(589a) Pruning of Catalytic Reaction Networks: A Comparison of Campbell's Degree of Rate Control with Reaction Step Resistance | AIChE

(589a) Pruning of Catalytic Reaction Networks: A Comparison of Campbell's Degree of Rate Control with Reaction Step Resistance

Authors 

Datta, R., Worcester Polytechnic Institute
Vilekar, S., Worcester Polytechnic Institute

Many complex catalytic reaction mechanisms start off as being “hypothetical,” i.e., they emerge from a researcher’s knowledge of the catalytic chemistry on a given catalyst coupled with intuition, or are based on a stoichiometric, or graph-theoretical, generator, but can soon become “real,” when modern quantum-chemical methods are used to predict their reaction energies and activation barriers. Whether the imagined molecular and kinetic complexity of a network is justified in reality, however, is an open question; one which we are not yet fully equipped to answer unequivocally.

The tools that are currently available for simplifying complex microkinetic models include: 1) a comparison of the energy landscape of different pathways; 2) a comparison of step reversibility, as proposed by Dumesic, and 3) Campbell’s degree of rate control (RDC). The first of these, although the most common, is mainly a qualitative tool, while the second, involves only a thermodynamic criterion, not kinetic. The most useful and rigorous tool so far is Campbell’s DRC, which involves a partial differential analysis of the relative change in the rate of overall reaction (OR) for a relative small change in the rate constant of a step, holding constant all other step rate as well as equilibrium constants, i.e., it is a numerical sensitivity analysis. With the help of the water-gas shift reaction network and other examples, we show that our alternate methodology, as a part of our Reaction Route (RR) Graph approach, which involves a comparison of reaction step “resistance,” is just as rigorous and, in fact, more revealing than Campbell’s DRC, and allows transparent pruning of complex catalytic reaction networks.