Chemical Engineering

Our chemical property prediction software allows the prediction of all relevant thermodynamic properties for any chemical compound with high accuracy. This allows chemical engineers to work with compounds even when no measured data is available.

Prediction Accuracy

The following sections present further insight into the prediction accuracy. At first pure properties are considered. Afterwards results for mixture properties are presented. Note that our software allows the prediction of many more properties not mentioned here. For details on all available features take a look at the shop.

Pure Properties

The prediction of pure properties is especially important when no measured data is available for the compound of interest. Knowledge of the vapor liquid equilibrium (VLE) of a pure compound is essential for many separation problems. Below we show a comparision of predicted VLE data T(pre) to thermodynamic reference data T(ref):

 

Deviation in Temperatures:

The mean value of the absolute deviation in temperature is 9 K. The coefficient of determination is 0.99. These are incredibly small deviations for the broad range of compounds and temperatures included.

 

Deviation in Volumes:

The mean value of the absolute relative deviation in volumes is 5%. Again the deviation from the reference data is very small. Such accurate volume predictions show a high potential for the prediction of transport properties like viscosities in future releases.

 

 

The reference data is generated by the PC-SAFT equation using parameters fitted to experimental data. This way a broad range of temperature and pressure conditions are included. More than 4000 datapoints for different compounds are shown. The deviations of the reference data from the original measured data is negligible. 

 

A broad range of functional groups are represented by the data above including: Alkanes, Alkenes, Alkynes, Cyclics, Multicyclics, Aromatics, Halogenes, Aldehydes, Ketones, Ethers, Esters, Maleates, Alcohols, Organic Acids, Nitriles, Nitrates, Amines, Thioethers, Silanes, Halosilanes, Organosilianes and Multifunctional-Compounds. For all these compounds the same set of parameter is used to translate the data of quantum mechanic predictions into equation of state parameter. There are no group specific corrections necessary. Therefore this model will most likely also be accurate for molecules which are not yet represented by the reference data.

 

For any overview of the methodology applied please read the following article: J. Phys. Chem. B 2008, 112, 5693-5701

 

Mixture Properties

A property of major interest for mixtures are binary vapor liquid equilibria (VLE). Their knowlege is essential in most separation processes. Two examples of fully predicted binary VLE's are shown below. The experimental data is reproduced with very small deviations.

VLE of n-decane / ethane. Comparision of the predicted values to experimental data.

VLE of 1-butanol / n-butaneComparision of the predicted values to experimental data.


Some predictions may show larger deviations than shown above. This is often mainly attributed to an error in the pure component vapor pressure prediction. Luckily for most pure components a lot of experimental data is available. This data can be used to increase the accuracy of the predictions. A single datapoint is often already sufficient.

 

Using a single experimental pure datapoint:

 

For the examples shown below a single pure experimental datapoint was used. For consistency the single datapoint is always the VLE data at 0.7 * Tc. Tc is defined as the critical temperature of the compound.

VLE of the acetone / n-pentane. Comparision of the predicted values to experimental data.

VLE of the acetone / n-pentane. Comparision of the predicted values augmented with a single pure compound datapoint to experimental data.


VLE of the n-butane / carbon dioxideComparision of the predicted values to experimental data.

VLE of the n-butane / carbon dioxideComparision of the predicted values augmented with a single pure compound datapoint to experimental data.


The results clearly indicate a significant improvement in accuracy. Even higher accuracies are possible if more pure component data is added. For most compounds at least one measured pure component datapoint is available (i.e. normal boiling point or critical temperature and pressure). An accuracy boost like shown below will therefore be possible in most cases.

 

We currently use the GFN-xTB methodology for the quantum mechanic predictions. This results in very short simulation times with a good prediction accuracy. In future releases other quantum mechanical methods will also be supported. This results in longer simulation times, but allows even higher accuracy in predictions.