Tools  |  Online calculators developed by the EgiChem Group

Estimation of diffusivities in supercritical carbon dioxide

Online calculator coming soon! Download the software for offline use.

Notes:

This tool estimates the binary diffusivity of a solute in supercritical carbon dioxide using a machine learning model trained with 4000 experimental data points from over 170 systems.

Requirements

Python 3 and the following Python libraries are required:

  • numpy
  • pandas
  • scikit-learn
  • joblib

Program is fully tested on:

  • Python 3.7
  • numpy 1.18.5
  • pandas 1.0.5
  • scikit-learn 0.23.1

Installation

  1. Install Python from python.org.
  2. Download the software by clickig Code >> Download ZIP.
  3. Unpack the zip file.
  4. Open the command line and run pip install -r requirements.txt to install the required libraries.
  5. You can now cd to the program folder and use it as described below.

Usage

Option 1) By providing the properties in order:

Call the program you desire and provide the properties in order. For istance, for the SC-CO2 program:

  1. Temperature (K)
  2. Density (g/cm3)
  3. Solute molecular mass (g/mol)
  4. Solute critical pressure (bar)
  5. Solute acentric factor (-)
python ml_scco2.py --properties YOUR_TEMPERATURE YOUR_DENSITY YOUR_MOLECULAR_MASS YOUR_CRITICAL_PRESSURE YOUR_ACENTRIC_FACTOR

Example:

python ml_scco2.py --properties 313.15 0.830000647 430.71 8.45543 0.8071 # Output: # Predicted diffusivities: # D12(1) = 5.81821846E-05 cm2/s

Option 2) By specifying each property:

In this case the order is irrelevant.

python ml_scco2.py --temperature YOUR_TEMPERATURE --density YOUR_DENSITY --molecularmass YOUR_MOLECULAR_MASS --criticalpressure YOUR_CRITICAL_PRESSURE --acentricfactor YOUR_ACENTRIC_FACTOR # OR using python ml_scco2.py -t YOUR_TEMPERATURE -d YOUR_DENSITY -mm YOUR_MOLECULAR_MASS -cp YOUR_CRITICAL_PRESSURE -af YOUR_ACENTRIC_FACTOR

Example:

python ml_scco2.py -t 313.15 -d 0.830000647 -mm 430.71 -cp 8.45543 -af 0.8071 # Output: # Predicted diffusivities: # D12(1) = 5.81821846E-05 cm2/s

Option 3) By specifying a CSV file with the input data:

The CSV file must include at least five columns with the following headers:

Case SC-CO2 Polar Nonpolar
Properties/Headers T
density
solute.M2
solute.Pc
solute.w
T
viscosity
solute.M2
solute.Pc
solvent.M1
solvent.elj
T
viscosity
solute.M2
solute.Pc
solvent.M1

You can provide any number of points as rows. See the examples folder.

python ml_scco2.py --csvfile YOUR_CSVFILE_PATH

Example:

# Using a file in the same directory with 3 points (rows) python ml_scco2.py --csvfile sample-scco2-data.csv # Output: # Predicted diffusivities: # D12(1) = 5.03732712E-05 cm2/s # D12(2) = 1.05026748E-04 cm2/s # D12(3) = 9.14913157E-05 cm2/s

Save results

Optionally you can use the --save or -s flag to save the results in an csv file.

python ml_scco2.py --csvfile YOUR_CSVFILE_PATH --save

Help

python ml_scco2.py -h

Citing

If you use the SC-CO2 program for a scientific publication please cite: