C4 olefin production conditions optimizing based on a hybrid model

Yancong Zhou; Chenheng Xu; Yongqiang Chen; Shanshan Li; Yancong Zhou; Chenheng Xu; Yongqiang Chen; Shanshan Li

doi:10.3934/mbe.2023553

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 7: 12433-12453. doi: 10.3934/mbe.2023553

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Research article

C4 olefin production conditions optimizing based on a hybrid model

1.
School of Information Engineering, Tianjin University of Commerce, Tianjin 300134, China
2.
School of Economics, Tianjin University of Commerce, Tianjin 300134, China
3.
School of Science, Tianjin University of Commerce, Tianjin 300134, China
^# The authors contributed equally to this work

Received: 04 April 2023 Revised: 04 May 2023 Accepted: 10 May 2023 Published: 24 May 2023

The yield of C4 olefin is often low due to the complexity of the associated products. Finding the optimal ethanol reaction conditions requires repeated manual experiments, which results in a large consumption of resources. Therefore, it is challenging to design ethanol reaction conditions to make the highest possible yield of C4 olefin. This paper introduces artificial intelligence technology to the optimization problem of C4 olefin production conditions. A sample incremental eXtreme Gradient Boosting tree based on Gaussian noise (GXGB) is proposed to establish the objective function of the variables to be optimized. The Sparrow Search Algorithm (SSA), which has an improved advantage in the optimization efficiency, is used to combine with GXGB. Therefore, a kind of hybrid model GXGB-SSA that can solve the optimization of complex problems is proposed. The purpose of this model is to find the combination of ethanol reaction conditions that makes the maximum yield of C4 olefin. In addition, due to the insufficient interpretation ability of GXGB on the data, the SHAP (SHapley Additive exPlanations) value method is creatively introduced to investigate the effect of each ethanol reaction condition on the yield of C4 olefin. The constraints of each decision variable for optimization are adjusted according to the analysis results. The experimental results have showed that the proposed GXGB-SSA model obtained the combination of ethanol reaction conditions that maximized the yield of C4 olefin. (i.e., when the Co loading is 1.1248 wt%, the Co/SiO₂ and HAP mass ratio is 1.8402, the ethanol concentration is 0.8992 ml/min, the total catalyst mass is 400 mg, and the reaction temperature is 420.37 ℃, the highest C4 olefin yield is obtained as 5611.46%). It is nearly 25.46 % higher compared to the current highest yield of 4472.81 % obtained from manual experiments.

Keywords:

Citation: Yancong Zhou, Chenheng Xu, Yongqiang Chen, Shanshan Li. C4 olefin production conditions optimizing based on a hybrid model[J]. Mathematical Biosciences and Engineering, 2023, 20(7): 12433-12453. doi: 10.3934/mbe.2023553

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Abstract

References

[1]	F. Yu, Z. J. Li, Y. L. An, P. Gao, L. S. Zhong, Y. H. Sun, Research progress on direct preparation of low carbon olefins by catalytic conversion of syngas, J. Fuel Chem., 44 (2016), 14. https://doi.org/10.3969/j.issn.0253-2409.2016.07.005 doi: 10.3969/j.issn.0253-2409.2016.07.005
[2]	Z. B. Guo, Y. Y. Zhang, X. Feng, Separation and purification of C_4 to C_6 hydrocarbons by metal-organic framework, J. Chem-NY., 78 (2020), 10. https://doi.org/10.6023/A20030081 doi: 10.6023/A20030081
[3]	H. T. Wang, G. Z. Qi, X. H. Li, W. Li, Coupling catalytic cracking of C4 olefins with methanol conversion to olefins over SAPO-34 catalyst, Chem. React. Eng. Process., 2 (2013), 47–53. https://doi.org/CNKI: SUN: HXFY.0.2013-02-007
[4]	X. H. Gao, Y. L. Wang, S. P. Lu, J. L. Zhang, S. B. Fan, T. S. Zhao, Microwave hydrothermal preparation of Fe-Zr catalysts and their performance in CO hydrogenation to olefins, J. Fuel Chem., 42 (2014), 219–224. https://doi.org/10.3969/j.issn.0253-2409.2014.02.014 doi: 10.3969/j.issn.0253-2409.2014.02.014
[5]	B. H. Liu, W. J. Wang, Research progress on catalysts for the synthesis of low carbon olefins by carbon dioxide hydrogenation, Anhui Chem. Industry, 45 (2019), 5. https://doi.org/CNKI: SUN: AHHG.0.2019-05-004
[6]	J. Wang, S. Q. Zhong, Z. K. Xie, C. F. Zhang, Analysis of ethanol dehydration to olefin process, Nat. Gas Chem. C1 Chem. Chem. Eng., 33 (2008), 27–32. https://doi.org/10.3969/j.issn.1001-9219.2008.05.007 doi: 10.3969/j.issn.1001-9219.2008.05.007
[7]	J. H. Zhu, J. Yu, Z. K. Duan, Z. G. Zhang, G. W. Xu, Preparation of low carbon olefins by Fe_2O_3/HZSM-5 catalyzed conversion of ethanol, J. Process Eng., 1 (2010), 6. https://doi.org/CNKI: SUN: HGYJ.0.2010-01-019
[8]	Y. C. Liu, Y. B. Xu, J. Y. Lu, ZSM-5-catalyzed ethanol to oligocene, Adv. Chem., 22 (2010), 6. https://doi.org/CNKI:SUN:HXJZ.0.2010-04-026
[9]	W. Xia, J. G. Wang, C. Qian, Y. X. Huang, C. Ma, Y. Fan, et al., Comprehensive experiments on the preparation of low carbon olefins from ethanol catalyzed by Zr/ZSM-5 molecular sieve, Exper. Technol. Manag., 38 (2021), 149–153. https://doi.org/10.16791/j.cnki.sjg.2021.08.032 doi: 10.16791/j.cnki.sjg.2021.08.032
[10]	G. S. Tang, X. W. Xiu, M. T. Wang, W. X. Chang, Effect of catalyst and temperature on the preparation of C4 olefins, Labor. Res. Explor., 41 (2022), 28–32. https://doi.org/10.19927/j.cnki.syyt.2022.11.006 doi: 10.19927/j.cnki.syyt.2022.11.006
[11]	S. Y. Li, Advances in ant colony optimization algorithms and their applications, Comput. Measur. Control, 11 (2003), 911–913. https://doi.org/10.3321/j.issn:1671-4598.2003.12.001 doi: 10.3321/j.issn:1671-4598.2003.12.001
[12]	J. Yang, S. S. Yang, Z. Y. Duan, L. Z. Zhu, Development and application of chemical experimental design and optimization methods, Guangdong Chem. Indust., 37 (2010), 2. https://doi.org/10.3969/j.issn.1007-1865.2010.10.035 doi: 10.3969/j.issn.1007-1865.2010.10.035
[13]	W. Fang, Population intelligence algorithm and its research in digital filter optimization design, Wuxi Jiangnan University. https://doi.org/10.7666/d.y1399305
[14]	Y. Deng, Q. Y. Jiang, Z. K. Cao, J. Shi, H. Zhou, An improved particle swarm algorithm for chemical process optimization, Comput. Appl. Chem., 28 (2011), 4. https://doi.org/10.3969/j.issn.1001-4160.2011.06.020 doi: 10.3969/j.issn.1001-4160.2011.06.020
[15]	D. Y. Diao, Z. F. Cao, Improved multi-objective particle swarm optimization algorithm for intermittent steaming process, Comput. Appl., 32 (2012), 4. https://doi.org/CNKI: SUN: JSJY.0.2012-S2-017
[16]	S. Fan, W. Zhong, H. Cheng, Q. Feng, Novel control vector parameterization method with differential evolution algorithm and its application in dynamic optimization of chemical processes, Chinese J. Chem. Eng., 21 (2013), 64–71. https://doi.org/10.1016/S1004-9541(13)60442-5 doi: 10.1016/S1004-9541(13)60442-5
[17]	X. F. Wang, X. H. Wang, H. Z. Du, P. Wang, Fuzzy rough set and support vector machine-based fault diagnosis for chemical processes, Control Decision Making, 30 (2015), 4. https://doi.org/10.13195/j.kzyjc.2014.0246 doi: 10.13195/j.kzyjc.2014.0246
[18]	Y. W. Wang, L. Zhang, Simulation and optimization of response surface method for synthesis of methyl chloroacetate by reaction distillation next door tower process, Nat. Gas Chem. C1 Chem. Chem. Eng., 45 (2020), 7. https://doi.org/CNKI: SUN: TRQH.0.2020-03-025
[19]	P. Gao, S. Liu, S. H. Cheng, F. S. Ouyang, M. Y. Zhao, Optimization of gasoline octane loss in S Zorb unit based on BP neural network and genetic algorithm, Petrol. Refin. Chem., 52 (2021), 8. https://doi.org/10.3969/j.issn.1005-2399.2021.07.020 doi: 10.3969/j.issn.1005-2399.2021.07.020
[20]	J. Chen, J. F. Tang, X. M. Jin, Y. H. Hua, J. Chu, Y. Wang, et al., Pilot experiments and simulation optimization of decarbonization process parameters of alcohol-amine method, JPT, 33 (2017), 9. https://doi.org/10.3969/j.issn.1001-8719.2017.05.020 doi: 10.3969/j.issn.1001-8719.2017.05.020
[21]	T. Chen, C. Guestrin, Xgboost: A scalable tree boosting system, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (2016), 785–794. https://doi.org/10.1145/2939672.2939785
[22]	D. Zhang, L. Qian, B. Mao, C. Huang, Y. Si, A data-driven design for fault detection of wind turbines using random forests and XGboost, IEEE Access, 6 (2018), 21020–21031. https://doi.org/10.1109/ACCESS.2018.2818678 doi: 10.1109/ACCESS.2018.2818678
[23]	J. Q. Shi, J. H. Zhang, Load forecasting method based on multi-model fusion Stacking integrated learning approach, Chinese J. Electr. Eng., 39 (2019), 10. https://doi.org/10.13334/j.0258-8013.pcsee.181510 doi: 10.13334/j.0258-8013.pcsee.181510
[24]	H. Jiang, Z. He, G. Ye, H. Zhang, Network intrusion detection based on PSO-XGBoost model, IEEE Access, 8 (2020), 58392–58401. https://doi.org/10.1109/ACCESS.2020.2982418 doi: 10.1109/ACCESS.2020.2982418
[25]	A. Ogunleye, Q. G. Wang, XGBoost model for chronic kidney disease diagnosis, IEEE/ACM Transact. Computat. Biol. Bioinform., 17 (2019), 2131–2140. https://doi.org/10.1109/TCBB.2019.2911071
[26]	B. S. Bhati, G. Chugh, F. AlmilUrjman, N. S. Bhati, An improved ensemble-based intrusion detection technique using XGBoost, Transact. Emerg. Telecommun. Technol., 1 (2020). https://doi.org/10.1002/ett.4076
[27]	Y. Liang, D. X. Niu, M. Q. Ye, W. C. Hong, Short-term load forecasting based on wavelet transform and least squares support vector machine optimized by improved cuckoo search, Energies, 9 (2016), 827. https://doi.org/10.3390/en9100827 doi: 10.3390/en9100827
[28]	G. Y. Liu, C. Shu, Z. W. Liang, B. H. Peng, L. F. Cheng, A modified sparrow search algorithm with application in 3D route planning for UAV, Sensors, 21 (2021), 1224. https://doi.org/10.3390/s21041224 doi: 10.3390/s21041224
[29]	C. Zhang, S. Ding, A stochastic configuration network based on chaotic sparrow search algorithm, Knowledge-Based Syst., 220 (2021), 106924. https://doi.org/10.1016/j.knosys.2021.106924 doi: 10.1016/j.knosys.2021.106924
[30]	Q. H. Mao, Q. Zhang, An improved sparrow algorithm incorporating Corsi variation and backward learning, Comput. Sci. Explor., 15 (2021), 10. https://doi.org/10.3778/j.issn.1673-9418.2010032 doi: 10.3778/j.issn.1673-9418.2010032
[31]	Y. Zhu, N. Yousefi, Optimal parameter identification of PEMFC stacks using adaptive sparrow search algorithm, Int. J. Hydrogen Energ., 46 (2021). https://doi.org/10.1016/j.ijhydene.2020.12.107 doi: 10.1016/j.ijhydene.2020.12.107
[32]	P. Wang, Y. Zhang, H. Yang, Research on economic optimization of microgrid cluster based on chaos sparrow search algorithm, Comput. Int. Neurosci., 3 (2021), 1–18. https://doi.org/10.1155/2021/5556780 doi: 10.1155/2021/5556780
[33]	S. Lundberg, S. I. Lee, A unified approach to interpreting model predictions, Adv. Neural Inform. Process. Syst., (2017), 30. https://doi.org/10.48550/arXiv.1705.07874 doi: 10.48550/arXiv.1705.07874
[34]	A. B. Parsa, A.Movahedi, H. Taghipour, S. Derrible, A. K. Mohammadian, Toward safer highways, application of XGBoost and SHAP for real-time accident detection and feature analysis, Accident Anal. Prev., 136 (2020), 105405. https://doi.org/10.1016/j.aap.2019.105405
[35]	Y. D. Gu, H. Liu, Improved load forecasting based on optimal feature combination for limit gradient lifting, Comput. Appl. Res., 38 (2021), 2767–2772. https://doi.org/10.19734/j.issn.1001-3695.2020.12.0552 doi: 10.19734/j.issn.1001-3695.2020.12.0552
[36]	Y. Luo, F. Wang, W. L. Ye, Explainable prediction model for acute kidney injury based on XGBoost and SHAP, J. Electron. Inf. Technol., 44 (2022), 12. https://doi.org/10.11999/JEIT210931 doi: 10.11999/JEIT210931
[37]	S. C. Chelgani, H. Nasiri, M. Alidokht, Interpretable modeling of metallurgical reactions in industrial coal pillar flotation circuits via XGBoost and SHAP - a "Conscious Laboratory" development, J. Min. Sci. Technol. English Edition, 31 (2021), 10. https://doi.org/10.3969/j.issn.2095-2686.2021.06.016 doi: 10.3969/j.issn.2095-2686.2021.06.016
[38]	H. Yang, E. Li, Y. F. Cai, J. P. Li, G. X. Yuan, The extraction of early warning features for the predicting financial distress based on XGboost model and shap framework, Int. J. Finan. Eng., 8 (2021), 2141004. https://doi.org/10.1142/S2424786321410048 doi: 10.1142/S2424786321410048
[39]	J. Xue, B. Shen, A novel swarm intelligence optimization approach: sparrow search algorithm, Syst. Sci. Control Eng., 8 (2020), 22–34. https://doi.org/10.1080/21642583.2019.1708830 doi: 10.1080/21642583.2019.1708830
[40]	J. Yuan, Z. Zhao, Y. Liu, B. He, Y. Gao, DMPPT control of photovoltaic microgrid based on improved sparrow search algorithm, IEEE Access, 9 (2021), 16623–16629. https://doi.org/10.1109/ACCESS.2021.3052960 doi: 10.1109/ACCESS.2021.3052960

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