UC Riverside

Renewable energy production processes have achieved significant technological and commercial maturity over the past two decades. Most carbon based renewable fuel gases contain significant quantities of CO2. Converting the CO2 along with methane into syngas is an attractive option since it can potentially increase utilization of distributed renewable carbon resources while creating additional revenue streams.

An integrated renewable power generation system where the SBR process was coupled with a Solid Oxide Fuel Cell (SOFC) was studied using the Aspen Plus model. The steam-biogas reforming (SBR) process performed over a Pd-Rh catalyst was compared with equilibrium values predicted by Aspen Plus. At steam to carbon ratio of 1.50 and temperature of 1073 K or above, positive CO2 conversion was obtained. Coke formation was significantly reduced during reforming reaction performed experimentally over the Pd-Rh compared to literature data. SBR integrated with combustion process works with an efficiency of 40% or higher. The variation of the catalytic support material composition helps to adjust H2/CO ratio and H2/CH4 yield. CeZrO2 addition suppressed coke formation, for improved oxygen storage and oxide reducibility. Pd-Rh catalysts exhibit stable performance for 200 h, although sintering occurred regardless the catalyst composition used.

A life cycle assessment was performed for methanol production pathway using syngas produced via bi-reforming pathway from CO2, H2O reforming with methane. GHG emission is about 203 kilograms of CO2e per metric tonne of methanol produced using the proposed bi-reforming pathway. GHG emission reduction is 0.29 kg/ CO2e/kg of CH3OH compared to the commercial scale production. With NG price $3.50/GJ and methanol price $400/tonne IRR is 57% with 5 years payback period.

A database for Wobbe Index, Methane Number, thermal conductivity, sound velocity of biogas, anaerobic digester gas and natural gas mixture was built. A prediction model for WI and MN of a gaseous fuel mixture was developed that uses thermal conductivity and sonic velocity. The model can predict the Wobbe Index with an average error of ±2.76% and Methane Number with an average error of ±1.65%. The prediction model coupled with a thermal conductivity sensor and sonic velocity measurement sensor enables the combustion of gas efficiently.