Research on the Catalytic Coupling of Ethanol to $C4$ Olefins: A Multi-Modal Study with BP-NN-Assisted Bayesian Optimization and PSO-Driven Variational Strategies
Abstract
This study focuses on the process conditions for the preparation of $C4$ olefins via ethanol-catalyzed coupling technology for the wide range of applications of $C4$ olefins in the production of chemicals and pharmaceuticals, a process of remarkable significance and value for the optimization of chemical products. In order to maximize the yield of $C4$ olefin under the given experimental conditions, this paper combines the BP neural network model of Bayesian optimization with the black-box optimization model, and further solves for the optimal catalyst combinations and reaction temperatures by the particle swarm algorithm with the introduction of a variational strategy. Through this comprehensive method, the key conditions for achieving the maximum yield of $C4$ olefins were determined, demonstrating the good application of this method in the optimization process of the chemical industry.
Keywords
BP neural network model for Bayesian optimization, Black-box optimization model, Particle swarm algorithm for variational strategies
References
- [1] V. Zacharopoulou, A. A. Lemonidou, Olefins from biomass intermediates: A review, Catalysts, 8(1) (2017), Article ID 2. https://doi.org/10.3390/catal8010002
- [2] S. Wang, Z. Jiang, J. Yang, Y. Tang, B. Liu, Design and research on preparation of $C4$ olefins by ethanol coupling based on logistic, Energy Rep., 8(4) (2022), 370-376. https://doi.org/10.1016/j.egyr.2022.01.215
- [3] M. Li, L. Zhao, S. Jin, D. Li, J. Huang, J. Liu, Process schemes of ethanol coupling to $C4$ olefins based on a genetic algorithm for back propagation neural network optimization, Heliyon, 8(12) (2022), Article ID e12301. https://doi.org/10.1016/j.heliyon.2022.e12301
- [4] Q. Zhang, Y. Zhang, Y. Zhu, Mechanism modeling and optimization design of ethanol coupling to prepare $C4$ olefins, Acad. J. Eng. Technol. Sci., 5(7) (2022), 12-18. https://doi.org/10.25236/AJETS.2022.050703
- [5] Y. Xie, Q. Li, W. Zhu, Q. Wu, J. Wan, H. Mai, Research on the optimization model of ethanol coupling to $C4$ olefins based on regression analysis, In: Int. Conf. Fuzzy Inf. Eng., Springer Nature, Singapore, (2022), 177-188. https://doi.org/10.1007/978-981-97-2891-6_14
- [6] R. Wang, Study on the process of ethanol coupling to prepare $C4$ olefins based on SPSS fitting, In: Int. Conf. Green Commun., Netw. (GCNIoT 2021), 12085 (2021), 262-267. https://doi.org/10.1117/12.2625435
- [7] Y. Hou, Y. Zhang, Research on the optimum conditions for preparing $C4$ olefin by ethanol coupling, In: 2nd Int. Conf. Mech., Electron., Electr. Automat. Control (METMS 2022), 12244 (2022), 298-304. https://doi.org/10.1117/12.2635297
- [8] J. Xiao, K. Luo, J. Zhong, Cross-scale modeling and collaborative optimization of ethanol-catalyzed coupling to produce $C4$ olefins: Nonlinear modeling and collaborative optimization strategies, Nonlinear Eng., 14(1) (2025), Article ID 20250167. https://doi.org/10.1515/nleng-2025-0167
- [9] Z. Yan, L. Yan, J. Ying, J. Guo, H. Yu, Study on the preparation of $C4$ alkenes by ethanol coupling based on bootstrap method, In: Int. Conf. Frontier Comput., Springer Nature, Singapore, (2022), 864-872. https://doi.org/10.1007/978-981-99-1428-9 108
- [10] R. Zhang, Y. Bai, M. Guo, Research on the preparation of $C4$ olefin by ethanol coupling based on regression analysis and XGBoost algorithm, In: Second IYSF Acad. Symp. Artif. Intell. Comput. Eng., 12079 (2021), 121-128. https://doi.org/10.1117/12.2623030
