【摘要】 为提高烯烃催化裂解(OCC)装置的丙烯和乙烯收率，在采集国内某企业100 kt/a OCC装置工业数据的基础上，通过数据预处理获得了835组数据样本，采用最大互信息系数和Pearson相关系数方法筛选出了13个输入特征变量，以双烯(丙烯和乙烯)收率为目标变量，分别采用XGBoost和BPNN方法建立了XGBoost和结构为13-64-13-1的BPNN回归模型。对比两种模型的回归结果，发现BPNN模型的整体预测效果更优；进而，结合遗传算法优化BPNN模型的装置操作变量，发现操作变量优化后，模型预测的双烯收率平均提升2.8百分点。这表明，所建BPNN模型可为OCC工业装置的运行优化提供指导。更多还原

【Abstract】 In order to improve the yield of propylene and ethylene in the olefin catalytic cracking(OCC) unit, based on the collection of industrial data from a 100 kt/a OCC unit of a domestic enterprise, 835 sets of data samples were obtained through data preprocessing. 13 input feature variables were selected using the maximum mutual information coefficient and Pearson correlation coefficient methods. With the yield of diene(propylene and ethylene) as the target variable, BPNN and XGBoost methods were used to establish XGBoost and BPNN classification prediction models with a structure of 13-64-13-1,respectively. Comparing the classification prediction results of the two models, it was found that the BPNN model had a better overall prediction performance; Furthermore, by combining genetic algorithm to optimize the operating variables of the BPNN model, it was found that after optimizing the operating variables, the model’s predicted diene yield increased by an average of 2.8 percentage points. This indicates that the established BPNN model can provide guidance for optimizing the operation of OCC industrial facilities.更多还原