In this work, the radial basis function neural network (RBFNN) and random forest (RF) algorithms were employed to develop generic AI models predicting mass transfer coefficient in amine-based CO₂ absorber. The models with operating parameters as input gave quite different prediction performance in different CO₂ absorption systems. To secure better applicability, extra parameters related to amine type and packing characteristics were introduced to reasonably describe mass transfer behaviors, respectively. Moreover, the generic models were proposed by considering all influencing factors of mass transfer in CO₂ absorber column. Furthermore, the performance of BPNN, RBFNN, and RF models was completely compared and fully discussed in terms of AARE. All three generic models could predict mass transfer coefficient of CO₂ absorber very well. It was found that the BPNN models provide the best predication with AAREs of below 5%. The developed generic model could serve as a fast and efficient tool for preliminary selection and evaluation of potential amines for CO₂ absorption. The framework of generic ML model development was also clearly presented, which could provide theoretical basis and practical guidance for the implementation and application of ML models in the carbon capture field.

中文翻译：

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用于胺基 CO2 吸收剂传质系数预测的通用 AI 模型，第二部分：RBFNN 和 RF 模型

在这项工作中，采用径向基函数神经网络 (RBFNN) 和随机森林 (RF) 算法来开发预测胺基 CO ₂吸收器传质系数的通用 AI 模型。_{以操作参数作为输入的模型在不同的CO 2}吸收系统中给出了完全不同的预测性能。为了确保更好的适用性，引入了与胺类型和填料特性相关的额外参数，以分别合理地描述传质行为。_{此外，通过考虑CO 2}传质的所有影响因素，提出了通用模型。吸收柱。此外，还根据 AARE 对 BPNN、RBFNN 和 RF 模型的性能进行了全面比较和充分讨论。这三个通用模型都可以很好地预测CO _{2吸收器的传质系数。}结果发现，BPNN 模型提供了低于 5% 的 AARE 的最佳预测。开发的通用模型可以作为一种快速有效的工具，用于初步选择和评估潜在的 CO ₂吸收胺。还清晰地提出了通用ML模型开发框架，可为ML模型在碳捕集领域的实施和应用提供理论依据和实践指导。