Category: Biotechnology

Bioeconomy Bright Spots, Challenges, and Key Factors Going Forward: Perceptions of Bioeconomy Stakeholders

By Evelyn Thomchick📧, Michael Jacobson, and Kusumal Ruamsook📧

In EFB Bioeconomy Journal, Volume 4, November 2024, 100068 (available online June 8, 2024). https://doi.org/10.1016/j.bioeco.2024.100068

The bioeconomy is a complex system involving a plethora of bio-products and several groups of stakeholders—such as government institutions, industry, environmental organizations, and civil society—across the bioeconomy supply chain. Successful bioeconomy activities hinge on the collective efforts and coordinated development across all involved. This study seeks to understand different stakeholder groups’ perceptions, expectations, needs, issues, goals, and constraints as related to the development of the U.S. bioeconomy, with biochar as a bioproduct of focus. Focus groups were held with a representative sample of stakeholders involved in the bioeconomy. Results show encouraging trends in increased interests and awareness, and carbon market development; while regulatory structure, production capacity and commercialization, and developing industry standards present key areas of challenges. Going forward, large-scale real-world research, commercial viability, and education are perceived to be imperative.

Keywords: Bioeconomy; Biochar; Stakeholders; Focus group study; Business development; The United States

View the full article from the publisher web site here.

Stochastic Simulation Uncertainty Analysis to Accelerate Flexible Biomanufacturing Process Development

By Wei Xie, R. R. Barton📧, Barry L. Nelson, and Keqi Wanga

In European Journal of Operational Research, 2023, 310 (1): 238–248. https://doi.org/10.1016/j.ejor.2023.01.055

Motivated by critical challenges and needs from biopharmaceuticals manufacturing, we propose a general metamodel-assisted stochastic simulation uncertainty analysis framework to accelerate the development of a simulation model with modular design for flexible production processes. There are often very limited process observations. Thus, there exist both simulation and model uncertainties in the system performance estimates. In biopharmaceutical manufacturing, model uncertainty often dominates. The proposed framework can produce a confidence interval that accounts for simulation and model uncertainties by using a metamodel-assisted bootstrapping approach. Furthermore, a variance decomposition is utilized to estimate the relative contributions from each source of model uncertainty, as well as simulation uncertainty. This information can be used to improve the system mean performance estimation. Asymptotic analysis provides theoretical support for our approach, while the empirical study demonstrates that it has good finite-sample performance.

Keywords: Hybrid Simulation Model; Biomanufacturing Systems; Uncertainty Quantification (UQ); Sensitivity Analysis (SA); Gaussian Process (GP)