(390e) Modeling of a Catalytic Microsensor for the Selective Detection and Quantification of Ethanol in Multi-Component Hydrocarbon Fuel Mixtures | AIChE

(390e) Modeling of a Catalytic Microsensor for the Selective Detection and Quantification of Ethanol in Multi-Component Hydrocarbon Fuel Mixtures


Gatt, J. E. - Presenter, Purdue University
Nair, H. - Presenter, Purdue University
Zhang, R. - Presenter, Purdue University
Baertsch, C. D. - Presenter, Purdue University

Micro-hotplate-based catalytic gas sensors are designed and fabricated to selectively quantify ethanol in hydrocarbon fuels. This sensor builds upon the concept of a catalytic sensor by introducing a reactant selective catalyst to uniquely oxidize only the target Volatile organic compound (VOC) while allowing all other non-target VOCs to pass by it without generating a response. The microsensor for ethanol quantification utilizing Fe2(MoO4)3 as a sensing catalytic substrate at 453 K in an inert environment (hydrocarbon mixtures included alkanes, alkenes, and aromatics) and in mixtures containing methanol were simulated via COMSOL multiphysics software package paired with Matlab v8. The microsensor is essentially modeled as a two dimensional, thermal-catalytic reactor, with dimensions representative of previously fabricated micro-channel reactors in the field (3000 μm by 500 μm by 50 μm). The system is assumed as a cuboid, divided into a gas phase and the solid catalytic layer. In the scenario of ethanol in an inert environment, complete ethanol conversion ranges were found to be 930 ppm ethanol for a sensor with a catalyst layer of 15 μm. These simulations show how sensor designs can be tailored to meet specific quantification requirements. Temperature profiles for multiple concentrations of ethanol were investigated specifically at the base of the sensor where the RTDs are fabricated within thermalchemical microsensors. A simple empirical relationship can be used to calculate ethanol concentrations in an inert environment based on a single temperature measurement. However, this approach fails in ethanol with methanol mixtures because both methanol and ethanol react on the catalytic sensor and thus multiple concentrations of each alcohol can give rise to similar temperature profiles. The impact that various concentrations of methanol have on the ethanol quantification capability of the sensor was investigated by conducting a series of simulations were ethanol concentrations were held constant at either 100 ppm or 250 ppm while methanol concentrations varied from 0-1250 ppm. By employing a genetic search algorithm, it was demonstrated that unknown concentrations of ethanol and methanol can be estimated from a resulting temperature profile. Accuracy for a simple sensor layout comprised of 4 evenly spaced RTDs proved to be within 8 ppm of ethanol and 26 ppm methanol for a target concentration of 250 ppm ethanol and 100 ppm methanol and was found to improve upon increasing the number of RTDs or their position along the sensor length. Coupling microsensor simulations with a genetic search algorithm will enable quantification of both ethanol and methanol in gas mixtures containing one or more of these reactive gases over a selective oxidation catalyst and can be used to optimize sensor positions to improve detection precision over different concentration ranges.