Nonlinear model predictive controller of hydrogenation of dimethyl oxalate for ethylene glycol production
-
Fakhrony Sholahudin Rohman
, Sharifah Rafidah Wan Alwi, Dinie Muhammad
, Ashraf Azmi und Muhamad Nazri Murat
Abstract
Ethylene glycol (EG) is a valuable commodity organic intermediate that is produced using the catalyzed gas-phase hydrogenation process of dimethyl oxalate (DMO) from syngas. The reactor process is challenging to control because of its nonlinearity and multivariable condition. Thus, this study proposes the application of Neural Wiener model predictive control (NWMPC) for DMO hydrogenation reactor control. The application of empirical-based MPC, such as NWMPC, is still new in DMO hydrogenation reactor control. In order to simulate the process, the DMO hydrogenation reactor is modeled using Aspen Plus and Aspen Dynamic software. The Neural Wiener (NW) model is developed based on state space and neural network modeling using a Linear-Nonlinear (L-N) identification approach. A validation test is also performed to verify the accuracy of the NW model. Based on the test, the model accuracy is acceptable with the coefficient of determination (R2) of 0.965 for EG output mole fraction (first output) and R2 of 0.936 for product temperature (second output). The NWMPC capability is evaluated with a PID controller to handle a setpoint change in EG output mole fraction and reject disturbance in the feed stream flow rate. The control performance results have demonstrated the superior ability of the NWMPC to handle such scenarios better than PID in terms of controller action speed and profile.
Funding source: Universiti Teknologi Malaysia
Award Identifier / Grant number: Q.J130000.21A2.07E17
-
Research ethics: Not applicable.
-
Informed consent: Not applicable.
-
Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.
-
Use of Large Language Models, AI and Machine Learning Tools: None declared.
-
Conflict of interest: The authors declare no competing interests.
-
Research funding: The financial support from Universiti Teknologi Malaysia Professional Development Research University Grant (UTM-PDRU) with Vote number Q.J130000.21A2.07E17 is greatly acknowledged.
-
Data availability: Datasets used and/or analysed in this study are available upon reasonable request.
References
1. Schwaar, RH, Ethylene oxide and ethylene glycol, SRI Consulting. ihs.com/pdf/RP002F_toc_173616110917062932.pdf [Accessed 15 November 2020]; 1997.Suche in Google Scholar
2. Jiang, CW, Zheng, ZW, Zhu, YP, Luo, ZH. Design of a two-stage fluidized bed reactor for preparation of diethyl oxalate from carbon monoxide. Chem Eng Res Des 2012;90:915–25.10.1016/j.cherd.2011.10.018Suche in Google Scholar
3. Taqvi, SA, Tufa, LD, Muhadizir, S. Optimization and dynamics of distillation column using Aspen Plus®, Procedia Eng 2016;148:978–84. https://doi.org/10.1016/j.proeng.2016.06.484.Suche in Google Scholar
4. Akpa, JG, Onuorah, P. Simulation and control of a reactor for the non-catalytic hydrolysis of ethylene oxide to ethylene glycol. Math Theory Model 2018;8:23–45.Suche in Google Scholar
5. Rohman, FS, Sulaiman, SHS, Aziz, N. Multivariable optimisation of hydrogenation of dimethyl oxalate for maximising productivity of ethylene glycol. Int J Hydrogen Energy 2021;46:30882–90.10.1016/j.ijhydene.2021.05.003Suche in Google Scholar
6. Muhammad, D, Ahmad, Z, Aziz, N. Low density polyethylene tubular reactor control using state space model predictive control. Chem Eng Commun 2021a;208:500–16. https://doi.org/10.1080/00986445.2019.1674816.Suche in Google Scholar
7. Zheng, J, Zhou, J, Lin, H, Duan, X, Williams, CT, Yuan, Y. CO-mediated deactivation mechanism of SIO2-supported copper catalysts during dimethyl oxalate hydrogenation to ethylene glycol. J Phys Chem C 2015;119:13758–66. https://doi.org/10.1021/acs.jpcc.5b03569.Suche in Google Scholar
8. Schoukens, M, Tiels, K. Identification of block-oriented nonlinear systems starting from linear approximations: a survey. Automatica 2017;85:272–92. https://doi.org/10.1016/j.automatica.2017.06.044.Suche in Google Scholar
9. Lawryńczuk, M. Identification of Wiener models for dynamic and steady-state performance with application to solid oxide fuel cell. Asian J Control 2019;21:1836–46. https://doi.org/10.1002/asjc.2038.Suche in Google Scholar
10. Lawryńczuk, M, Tatjewski, P. Offset-free state-space nonlinear predictive control for Wiener systems. Inf Sci 2020;511:127–51. https://doi.org/10.1016/j.ins.2019.09.042.Suche in Google Scholar
11. Giri, F, Bai, EW. Block-oriented nonlinear system identification. London: Springer; 2010.10.1007/978-1-84996-513-2Suche in Google Scholar
12. Yu, BY, Chien, IL. Design and optimization of dimethyl oxalate (DMO) hydrogenation process to produce ethylene glycol (EG). Chem Eng Res Des 2017;121:173–90. https://doi.org/10.1016/j.cherd.2017.03.012.Suche in Google Scholar
13. Zhu, YP, Chen, GQ, Luo, ZH. Iterative multiscale computational fluid dynamics – single-particle model for intraparticle transfer and catalytic hydrogenation reaction of dimethyl oxalate in a fluidized-bed reactor. Ind Eng Chem Res 2014;53:110–22. https://doi.org/10.1021/ie403227z.Suche in Google Scholar
14. Yang, Q, Zhang, D, Zhou, H, Zhang, C. Process simulation, analysis and optimization of a coal to ethylene glycol process. Energy 2018;155:521–34. https://doi.org/10.1016/j.energy.2018.04.153.Suche in Google Scholar
15. Eden, MR. Introduction to Aspen plus simulation. Auburn: Auburn University; 2012.Suche in Google Scholar
16. Li, S, Wang, Y, Zhang, J, Wang, S, Xu, Y, Zhao, Y, et al.. Kinetics study of hydrogenation of dimethyl oxalate over Cu/SiO2 catalyst. Ind Eng Chem Res 2015;54:1243–50.10.1021/ie5043038Suche in Google Scholar
17. Pearson, RK, Pottmann, M. Gray-box identification of block-oriented nonlinear models. J Process Control 2000;10:301–15. https://doi.org/10.1016/s0959-1524(99)00055-4.Suche in Google Scholar
18. Sudibyo. Study of different nonlinear models and optimizers for model predictive control in methyl tertiary butyl ether reactive distillation column. Penang, Malaysia: University Sains Malaysia; 2018.Suche in Google Scholar
19. Zhu, Y. Multivariable system identification for process control. Amsterdam: Elsevier Science & Technology Books; 2001.10.1016/B978-008043985-3/50012-0Suche in Google Scholar
20. Iqbal, IM, Aziz, N. Comparison of various wiener model identification approach in modelling nonlinear process. In: 3rd conference on data mining and optimization (DMO). Putra Jaya, Malaysia; 2011.Suche in Google Scholar
21. Cervantes, AL, Agamennoni, OE, Figueroa, JL. A nonlinear model predictive control system based on Wiener piecewise linear models. J Process Control 2003;13:655–66. https://doi.org/10.1016/s0959-1524(02)00121-x.Suche in Google Scholar
22. Muhammad, D, Ahmad, Z, Aziz, N. Modeling and nonlinearity studies of low density polyethylene (LDPE) tubular reactor. Mater Today Proc 2021b;42:39–44. https://doi.org/10.1016/j.matpr.2020.09.238.Suche in Google Scholar
23. Ławryńczuk, M. MPC algorithms using input-output wiener models. In: Nonlinear predictive control using Wiener models: computationally efficient approaches for polynomial and neural structures. Cham, Switzerland: Springer International Publishing; 2022a:71–141 pp.10.1007/978-3-030-83815-7_3Suche in Google Scholar
24. Ławryńczuk, M. Wiener models. In: Nonlinear predictive control using Wiener models: computationally efficient approaches for polynomial and neural structures. Cham, Switzerland: Springer International Publishing; 2022b:41–68 pp.10.1007/978-3-030-83815-7_2Suche in Google Scholar
25. Pearson, RK. Selecting nonlinear model structures for computer control. J Process Control 2003;13:1–26. https://doi.org/10.1016/S0959-1524(02)00022-7.Suche in Google Scholar
26. Seborg, DE, Edgar, TF, Mellichamp, DA. Process dynamics and control, 2nd ed.. Hoboken, NJ: John Wiley & Sons; 2004.Suche in Google Scholar
27. Muhammad, D. Low density polyethylene grade transition and conversion control using neural wiener model predictive control with soft sensor PhD Thesis. Universiti Sains Malaysia; 2021.10.1002/apj.2699Suche in Google Scholar
© 2024 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Frontmatter
- Research Articles
- Energy cost prediction for chromium removal by nanofiltration membrane
- Forecasting gasification sustainability through enhanced K-nearest neighbour models for hydrogen and nitrogen amount
- Applying machine learning for biomass gasification prediction: enhancing efficiency and sustainability
- Enhancing prediction of elemental composition through machine learning decision tree models for biomass gasification optimization
- Nonlinear model predictive controller of hydrogenation of dimethyl oxalate for ethylene glycol production
- Dynamic optimization of boiler for minimizing energy consumption in the intentionally transient process operation: effect of different interval number
- Heat transfer efficiency in gas–solid fluidized beds with flat and corrugated walls
- Ant lion based optimization for performance improvement of methanol production
- Boundary Element Method for Viscous Flow through Out-phase Slip-patterned Microchannel under the Influence of Inclined Magnetic Field
- Artificial neural network models for forecasting the extracted yield of essential oils from Curcuma longa L. by hydro-distillation
Artikel in diesem Heft
- Frontmatter
- Research Articles
- Energy cost prediction for chromium removal by nanofiltration membrane
- Forecasting gasification sustainability through enhanced K-nearest neighbour models for hydrogen and nitrogen amount
- Applying machine learning for biomass gasification prediction: enhancing efficiency and sustainability
- Enhancing prediction of elemental composition through machine learning decision tree models for biomass gasification optimization
- Nonlinear model predictive controller of hydrogenation of dimethyl oxalate for ethylene glycol production
- Dynamic optimization of boiler for minimizing energy consumption in the intentionally transient process operation: effect of different interval number
- Heat transfer efficiency in gas–solid fluidized beds with flat and corrugated walls
- Ant lion based optimization for performance improvement of methanol production
- Boundary Element Method for Viscous Flow through Out-phase Slip-patterned Microchannel under the Influence of Inclined Magnetic Field
- Artificial neural network models for forecasting the extracted yield of essential oils from Curcuma longa L. by hydro-distillation