(29h) Modeling and Uncertainty Analysis of Microbial Electrosynthesis of Acetate for Martian Colonization

The primary goal of the Center for Utilization of Biological Engineering in Space (CUBES) is efficient exploitation of Martian in-situ resources for integrated bio-manufacturing of the necessary products for Martian colonization, including biologically-produced pharmaceuticals, cell-based treatments/therapeutics, and materials for on-demand diverse additive manufacturing applications [1]. A key module of the bio-manufacturing system is a novel, biologically-driven process for carbon fixation through microbial electrosynthesis, in which acetate is generated as a product of microbial growth. Acetate is an essential carbon source for downstream bioplastics and fuel production.

In this work, we present a framework for modeling a continuous bioreactor for carbon fixation through microbial electrosynthesis. An important requirement of the bioreactor model is the ability to reliably forecast the acetate synthesis under climate conditions of Mars and operational uncertainties within the integrated biomanufacturing system. We first present a first-principles dynamic model for a reverse microbial fuel cell reactor, where the bacteria on the cathode surface reduce CO₂ into acetate via a supply of electrons [2]. The underlying physical phenomena include coupled micro-scale reaction and transport processes within the biofilm, along with macro-scale mass balances in the bulk liquid [3]. The reaction kinetics are described by the Butler Volmer model for the anode and the Butler-Volmer-Monod model for the cathode [4]. The cathode surface potential is treated as a time-dependent variable related to the efficiency of a photovoltaic panel absorbing light incident to the Martian surface. The climate conditions on Mars due to dust and cloud formations affect the availability of light, acting as a key disturbance to the production rate of acetate. We utilize the RedSun climate modeling software, developed within CUBES [5], to provide a description for the state of the Martian climate and in-situ variables such as temperature, pressure, and the spectral flux based on a geospatial grid of Mars.

For a nominal period of a human mission on Mars, we present a comprehensive parametric study on the system behavior to assess the effects of various reactor design and mission parameters on the quantities of interest, such as the biofilm growth rate and acetate production. The aforementioned findings inform the practical engineering and operational decisions of the integrated biomanufacturing system. Finally, we utilize deep learning methods [6] to develop a surrogate model for the first-principles bioreactor model in order to systematically study the performance of the bioreactor under the varying climate conditions of Mars as well as operational uncertainties of the biomanufacturing system.

References

[1] Menezes A., A., Cumbers J., Hogan J.A., Arkin A., “Towards synthetic biological approaches to resource utilization on space missions”, Journal of the Royal Society Interface, 2015, 12 (102), 20140715.

[2] Kazemi, M., Biria , D., Rismani-Yazdi, H., “Modelling bio-electrosynthesis in a reverse microbial fuel cell to produce acetate from CO₂ and H₂O”, Phys.Chem.Chem.Phys., 2015, 17, 12561

[3] International Water Association Task Group on Biofilm Modeling, “Mathematical Modeling of Biofilms”, IWA Publishing, Water Intelligence Online, 2006

[4] Hamelers H.V.M, Ter Heijne A., Stein N., Rozendal R.A., Buisman C., J., N., “Butler–Volmer–Monod model for describing bio-anode polarization curves”, Bioresource Technology, 2011, 102, 381-387

[5] “Abel A., Berliner A.J., Mirkovic M., Collins W., Arkin A.P., Clark D.S, “Traversing Photovoltaic and Photoelectrochemical Production Capacity Across the Martian Surface”, to be submitted for publication.

[6]Tripathy R. K. , Bilionis I., Wiart J., “Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification”,Journal of Computational Physics, 2015, 375, 565-588.