West awarded NSF CAREER Award
Associate Professor of Chemical Engineering Richard West has received a CAREER Award from the National Science Foundation for Predictive Kinetic Modeling of Halogenated Hydrocarbon Combustion. West will research how to predict the complex networks of chemical reactions that comprise a chemical mechanism, specifically combustion, which could ultimately provide real-world, life-saving solutions in preventing fires aboard airplanes or in car wrecks.
Halogenated hydrocarbons (HHCs) are widely used as both refrigerants and fire suppressants. Driven by environmental and economic considerations, there is rapid innovation in the industry, but the next generation of HHC compounds raise fire safety concerns.
“We’ve been trying to predict combustion—how hydrocarbons like fuels will burn—for a while. So people are now generating models with thousands of different intermediate steps. This research project is about trying to take those same techniques but extending them to model hydrocarbons that have chlorine, bromine, iodine or fluorine attached,” explained West.
The challenge is finding a way to have computers predict what the parameters will be because there are thousands of individual reactions. If this can be accomplished, there will be a precise idea of how certain compounds burn—enabling the prevention of dangerous fires. West’s research will use a computational approach known as machine learning to help model the complex reacting systems, leading to a breakthrough development of an automated reaction mechanism generation tool to create detailed kinetic models for combustion of HHCs.
“Being able to predict how a material will burn before making a lot of it would save an awful lot of time and expense,” West said. “What we need is a way to predict the combustion properties of halogenic compounds. That means building detailed models with thousands of intermediates just based on someone drawing a picture of a molecule and saying, ‘what would happen to this?’”
The end result could be a compound stored on airplanes to be injected in case of fire, he said. If they know the exact chain of reactions that causes the fire, they can create a compound that will effectively contain or extinguish it, potentially saving lives.
Abstract Source: NSF
Halogenated hydrocarbons (HHCs) are widely used as both refrigerants and fire suppressants. Driven by environmental and economic considerations, there is rapid innovation in the industry, but the next generation of HHC compounds raise fire safety concerns. Predicting the combustion behavior of these novel HHCs earlier in the design process will save much time, effort, and expense. The chemical kinetic models for describing HHC combustion are highly complex, comprising thousands of elementary reactions involving hundreds of chemical species. To effectively predict these combustion behaviors, we must automate the construction of kinetic models. This project will use a computational approach known as machine learning to help model these complex reacting systems. This breakthrough will enable us to develop an automated reaction mechanism generation tool to create detailed kinetic models for combustion of HHCs. The methodology proposed in this work are not only novel and necessary, but will be widely applicable in other aspects of automated mechanism generation. The integrated educational objective of this CAREER project is to develop a series of computational modules teaching students to solve problems throughout their chemical engineering curriculum.
The research approach is to extend and apply automated Reaction Mechanism Generator (RMG) software to create detailed kinetic models for combustion of any mix of hydrocarbons containing any combination of halogen atoms. Optimized decision-tree and novel convolutional neural network algorithms from the field of machine learning will be extended to enable the necessary restructuring of parameter estimation codes. Quantum chemistry calculations will be automated to supplement literature searches to generate the necessary training data. The model-generating tool will be validated against available experimental data from key example compounds, and used to explain the remarkable combustion behavior of these compounds. The educational program is aligned with the research, developing a series of computational modules that will be integrated into existing classes. These modules will teach students to use Python and SciPy to solve chemical engineering problems. The integration of teaching modules for scientific computing throughout the undergraduate chemical engineering curriculum will help prepare a generation of graduate engineers for a workplace in which data analysis, processing, and computation are increasingly important.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.