An Industrial Data-Based Model to Reduce Octane Number Loss of Refined Gasoline for S Zorb Process

Cover Page

Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription Access

Abstract

S Zorb process is one of the main technologies for deep desulfurization of gasoline from fluid catalytic cracking (FCC) process, which by the process will also cause some research octane number (RON) loss of gasoline. Establishing a data-driven model with data mining technologies to optimize production is one of the development directions in petrochemical field. Based on the industrial data from a 1.20 Mt/a S Zorb unit in China in recent three years, 422 modeling samples and 22 modeling variables were screened out and then three data-driven models were established by back propagation neural network (BPNN), radial basis function neural network (RBFNN) and generalized regression neural network (GRNN) to predict RON of refined gasoline (r-RON). The results show that the BPNN model has the best prediction effect and generalization ability. Genetic algorithm (GA), particle swarm optimization algorithm (PSO) and simulated annealing algorithm (SA) in combination with the BPNN model respectively were used to optimize the operation variables to reduce the r-RON loss. The results indicate that the optimized performance of PSO-BPNN model is best because of its largest reduction in r-RON loss at 48.55%. The validity of the PSO-BPNN model was verified in the S Zorb unit and the research methods to establish a data-driven model for reducing r-RON loss are also worthy of reference for other S Zorb units.

About the authors

Chen Bo

International Joint Research Center of Green Energy Chemical Engineering, East China University of Science and Technology

Email: petrochem@ips.ac.ru
200237, Shanghai, China

Wang Jie

International Joint Research Center of Green Energy Chemical Engineering, East China University of Science and Technology

Email: petrochem@ips.ac.ru
200237, Shanghai, China

Liu Song

SINOPEC Shanghai Gaoqiao Petrochemical Co., Ltd

Email: petrochem@ips.ac.ru
200129, Shanghai, China

Ouyang Fusheng

International Joint Research Center of Green Energy Chemical Engineering, East China University of Science and Technology

Email: ouyfsh@ecust.edu.cn
200237, Shanghai, China

Xiong Da

International Joint Research Center of Green Energy Chemical Engineering, East China University of Science and Technology

Email: petrochem@ips.ac.ru
200237, Shanghai, China

Zhao Mingyang

SINOPEC Shanghai Gaoqiao Petrochemical Co., Ltd

Author for correspondence.
Email: petrochem@ips.ac.ru
200129, Shanghai, China

References

  1. Qiu L.M., Xiang Y.J., Xin M.D., Zou K., Zheng A.G., Xu G.T. Structural verification of nickel sulfide on spent S Zorb adsorbent as studied by HRTEM and XPS // J. Mol. Struct. 2020. V. 1202. P. 127215-127215. https://doi.org/10.1016/j.molstruc.2019.127215
  2. Ribeiro E Sousa L.R., Miranda T., e Sousa R.L., Tinoco J. The use of data mining techniques in rockburst risk assessment // Engineering. 2017. V. 3. P. 552-558. https://doi.org/10.1016/J.ENG.2017.04.002
  3. Ouyang F.S., Zhang J.H., Fang W.G. Optimizing product distribution in the heavy oil catalytic cracking (MIP) process // Petrol. Sci. Technol. 2017. V. 35. P. 1315-1320. https://doi.org/10.1080/10916466.2017.1297826
  4. Sadighi S., Mohaddecy R.S., Norouzian A. Optimizing an industrial scale naphtha catalytic reforming plant using a hybrid artificial neural network and genetic algorithm technique // Bull. Chem. React. Eng. Catal. 2015. V. 10. P. 210-220. https://doi.org/10.9767/bcrec.10.2.7171.210-220
  5. Zhu W.B., Webb Z.T., Mao K., Romagnoli J. A deep learning approach for process data visualization using t-distributed stochastic neighbor embedding // Ind. Eng. Chem. Res. 2019. V. 58. P. 9564-9575. https://doi.org/10.1021/acs.iecr.9b00975
  6. Chang P., Li Z.Y., Wang G.M., Wang P. An effective deep recurrent network with high-order statistic information for fault monitoring in wastewater treatment process // Expert Syst. Appl. 2020. V. 167. P. 114141. https://doi.org/10.1016/j.eswa.2020.114141
  7. Martínez-Martínez J.M., Escandell-Montero P., Soria-Olivas E., Martín-Guerrero J.D., Serrano-López A.J. A new visualization tool for data mining techniques // Prog. Artif. Intell. 2016. V. 5. P. 137-154. https://doi.org/10.1007/s13748-015-0079-4
  8. Chang Z.H., Zhang Y., Chen W.B. Electricity price prediction based on hybrid model of adam optimized LSTM neural network and wavelet transform // Energy. 2019. V. 187. P. 115804. https://doi.org/10.1016/j.energy.2019.07.134
  9. Luor D.C. A comparative assessment of data standardization on support vector machine for classification problems // Intell. Data Anal. 2015. V. 19. P. 529-546. https://doi.org/10.3233/IDA-150730
  10. Duan H.M., Pang X.Y. A multivariate grey prediction model based on energy logistic equation and its application in energy prediction in China // Energy. 2021. V. 229. P. 120716. https://doi.org/10.1016/J.ENERGY.2021.120716
  11. Fang S.E., Chen S. Model-free damage localization of structures using wavelet based grey relational analysis // Smart Mater. Struct. 2020. V. 29. № 8. P. 085046. https://doi.org/10.1088/1361-665X/ab99da
  12. Cai Y.G., Xi M.C., Xue Q.H. Study on the applications of neural networks for processing deformation monitoring data // Appl. Mech. Mater. 2014. V. 501-504. P. 2149-2153. https://doi.org/10.4028/www.scientific.net/AMM.501-504.2149
  13. Zhang E.L., Hou L., Shen C., Shi Y.L., Zhang Y.X. Sound quality prediction of vehicle interior noise and mathematical modeling using a back propagation neural network (BPNN) based on particle swarm optimization (PSO) // Meas. Sci. Technol. 2016. V. 27. P. 015801. https://doi.org/10.1088/0957-0233/27/1/015801
  14. Liu Xm., Liu Jc., Xu Yr. Motion control of underwater vehicle based on least disturbance BP algorithm // J. Marine. Sci. Appl. 2002. V. 1. P. 16-20. https://doi.org/10.1007/BF02921411
  15. Potts M.A.S., Broomhead D.S. Time series prediction with a radial basis function neural network // Proc. SPIE. 1991. V. 1565. P. 255-266. https://doi.org/10.1117/12.49782
  16. Zhao Y.P., Zhou X.L. K-means clustering algorithm and its improvement research // J. Phys.: Conf. Ser. 2021. V. 1873. P. 012074. https://doi.org/10.1088/1742-6596/1873/1/012074
  17. Yousef W.A. Estimating the standard error of cross-validation-based estimators of classifier performance // Pattern Recognit. Lett. 2021. V. 146. P. 115-125. https://doi.org/10.1016/J.PATREC.2021.02.022
  18. Liang F., Gao J., Xu L. Thermal performance investigation of the miniature revolving heat pipes using artificial neural networks and genetic algorithms // Int. J. Heat Mass Transf. 2020. V. 151. P. 119394. https://doi.org/10.1016/j.ijheatmasstransfer.2020.119394
  19. Ying J.L., Xiao J.C. Simulated annealing algorithm improved BP learning algorithm // Appl. Mech. Mater. 2014. V. 513-517. P. 734-737. https://doi.org/10.4028/www.scientific.net/AMM.513-517.734
  20. Wang H.L., Hu Z.B., Sun Y.Q., Su Q.H., Xia X.W. Modified backtracking search optimization algorithm inspired by simulated annealing for constrained engineering optimization problems // Comput. Intell. Neurosci. 2018. V. 2018. article ID 9167414. 27 pp. https://doi.org/10.1155/2018/9167414

Supplementary files

Supplementary Files
Action
1. JATS XML
Download

Copyright (c) 2023 Russian Academy of Sciences