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A machine learning model for predicting the mass transfer performance of rotating packed beds based on a least squares support vector machine approach
Chemical Engineering and Processing: Process Intensification ( IF 4.3 ) Pub Date : 2021-04-27 , DOI: 10.1016/j.cep.2021.108432
Wei Zhang , Peng Xie , Yuxing Li , Jianlu Zhu

Rotating packed beds (RPBs) have been widely noted due to its superior gas-liquid mass transfer performance compared to conventional packed bed for CO2 absorption. The overall volumetric gas-side mass transfer coefficient (KGa) is selected as one of the key parameters for the screening and evaluation of RPBs. Existing theoretical and semi-empirical models for the KGa are easy to be used but have a poor accuracy and generalization ability. In this paper, a machine learning model based on least squares support vector machine (LSSVM) is developed to predict the KGa more accurately for CO2-NaOH chemical absorption system in different types of RPBs. Unlike the conventional prediction models, the input parameters are selected by multiple correlation analysis in the model establishment. Then, the proposed model is comprehensively evaluated by using four evaluation indicators, including determination coefficient, mean relative error, Root mean square error and standard deviations. The results show that the proposed model has the prediction performance with R2 = 0.9808 and RMSE = 0.0055 for testing set. In addition, the model performance is compared with the multiple nonlinear regression and artificial neutral networks. The results show that the proposed model has a superior performance for predicting the KGa of CO2 absorption in RPBs.



中文翻译:

基于最小二乘支持向量机方法的预测旋转填充床传质性能的机器学习模型

旋转填料床(RPB)由于与常规填料床相比具有优越的气液传质性能而广受关注 CØ2个吸收。气体侧总体积传质系数(ķG一种选择)作为RPB筛选和评估的关键参数之一。现有的理论和半经验模型ķG一种易于使用,但准确性和泛化能力较差。本文提出了一种基于最小二乘支持向量机(LSSVM)的机器学习模型来预测ķG一种更精确地用于不同类型RPB中的CO 2 -NaOH化学吸收系统。与传统的预测模型不同,在模型建立过程中通过多重相关分析来选择输入参数。然后,通过确定系数,平均相对误差,均方根误差和标准偏差这四个评价指标对提出的模型进行综合评价。结果表明,该模型具有较好的预测性能。[R2个 = 0.9808和 [R中号小号Ë = 0.0055(测试集)。此外,将模型性能与多元非线性回归和人工中性网络进行了比较。结果表明,所提出的模型具有较好的预测性能。ķG一种CØ2个 RPB中的吸收。

更新日期:2021-05-03
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