Application of machine learning to pyrolysis reaction networks: Reducing model solution time to enable process optimization

Abstract 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. Here we present a machine learning approach using artificial neural networks and decision trees to reduce the computational expense of detailed kinetic models by four orders of magnitude, enabling their use in comprehensive models. The trained neural networks generalize very well, predicting the outputs of the detailed kinetic model with over 99.9% accuracy on new data. The machine learning approach we outline is not specific to kinetic modeling and can be applied to any set of input and output data, even if the underlying relationship between inputs and outputs is unknown.