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A new Python algorithm predicts aqueous physical properties for AspenTech software, showing good accuracy for density and heat capacity but limitations with viscosity and thermal conductivity at high concentrations.

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Area of Science:

  • Chemical Engineering
  • Computational Chemistry
  • Thermodynamics

Background:

  • Accurate prediction of aqueous physical properties is crucial for chemical process design and simulation.
  • Existing models in commercial software like AspenTech may lack parameters for all relevant substances.
  • Open-source solutions can enhance the flexibility and accessibility of property prediction tools.

Purpose of the Study:

  • To develop and validate a Python-based open-source algorithm for predicting aqueous physical properties.
  • To ensure compatibility with AspenTech's electrolyte property models.
  • To provide an alternative for estimating parameters not available in commercial databases.

Main Methods:

  • Developed a Python algorithm utilizing pure component property predictions and NRTL parameters from UNIFAC simulations.
  • Validated the algorithm against experimental data for 37 binary aqueous mixtures.
  • Compared predicted properties (density, heat capacity, viscosity, thermal conductivity) with experimental values.

Main Results:

  • Achieved Mean Absolute Percentage Errors (MAPE) of 2.88% for density, 0.355% for heat capacity, 12.1% for viscosity, and 10.1% for thermal conductivity.
  • Identified limitations in accurately reflecting density and viscosity trends at high concentrations for certain substances.
  • Demonstrated that inaccurate viscosity and thermal conductivity predictions impacted heat transfer coefficient calculations in a falling-film evaporator simulation.

Conclusions:

  • The developed algorithm offers a valuable tool for estimating initial physical property parameters for models not present in commercial databases.
  • Caution is advised when using the algorithm for high-concentration systems or for properties like viscosity and thermal conductivity due to potential significant errors.
  • Further refinement may be needed to improve accuracy for specific challenging conditions and properties.