Prediction and Estimation of 95% Distillation Point Diesel in Delayed Coking Unit Based on Kernel Partial Least Squares

Authors

Cheng Jian, Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China;
Lingbo Zhang, Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China;
Xingsheng Gu, Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China;

Abstract

Abstract: In order to save labor cost and time of 95% distillation point of diesel analysis, and improve the efficiency of adjusting the device, an algorithm was proposed to predict 95% distillation point of diesel based on Kernel Partial Least Squares(KPLS), on the basis of studying the process of delayed coking unit and analyzing the main factors related to 95% distillation point of diesel. The experiment results show that this algorithm can deal with the non-linear relationship among the variables effectively and quickly. Furthermore, the results also show that the model based on KPLS is better than that based on partial least squares. So this algorithm can be used to guide production and improve the profit of enterprise in practice.

Recommended Citation

Jian, Cheng; Zhang, Lingbo; and Gu, Xingsheng (2020) "Prediction and Estimation of 95% Distillation Point Diesel in Delayed Coking Unit Based on Kernel Partial Least Squares," Journal of System Simulation: Vol. 27: Iss. 3, Article 21.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol27/iss3/21

First Page

598

Revised Date

2014-10-30

DOI Link

https://doi.org/

Last Page

602

CLC

TP301

Recommended Citation

Cheng Jian, Zhang Lingbo, Gu Xingsheng. Prediction and Estimation of 95% Distillation Point Diesel in Delayed Coking Unit Based on Kernel Partial Least Squares[J]. Journal of System Simulation, 2015, 27(3): 598-602.