Automated reaction mechanism generation : data collaboration, Heteroatom implementation, and model validation

Nearly two-thirds of the United States' transportation fuels are derived from non-renewable fossil fuels. This demand of fossil fuels requires the United States to import ~ 60% of its total fuel consumption. Relying so heavily on foreign oil is a threat to national security, not to mention that burning all of these fossil fuels produces increased levels of CO₂, a greenhouse gas that contributes to global warming. This is not a sustainable model. The United States government has recently passed legislation that requires greenhouse gas emissions to be reduced to 80% of the 2005 level by the year 2050. Furthermore, new legislation under the Energy Independence and Security Act (EISA) requires that 36 billion gallons of renewable fuel be blended into transportation fuel by 2022. Solving these types of problems will require the fuel industry to shift away from petroleum fuels to biomass-derived oxygenated hydrocarbon fuels. These fuels are generated through different biological pathways, using different "bugs." The question of which fuel molecules should we be burning, and thus, which bugs should we be engineering, arises. To answer that question, a detailed understanding of the fuel chemistry under a wide range of operating conditions, i.e. temperature, pressure, fuel equivalence ratio, and fuel percentage, must be known. Understanding any fuel chemistry fully requires significant collaboration: experimental datasets that span a range of temperatures, pressures, and equivalence ratios, high-level ab initio quantum chemistry calculations for single species and reactions, and a comprehensive reaction mechanism and reactor model that utilizes the theoretical calculations to make predictions. A shortcoming in any of these three fields limits the knowledge gained from the others. This thesis addresses the third field of the collaboration, namely constructing accurate reaction mechanisms for chemical systems. In this thesis, reaction mechanisms are constructed automatically using a software package Reaction Mechanism Generator (RMG) that has been developed in the Green Group over the last decade. The predictive capability of any mechanism depends on the parameters employed. For kinetic models, these parameters consist of species thermochemistry and reaction rate coefficients. Many parameters have been reported in the literature, and it would be beneficial if RMG would utilize these values instead of relying on estimation routines purely. To this end, the PrIMe Warehouse C/H/O chemistry has been validated and a means of incorporating said data in the RMG database has been implemented. Thus, all kinetic models built by RMG may utilize the community's reported thermochemical parameters.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Chemical Engineering.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2011

Author dc:creator

• Harper, Michael Richard, Jr

Advisor dc:contributor.advisor

• William H. Green.

• Chemical Engineering.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/65756

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/65756