Determination of Chemical Kinetic Rate Constants of a Model for Carbothermal Processing of Lunar Regolith Simulant Using Methane

Abstract

We have previously developed a chemical conversion model of the carbothermal processing of lunar regolith using methane to predict the rate of production of carbon monoxide. In this carbothermal process, gaseous methane is pyrolyzed as it flows over the hot surface of a molten zone of lunar regolith and is converted to carbon and hydrogen. Hydrogen is carried away by the exiting stream of gases and carbon is deposited on the melt surface. The deposited carbon mixes with the melt and reacts with the metal oxides in it to produce carbon monoxide that bubbles out of the melt. In our model, we assume that the flux of carbon deposited is equal to the product of the surface reaction rate constant gamma and the concentration of methane adjacent to the melt surface. Similarly, the rate of consumption of carbon per unit volume in the melt is equal to the product of the melt reaction rate constant k and the concentrations of carbon and metal oxide in the melt. In this paper, we describe our effort to determine gamma and k by comparison of the predictions from our model with test data obtained by ORBITEC (Orbital Technologies Corporation). The concentration of methane adjacent to the melt surface is a necessary input to the model. It is inferred from the test data by a mass balance of methane, adopting the usual assumptions of the continuously-stirred-tank-reactor model, whereby the average concentration of a given gaseous species equals its exit concentration. The reaction rates gamma and k have been determined by a non-linear least-squares fit to the test data for the production of carbon monoxide and the fraction of the incoming methane that is converted. The comparison of test data with our model predictions using the determined chemical kinetic rate constants provides a consistent interpretation of the process over the full range of temperatures, pressures, and methane flow rates used in the tests, thereby increasing our confidence to use the model for scale-up purposes