Exploring Alternatives to Create Digital Twins from and for Process Simulation

Authors

  • Jaime Barbero-Sánchez

    Department of Chemical Engineering, University of Castilla La Mancha, Avda. Camilo José Cela 12, Ciudad Real, 13071, Spain
  • Alicia Megía-Ortega

    Department of Chemical Engineering, University of Castilla La Mancha, Avda. Camilo José Cela 12, Ciudad Real, 13071, Spain
  • Víctor R. Ferro

    Department of Chemical Engineering, Autonomous University of Madrid, Cantoblanco, Madrid, 28049, Spain
  • Jose-Luis Valverde

    Department of Chemical Engineering, University of Castilla La Mancha, Avda. Camilo José Cela 12, Ciudad Real, 13071, Spain

DOI:

https://doi.org/10.30564/jcsr.v6i1.6168
Received: 19 December 2023 | Revised: 10 January 2024 | Accepted: 12 January 2024 | Published Online: 19 January 2024

Abstract

In this work, Digital Twins based on Neural Networks for the steady state production of styrene were generated. Thus, both the Aspen Technology AI Model Builder (alternative 1) and a homemade MS Excel VBA code connected to Aspen HYSYS and Aspen Plus (alternative 2) were used with this same aim. The raw data used for generating the Digital Twins were obtained from process simulations using Aspen HYSYS and/or Aspen Plus, which were connected through a recycle-like stream via automation for solving the entire simulation flowsheet. Aspen HYSYS was used for solving the pre-heating, reaction, and stabilization sections of the process whereas Aspen Plus ensured the computing of the separation and purification columns. Both alternatives led to an excellent prediction showing the capability of creating Digital Twins from and for process simulation.

Keywords:

Digital Twin, Aspen Hybrid Model Builder, Aspen HYSYS, Aspen Plus, Automation, MS Excel-VBA

References

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How to Cite

Barbero-Sánchez, J., Megía-Ortega, A., R. Ferro, V., & Valverde, J.-L. (2024). Exploring Alternatives to Create Digital Twins from and for Process Simulation. Journal of Computer Science Research, 6(1), 16–30. https://doi.org/10.30564/jcsr.v6i1.6168

Issue

Vol. 6 , Iss. 1 (January 2024)

Article Type

Article

License

Copyright © 2024 Author(s)

Creative Commons License

This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

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