The development of models for a better representation of real processes is the core of the further development of the simulation software. Model development is done through the principles of chemical engineering but also control engineering and for the improvement of mathematical simulation techniques. Process simulation is therefore a field where practitioners from chemistry, physics, computer science, mathematics, and engineering work together.
Efforts are made to develop new and improved models for the calculation of properties. This includes for example the description of 
* thermophysical properties like vapor pressures, viscosities, caloric data, etc. of pure components and mixtures
* properties of different apparatus like reactors, distillation columns, pumps, etc.
* chemical reactions and kinetics
* environmental and safety-related data
There are two main types of models:
# Simple equations and correlations where parameters are fitted to experimental data.
# Predictive methods where properties are estimated.
The equations and correlations are normally preferred because they describe the property (almost) exactly. To obtain reliable parameters it is necessary to have experimental data which are usually obtained from factual data banks or, if no data are publicly available, from measurements. 
Using predictive methods is more cost effective than experimental work and also than data from data banks. Despite this advantage predicted properties are normally only used in early stages of the process development to find first approximate solutions and to exclude false pathways because these estimation methods normally introduce higher errors than correlations obtained from real data. 
Process simulation has encouraged the development of mathematical models in the fields of numerics and the solving of complex problems.