Accelerated modeling and design of a mixed refrigerant cryogenic process using a data-driven approach

Hosein Alimardani, Mehrdad Asgari*, Roohangiz Shivaee-Gariz, Javad Tamnanloo

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Cryogenic processes with mixed refrigerants are prevalent in energy-intensive chemical industries, enhancing energy efficiency while reducing costs and unit size. However, the curse of dimensionality and process design constraints pose significant hurdles for effective screening and optimization. To tackle this, we developed a neural network model for natural gas liquefaction prediction. Trained on an extensive Aspen HYSYS database, our ML model accurately simulates LNG processes, with an impressive R2 test value of 99.63, operating almost ten million times faster than HYSYS. It effectively addresses vital process design constraints, including liquid slugging and temperature cross, crucial for optimization. By integrating the ML model with genetic and Nelder–Mead algorithms, we achieve an 8.9% reduction in total exergy, outperforming Aspen HYSYS within the same time frame. Our study underscores ML's significance in modeling energy-intensive chemical processes, providing insights into the exergy profile and enabling feature importance analysis.

Original languageEnglish
Article number100143
JournalDigital Chemical Engineering
Volume10
Number of pages17
ISSN2772-5081
DOIs
Publication statusPublished - 2024

Keywords

  • Data-driven techniques
  • Machine learning
  • Optimization
  • Process modeling
  • Sub-ambient process

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