Welcome to ligpy’s documentation!¶
Introduction¶
Biomass valorization through thermochemical conversion of lignocellulosic feedstocks is limited by our lack of detailed kinetic models. In addition to adding mechanistic understanding, more detailed models are needed to optimize industrial biomass pyrolysis processes for producing fuels and chemicals. To this end, we developed a kinetic model for lignin pyrolysis involving ~100 species and 400 reactions which is capable of predicting the temporal evolution of molecules and functional groups during lignin pyrolysis. The model provides information beyond the lumped yields of common pyrolysis models without any fitting, allowing it to cover a wider range of feedstocks and reaction conditions. Good agreement is observed with slow pyrolysis experiments, and an exhaustive global sensitivity analysis using over two million simulations sheds light on reactions that contribute most to the variance in model predictions (sensitivity analysis results and a package to visualize them are available here). Model predictions for fast pyrolysis are available, however, recently developed experimental techniques for kinetically-controlled fast pyrolysis of biomass have yet to be applied to lignin.
ligpy is the package developed to solve the kinetic model we describe in our 2016 IECR paper, **Detailed kinetic modeling of lignin pyrolysis for process optimization**.
To start using ligpy see the “Installing ligpy” and “Getting started” pages below.
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Contents¶
Package Structure¶
ligpy’s architecture is shown below. Blue rectangles are python modules and red ovals are data files that describe the kinetic reaction scheme or the composition of starting lignin species.
Shapes with a thick border (generate_bash_script, ddasac_utils, DDASAC, and analysis_tools) refer to pieces of the code that will likely need to be replaced or modified by users who do not have access to the same computing environment that we developed this program on at the University of Washington (a computer called cmole using a modified version of the DDASAC ODE solver).
Dashed lines are user input that is possible, but infrequent.
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