Fuel cells offer potential advantages to traditional Naval power generation technologies including reduced air flow, increased operating efficiencies, and integration into modern ship architectures for distributed power production. Due to the requirements for logistic fuels usage, their deep desulfurization is essential for the protection of catalyzed systems necessary for fuel processing and fuel cell operation.

This effort describes the modeling of adsorption systems for deep desulfurization of logistic fuels using probability methods. The model creates probability functions to determine whether sulfur-containing compounds such as thiophenes, benzo- and di-benzothiophenes are removed as they pass through the sorbent bed. A coarse, working model was developed that is easily tuned to utilize system specific parameters such as sorbent bed geometry, fuel components, and sorbent characteristics. Additional modeling efforts include adsorbent site usage rates and increased complexity of sorbent and fuel characteristics. The computational challenges associated with modeling adsorption via probability methods are also discussed.

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