Fuel cell systems are being deployed in stationary applications for the generation of electricity, heat, and hydrogen. These systems use a variety of fuel cell types, ranging from the low temperature polymer electrolyte fuel cell (PEFC) to the high temperature solid oxide fuel cell (SOFC). Depending on the application and location, these systems are being designed to operate on reformate or syngas produced from various fuels that include natural gas, biogas, coal gas, etc. All of these fuels contain species that can potentially damage the fuel cell anode or other unit operations and processes that precede the fuel cell stack. These detrimental effects include loss in performance or durability, and attenuating these effects requires additional components to reduce the impurity concentrations to tolerable levels, if not eliminate the impurity entirely. These impurity management components increase the complexity of the fuel cell system, and they add to the system's capital and operating costs (such as regeneration, replacement and disposal of spent material and maintenance). This project reviewed the public domain information available on the impurities encountered in stationary fuel cell systems, and the effects of the impurities on the fuel cells. A database has been set up that classifies the impurities, especially in renewable fuels, such as landfill gas and anaerobic digester gas. It documents the known deleterious effects on fuel cells, and the maximum allowable concentrations of select impurities suggested by manufacturers and researchers. The literature review helped to identify the impurity removal strategies that are available, and their effectiveness, capacity, and cost. A generic model of a stationary fuel-cell based power plant operating on digester and landfill gas has been developed
it includes a gas processing unit, followed by a fuel cell system. The model includes the key impurity removal steps to enable predictions of impurity breakthrough, component sizing, and utility needs. These data, along with process efficiency results from the model, were subsequently used to calculate the cost of electricity. Sensitivity analyses were conducted to correlate the concentrations of key impurities in the fuel gas feedstock to the cost of electricity.