(375aa) Molecular Design of Biofuel Additives for Feedstock Flexibility
Biofuels blended with gasoline are one of the few alternatives that has not required significant new infrastructure or change on the part of consumers or auto manufacturers. As a result, biofuels are today the most widely deployed substitute for conventional fossil fuels in transportation. In the U.S., biofuels can currently be blended up to 10% (ethanol) and 20% (biodiesel) in every gallon of fuel.
The two most common renewable feedstock sources for making biofuels are plants rich in sugars and bio-derived oils. Ethanol can be produced from a number of feedstock; in addition to corn and sugarcane, other feedstocks include switchgrass, woody biomass, agricultural residue, wood residue, and municipal solid waste. However, different feedstocks produce different fuel characteristics. For example, the type of feedstock impacts the cold flow properties of the fuel, engine performances and efficiency, and fuel degradation (DARPA, 2010). It has been shown that the deviation in properties is a result of the variations in the residuals left in the blend from different sources of feedstock. Therefore, it is desired to molecularly design biofuel additives to account for the effect of residuals in the blend in order to achieve the performance properties of the petroleum based fuel. In this way, biofuels can be achieved that are adaptable to a range or blend of feedstock and the desirable fuel characteristics like higher energy density and wide operating temperature range. Moreover, such a sustainable biofuel must also meet the specifications required by the transportation and the aviation industry.
To meet this objective, fuel characterization data would be combined with property clustering techniques in a reverse problem formulation (Solvason, 2010). The characterization data consists of multitude of property values specified by aviation industry to ensure an adequate performance. To facilitate an efficient design we propose consolidating these various properties into a latent property domain using multivariate statistical techniques. Characterization based molecular design is then used to build additives that match the fuel specifications in the latent property space. Sustainability is controlled by environmental and ecological constraints on the design of the additives. Additives found can then be used to offset the impact the residuals on the property of the biofuel blend.
Defense Advanced Research Projects Agency (DARPA), ?Fact Sheet,? March 2010.
C.C. Solvason, N.G. Chemmangattuvalappil, M.R. Eden (2010), ?Multi-Scale Chemical Product Design using the Reverse Problem Formulation,? 20th European Symposium on Computer Aided Process Engineering ? ESCAPE20.