(219b) Kinexns (Kinetics of Reactions): an Open Source Python Package for Chemical Kinetic Modeling

Chemical kinetic models are usually composed of a set of stiff ordinary differential equations (ODEs), where a number of rate parameters have to be estimated to fit with experimental observations. This presents a twofold challenge—firstly, to solve the stiff set of ODEs accurately and efficiently; and secondly, to estimate the model parameters precisely by optimizing the objective function. In recent years, stochastic optimization methods for parameter estimation have gained popularity over the classical optimization methods as the former do not require a reasonable initial guess and have the capability to escape local minima. Therefore, in this study, we develop an open source python package kinexns to efficiently solve chemical kinetic model using CVode stiff ODE solver, perform sensitivity analysis to determine important model parameters, and optimize the model parameters by using the different stochastic algorithms. We also systematically examine ten different stochastic optimization algorithms and evaluate their performance to estimate the model parameters for previously developed propane oxidation mechanism—popularly known as the San Diego mechanism.¹ The different algorithms we considered are: Monte Carlo (MC), Latin-Hypercube Sampling (LHS), Maximum Likelihood Estimation (MLE), Markov-Chain Monte-Carlo (MCMC), Shuffled Complex Evolution Algorithm (SCE-UA), Simulated Annealing (SA), RObust Parameter Estimation (ROPE), Artificial Bee Colony (ABC), Fitness Scaled Chaotic Artificial Bee Colony (FSCABC), and Dynamically Dimensioned Search algorithm (DDS). Our results indicate the MLE and DDS provide better parameter approximation among all the algorithms evaluated. This package is finally used to develop a robust kinetic model for fast pyrolysis of xylose by identifying important reactions through sensitivity analysis and optimizing the associated rate parameters.

(1) Chemical Mechanism: Combustion Research Group at UC San Diego https://web.eng.ucsd.edu/mae/groups/combustion/mechanism.html.