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Data on propylene/propane separation by the externally heat-integrated distillation column (EHIDiC) using data-driven

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Data in Brief
|May 13, 2020
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Summary

This study presents simulation data for externally heat-integrated distillation columns (EHIDiC) for propylene/propane separation. The generated dataset aids in designing and optimizing EHIDiC processes, particularly with machine learning approaches.

Keywords:
Data-drivenHeat-integrated distillation columnPropylene-propane separation

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Area of Science:

  • Chemical Engineering
  • Process Systems Engineering

Background:

  • Externally Heat-Integrated Distillation Columns (EHIDiC) are complex, highly coupled systems.
  • Optimizing EHIDiC performance requires comprehensive operational data.

Purpose of the Study:

  • To generate and describe a dataset from rigorously simulated EHIDiC for propylene/propane separation.
  • To provide data for enhanced understanding and optimization of EHIDiC.

Main Methods:

  • Utilized Aspen Plus-Matlab communication platform for rigorous simulation.
  • Collected 900 data points by stochastically varying input variables and external exchanger parameters.
  • Performed statistical analysis on simulation outputs including flow rate, temperature, pressure, and Total Annualized Cost (TAC).

Main Results:

  • Generated a comprehensive dataset covering a wide operational window for EHIDiC.
  • Detailed simulation results including stream properties and economic factors (TAC).
  • The data facilitates analysis of highly coupled EHIDiC systems.

Conclusions:

  • The generated dataset is valuable for designing and optimizing both steady-state and dynamic EHIDiC schemes.
  • The data is particularly suited for applications involving machine learning methods in process optimization.
  • This work supports data-driven approaches for improving distillation column efficiency.