Dataset for Chemical Kinetics Bayesian Inference Toolbox (CKBIT)

The robust estimation of chemical kinetic parameters and their associated uncertainty is essential in the field of chemistry and catalysis. The Chemical Kinetics Bayesian Inference Toolbox (CKBIT) is a Python software library introduced to enable users to implement advanced Bayesian inference techniques for kinetic parameter estimation and uncertainty quantification. Leveraging functionalities of other open source Python packages and offering simplified implementation through minimal user-required coding and straightforward Excel input files, CKBIT aspires to make the inference method easily accessible for chemical kinetics. CKBIT provides maximum a posteriori, Markov chain Monte Carlo, and variational inference estimation options. Users may apply these functionalities to estimate activation energies, reaction orders, and pre-exponential terms from chemical reaction data from batch reactors, continuous stirred-tank reactors, and plug flow reactors. The availability of prior distribution specification and the implementation of hierarchical modeling in CKBIT provide a heightened level of accuracy in estimates of kinetic parameters and their uncertainties.化学与催化领域中，化学动力学参数及其关联不确定性的稳健估计至关重要。化学动力学贝叶斯推断工具箱（Chemical Kinetics Bayesian Inference Toolbox，CKBIT）是一款Python软件库，旨在帮助用户运用先进的贝叶斯推断技术开展动力学参数估计与不确定性量化工作。该工具箱依托其他开源Python软件包的功能，通过极简的用户编码要求与简洁的Excel输入文件实现了简化部署，致力于让化学动力学领域的研究者能够便捷地使用贝叶斯推断方法。CKBIT提供了最大后验估计、马尔可夫链蒙特卡洛估计与变分推断估计三种可选方案。用户可借助该工具箱的这些功能，基于间歇反应器、连续搅拌釜式反应器与平推流反应器的化学反应数据，估算活化能、反应级数与指前因子。CKBIT支持先验分布设定，并实现了分层建模，这能够提升动力学参数及其不确定性估计结果的精准度。