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Molecule-Based Modeling of Heavy Hydrocarbon Structure and Reactions:
Discrete and Statistical Approaches

Professor Michael T. Klein
Rutgers, The State University of New Jersey
Department of Chemical and Biochemical Engineering
School of Engineering
98 Brett Road
Piscataway, New Jersey 08854-8058
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Department of Chemical and Biochemical Engineering

ABSTRACT

The considerable interest in molecule-based kinetic models for complex chemistries is motivated by the need to predict product properties as a function of chemical process conditions.  This is because the molecular composition is an optimal starting point for the prediction of mixture properties. Reactivity is an especially significant property that can be discerned given a molecule’s (and its reaction environment’s) structure. Other prop­erties fall into perfor­mance and environmental classes. Thus, the potential advantages of molecule-based model­ing are clear. Less readily apparent, how­ever, is that the development and operation of molec­ular models comes with a large requirement for model construction and solution time as well as reactivity information.

The essential challenge of building detailed kinetic models for heavy hydrocarbons is due to the staggering complexity of not only the reaction mixtures but the complexity of each molecule within the mixture.  There will often be thousands of “multi-functional” component species.  The sheer size of the thus-im­plied modeling problem engenders a con­flict between the need for molecular detail and the formulation and solution of the model. 

Our recent work has explored automated strategies to represent heavy hydrocarbon structure and reaction both in terms of discrete molecules and probability density functions (pdf’s) for molecular attributes.  Monte Carlo simulation of feedstock structure is one method to cast the modeling problem in molecular terms.  This technique samples pdf’s for the attributes of the heavy hydrocarbon molecular structures to construct a representative molecular sample whose properties are compared against measured properties.  Optimization methods are used to minimize the weighted sum of squares difference, and the final set of pdf parameters are the mathematical representation of heavy hydrocarbon structure.  Subsequent reaction models can be based on discrete molecules or the molecular attributes of the pdf’s.  The latter “Attribute Reaction Model” provides a large reduction in the number of reaction equations and thus solution time.

The relative merits of these strategies are discussed.