In this paper, a methodology is developed for sequential design of experiments (SDoE) for process systems and applied to a solvent-based CO₂ capture system. In this approach, the prior knowledge of the system is used to prioritize process data collection at specific operating conditions. These data are then incorporated into a Bayesian inference methodology for updating a stochastic model by refining estimations of its underlying parameters, and the updated model is then used to generate the next set of test runs. Thus, the new knowledge obtained from the data is used to guide subsequent iterations of the experimental runs, ensuring that the overall data collection is maximally informative given that most experimental campaigns, especially at pilot or higher-scale plants, are costly, time-consuming, and resource-limited. The test run objective for this work was to minimize the maximum model prediction uncertainty for key output variables, but the methodology is generic and can be readily applied to other test run objectives. This methodology is applied to an aqueous monoethanolamine (MEA) pilot plant campaign at the National Carbon Capture Center (NCCC) in Wilsonville, Alabama, USA. The SDoE framework was utilized for two iterations, while collecting 18 sets of data representing different process conditions, and this resulted in an overall average reduction in uncertainty of approximately 50% in the prediction of CO₂ capture percentage. Moreover, 11 additional data sets were obtained with variation of absorber packing height for further model validation. This work shows the capability of the SDoE framework to maximize learning given limited resources, allowing for the reduction of model uncertainty, which is of great importance for many applications including reduction of technical risk associated with scale-up and economic analysis.

在本文中，为过程系统的实验顺序设计（SE）开发了一种方法，并将其应用于基于溶剂的CO ₂捕获系统。在这种方法中，系统的先验知识用于对特定操作条件下的过程数据收集进行优先排序。然后将这些数据合并到贝叶斯推理方法中，以通过细化其基础参数的估计来更新随机模型，然后将更新后的模型用于生成下一组测试运行。因此，从数据中获得的新知识将用于指导实验运行的后续迭代，从而确保总体数据收集具有最大的参考价值，因为大多数实验活动（尤其是中试工厂或规模较大的工厂）都是昂贵且费时的，并且资源有限。这项工作的测试目标是最大程度地减少关键输出变量的最大模型预测不确定性，但是该方法是通用的，可以很容易地应用于其他测试目标。该方法适用于美国阿拉巴马州威尔逊维尔国家碳捕集中心（NCCC）的单乙醇胺水溶液（MEA）中试工厂活动。使用sE框架进行了两次迭代，同时收集了代表不同过程条件的18组数据，这导致在预测CO的过程中不确定性总体平均降低了约50％_2个捕获百分比。此外，获得了11个其他数据集，这些数据集具有不同的吸收器填充高度，用于进一步的模型验证。这项工作表明，在有限资源的情况下，SE框架具有最大化学习的能力，从而可以减少模型的不确定性，这对于许多应用（包括降低与规模扩大和经济分析相关的技术风险）非常重要。