文献类型：期刊文献

中文题名：Optimization of Product Distribution for MIP Units Using Data Mining

英文题名：Optimization of Product Distribution for MIP Units Using Data Mining

作者：Wang, Qing[1];Zhang, Xiaoguo[1,2];Mei, Junwei;Gao, Zhibo[1];Yang, Kuizhi[2];Yang, Dawei[2];Ouyang, Fusheng[1]

机构：[1]East China Univ Sci & Technol, Int Joint Res Ctr Green Energy Chem Engn, Shanghai 200237, Peoples R China;[2]SINOPEC Changling Petrochem Co Ltd, Yueyang 414012, Peoples R China

年份：2024

卷号：26

期号：2

起止页码：146

中文期刊名：China Petroleum Processing & Petrochemical Technology

外文期刊名：CHINA PETROLEUM PROCESSING & PETROCHEMICAL TECHNOLOGY

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001266840000014)】；CSCD:【CSCD2023_2024】；

基金：This work was supported by the National Natural Science Foundation of China (No. U22B20141) and the SINOPEC funded project (No. 31900000-21-ZC0607-0009).

语种：英文

中文关键词：MIP process;K-Means++;BP neural network model;XGBoost algorithm;genetic algorithm

外文关键词：MIP process; K-Means plus plus; BP neural network model; XGBoost algorithm; genetic algorithm

摘要：Based on data from a petrochemical company’s MIP unit over the past three years,19 input variables and 2 output variables were selected for modeling using the maximum information coefficient and Pearson correlation coefficient among 155 variables,which included properties of feedstock oil and spent catalyst,operational variables,and material flows.The distillation range variables were reduced using factor analysis,and the feedstock oils were clustered into three types using the K-means++algorithm.Each feedstock oil type was then used as an input variable for modeling.An XGBoost model and a back propagation(BP)neural network model with a structure of 20-15-15-2 were developed to predict the combined yield of gasoline and propylene,as well as the coke yield.In the test set,the BP neural network model demonstrated better fitting and generalization abilities with a mean absolute percentage error and determination coefficient of 1.48%and 0.738,respectively,compared to the XGBoost model.It was therefore chosen for further optimization work.The genetic algorithm was utilized to optimize operational variables in order to increase the combined yield of gasoline and propylene while controlling the growth of coke yield.Seven commercial test results in the MIP unit showed an average increase of 1.39 percentage points for the combined yield of gasoline and propylene and an average decrease of 0.11 percentage points for coke yield.These results indicate that the model effectively improves the combined yield of gasoline and propylene while controlling the increase in coke yield.
Based on data from a petrochemical company's MIP unit over the past three years, 19 input variables and 2 output variables were selected for modeling using the maximum information coefficient and Pearson correlation coefficient among 155 variables, which included properties of feedstock oil and spent catalyst, operational variables, and material flows. The distillation range variables were reduced using factor analysis, and the feedstock oils were clustered into three types using the K-means++ algorithm. Each feedstock oil type was then used as an input variable for modeling. An XGBoost model and a back propagation (BP) neural network model with a structure of 20-15-15-2 were developed to predict the combined yield of gasoline and propylene, as well as the coke yield. In the test set, the BP neural network model demonstrated better fitting and generalization abilities with a mean absolute percentage error and determination coefficient of 1.48% and 0.738, respectively, compared to the XGBoost model. It was therefore chosen for further optimization work. The genetic algorithm was utilized to optimize operational variables in order to increase the combined yield of gasoline and propylene while controlling the growth of coke yield. Seven commercial test results in the MIP unit showed an average increase of 1.39 percentage points for the combined yield of gasoline and propylene and an average decrease of 0.11 percentage points for coke yield. These results indicate that the model effectively improves the combined yield of gasoline and propylene while controlling the increase in coke yield.