Multiphase Metabolic Modeling of Biochemical Producing Bacterial Communities in Bubble Column Reactors

Development of cost-effective technologies for sustainable production of commodity fuels and chemicals remains a paramount challenge for the United States. Bacteria-based chemical production represents an alternative to petrochemical processes due to the microbes’ evolved ability to utilize diverse feedstocks and synthesize a broad array of biochemicals.

However, the effectiveness of microbial systems has only been demonstrated at large scale for a limited number of applications, such as conversion of corn-derived sugar to the fuel additive ethanol. To succeed in the marketplace, microbial systems must offer more flexibility with respect to the feedstock they consume as well as the biochemicals they produce.

Industrial waste gases rich in CO and CO2 represent an abundant and cheap source of carbon for upgrading to more valuable chemicals. Over the past decade, gas fermentation with CO2-consuming (acetogenic) bacteria has been shown to be a promising technology for large-scale conversion of such waste gases to ethanol. However, gas conversion to more valuable chemical products is considerably more challenging.

The goal of this project is to develop the gas fermentation process simulation and optimization technology necessary to advance microbial systems for large-scale production of platform chemicals, the building blocks of a wide range of chemical products. The proposed research will significantly advance a novel microbial paradigm for conversion of cheap waste gases into value-added products by focusing on critical process systems engineering challenges.

The research team will recruit women and URM students into the project through the NSF-sponsored Northeast Alliance in Minority and Graduate Education and the Professoriate program at UMass.

Large-scale gas fermentation requires specialized bubble column reactor technologies and sophisticated operating strategies to maintain desirable multiphase hydrodynamics, achieve high gas-liquid mass transfer rates, minimize the inhibitory effects of dissolved gases and synthesized byproducts on cellular growth, achieve high cell densities, and ensure high volumetric productivity over a range of feed conditions. While bubble column reactors have been extensively investigated for CO fermentation, studies have been limited to acetogen monocultures and existing process engineering challenges have received very little attention.

We have been developing a bubble column modeling approach based on combining genome-scale stoichiometric models of microbial metabolism with multiphase transport equations governing column hydrodynamics. In this project, these sophisticated models consisting of linear programs (LPs) and partial differential equations (PDEs) will be utilized to develop dynamic process modeling and optimization strategies for coculture bubble column reactors.

Our target is the conversion of CO-rich gas streams containing CO2 and/or H2 to the platform chemicals butyrate and 2,3-butanediol. The proposed research has three specific aims: (1) Perform monoculture and coculture continuous-flow stirred tank reactor experiments with the acetogen Clostridium autoethanogenum and computationally identify promising gut bacteria by quantifying their metabolic activity; (2) Develop dynamic models of bubble column bioreactors for in silico investigation of coculture stability and performance for different gut bacteria paired with C. autoethanogenum; and (3) Perform rigorous optimization of the most promising coculture systems to determine dynamic startup and steady-state operating policies to maximize volumetric productivity.

Collectively, the three aims will yield innovations in bioreactor process engineering through the systematic development of optimization approaches for LP-PDE models and in gas fermentation for renewable chemical production through the integrated development of dynamic modeling and optimization technologies.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.