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-Comprehensive models of biomass pyrolysis are needed to develop renewable fuels and chemicals from biomass. Unfortunately, the detailed kinetic schemes required to optimize industrial biomass pyrolysis processes are too computationally expensive to include in models that account for both kinetics and transport within reacting particles. *lignet* is a project to train neural nets (and a decision tree) that can reproduce the results of my detailed ODE-based kinetic model for lignin pyrolysis, *ligpy* [(available here)](https://github.com/houghb/ligpy). The trained neural networks generalize very well, predicting the outputs of the detailed kinetic model with over 99.9% accuracy on new data, and **reduce the computational cost by four orders of magnitude!**
+Comprehensive models of biomass pyrolysis are needed to develop renewable fuels and chemicals from biomass. Unfortunately, the detailed kinetic schemes required to optimize industrial biomass pyrolysis processes are too computationally expensive to include in models that account for both kinetics and transport within reacting particles. *lignet* is a project to train neural nets (and a decision tree) that can reproduce the results of my detailed ODE-based kinetic model for lignin pyrolysis, *ligpy* [(available here)](https://github.com/houghb/ligpy). The trained neural networks generalize very well, predicting the outputs of the detailed kinetic model with over 99.9% accuracy on new data, and **reduce the computational cost by four orders of magnitude.**
 
 *lignet* was written to generate results for our 2016 paper, **Application of machine learning to pyrolysis reaction networks: reducing model solution time to enable process optimization** (a link to the paper will be added when it is published).