Startseite Performance evaluation of adaptive based model predictive control for ethylene glycol production from dimethyl oxide hydrogenation
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Performance evaluation of adaptive based model predictive control for ethylene glycol production from dimethyl oxide hydrogenation

  • Fakhrony Sholahudin Rohman , Muhammad Syafiq Sulaiman , Muhamad Nazri Murat und Norashid Aziz EMAIL logo
Veröffentlicht/Copyright: 22. November 2022
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Abstract

Advance process control is a proven control and optimization technology delivering measurable and sustainable improvements in production yield, coupled with the added value of energy savings. In this work, an adaptive based model predictive control (aMPC) is developed and implemented to control the hydrogenation of dimethyl oxide to ethylene glycol (EG) in a plug flow reactor. The aMPC is compared with 3 other control schemes; proportional-integral (PI), decoupled PI (dPI) and linear model predictive control. The aim is to determine the reliability of aMPC in controlling the production rate and reactor temperature for an optimized hydrogenation reactor. The aspects compared include set point tracking, disturbance rejection and robustness test. The analysis concludes that the aMPC offers the best overall performance compared to the other control schemes.


Corresponding author: Norashid Aziz, School of Chemical Engineering, Engineering Campus, Universiti Sains Malaysia, Seri Ampangan, 14300 Nibong Tebal, Seberang Perai Selatan, Penang, Malaysia, E-mail:

Funding source: Universiti Sains Malaysia

Award Identifier / Grant number: Research University Grant (RUI) 203.PJKIMIA.801414

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: This study was supported by Universiti Sains Malaysia through Research University Grant (RUI) 203.PJKIMIA.8014146.

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

Appendix A: State space model details

References

Ackermann, J., and D. Kaesbauer. 2001. “Design of Robust PID Controllers.” European Control Conference (ECC) 2001: 522–7, https://doi.org/10.23919/ECC.2001.7075960.Suche in Google Scholar

Adetola, V., D. DeHaan, and M. Guay. 2009. “Adaptive Model Predictive Control for Constrained Nonlinear Systems.” Systems & Control Letters 58 (5): 320–6.10.1016/j.sysconle.2008.12.002Suche in Google Scholar

Akpa, J. G., and P. Onuorah. 2018. “Simulation and Control of a Reactor for the Non-catalytic Hydrolysis of Ethylene Oxide to Ethylene Glycol.” Mathematical Theory and Modeling 8 (2): 23–45.Suche in Google Scholar

Al-Arfaj, M. A., and W. L. Luyben. 2002. “Control of Ethylene Glycol Reactive Distillation Column.” AIChE Journal 48 (4): 905–8, https://doi.org/10.1002/aic.690480424.Suche in Google Scholar

Camacho, E. F., and C. Bordons. 2013. Model Predictive Control. Berlin: Springer Science & Business Media.Suche in Google Scholar

Dinie, M., and N. Aziz. 2017. “Review: Control Schemes for Low Density Polyethylene Reactor.” Chemical Engineering Transaction 56: 769.Suche in Google Scholar

Gumussoy, S., A. A. Ozdemir, T. McKelvey, L. Ljung, M. Gibanica, and R. Singh. 2018. “Improving Linear State-Space Models with Additional Iterations.” IFAC-PapersOnLine 51: 341–6, https://doi.org/10.1016/j.ifacol.2018.09.158.Suche in Google Scholar

Jiang, C. W., Z. W. Zheng, Y. P. Zhu, and Z. H. Luo. 2012. “Design of a Two-Stage Fluidized Bed Reactor for Preparation of Diethyl Oxalate from Carbon Monoxide.” Chemical Engineering Research and Design 90: 915–25, https://doi.org/10.1016/j.cherd.2011.10.018.Suche in Google Scholar

Jin, Q., L. Zhao, F. Hao, and S. Liu. 2012. “Design of a Multivariable Internal Model Controller Based on Singular Value Decomposition.” Canadian Journal of Chemical Engineering 91 (6): 1103–14, https://doi.org/10.1002/cjce.21735.Suche in Google Scholar

Kiss, A. A. 2019. “Novel Catalytic Reactive Distillation Processes for a Sustainable Chemical Industry.” Topics in Catalysis 62: 1132–48, https://doi.org/10.1007/s11244-018-1052-9.Suche in Google Scholar

Kumar, A., and P. Daoutidis. 1999. “Modeling, Analysis and Control of Ethylene Glycol Reactive Distillation Column.” AIChE Journal 45 (1): 51–68, https://doi.org/10.1002/aic.690450106.Suche in Google Scholar

Li, S., Y. Wang, J. Zhang, S. Wang, Y. Xu, Y. Zhao, and X. Ma. 2015. “Kinetics Study of Hydrogenation of Dimethyl Oxalate over Cu/Sio2 Catalyst.” Industrial & Engineering Chemistry Research 54: 1243–50, https://doi.org/10.1021/ie5043038.Suche in Google Scholar

Lopez, B. T., J. J. E. Slotine, and J. P. How. 2019. “Dynamic Tube MPC for Nonlinear Systems,” In American Control Conference (ACC), 1–8.10.23919/ACC.2019.8814758Suche in Google Scholar

Pipino, H. A., C. A. Cappelletti, and E. J. Adam. 2020. “Adaptive Multi-Model Predictive Control Applied to Continuous Stirred Tank Reactor.” Computers & Chemical Engineering 145: 107195, https://doi.org/10.1016/j.compchemeng.2020.107195.Suche in Google Scholar

Rohman, F. S., and N. Aziz. 2019. “Performance Metrics Analysis of Dynamic Multi-Objective Optimization for Energy Consumption and Productivity Improvement in Batch Electrodialysis.” Chemical Engineering Communications 208: 517–29.10.1080/00986445.2019.1674817Suche in Google Scholar

Rohman, F. S., S. H. S. Sulaiman, and N. Aziz. 2021. “Multivariable Optimisation of Hydrogenation of Dimethyl Oxalate for Maximising Productivity of Ethylene Glycol.” International Journal of Hydrogen Energy 46 (60): 30882–90, https://doi.org/10.1016/j.ijhydene.2021.05.003.Suche in Google Scholar

Rosendo, M. L., P. C. Eduardo, and A. R. Jose. 2000. “A Robust PI Control Configuration for a High-Purity Ethylene Glycol Reactive Distillation Column.” Chemical Engineering Science 55: 4925–37, https://doi.org/10.1016/s0009-2509(00)00124-x.Suche in Google Scholar

Seborg, D. E., T. F. Edgar, and D. A. Mellichamp. 2004. Process Dynamics and Control, 2nd ed. Hoboken, New Jersey: John Wiley & Sons.Suche in Google Scholar

Song, H., R. Jin, M. Kang, and J. Chen. 2013. “Progress in Synthesis of Ethylene Glycol through C1 Chemical Industry Routes.” Chinese Journal of Catalysis 34: 1035–50, https://doi.org/10.1016/s1872-2067(12)60529-4.Suche in Google Scholar

Sulaiman, M. S., F. S. Rohman, and N. Aziz. 2021. “Optimization Process for Ethylene Glycol Production Using the Pareto Solution.” IOP Conference Series: Materials Science and Engineering 1011: 012003, https://doi.org/10.1088/1757-899x/1011/1/012003.Suche in Google Scholar

Tatjewski, P., and M. Ławryńczuk. 2021. “Nonlinear Predictive Control, Automatic Control, Robotics, and Information Processing.” Studies in Systems, Decision and Control 296: 189–228.10.1007/978-3-030-48587-0_7Suche in Google Scholar

Valencia-Palomo, G., and J. A. Rossiter. 2007. Comparison Between an Auto-Tuned PI Controller, A Predictive Controller and a Predictive Functional Controller in Elementary Dynamic Systems. Sheffield, UK: Automatic Control and Systems Engineering, University of Sheffield.Suche in Google Scholar

Vandoren, V. 2011. “Disturbance-Rejection vs. Setpoint-Tracking Controllers,” PhD, PE.Suche in Google Scholar

Wang, L. 2020. PID Control System Design and Automatic Tuning using MATLAB/Simulink. Hoboken, NJ: John Wiley & Sons Inc.10.1002/9781119469414Suche in Google Scholar

Wang, Q. G., Z. Ye, W. J. Cai, and C. C. Hang. 2008. “Loop Pairing Analysis.” Lecture Notes in Control and Information Sciences 373: 9–38.10.1007/978-3-540-78482-1_2Suche in Google Scholar

Xiong, Q., W. J. Cai, and M. J. He. 2005. “A Practical Loop Pairing Criterion for Multivariable Processes.” Journal of Process Control 15 (7): 741–7, https://doi.org/10.1016/j.jprocont.2005.03.008.Suche in Google Scholar

Yu, B. Y., and I. L. Chien. 2017. “Design and Optimization of Dimethyl Oxalate (DMO) Hydrogenation Process to Produce Ethylene Glycol (EG).” Chemical Engineering Research and Design 121: 173–90, https://doi.org/10.1016/j.cherd.2017.03.012.Suche in Google Scholar

Zak, S. H. 2017. An Introduction to Model-Based Predictive Control (MPC), 680. West Lafayette, Indiana: ECE, Purdue University.Suche in Google Scholar

Zhang, R., S. Wu, and F. Gao. 2017. “State Space Model Predictive Control for Advanced Process Operation: A Review of Recent Development, New Results, and Insight.” Industrial & Engineering Chemistry Research 56: 5360–94, https://doi.org/10.1021/acs.iecr.7b01319.Suche in Google Scholar

Received: 2022-05-01
Accepted: 2022-11-03
Published Online: 2022-11-22

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