气固流化床内压力分布的快速预测是其运行监测和优化的关键，本文针对气固流化床中压力分布的快速预测方法进行了深入研究。首先利用双流体模型(TFM) 对流化床进行数值模拟计算，得到床内流场压力分布的变工况数据集。基于所得到的模拟数据集，采用本征正交分解(POD) 分析并提取特征向量和对应系数。采用支持向量机回归(SVR) 拟合投影系数，从而实现基于特征向量的气固流化床时均压力分布预测。结果表明单个工况预测所需时间为0.04 CPU 小时，远小于模拟所需的332CPU 小时；平均床层压降的预测结果与模拟结果高度一致，且压力平均百分比误差在0.01％内，POD 能够高精度快速预测流化床压力分布。本文验证了POD 在气固多相流领域应用的准确性和有效性，为流化床的工程应用和优化提供了新的研究方法和工具。

The rapid prediction of pressure distribution in gas-solid fluidized beds is crucial for their operation monitoring and optimization. This paper presents an in-depth study of the rapid prediction method of pressure distribution in gas-solid fluidized beds. First, numerical simulations of the fluidized bed were performed using the two-fluid model (TFM) to obtain a variable-case dataset of the pressure distribution in the flow field inside the bed. The obtained simulation dataset was analyzed using proper orthogonal decomposition (POD) to extract eigenvectors and corresponding coefficients. Support vector machine regression (SVR) was then used to fit the projection coefficients, allowing the prediction of time-averaged pressure distribution based on feature vectors for gas-solid fluidized beds. The results indicate that predicting a single case takes only 0.04 CPU hours, significantly less than the 332 CPU hours required for simulation; the prediction results of the average bed pressure drop are highly consistent with the simulation results, and the mean absolute percentage error is within 0.01%. Therefore, POD can accurately and quickly predict the pressure distribution of fluidized beds. This paper examines the accuracy and validity of using POD in the gas-solid multiphase flow field. It provides a new research method and tool for optimizing fluidized bed engineering applications.