A System Dynamics Model of Early-Stage Transition Dynamics in the Bioproducts Industry
The U.S. Department of Energy, Bioenergy Technologies Office has a broad understanding of different conversion processes that produce bioproducts (chemicals derived from biomass feedstocks) and the techno-economic attributes of those processes. However, bioproducts have in many cases proven to be difficult to scale up to commercial production, and there is currently a need for greater understanding around the possible successful scenarios for advancing the bioproducts industry. Insight into the myriad factors that impact the success of individual bioproducts and the growth of the bioproducts industry will enable bioproduct stakeholders to evaluate strategies for development and investment that could achieve the greatest impact. An understanding of these factors will also enable the identification of synergies between the bioproducts and biofuels industries that occur through shared learning and co-production. The Bioproduct Transition Dynamics (BTD) model explores questions such as the following: what are the factors that separate a successful, commercially produced bioproduct like succinic acid from one that never progresses beyond bench-scale lab research? Can these factors be influenced by stakeholders, and to what extent? The BTD model uses system dynamics to capture the impacts of investor decision-making, bioproduct techno-economics, and end use factors during the early stages of bioproduct development. Key components of the BTD model include techno-economic benchmarks that inform the investor decision-making process, failures and setbacks in development and the resulting need for additional work, and the impacts of effective or ineffective management during each development stage. These factors and their interactions are tracked through bench-scale laboratory research, piloting, demoing, and the construction and operation of the first commercial-scale plant. Bioproduct development is linked to the biofuels industry through a shared-learning model, enabling the benefit to biofuels from bioproduct technology and feedstock supply chain development to be quantified. A wide variety of bioproducts can be analyzed using the BTD model, including bioproducts that have been developed through economic or policy-driven mechanisms, and those with either niche (low volume) or scalable (high volume) demand. The BTD model also has the potential to apply to technology development processes outside the bioproducts industry; background research performed for this project has shown that the early stages of technology development are similar, and impacted by similar factors, regardless of the exact industry. This presentation will cover the BTD model logic, input data, validation process, and results of a sensitivity analysis based on techno-economic data for succinic acid. The sensitivity analysis was performed to identify factors with the largest impact on the eventual success or failure of a bioproduct. Results indicate that management effectiveness, which affects how efficiently money spent is converted into technological advancements, is the single most important factor in determining whether a bioproduct reaches commercial-scale production. The capacity of the first commercial plant and the type of biomass feedstock used, as well as the presence of government support in the form of cost-sharing, were also identified as significant factors.
- Research Organization:
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Transportation Office. Bioenergy Technologies Office
- DOE Contract Number:
- AC36-08GO28308
- OSTI ID:
- 1458911
- Report Number(s):
- NREL/PR-6A20-71774
- Resource Relation:
- Conference: Presented at the International Symposium on Sustainable Systems and Technology 2018, 26-28 June 2018, Buffalo, New York
- Country of Publication:
- United States
- Language:
- English
Similar Records
WBS 4.1.2.31 Bioproduct Transition System Dynamics
Supporting Bioproducts Industry Growth with a System Dynamics Decision-Support Tool