Coupling Experiment of Compact Integrated Fuel Processors with 75kW PEM Fuel Cells

A compact autothermal reformer suitable for liquid fuel for instance methanol et al. was developed. The fuel reformer was combined with polymer electrolyte membrane fuel cells (PEM FC) and a system test of the process chain was successfully performed. The fuel processor consists of a fuel evaporating step, two-stage reformer and a two-stage reactor of water gas shift (WGS, one for high temperature water gas shift and the other for low temperature water gas shifter) and a four-stage preferential oxidation (PROX) reactor and some internal heat exchanger in order to achieve optimized heat integration. The fuel processor is designed to provide enough hydrogen for 75kWel fuel cells. After the initial step of methanol ATR, CO WGS and CO PROX steps are used for 'clean-up' CO. The exhaust gas from FC anode feedback to the fuel processor to vaporizes the feedstock of methanol and water by a catalytic combusting-evaporator. The hydrogen source system can produce hydrogen 70.5 m3/hr and its specific gravity power and specific volume power reach 255W/kg and 450W/L respectively. During three hours coupling experiment, the fuel processing system and the fuel cells all has been running smoothly. The volume concentration of H2 and CO in product gas (dry basis) was kept in 53% and 20ppm respectively, completely meeting the requirements of PEM fuel cells. The conversion efficiency of the hydrogen producing system based on LHV of fuel and hydrogen can exceed 95.85%. The fuel cells stacks put up strong resistance to CO and its maximum electronic load to the fuel cells reaches 75.5kW. It indicates that it is feasible technically for supplying hydrogen for Proton Exchange Membrane Fuel Cells by catalytic reforming of hydrogen-rich liquid fuel on-board or on-site.

The Second Phase of the Global Land–Atmosphere Coupling Experiment: Soil Moisture Contributions to Subseasonal Forecast Skill

The second phase of the Global Land–Atmosphere Coupling Experiment (GLACE-2) is a multi-institutional numerical modeling experiment focused on quantifying, for boreal summer, the subseasonal (out to two months) forecast skill for precipitation and air temperature that can be derived from the realistic initialization of land surface states, notably soil moisture. An overview of the experiment and model behavior at the global scale is described here, along with a determination and characterization of multimodel “consensus” skill. The models show modest but significant skill in predicting air temperatures, especially where the rain gauge network is dense. Given that precipitation is the chief driver of soil moisture, and thereby assuming that rain gauge density is a reasonable proxy for the adequacy of the observational network contributing to soil moisture initialization, this result indeed highlights the potential contribution of enhanced observations to prediction. Land-derived precipitation forecast skill is much weaker than that for air temperature. The skill for predicting air temperature, and to some extent precipitation, increases with the magnitude of the initial soil moisture anomaly. GLACE-2 results are examined further to provide insight into the asymmetric impacts of wet and dry soil moisture initialization on skill.