Startseite Naturwissenschaften Nonlinear model predictive controller of hydrogenation of dimethyl oxalate for ethylene glycol production
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Nonlinear model predictive controller of hydrogenation of dimethyl oxalate for ethylene glycol production

  • Fakhrony Sholahudin Rohman , Sharifah Rafidah Wan Alwi EMAIL logo , Dinie Muhammad , Ashraf Azmi und Muhamad Nazri Murat
Veröffentlicht/Copyright: 13. November 2024
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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.


Corresponding author: Sharifah Rafidah Wan Alwi, Process Systems Engineering Centre (PROSPECT), Research Institute of Sustainable Environment (RISE), Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia; and Chemical Engineering Department, Faculty of Chemical and Energy Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia, E-mail:

Award Identifier / Grant number: Q.J130000.21A2.07E17

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors declare no competing interests.

  6. 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.

  7. Data availability: Datasets used and/or analysed in this study are available upon reasonable request.

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Received: 2024-04-07
Accepted: 2024-08-13
Published Online: 2024-11-13

© 2024 Walter de Gruyter GmbH, Berlin/Boston

Heruntergeladen am 31.12.2025 von https://www.degruyterbrill.com/document/doi/10.1515/cppm-2024-0025/pdf
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