys to industry development in the simulations:

• Profitability at the point of production

• High rates of industry learning

• An aggressive start in building pilot, demo, and pioneer-scale plants

• For ethanol, a high level of infrastructure investment to sustain low enough point-of-use prices.

The simulated development of the industry can be negatively affected by:

• Unstable, higher than anticipated feedstock prices

• Boom/bust development of production capacity

• Potential for biofuel price instability.