Modeling of Lambda Sensor Output With Exhaust Gas Mixtures From Natural Gas-Fueled Engines

The increasingly strict emission regulations may require implementing Non-Selective Catalytic Reduction (NSCR) system as a promising emission control technology for stationary rich burn spark ignition engines. Many recent investigations used NSCR systems for stationary natural gas fueled engines showed that NSCR systems were unable to consistently control the emissions level below the compliance limits. Modeling of NSCR components to better understand, and then exploit, the underlying physical processes that occur in the lambda sensor and the catalyst media is now considered an essential step toward the required NSCR system performance. This paper presents the work done to date on a modeling of lambda sensor that provides feedback to the air-to-fuel controller. Several recent experimental studies indicate that the voltage signal from the lambda sensor may not be interpreted correctly because of the physical nature in the way the sensor senses the exhaust gas concentration. Correct interpretation of the sensor output signal is necessary to achieve consistently low emissions level. The goal of this modeling study is to improve the understanding of the physical processes that occur within the sensor, investigate the cross-sensitivity of various exhaust gas species on the sensor performance, and finally this model serves as a tool to improve NSCR control strategies. This model simulates the output from a planar switch type lambda sensor. The model consists of three modules. The first module models the multi-component mass transport through the sensor protective layer. Diffusion fluxes are calculated using the Maxwell-Stefan equation. The second module includes all the surface catalytic reactions that take place on the sensor platinum electrodes. All kinetic reactions are modeled based on the Langmuir-Hinshelwood kinetic mechanism. The model incorporates for the first time methane catalytic reactions on the sensor platinum electrode. The third module is responsible for simulating the reactions that occur on the electrolyte material and determine the sensor output voltage. The model results are validated using field test data obtained from a mapping study of a natural gas-fueled engine equipped with NSCR system. The data showed that the lambda sensor output voltage is influenced by the reducing species concentration, such as carbon monoxide (CO) and hydrogen (H2). The results from the developed model and the experimental data showed strong correlations between CO and H2 with the sensor output voltage within the lambda operating range between 0.994 to 1.007 (catalytic converter operating window). This model also showed that methane does not significantly influence the lambda sensor performance compared to the effect of CO and H2.