The concept of molecular engineering to fit a particular application is of vast importance in the fuels industry. This idea has allowed our group to create strategies for improving diesel, gasoline, and alternative fuels. The first step is accomplished by identifying molecules or types of molecules which have potential for improvement, such as naphthenics in LCO, aromatics in gasoline, or glycerol from biofuels. Representative model compounds are then chosen and converted via heterogeneous catalysis with all reaction products tracked. The desired reaction products are then identified and maximized by optimization of the catalyst and reaction conditions. The best catalyst is then tested with real feeds and a successful strategy is born. This has been implemented for a variety of situations in both the oil and alternative fuels energy industries involving hydrogenation, ring contraction/ring opening, isomerization, decarbox/onylation, cracking, as well as others. In each case, careful attention is paid to catalyst characterization in order to both learn about important reaction intermediates and provide direction towards catalyst optimization.

Stemming from this approach is the need to know important fuel properties of each potential reaction product in order to better determine which products should be maximized. Because of this, quantitative structure-property relationships (QSPRs) have been heavily utilized in order to predict a variety of fuel properties, including cetane number (CN), octane number (ON), threshold sooting index (TSI), and micropyrolysis index (MPI). These relationships are created by correlating molecular descriptors to fuel properties through the use of genetic algorithms, artificial neural networks, principal components, and other statistical techniques. The end result is a reliable fuel property of each potential reaction product which has been calculated based only upon its molecular structure.

Quantitative structure-activity relationships (QSAR) have also been utilized to predict primary product distributions. By measuring the primary product distributions of several different feed molecules, and creating models which predict several different ratios of products, we now have the ability to predict for several compounds what their reaction behavior will be over specific catalysts as well as the fuel properties of each of these potential reaction products. This is a great tool for determining how a particular catalyst will influence the properties of a fuel.

A new fuel property was also developed in order to determine the sooting tendency that a specific fuel will produce through pyrolysis alone. This technique, coined the micropyrolysis index (MPI), involves injecting 20μL of a fuel across a hot bed of α-Al₂O₃ in a flow of helium. By measuring the amount of carbon that has deposited on the alumina via temperature programmed oxidation (TPO), one is measuring the kinetic tendency of that particular fuel to form soot via pyrolysis. This is an essential tool in molecular engineering as a reliable sooting measurement is very important for nearly every fuels upgrading situation. This has also led to a better understanding of how molecular structure impacts sooting tendency, and the ability to characterize the soot which was formed has led to further studies with SEM, Raman, and same spot TEM.