Directed kinetic model building: Seeding as a model reduction tool

The need for detailed molecular information from kinetic models has given r ise to the practice of modeling the chemistry at either the molecular or me chanistic level. These models are often used to predict the product spectru m of complex process chemistries involving complex feedstocks and, hence, t hey are extremely large and often consume prohibitively large CPU times. Th ese models therefore need to be tailored to emphasize mainly the important chemistry only. To this end, we have developed a model reduction technique involving the "seeding" of key intermediates and molecules along the import ant reaction paths in the complex chemistry. This technique directs the mod el growth toward the most important and experimentally observed products at the expense of the unimportant part of the reaction network. The technique is illustrated using the acid cracking chemistry for n-heptane as an illus trative example. Comparison of a reduced model and a full model reveals con siderable time and size savings without loss of accuracy.