Nano Sensor Networks for Tailored Operation of Highly Efficient Gas-To-Liquid Fuels Catalysts

Fischer-Tropsch synthesis, a major process for converting natural gas to liquid hydrocarbons (GTL) suffers from selectivity limitations. While GTL reactor produces highly useful hydrocarbons in the form of liquid fuels such as gasoline, it also produces low-grade hydrocarbons such as methane. Selectivity refers to the ratio of highly useful hydrocarbons to the total product output. The literature is replete with various catalyst formulations which seek to improve selectivity to specific product spectrum via for example, molecular size or shape exclusion using zeolites or control of growing surface chain length with particle (site) geometry. Existing strategies for selectivity improvement, such as manipulation of reactor operating factors (temperature, pressure, etc) and catalyst design preparation variables may be classified as top-down approaches. In this work, a bottom-up approach is proposed in which surface processes can be controlled via Nano Sensor Network (NSN) involving the turning on or off of elementary steps consisting undesired species, and redirection of surface efforts to step(s) leading to the wanted products. The overall effect of this nano-level communications will lead to superior selectivity than hitherto possible by reducing the rate of Hydrogenation To Paraffin (HTP) reactions. Our numerical and simulation results reveal an exponential relationship between reduction in rates of HTP reactions and selectivity. It also confirms a considerable improvement of overall selectivity in a catalyst that is equipped by a high reliable NSN in comparison with extant catalyst technologies and current commercial Fischer-Tropsch reactors . 1 This work was done while the last author was with the School of Chemical Engineering at University of New South Wales.