In this study, a Bayesian approach is demonstrated to identify parameters in adsorption isotherm models. First, we demonstrate the data fusion with Bayesian analysis for which we use CO₂ sorption on UiO-66 sorbents. This material has been studied by different groups to report CO₂ adsorption data in the NIST database under different conditions. We unify the data in two different ways depending upon the assumption around the error distribution of the data. In the first case study, all the data are assumed to have a common error distribution regardless of the source, and in the second approach, the error distributions of data from different sources are distinguished from each other. We apply a Bayesian approach to perform an optimal experimental design to minimize the uncertainty in adsorption isotherm parameters.

在这项研究中，贝叶斯方法被证明可以识别吸附等温线模型中的参数。首先，我们通过贝叶斯分析证明数据融合，为此我们在UiO-66吸附剂上使用CO ₂吸附。该材料已由不同小组研究以报告CO ₂NIST数据库中不同条件下的吸附数据。根据围绕数据错误分布的假设，我们以两种不同的方式统一数据。在第一个案例研究中，假设所有数据均具有相同的错误分布，而与来源无关，而在第二个方法中，则将来自不同来源的数据的错误分布区分开来。我们应用贝叶斯方法进行最佳实验设计，以最大程度降低吸附等温线参数的不确定性。