Reaction network design and hybrid modeling of S Zorb

doi:10.1016/j.cjche.2024.04.013

中国化学工程学报 ›› 2024, Vol. 73 ›› Issue (9): 301-310.DOI: 10.1016/j.cjche.2024.04.013

Reaction network design and hybrid modeling of S Zorb

Kai Ji¹, Zhencheng Ye^1,2, Feng Qian¹

1. Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China;
2. Qingyuan Innovation Laboratory, Quanzhou, 362801, China

收稿日期:2024-01-03 修回日期:2024-04-20 接受日期:2024-04-24 出版日期:2024-11-21 发布日期:2024-05-15
通讯作者: Zhencheng Ye,E-mail:yzc@ecust.edu.cn;Feng Qian,E-mail:fqian@ecust.edu.cn
基金资助:
This work was supported by the National Natural Science Foundation of China (Basic Science Center Program: 61988101), the Shanghai Committee of Science and Technology, China (22DZ1101500), Major Program of Qingyuan Innovation Laboratory (00122002), Fundamental Research Funds for the Central Universities (222202417006).

Reaction network design and hybrid modeling of S Zorb

Kai Ji¹, Zhencheng Ye^1,2, Feng Qian¹

1. Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China;
2. Qingyuan Innovation Laboratory, Quanzhou, 362801, China

Received:2024-01-03 Revised:2024-04-20 Accepted:2024-04-24 Online:2024-11-21 Published:2024-05-15
Contact: Zhencheng Ye,E-mail:yzc@ecust.edu.cn;Feng Qian,E-mail:fqian@ecust.edu.cn
Supported by:
This work was supported by the National Natural Science Foundation of China (Basic Science Center Program: 61988101), the Shanghai Committee of Science and Technology, China (22DZ1101500), Major Program of Qingyuan Innovation Laboratory (00122002), Fundamental Research Funds for the Central Universities (222202417006).

摘要/Abstract

摘要： At present, many countries are becoming more and more stringent in terms of sulfur content in fuel oil. S Zorb is a kind of desulfurization technology with advantages of exceptional desulfurization efficiency and small impact on octane number. To meet the needs of environmental requirements and the trend of digitalization in the petrochemical industry, a first-principle model of S Zorb was established based on industry data. In order to describe the desulfurization and the other side reactions, a reaction network was designed and the kinetic parameters were estimated by the particle swarm optimization algorithm. Two hybrid models based on the first-principle model and support vector regression method were established to correct the mass fraction of sulfur and predict the research octane number of the refined gasoline respectively. The results indicate that the hybrid models can predict the mass fraction of PIONA, sulfur content and research octane number of the refined gasoline accurately, of which the mean absolute percentage errors are less than 6%. Hybrid models were then applied to optimize the decision variables to minimize the research octane number loss. Optimization results show that the average reduction of the loss of research octane number is 21.8%, which suggests that the models developed hold promise for guiding practical production.

关键词: Desulfurization, Simulation, Reaction kinetics, Optimization

Abstract: At present, many countries are becoming more and more stringent in terms of sulfur content in fuel oil. S Zorb is a kind of desulfurization technology with advantages of exceptional desulfurization efficiency and small impact on octane number. To meet the needs of environmental requirements and the trend of digitalization in the petrochemical industry, a first-principle model of S Zorb was established based on industry data. In order to describe the desulfurization and the other side reactions, a reaction network was designed and the kinetic parameters were estimated by the particle swarm optimization algorithm. Two hybrid models based on the first-principle model and support vector regression method were established to correct the mass fraction of sulfur and predict the research octane number of the refined gasoline respectively. The results indicate that the hybrid models can predict the mass fraction of PIONA, sulfur content and research octane number of the refined gasoline accurately, of which the mean absolute percentage errors are less than 6%. Hybrid models were then applied to optimize the decision variables to minimize the research octane number loss. Optimization results show that the average reduction of the loss of research octane number is 21.8%, which suggests that the models developed hold promise for guiding practical production.

Key words: Desulfurization, Simulation, Reaction kinetics, Optimization

Kai Ji, Zhencheng Ye, Feng Qian. Reaction network design and hybrid modeling of S Zorb[J]. 中国化学工程学报, 2024, 73(9): 301-310.

Kai Ji, Zhencheng Ye, Feng Qian. Reaction network design and hybrid modeling of S Zorb[J]. Chinese Journal of Chemical Engineering, 2024, 73(9): 301-310.

参考文献

[1] B. Saha, S. Vedachalam, A.K. Dalai, Review on recent advances in adsorptive desulfurization, Fuel Process. Technol. 214 (2021) 106685.
[2] R. Ullah, M. Tuzen, Interactions of Ni/ZnO with alumina support and their influence on deep reactive adsorption desulfurization, J. Mol. Liq. 365 (2022) 120082.
[3] L. li, F. Ju, Y. Sun, H. Pan, H. Ling, Self-sulfidation adsorbent for reactive adsorption desulfurization, Fuel 313 (2022) 122696.
[4] Y.C. Lyu, Z.W. Sun, X.T. Meng, Y. Wu, X.M. Liu, Y. Hu, Scale-up reactivation of spent S-Zorb adsorbents for gasoline desulfurization, J. Hazard Mater. 423 (Pt A) (2022) 126903.
[5] W.J. Song, W. Du, C. Fan, M.L. Yang, F. Qian, Adaptive weighted hybrid modeling of hydrocracking process and its operational optimization, Ind. Eng. Chem. Res. 60 (9) (2021) 3617-3632.
[6] M. Morales-Blancas, F.S. Mederos-Nieto, I. Elizalde, J. Felipe Sanchez-Minero, F. Trejo-Zarraga, Discrete lumping kinetic models for hydrodesulfuration and hydrocracking of a mixture of FCC feedstock and light gasoil, Chem. Pap. 76 (8) (2022) 4885-4891.
[7] O. Patierno, P. Cipriani, F. Pochetti, M. Giona, Pyrolysis of automotive shredder residues: a lumped kinetic characterization, Chem. Eng. J. 70 (2) (1998) 157-163.
[8] O.P. Hamadi, T. Varga, Novel distributed parameter model-based continuous lumping approach: an application to a pilot-plant hydrocracking reactor, Chem. Eng. Sci. 271 (2023) 118572.
[9] M.F. Wang, H.P. Yang, J. Wang, T. Gu, M. Zhong, Z.H. Dai, J. Li, Structure-oriented lumping method: an effective tool for molecular refining, Ind. Eng. Chem. Res. 62 (33) (2023) 12845-12854.
[10] Z.Y. Pang, P. Huang, C. Lian, C. Peng, X.C. Fang, H.L. Liu, Data-driven prediction of product yields and control framework of hydrocracking unit, Chem. Eng. Sci. 283 (2024) 119386.
[11] R.S. Qin, J.S. Zhao, Adaptive multiscale convolutional neural network model for chemical process fault diagnosis, Chin. J. Chem. Eng. 50 (2022) 398-411.
[12] H.S. Duan, X. Meng, J. Tang, J.F. Qiao, Prediction of NO concentration using modular long short-term memory neural network for municipal solid waste incineration, Chin. J. Chem. Eng. 56 (2023) 46-57.
[13] L. Xie, G.M. Zhou, C.S. Huo, J.W. He, Z.H. Luo, J. Yang, Development of effective voidage correlations in pilot-scale liquid-solid fluidized bed based on data-driven modeling, Ind. Eng. Chem. Res. 62 (25) (2023) 9901-9909.
[14] K.X. Bi, T. Qiu, An intelligent SVM modeling process for crude oil properties prediction based on a hybrid GA-PSO method, Chin. J. Chem. Eng. 27 (8) (2019) 1888-1894.
[15] Z.Z. Alireza Baghban Jafar Sasanipour, Sajjad Habibzadeh, Estimating solubility of supercritical H₂S in ionic liquids through a hybrid LSSVM chemical structure model, Chin. J. Chem. Eng. 27 (3) (2019) 620-627.
[16] W. Li, J.L. Yang, P.H. Yang, S. Li, Characteristic selection and prediction of octane number loss in gasoline refinement process, E3S Web Conf. 245 (2021) 01040.
[17] J. Ma, J. Lin, Intelligent forecast model of octane number loss using BP neural network and random forests, in: 2022 IEEE 4th Int. Conf. Civ. Aviat. Saf. Inf. Technol. ICCASIT, 2022: pp. 268-271.
[18] J. Guo, Y.J. Lou, W.Y. Wang, X.H. Wu, Optimization modeling and empirical research on gasoline octane loss based on data analysis, J. Adv. Transport. 2021 (2021) 5553069.
[19] B. Chen, J. Wang, S. Liu, F.S. Ouyang, D. Xiong, M.Y. Zhao, An industrial data-based model to reduce octane number loss of refined gasoline for S zorb process, Petrol. Chem 63 (3) (2023) 299-309.
[20] H. Jia, W. Du, C. Fan, M. Yang, Mechanism modeling of S-zorb reactors and parameter estimation with improved whale algorithm, J. Chem. Eng. Chin. Univ. 32 (6) (2018) 1395-1402.
[21] C. Cao, Z. Shao, S zorb device online product prediction and multi-objective operation optimization analysis based on SSA-RELM, Acta Pet. Sin. Process. Sect. 38 (6) (2022) 1305-1316.
[22] G. Soave, Equilibrium constants from a modified Redlich-Kwong equation of state, Chem. Eng. Sci. 27 (6) (1972) 1197-1203.
[23] R.C. Reid, J.M. Prausnitz, B.E. Poling, The Properties of Gases and Liquids, fourth ed., McGraw-Hill, New York, 1987.
[24] H. Yu, J.G. Zhao, X.M. Hou, B.X. Shen, Thermodynamic analysis on the process of S Zorb reaction adsorption desulfurization, Chem. Ind. Eng. Prog. 33 (11) (2014) 2843-2847.
[25] A. Ryzhikov, I. Bezverkhyy, J.P. Bellat, Reactive adsorption of thiophene on Ni/ZnO: role of hydrogen pretreatment and nature of the rate determining step, Appl. Catal. B Environ. 84 (3-4) (2008) 766-772.
[26] J. Kennedy, R. Eberhart, Particle swarm optimization, Proceedings of ICNN'95 - International Conference on Neural Networks. Perth, WA, Australia. IEEE, (1995) 1942-1948.
[27] J. Long, K. Deng, R.C. He, Closed-loop scheduling optimization strategy based on particle swarm optimization with niche technology and soft sensor method of attributes-applied to gasoline blending process, Chin. J. Chem. Eng. 61 (2023) 43-57.
[28] D.L. Chen, Y.Q. Luo, X.G. Yuan, Cascade refrigeration system synthesis based on hybrid simulated annealing and particle swarm optimization algorithm, Chin. J. Chem. Eng. 58 (2023) 244-255.
[29] S.R. Sun, A. Yang, C.L. Chang, G.Q. Hua, J.Z. Ren, Z.G. Lei, W.F. Shen, Improved multiobjective particle swarm optimization integrating mutation and changing inertia weight strategy for optimal design of the extractive single and double dividing wall column, Ind. Eng. Chem. Res. 62 (43) (2023) 17923-17936.
[30] Y. Shi, R. Eberhart, A modified particle swarm optimizer, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH₈360). Anchorage, AK, USA. IEEE, (1998) 69-73.
[31] C. Cortes, V. Vapnik, Support-vector networks, Mach. Learn. 20 (3) (1995) 273-297.

Reaction network design and hybrid modeling of S Zorb

Reaction network design and hybrid modeling of S Zorb

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	Zhongxiang Ding, Wei Song, Tong Zhou, Weihua Cui, Changsong Wang. Thiourea crystal growth kinetics, mechanism and process optimization during cooling crystallization[J]. 中国化学工程学报, 2024, 73(9): 62-69.
[2]	Limin Wang, Jinrong Duan, Bei Liu, Zhi Li, Guangjin Chen. The effect of ethylene-vinyl acetate copolymer on the formation process of wax crystals and hydrates[J]. 中国化学工程学报, 2024, 73(9): 109-119.
[3]	Jie Zhang, Lei Shi, Chuanxian Li, Fei Yang, Bo Yao, Guangyu Sun. Research methods and devices for hydrate characteristics during oil and gas transportation: A review[J]. 中国化学工程学报, 2024, 73(9): 130-145.
[4]	Jing Chen, Wenqi Zhong, Guanwen Zhou, Jinming Li, Shasha Ding. Desulfurization characteristics of slaked lime and regulation optimization of circulating fluidized bed flue gas desulfurization process—A combined experimental and numerical simulation study[J]. 中国化学工程学报, 2024, 73(9): 163-175.
[5]	Can Ding, Minglei Yang, Yunmeng Zhao, Wenli Du. Graph convolutional network for axial concentration profiles prediction in simulated moving bed[J]. 中国化学工程学报, 2024, 73(9): 270-280.
[6]	Maoqiao Xiang, Wenjun Ding, Qinghua Dong, Qingshan Zhu. Synthesis methods and powder quality of titanium monocarbide[J]. 中国化学工程学报, 2024, 72(8): 10-18.
[7]	Pengcheng Lu, Yaoyao Li, Jianjun Zhang, Yuchao Zhao, Qingqiang Wang, Ying Chen, Nan Jin, Xiugang Yu. Continuous synthesis of N, N-dicyanoethylaniline in microreactors: Reaction kinetics and process intensification[J]. 中国化学工程学报, 2024, 72(8): 95-105.
[8]	Tianxiao Huang, Binhang Yan. Evaluation and application of kinetic models for Cu-catalyzed acetylene hydrochlorination[J]. 中国化学工程学报, 2024, 72(8): 209-219.
[9]	Baowei Niu, Yanjie Yi, Yuwen Wei, Fuzhen Zhang, Lili Wang, Li Xia, Xiaoyan Sun, Shuguang Xiang. Phase equilibrium data prediction and process optimizationin butadiene extraction process[J]. 中国化学工程学报, 2024, 71(7): 1-12.
[10]	Chunliang Liu, Jianhui Zhong, Ranran Wei, Jiuxu Ruan, Kaicong Wang, Zhaoyou Zhu, Yinglong Wang, Limei Zhong. Process design and intensification of multicomponent azeotropes special distillation separation via molecular simulation and system optimization[J]. 中国化学工程学报, 2024, 71(7): 24-44.
[11]	Dandan Ren, Jiale Xie, Tianle Chen, Haibin Qu, Xingchu Gong. Design and optimization of a greener sinomenine hydrochloride preparation process considering variations among different batches of the medicinal herb[J]. 中国化学工程学报, 2024, 71(7): 77-90.
[12]	Junjie Cai, Xijian Li, Hao Sui, Honggao Xie. Study on the evolution of solid–liquid–gas in multi-scale pore methane in tectonic coal[J]. 中国化学工程学报, 2024, 71(7): 122-131.
[13]	Zhiwei Zhu, Minglei Yang, Wangli He, Renchu He, Yunmeng Zhao, Feng Qian. A deep reinforcement learning approach to gasoline blending real-time optimization under uncertainty[J]. 中国化学工程学报, 2024, 71(7): 183-192.
[14]	Kaixuan Gao, Xiwei Ke, Bingjun Du, Zhenchuan Wang, Yan Jin, Zhong Huang, Yanhong Li, Xuemin Liu. Simulation of gas-solid flow characteristics of the circulating fluidized bed boiler under pure-oxygen combustion conditions[J]. 中国化学工程学报, 2024, 70(6): 9-19.
[15]	Huashuai Wu, Gang Wang, Yong Yang, Yongwang Li. Modeling analysis of cobalt-based Fischer-Tropsch catalyst particles[J]. 中国化学工程学报, 2024, 70(6): 82-92.