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 c...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.展开更多
On the basis of the reaction rules and its influencing factors of sulfur compounds in MIP naphtha, a correlation model for describing the correlation between mass fraction of sulfur in MIP naphtha, mass fraction of su...On the basis of the reaction rules and its influencing factors of sulfur compounds in MIP naphtha, a correlation model for describing the correlation between mass fraction of sulfur in MIP naphtha, mass fraction of sulfur in feedstock and volume fraction of olefin in naphtha was developed and the model＇s parameters were estimated. The residual error distribution and statistical study showed that the developed model was reasonable and reliable and able to predict the mass fraction of sulfur compounds in naphtha. The correlation model can provide theoretical guidance and operation base for adjusting process parameters to produce EURO IV gasoline by the MIP units. The model was validated by its application on the MIP unit of Qingdao Refining ＆ Chemical Company. On this unit, the tail oil with low sulfur content obtained via hydrotreating gas oil was used as the feedstock and the olefin content of naphtha was reduced by promoting hydrogen transfer reaction through adjustment of process parameters. Thus, EURO IV clean gasoline was manufactured by this MIP unit.展开更多
基金the National Natural Science Foundation of China(No.U22B20141)the SINOPEC funded project(No.31900000-21-ZC0607-0009).
文摘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.
文摘On the basis of the reaction rules and its influencing factors of sulfur compounds in MIP naphtha, a correlation model for describing the correlation between mass fraction of sulfur in MIP naphtha, mass fraction of sulfur in feedstock and volume fraction of olefin in naphtha was developed and the model＇s parameters were estimated. The residual error distribution and statistical study showed that the developed model was reasonable and reliable and able to predict the mass fraction of sulfur compounds in naphtha. The correlation model can provide theoretical guidance and operation base for adjusting process parameters to produce EURO IV gasoline by the MIP units. The model was validated by its application on the MIP unit of Qingdao Refining ＆ Chemical Company. On this unit, the tail oil with low sulfur content obtained via hydrotreating gas oil was used as the feedstock and the olefin content of naphtha was reduced by promoting hydrogen transfer reaction through adjustment of process parameters. Thus, EURO IV clean gasoline was manufactured by this MIP unit.
