摘要
催化裂化干气脱硫装置尾气中的硫化氢质量分数需要控制在10μg/g以下。基于某石化企业催化裂化干气脱硫装置的705组运行数据,采用最大互信息系数和Pearson相关系数法从44个变量中筛选出了25个变量作为输入变量,以净化干气中的硫化氢含量作为目标变量(转化为分类问题),分别采用XGBoost方法和DNN方法建立了针对目标变量的二值分类预测模型。结果表明,DNN模型的综合评价指标优于XGBoost模型,尤其是对不达标样本的分类准确性和处理复杂问题的稳定性方面更优。将DNN模型与遗传算法相结合,对测试集中未达标样本的操作变量进行了优化,使未达标样本全部实现达标。因此,所建DNN模型可为催化裂化干气脱硫装置的优化运行提供指导。
Hydrogen sulfide concentration of the tail gas from the methyldiethanolamine(MDEA)desulfurization zone for dry gas from fluid catalytic cracking(FCC)unit must be controlled below 10μg/g.Based on the 705 sets of historical data from the dry gas desulfurization zone in the FCC unit of a petrochemical enterprise,25 modeling variables were screened from 44 variables by the maximum mutual information coefficient and Pearson correlation coefficient methods,transforming hydrogen sulfide concentration in the tail gas into a classification problem as the target variable.An XGBoost and a DNN binary classification models for predicting target variable were established respectively.The evaluation results of the DNN model are better than that of XGBoost,particularly in the accuracy of classifying substandard samples and the stability of handling complex problems.Combining the DNN model with the Genetic Algorithm,the operating variables in the substandard samples from the test set were optimized to achieve the standard.Therefore,the DNN model can provide guidance to optimize the operation of MDEA desulfurization zone in FCC unit.
作者
魏敏
刘锦泽
欧阳福生
吕涯
Wei Min;Liu Jinze;Ouyang Fusheng;LüYa(Institute of Petroleum Processing,School of Chemical Engineering,East China University of Science and Technology,Shanghai 200237;Petro-Cyber Works Information Technology Co.,Ltd.)
出处
《石油炼制与化工》
北大核心
2025年第9期89-98,共10页
Petroleum Processing and Petrochemicals
基金
中国石油化工股份有限公司合作项目(321022)。
关键词
硫化氢
MDEA脱硫
数据挖掘技术
遗传算法
hydrogen sulfide
MDEA desulfurization
data mining techniques
genetic algorithm
